fix(pi0fast): remove extra <bos> causing degenerate generation

pi0_fast decoding appended a second <bos> before the action block
(sample_actions_fast / sample_actions_fast_kv_cache), and
ActionTokenizerProcessorStep prepended the same <bos> to the training
target. The language prompt already begins with <bos> (standard PaliGemma
prefix "[image] <bos> prompt \n"), so this produced a ";\n <bos>" pattern
that does not exist in the openpi FAST format
("<bos> Task:...;\n Action: <fast> | <eos>", single leading <bos>,
"Action:" directly after ";\n").

On lerobot/pi0fast-base the extra <bos> pushed the model into a bos->bos
attractor, yielding degenerate generation (endless <bos> / random unicode)
instead of "Action: <fast>". Verified on stock PaliGemma with the same
weights: +bos -> <bos> collapse (logit ~51); -bos -> coherent, and on real
robot observations decoding now yields valid FAST action tokens.

Remove the extra <bos> in all three places so the prefix matches the
openpi format.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

.github/workflows/documentation.yml

@@ -55,7 +55,7 @@ jobs:
       github.repository == 'huggingface/lerobot'
     permissions:
       contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
     with:
       commit_sha: ${{ github.sha }}
       package: lerobot
@@ -78,7 +78,7 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
     with:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}

README.md

@@ -87,7 +87,7 @@ Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co
 
 ## SoTA Models
 
-LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
+LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, Vision-Language-Action (VLA) models, World Models, and Reward Models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
 
 <p align="center">
   <img alt="Gr00t Architecture" src="./media/readme/VLA_architecture.jpg" width="640px">
@@ -101,13 +101,13 @@ lerobot-train \
   --dataset.repo_id=lerobot/aloha_mobile_cabinet
 ```
 
-| Category                   | Models                                                                                                                                                                                                                                                                                                                                                     |
-| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md)                                                                                                                                    |
-| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                                                                                                                                                                |
-| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx) |
-| **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx) (more coming soon)                                                                                                                                                                                                                                                                                                  |
-| **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                         |
+| Category                   | Models                                                                                                                                                                                                                                                                                                                                                                                     |
+| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md)                                                                                                                                                                    |
+| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                                                                                                                                                                                                |
+| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.7](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx), [EVO1](./docs/source/evo1.mdx) |
+| **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx), [LingBot-VA](./docs/source/lingbot_va.mdx), [FastWAM](./docs/source/fastwam.mdx)                                                                                                                                                                                                                                                                   |
+| **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                                                         |
 
 Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub
 

docs/source/_toctree.yml

@@ -73,8 +73,10 @@
     title: LingBot-VA
   - local: fastwam
     title: FastWAM
+  - local: evo1
+    title: EVO1
   - local: groot
-    title: NVIDIA GR00T N1.5
+    title: NVIDIA GR00T
   - local: xvla
     title: X-VLA
   - local: multi_task_dit
@@ -167,6 +169,8 @@
 - sections:
   - local: phone_teleop
     title: Phone
+  - local: isaac_teleop
+    title: Isaac Teleop
   title: "Teleoperators"
 - sections:
   - local: cameras

docs/source/bring_your_own_policies.mdx

@@ -295,11 +295,12 @@ The file names are load-bearing: the factory does lazy imports by name, and the
 
 ### Wiring
 
-Three places need to know about your policy. All by name.
+Four places need to know about your policy. All by name.
 
 1. **`policies/__init__.py`** — re-export `MyPolicyConfig` and add it to `__all__`. **Don't** re-export the modeling class; it loads lazily through the factory (so `import lerobot` stays fast).
 2. **`factory.py:get_policy_class`** — add a branch returning `MyPolicy` from a lazy import.
 3. **`factory.py:make_policy_config`** and **`factory.py:make_pre_post_processors`** — same idea, two more branches.
+4. **`templates/lerobot_modelcard_template.md` and the root `README.md`** — the template is what `push_model_to_hub` renders into the model card of every checkpoint trained with your policy: add a one-line description of your policy in the `model_name` branches, map it in `policy_docs` so cards link to your MDX guide, and optionally add an architecture image to `diagrams`. Then add your policy to the models table in the root `README.md`, under the right category, linking to your doc page.
 
 Mirror an existing policy that's structurally similar to yours; the diff is small.
 
@@ -371,6 +372,8 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
 - [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
 - [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
 - [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
+- [ ] `templates/lerobot_modelcard_template.md` has a description entry and a `policy_docs` link for your policy.
+- [ ] The models table in the root `README.md` lists your policy in the right category, linking to your doc page.
 - [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).
 
 The fastest way to get a clean PR is to copy the directory of the existing policy closest to yours, rename, and replace contents method by method. Don't wait until everything is polished — open a draft PR early and iterate with us; reviewers would much rather give feedback on a half-finished branch than a fully-merged one.

docs/source/envhub_isaaclab_arena.mdx

@@ -193,7 +193,7 @@ To learn more about training policies with LeRobot, please refer to the training
 
 - [SmolVLA](./smolvla)
 - [Pi0.5](./pi05)
-- [GR00T N1.5](./groot)
+- [GR00T N1.7](./groot)
 
 Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:
 

docs/source/evo1.mdx

@@ -0,0 +1,191 @@
+# EVO1
+
+EVO1 is a Vision-Language-Action policy for robot control built around an InternVL3 backbone and a continuous flow-matching action head. This LeRobot integration exposes EVO1 as a standard policy type so it can be trained and evaluated with the usual LeRobot dataset, checkpoint, and processor APIs.
+
+## Model Overview
+
+The policy embeds one or more camera images and the language task prompt with InternVL3, pads robot state/action vectors to fixed maximum dimensions, and predicts future action chunks with a flow-matching action head. During inference, the policy samples an action chunk and returns `n_action_steps` actions from that chunk before sampling again.
+
+### What the LeRobot Integration Covers
+
+- Standard `policy.type=evo1` configuration through LeRobot
+- InternVL3 image/text embedding with optional FlashAttention fallback
+- Stage-based finetuning controls for action-head-only and VLM finetuning runs
+- Continuous flow-matching action prediction
+- Checkpoint save/load through LeRobot policy APIs
+- Training with `lerobot-train` and evaluation with standard policy inference APIs
+
+The broader EVO1 project may include additional training scripts and dataset tooling. This page focuses on the LeRobot robot-control policy path.
+
+## Installation Requirements
+
+1. Install LeRobot by following the [Installation Guide](./installation).
+2. Install EVO1 dependencies:
+
+   ```bash
+   pip install -e ".[evo1]"
+   ```
+
+   For LIBERO evaluation, install the LIBERO extra as well:
+
+   ```bash
+   pip install -e ".[evo1,libero]"
+   ```
+
+3. Install a `flash-attn` wheel only if it is compatible with your Python, PyTorch, CUDA, and GPU stack. EVO1 falls back to standard attention when `flash_attn` is not available.
+
+EVO1 uses the native Hugging Face `transformers` InternVL implementation, so `policy.vlm_model_name` must point to a natively converted checkpoint such as `OpenGVLab/InternVL3-1B-hf` (note the `-hf` suffix). The first run may download the configured VLM checkpoint unless `policy.vlm_model_name` points to a local model directory.
+
+## Data Requirements
+
+EVO1 expects a LeRobot dataset with:
+
+- One to `policy.max_views` visual observations, for example `observation.images.image`
+- `observation.state`
+- `action`
+- A language task instruction in the dataset `task` field, or another field configured with `policy.task_field`
+
+State and action vectors are padded to `policy.max_state_dim` and `policy.max_action_dim`. Predictions are cropped back to the dataset action dimension before being returned.
+
+## Usage
+
+To use EVO1 in a LeRobot configuration, specify:
+
+```python
+policy.type=evo1
+```
+
+By default, a new EVO1 policy initializes its VLM from:
+
+```python
+policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf
+```
+
+Once a LeRobot-format EVO1 checkpoint is available, load it with:
+
+```python
+policy.path=your-org/your-evo1-checkpoint
+```
+
+## Training
+
+### Stage 1
+
+Stage 1 freezes the VLM and trains the action head:
+
+```bash
+lerobot-train \
+  --dataset.repo_id=your_org/your_dataset \
+  --policy.type=evo1 \
+  --policy.training_stage=stage1 \
+  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
+  --policy.device=cuda \
+  --policy.chunk_size=50 \
+  --policy.n_action_steps=50 \
+  --policy.max_state_dim=24 \
+  --policy.max_action_dim=24 \
+  --policy.optimizer_lr=1e-5 \
+  --batch_size=4 \
+  --steps=5000 \
+  --output_dir=./outputs/evo1_stage1
+```
+
+### Stage 2
+
+Stage 2 finetunes the VLM branches and action head. A common workflow starts from a Stage 1 checkpoint:
+
+```bash
+lerobot-train \
+  --dataset.repo_id=your_org/your_dataset \
+  --policy.path=./outputs/evo1_stage1/checkpoints/005000/pretrained_model \
+  --policy.training_stage=stage2 \
+  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
+  --policy.device=cuda \
+  --policy.chunk_size=50 \
+  --policy.n_action_steps=50 \
+  --policy.max_state_dim=24 \
+  --policy.max_action_dim=24 \
+  --policy.optimizer_lr=1e-5 \
+  --batch_size=4 \
+  --steps=80000 \
+  --output_dir=./outputs/evo1_stage2
+```
+
+By default, `policy.training_stage` reapplies the finetuning defaults for that stage. This is important when
+starting Stage 2 from a Stage 1 checkpoint, because the Stage 1 checkpoint config stores the VLM finetuning
+flags as disabled. These stage defaults take precedence over saved or manually supplied `policy.finetune_*`
+flags unless `policy.apply_training_stage_defaults=false`, so set that flag only when manually controlling
+every finetuning flag.
+
+### Key Training Parameters
+
+| Parameter                                     | Default                     | Description                                                       |
+| --------------------------------------------- | --------------------------- | ----------------------------------------------------------------- |
+| `policy.vlm_model_name`                       | `OpenGVLab/InternVL3-1B-hf` | Natively converted InternVL3 checkpoint or local model directory  |
+| `policy.training_stage`                       | `stage1`                    | `stage1` trains the action head; `stage2` finetunes VLM branches  |
+| `policy.apply_training_stage_defaults`        | `true`                      | Reapplies stage finetuning defaults after loading a checkpoint    |
+| `policy.vlm_num_layers`                       | `14`                        | Number of InternVL3 language layers kept for the policy           |
+| `policy.vlm_dtype`                            | `bfloat16`                  | Requested VLM dtype                                               |
+| `policy.use_flash_attn`                       | `true`                      | Requests FlashAttention when installed; otherwise falls back      |
+| `policy.enable_gradient_checkpointing`        | `true`                      | Enables checkpointing on supported InternVL3 modules              |
+| `policy.gradient_checkpointing_use_reentrant` | `false`                     | Reentrant setting passed to gradient checkpointing when supported |
+| `policy.chunk_size`                           | `50`                        | Number of future actions predicted per chunk                      |
+| `policy.n_action_steps`                       | `50`                        | Number of actions consumed from a sampled chunk                   |
+| `policy.max_state_dim`                        | `24`                        | State padding dimension                                           |
+| `policy.max_action_dim`                       | `24`                        | Action padding dimension                                          |
+| `policy.postprocess_action_dim`               | `null`                      | Optional action dimension returned after EVO1 postprocessing      |
+| `policy.binarize_gripper`                     | `false`                     | Binarizes the postprocessed gripper channel for LIBERO-style eval |
+| `policy.task_field`                           | `task`                      | Batch field used as the language prompt                           |
+
+## Inference
+
+Try it out with a trained EVO1 checkpoint:
+
+```bash
+lerobot-rollout \
+  --policy.path=your-org/your-evo1-checkpoint \
+  --inference.type=rtc \ # optional
+  ...
+```
+
+## Results
+
+### LIBERO Evaluation
+
+> [!NOTE]
+> Benchmark results for a `lerobot`-hosted LIBERO checkpoint trained with this implementation
+> will be added once training completes.
+
+The official EVO1 LIBERO rollout protocol uses the raw LIBERO camera feature names
+(`observation.images.agentview_image` and `observation.images.robot0_eye_in_hand_image`), replans every
+14 actions, and binarizes the gripper command before stepping the simulator. The EVO1 policy postprocessor
+can crop the padded 24D action back to the 7D LIBERO action space and apply that gripper binarization. To
+evaluate a LIBERO checkpoint under the same one-episode-per-task setting, keep the raw camera names instead
+of the default `image`/`image2` mapping and set the LIBERO action postprocessing flags:
+
+```bash
+lerobot-eval \
+  --policy.path=your-org/your-evo1-libero-checkpoint \
+  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
+  --policy.device=cuda \
+  --policy.use_flash_attn=true \
+  --policy.n_action_steps=14 \
+  --policy.postprocess_action_dim=7 \
+  --policy.binarize_gripper=true \
+  --env.type=libero \
+  --env.task=libero_object \
+  --env.camera_name_mapping="{agentview_image: agentview_image, robot0_eye_in_hand_image: robot0_eye_in_hand_image}" \
+  --env.observation_height=448 \
+  --env.observation_width=448 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=1
+```
+
+## References
+
+- [EVO1 repository](https://github.com/MINT-SJTU/Evo-1)
+- [InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf)
+
+## License
+
+This LeRobot integration follows the Apache 2.0 License used by LeRobot. Check the upstream EVO1 and InternVL3 model pages for the licenses of released checkpoints and data.

docs/source/groot.mdx

@@ -1,16 +1,19 @@
-# GR00T N1.5 Policy
+# GR00T Policy
 
-GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
+GR00T is an NVIDIA foundation model family for generalized humanoid robot reasoning and skills. It is a cross-embodiment policy that accepts multimodal input, including language, images, and proprioception, to perform manipulation tasks in diverse environments.
 
-This document outlines the specifics of its integration and usage within the LeRobot framework.
+LeRobot integrates GR00T N1.7 through the `groot` policy type.
+
+> [!WARNING]
+> **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints and configs are rejected with a migration note. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).
 
 ## Model Overview
 
-NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
+GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
 
-Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
 
-GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
+GR00T uses pre-trained vision and language encoders with a flow matching action transformer to model a chunk of actions conditioned on vision, language, and proprioception.
 
 <img
   src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
@@ -28,33 +31,24 @@ This approach allows the model to be highly adaptable through post-training for
 
 ## Installation Requirements
 
-As of today, GR00T N1.5 requires flash attention for it's internal working.
-
-We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
-
-1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
-2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
+GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra:
 
 ```bash
-# Check https://pytorch.org/get-started/locally/ for your system
-pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
-pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
-pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
-python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
+pip install "lerobot[groot]"
 ```
 
-3. Install LeRobot by running:
+For a source checkout:
 
 ```bash
-pip install lerobot[groot]
+pip install -e ".[groot]"
 ```
 
 ## Usage
 
-To use GR00T in your LeRobot configuration, specify the policy type as:
+To use GR00T N1.7:
 
-```python
-policy.type=groot
+```bash
+--policy.type=groot
 ```
 
 ## Training
@@ -63,72 +57,171 @@ policy.type=groot
 
 Here's a complete training command for finetuning the base GR00T model on your own dataset:
 
+This command is using the `new_embodiment` flag, which is used for the SO-101 robot, [read more about how GR00T handles different embodiments.](https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/policy.md#--embodiment-tag).
+
 ```bash
-# Using a multi-GPU setup
-accelerate launch \
-  --multi_gpu \
-  --num_processes=$NUM_GPUS \
-  $(which lerobot-train) \
-  --output_dir=$OUTPUT_DIR \
-  --save_checkpoint=true \
-  --batch_size=$BATCH_SIZE \
-  --steps=$NUM_STEPS \
-  --save_freq=$SAVE_FREQ \
-  --log_freq=$LOG_FREQ \
-  --policy.push_to_hub=true \
+# install extra deps for training
+pip install "lerobot[training]"
+
+hf auth login
+wandb login
+
+export DATASET_NAME=your_data_set
+export HF_USER=your_hf_username
+export DATASET=$HF_USER/$DATASET_NAME
+export REPO_ID="${DATASET}_GR00T17" #this is the model that will be uploaded to huggingface
+export OUTPUT_DIR=outputs/train/$REPO_ID
+
+lerobot-train \
+  --dataset.repo_id=$DATASET \
+  --dataset.image_transforms.enable=true \
   --policy.type=groot \
+  --policy.device=cuda \
+  --policy.base_model_path=nvidia/GR00T-N1.7-3B \
+  --policy.embodiment_tag=new_embodiment \
+  --policy.chunk_size=16 \
+  --policy.n_action_steps=16 \
+  --policy.use_relative_actions=true \
+  --policy.relative_exclude_joints='["gripper"]' \
+  --policy.use_bf16=true \
+  --policy.push_to_hub=true \
   --policy.repo_id=$REPO_ID \
-  --policy.tune_diffusion_model=false \
-  --dataset.repo_id=$DATASET_ID \
+  --seed=42 \
+  --batch_size=64 \
+  --steps=20000 \
+  --save_checkpoint=true \
+  --save_freq=5000 \
+  --use_policy_training_preset=true \
+  --env_eval_freq=0 \
+  --eval_steps=0 \
+  --log_freq=10 \
+  --output_dir=$OUTPUT_DIR \
+  --job_name=$DATASET \
   --wandb.enable=true \
-  --wandb.disable_artifact=true \
-  --job_name=$JOB_NAME
+  --wandb.disable_artifact=true
+
 ```
 
 ## Performance Results
 
-### Libero Benchmark Results
+### LIBERO Benchmark Results
 
 > [!NOTE]
-> Follow our instructions for Libero usage: [Libero](./libero)
+> Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`.
 
-GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
+GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
 
-| Benchmark          | LeRobot Implementation | GR00T Reference |
-| ------------------ | ---------------------- | --------------- |
-| **Libero Spatial** | 82.0%                  | 92.0%           |
-| **Libero Object**  | 99.0%                  | 92.0%           |
-| **Libero Long**    | 82.0%                  | 76.0%           |
-| **Average**        | 87.0%                  | 87.0%           |
+### Train on LIBERO
 
-These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+Example training command for a LIBERO suite (here `libero_spatial`):
+
+```bash
+IMAGE_TRANSFORMS='{
+  "brightness": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"brightness": [0.7, 1.3]}},
+  "contrast":   {"weight": 1.0, "type": "ColorJitter", "kwargs": {"contrast":   [0.6, 1.4]}},
+  "saturation": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"saturation": [0.5, 1.5]}},
+  "hue":        {"weight": 1.0, "type": "ColorJitter", "kwargs": {"hue":        [-0.08, 0.08]}}
+}'
+
+lerobot-train \
+  --dataset.repo_id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \
+  --dataset.root=/datasets/libero_spatial \
+  --dataset.revision=main \
+  --dataset.video_backend=pyav \
+  --dataset.image_transforms.enable=true \
+  --dataset.image_transforms.max_num_transforms=4 \
+  --dataset.image_transforms.tfs="$IMAGE_TRANSFORMS" \
+  --policy.type=groot \
+  --policy.base_model_path=nvidia/GR00T-N1.7-3B \
+  --policy.embodiment_tag=libero_sim \
+  --policy.push_to_hub=false \
+  --policy.use_relative_actions=false \
+  --policy.max_steps=20000 \
+  --batch_size=320 \
+  --steps=20000 \
+  --save_freq=2000 \
+  --env_eval_freq=0 \
+  --eval_steps=0 \
+  --log_freq=10 \
+  --wandb.enable=true \
+  --wandb.project=lerobot \
+  --wandb.mode=online \
+  --wandb.disable_artifact=true \
+  --num_workers=4 \
+  --prefetch_factor=2 \
+  --persistent_workers=true \
+  --output_dir=$OUTPUT_DIR \
+  --job_name=$JOB_NAME
+```
+
+This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/LIBERO/README.md).
+
+### GR00T N1.7 LIBERO Results
+
+Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):
+
+| Suite            | Success rate | Checkpoint                                                                                                    |
+| ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- |
+| LIBERO Spatial   |          95% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) |
+| LIBERO Object    |         100% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640)   |
+| LIBERO Goal      |          98% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640)       |
+| LIBERO 10 (Long) |          93% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640)           |
+| **Average**      |    **96.5%** |                                                                                                               |
+
+```bash
+export MODEL_ID=your_trained_model_on_huggingface
+
+lerobot-eval \
+  --policy.type=groot \
+  --policy.base_model_path=$MODEL_ID \
+  --policy.embodiment_tag=libero_sim \
+  --env.type=libero \
+  --env.task=libero_spatial \
+  --eval.n_episodes=50
+```
+
+Use `eval.n_episodes >= 50` per suite when reporting success rates.
 
 ### Evaluate in your hardware setup
 
 Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example:
 
 ```bash
-lerobot-rollout\
-  --strategy.type=sentry \
-  --strategy.upload_every_n_episodes=5 \
-  --robot.type=bi_so_follower \
-  --robot.left_arm_port=/dev/ttyACM1 \
-  --robot.right_arm_port=/dev/ttyACM0 \
-  --robot.id=bimanual_follower \
-  --robot.cameras='{ right: {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30},
-    left: {"type": "opencv", "index_or_path": 2, "width": 640, "height": 480, "fps": 30},
-    top: {"type": "opencv", "index_or_path": 4, "width": 640, "height": 480, "fps": 30},
-  }' \
+# install extra deps for roullout and real hardware
+pip install "lerobot[feetech,viz]"
+
+export MODEL_ID=your_trained_model_on_huggingface
+
+# make sure that camera index matches your setup!
+# find index using `uv run lerobot-find-cameras opencv`
+WRIST_CAM='wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30, fourcc: "MJPG"}'
+FRONT_CAM='front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30, fourcc: "MJPG"}'
+export ROBOT_CAMERAS="{ $WRIST_CAM, $FRONT_CAM }"
+export ROBOT_ID=follower_robot
+export ROBOT_PORT=/dev/ttyACM0
+
+uv run lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=$MODEL_ID \
+  --policy.base_model_path=nvidia/GR00T-N1.7-3B \
+  --policy.n_action_steps=8 \
+  --robot.type=so101_follower \
+  --robot.port=$ROBOT_PORT \
+  --robot.id=$ROBOT_ID \
+  --robot.cameras="$ROBOT_CAMERAS" \
+  --task="place the vial in the rack" \
+  --duration=60 \
+  --device=cuda \
   --display_data=true \
-  --dataset.repo_id=<user>/eval_groot-bimanual  \
-  --dataset.single_task="Grab and handover the red cube to the other arm" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.rgb_encoder.vcodec=auto \
-  --policy.path=<user>/groot-bimanual \ # your trained model
-  --duration=600
+  --inference.type=rtc \
+  --inference.rtc.enabled=True \ # set to False if it causes inference instability
+  --inference.rtc.execution_horizon=8 \
+  --inference.queue_threshold=0
 ```
 
+> [!NOTE]
+> Value of `inference.queue_threshold` should not exceed 5 to ensure stable inference.
+
 ## License
 
-This model follows NVIDIA's proprietary license, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T). Future versions (starting from N1.7) will follow **Apache 2.0 License**.
+GR00T N1.7 is released under the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

docs/source/isaac_teleop.mdx

@@ -0,0 +1,397 @@
+# Isaac Teleop
+
+Control your robot with NVIDIA [Isaac Teleop](https://github.com/NVIDIA/IsaacTeleop), a
+multi-modal teleoperation framework. Isaac Teleop drives a single `TeleopSession` from a range
+of input devices — XR (VR) controllers, hand tracking, full-body tracking, Manus gloves, foot
+pedals, and more.
+
+In LeRobot, Isaac Teleop ships as a self-contained example under
+[`examples/isaac_teleop_to_so101/`](https://github.com/huggingface/lerobot/tree/main/examples/isaac_teleop_to_so101).
+Each Isaac Teleop input device is its own `Teleoperator` subclass in the example's
+`isaac_teleop` package, sharing one session lifecycle (see `IsaacTeleopTeleoperator`). The
+devices available today are the **XR controller** (`XRController`) and a back-drivable
+**SO-101 leader arm** (`SO101LeaderArm`); Manus gloves and hand/full-body tracking are the
+natural next devices. This guide focuses on the XR controller; the SO-101 leader is summarized
+under [Run the example](#step-3-run-the-example).
+
+**In this guide you'll learn:**
+
+- How an Isaac Teleop device drives a robot end‑effector (EE) target
+- How the _clutch_ (squeeze/grip on the XR controller) engages teleoperation without jerking the arm
+- How to run the SO‑101 teleoperation example and tune motion / gripper / IK
+
+## Installation
+
+The example lives in the LeRobot repository (it is not part of the `lerobot` pip package), so
+clone the repo and install from source. The canonical, always-up-to-date install and usage
+reference is the example's
+[`README.md`](https://github.com/huggingface/lerobot/tree/main/examples/isaac_teleop_to_so101/README.md);
+in short:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+uv pip install -e ".[feetech,kinematics,dataset]" "huggingface_hub>=1.5"
+uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131" "scipy>=1.14"
+```
+
+`isaacteleop` is published on public PyPI (Linux only). The `cloudxr` extra brings the CloudXR
+runtime bindings; `retargeters-lite` is the scipy-based retargeter path that resolves on both
+x86_64 and ARM (on aarch64 — e.g. a DGX Spark — the full `retargeters` extra does not resolve
+because of its `dex-retargeting`/`nlopt` pins, which is why it is not the default here). On
+x86_64 you can additionally install the full retargeter stack:
+
+```bash
+uv pip install "isaacteleop[retargeters]~=1.3.131"
+```
+
+### Set up CloudXR and connect a headset
+
+Isaac Teleop streams the headset to your machine over **NVIDIA CloudXR**, which provides the
+OpenXR runtime the session connects to. By default LeTeleop **auto-launches the CloudXR runtime
+for you** when you call `teleop_device.connect()` — you no longer have to run `python -m
+isaacteleop.cloudxr` and `source cloudxr.env` in a separate shell. All you need is a supported
+headset connected and the CloudXR firewall ports open. Follow the Isaac Teleop
+[Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html) for the
+headset-pairing and firewall details.
+
+**First run (EULA).** The very first launch must accept the NVIDIA CloudXR EULA. The auto-launch
+prompts for it **on stdin**, so on a headless machine it will hang waiting for input. Bootstrap
+the EULA once, interactively, with:
+
+```bash
+python -m isaacteleop.cloudxr --accept-eula   # one-time: accept the CloudXR EULA
+```
+
+After that, `connect()` launches the runtime non-interactively. The launch **blocks for ~30s**
+while the runtime comes up.
+
+**Configuration.** Two fields on `IsaacTeleopConfig` (shared by every device) control this:
+
+- `auto_launch_cloudxr` (default `True`) — whether `connect()` starts the runtime. Set `False`
+  when CloudXR is already running externally.
+- `cloudxr_env_file` (default `None`) — an optional CloudXR device-profile `.env` selecting the
+  headset transport (e.g. an Apple Vision Pro profile). This is launcher **input**; it is not the
+  `~/.cloudxr/run/cloudxr.env` **output** file the old manual flow told you to `source`. `None`
+  keeps the default auto-WebRTC profile — though the SO-101 example overrides it to the
+  `default.env` shipped next to `teleoperate.py` unless you pass `--teleop.cloudxr_env_file`.
+
+**Opting out.** To skip the auto-launch (CloudXR already running), either set
+`auto_launch_cloudxr=False` or export:
+
+```bash
+export LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1
+```
+
+The **env var takes precedence over the config field**: if `LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1` is
+set, the auto-launch is skipped even when `auto_launch_cloudxr=True`. This variable is
+**independent** of Isaac Lab's `ISAACLAB_CXR_SKIP_AUTOLAUNCH` — setting one does not affect the
+other.
+
+**One teleoperator per process.** The CloudXR runtime configures the environment process-wide (a
+singleton), so run a single Isaac Teleop teleoperator per process.
+
+**Shutting down.** Always call `teleop_device.disconnect()` on exit — including on Ctrl-C. Wrap
+your teleoperation loop in `try/finally` and call `disconnect()` in the `finally`. This tears down
+the OpenXR session **before** the CloudXR runtime, which is the required order; the launcher's
+`atexit` hook only reaps the runtime and does not run the session's `__exit__`, so without an
+explicit `disconnect()` an interrupted run shuts down in the wrong order.
+
+```python
+teleop_device.connect()
+try:
+    while True:
+        action = teleop_device.get_action()
+        # ... drive the robot ...
+finally:
+    teleop_device.disconnect()
+```
+
+See [System Requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html)
+for supported OS / GPU / CloudXR versions and headsets.
+
+## How it works
+
+The XR controller is one Isaac Teleop **input** device. `XRController` is a deliberately thin
+reader: it exposes the **raw** controller grip pose — already statically rebased into the robot
+base frame — plus the squeeze and trigger analog values. It has **no** retargeters and **no**
+clutch logic of its own. The clutch (engage latch + delta rebasing onto the EE) and the gripper
+mapping live downstream in the example loop, which then feeds LeRobot's existing closed‑loop
+Cartesian IK pipeline — the same one the phone teleoperator uses. The device‑specific pieces are
+`XRController`, the loop's `Clutch`, and `MapXRControllerActionToRobotAction`; everything downstream
+(`EEBoundsAndSafety`, `InverseKinematicsEEToJoints`) is shared, and a future device (e.g. Manus
+gloves) would swap in its own `teleop_<device>.py` + processor while reusing the rest.
+
+`XRController._build_pipeline` wires Isaac Teleop's `ControllersSource` — statically rebased into
+the robot base frame by the native `ControllerTransform` (`base_T_anchor`) — and exposes the
+transformed controller stream verbatim. `get_action()` reads the grip pose, squeeze, and trigger
+straight off it; the session is always stepped `RUNNING` (there is no clutch retargeter to gate).
+
+The `Clutch` class (in `examples/isaac_teleop_to_so101/isaac_teleop/clutch.py`, driven by the
+loop in `common.py`) mirrors Isaac Teleop's `SO101ClutchRetargeter`, but lives in-loop so the
+device can stay a thin reader:
+
+- It latches its engage origin on the squeeze **engage edge** (the frame the squeeze first crosses
+  `clutch_threshold`) and rebases both position and orientation around it, so engaging does not
+  teleport the arm. `Clutch.rebase` returns the absolute base-frame target as a `(pos, quat)`
+  pair, which the loop concatenates into the 7D `ee_pose` fed to the processor.
+- The analog trigger becomes a gripper `closedness` in `[0, 1]` (0 = open, 1 = closed),
+  proportional to the trigger pull, which `MapXRControllerActionToRobotAction` maps to a jaw target.
+
+See the Isaac Teleop
+[Retargeting interface](https://nvidia.github.io/IsaacTeleop/main/references/retargeting/index.html)
+and [architecture overview](https://nvidia.github.io/IsaacTeleop/main/overview/architecture.html)
+for how source nodes and retargeters compose.
+
+```text
+  VR controller (OpenXR)
+        │
+        ▼
+  XRController.get_action()          ── raw base-frame grip_pos / grip_quat + squeeze + trigger
+        │                                (TeleopSession always stepped RUNNING; clutch lives downstream)
+        ▼
+  Clutch.rebase(grip_pos, grip_quat) ── engage-relative delta applied to the EE home (pos + orient)
+        │  ee_pose (7) / closedness       → absolute ee_pose; closedness = trigger
+        ▼
+  MapXRControllerActionToRobotAction ── absolute ee.x/y/z; ee.w* = orientation rotvec target;
+        │  ee.x/y/z / ee.w* / ee.gripper_pos   ee.gripper_pos = (1 - closedness) * 100
+        ▼
+  EEBoundsAndSafety                ── workspace clip + per-frame step clamp (clamp+warn)
+        │
+        ▼
+  InverseKinematicsEEToJoints      ── closed-loop Placo IK; position + soft-orientation
+        │  (orientation_weight=0.01)   (passes ee.gripper_pos → gripper.pos)
+        ▼
+  SO-101 follower joint targets
+```
+
+### The clutch: owned by the example loop
+
+Unlike the phone pipeline (which splits the clutch across `MapPhoneActionToRobotAction` and
+`EEReferenceAndDelta`), the XR clutch lives entirely in the example loop's `Clutch` class. It emits
+an **absolute** EE pose, so there is no `EEReferenceAndDelta` stage and no delta accumulation in the
+processor — `MapXRControllerActionToRobotAction` is a pure, stateless per‑frame mapping.
+
+The clutch latches its engage origin on the squeeze **engage edge** (the moment the squeeze crosses
+`clutch_threshold`) and drives the EE from the motion _relative_ to that origin, so the arm does not
+teleport on engage. On **every** engage — startup and mid‑task re‑clutch alike — the home
+_position_ is latched from forward kinematics on the arm's **measured joints**, so the home equals
+where the arm physically is even if it moved while disengaged, and the engage is jump‑free. The
+home _orientation_ keeps the last commanded rotation: the 5‑DOF arm tracks orientation only
+softly, so latching the measured wrist orientation would inject its tracking offset into the
+command on every re‑clutch.
+
+## Controls
+
+- **Squeeze / grip** — the **clutch** (deadman). Hold it past `clutch_threshold` to engage
+  teleoperation; release to pause. Each engage re‑captures the origin, so you can reposition
+  your hand while paused and re‑engage without the arm jumping (index/clutch style).
+- **Trigger** — the **gripper**, controlled **analog**. The jaw tracks the trigger
+  proportionally — a half‑pressed trigger leaves the jaw half‑closed — via a closedness in
+  `[0, 1]` (0 = open, 1 = closed) that maps to an absolute gripper joint target.
+- **Controller orientation** — the **wrist**. The clutch rebases the controller orientation
+  (engage‑relative, base‑frame) into a soft IK orientation target the wrist tracks alongside
+  position. On the 5‑DOF SO‑101 the wrist follows the hand only partially by design — see
+  `orientation_weight` below.
+
+## Get started
+
+### Step 1: Create the teleoperator
+
+```python
+# Run from the repo root so the `examples` package is importable.
+from examples.isaac_teleop_to_so101.isaac_teleop import XRController, XRControllerConfig
+
+teleop_config = XRControllerConfig(
+    hand_side="right",      # "left" or "right" controller
+    clutch_threshold=0.5,   # squeeze value above which the clutch engages
+)
+teleop_device = XRController(teleop_config)
+```
+
+`XRController.get_action()` returns the **raw** base‑frame controller pose, not a clutch‑rebased
+target: `grip_pos` (3,) `[x, y, z]` [m] and `grip_quat` (4,) `[qx, qy, qz, qw]` in the robot base
+frame, plus scalar `squeeze` and `trigger` analog values in `[0, 1]`. The example loop's `Clutch`
+turns these into the absolute `ee_pose`, and the squeeze is thresholded by the loop against
+`clutch_threshold` to engage.
+
+### Step 2: Connect
+
+Calling `teleop_device.connect()` first auto-launches the CloudXR runtime (unless you opted out —
+see [Set up CloudXR and connect a headset](#set-up-cloudxr-and-connect-a-headset); this blocks for
+~30s and on the first run prompts for the EULA on stdin), then starts the Isaac Teleop
+[`TeleopSession`](https://nvidia.github.io/IsaacTeleop/main/getting_started/teleop_session.html)
+(opens the OpenXR session and discovers the controllers). XR controllers are self‑calibrating, so
+there is no manual calibration step — the clutch handles re‑centering each time you engage. Pair
+`connect()` with a `try/finally` that calls `disconnect()` so the session tears down before the
+runtime on exit/Ctrl-C.
+
+### Step 3: Run the example
+
+The example assumes you configured your robot (SO‑101 follower) and set the correct serial port.
+
+The **robot URDF and its meshes are fetched automatically** on first run: the XR device downloads
+the SO-101 URDF from the
+[`lerobot/robot-urdfs` Hugging Face bucket](https://huggingface.co/buckets/lerobot/robot-urdfs/tree/so101)
+into the LeRobot cache (`HF_LEROBOT_HOME/robot-urdfs/so101/`) and reuses it after, so there is no
+separate download step :
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm --teleop.type=xr_controller
+```
+
+The CLI is `lerobot-teleoperate`-style (draccus): `--robot.*` configures the SO-101 follower and
+`--teleop.type` selects the Isaac input device (`xr_controller` | `so101_leader`), with
+`--teleop.*` its device knobs. `--teleop.type=xr_controller` runs the XR-controller path described
+above. The startup safety contract: by default it slews all joints to a default reset pose over
+`--reset_duration` seconds (`--reset_to_origin=false` keeps the arm where it is), then seeds the
+clutch home from the arm's measured pose so the first engage is jump-free; the follower is
+commanded only while the clutch is engaged.
+
+**Customizing the reset pose.** The reset pose ships as a built-in default (a comfortable mid-range
+pose) and works out of the box — you do **not** need to record anything. To tailor it to your setup,
+back-drive the arm to the pose you want and run
+`python -m examples.isaac_teleop_to_so101.override_reset_pose --id <robot.id>`; it writes the
+current joints to a per-arm file in the LeRobot cache
+(`HF_LEROBOT_HOME/reset_poses/<robot.name>/<robot.id>.json`, keyed like calibration), which then takes
+priority over the built-in default on the next run. Because it lives in the user-local cache (not
+the repo), your override stays on your machine, and both `teleoperate` and `record` honor it
+when launched with the same `--robot.id`.
+
+The other device, `--teleop.type=so101_leader`, mirrors the follower 1:1 from a back-drivable
+SO-101 _leader arm_ whose joints are streamed by Isaac Teleop's native `so101_leader` plugin (no
+clutch, no IK — the leader and follower share the SO-101 kinematics).
+
+The `so101_leader_plugin` binary is a C++ plugin that is **not** part of the `isaacteleop` pip
+package — you build it from the Isaac Teleop source tree. Follow
+[Build Isaac Teleop from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html)
+(in short, from your Isaac Teleop checkout: `cmake -B build && cmake --build build --parallel &&
+cmake --install build`); the build installs the plugins under `<IsaacTeleop>/install/plugins/`, so
+the binary lands at `install/plugins/so101_leader/so101_leader_plugin` — the `--launch_plugin` path
+below. See the plugin's own `README.md` (next to the binary) for its serial/calibration details.
+
+Point `--teleop.port` at the physical leader's serial port and `--launch_plugin` at that plugin
+binary to have the script spawn it after CloudXR is up:
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm --teleop.type=so101_leader \
+    --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+    --launch_plugin=/code/Teleop/install/plugins/so101_leader/so101_leader_plugin
+```
+
+(Note `so101_leader` here is the _Isaac_ leader, resolved against the Isaac Teleop device
+registry, distinct from `lerobot-teleoperate`'s serial `so101_leader`.) When a `--teleop.port` is
+set, the plugin's tick→radian calibration is inferred from `--teleop.id` and passed to the plugin
+as its third positional arg — the LeRobot-format JSON at
+`HF_LEROBOT_CALIBRATION/teleoperators/so_leader/<id>.json`, the same file the serial SO-101 leader
+uses (`lerobot-calibrate --teleop.type=so101_leader --teleop.id=<id>`). If it is missing the script
+warns and the plugin uses built-in defaults. Run `python -m examples.isaac_teleop_to_so101.teleoperate --help` for all flags. Its
+startup safety contract: by default the follower is
+slewed to the leader's first reading over `--align_duration` seconds (`--align=false` to skip) so
+the arm does not snap when the mirror begins, and while the leader stream is stale the follower is
+held at its measured pose.
+
+The URDF fetch uses `huggingface_hub` (already a LeRobot dependency) against the public
+`lerobot/robot-urdfs` bucket, so it needs no login. It is cached under
+`HF_LEROBOT_HOME/robot-urdfs/so101/`; delete that folder to force a re‑download.
+
+Then, in your headset: squeeze and hold the grip to engage, move the controller to drive the
+arm, twist/tilt it to orient the wrist, and press the trigger to close the gripper
+(proportionally — release to open).
+
+To record a dataset (not just teleoperate), use `record.py` in the same folder. It dispatches on
+`--teleop.type` (`xr_controller` | `so101_leader`) exactly like `teleoperate.py`, so either device
+can drive the follower, and it saves the commanded joints to a LeRobot dataset (`lerobot-record`-style
+`--dataset.*` flags). See its module docstring for the full CLI and the keyboard recording shortcuts.
+
+## Important pipeline steps and options
+
+The clutch already produces an absolute base‑frame pose, so the processor side is a thin
+**absolute‑pose** path — there is no frame remap, no delta accumulation, and no
+`EEReferenceAndDelta` stage.
+
+- `MapXRControllerActionToRobotAction` is a stateless per‑frame mapping from the device output to
+  the IK input contract. It writes the absolute base‑frame position, encodes the absolute
+  orientation as a rotvec target, and inverts the closedness into a motor gripper target:
+
+  ```python
+  action["ee.x"], action["ee.y"], action["ee.z"] = ee_pose[:3]   # absolute, base frame [m]
+  action["ee.wx"], action["ee.wy"], action["ee.wz"] = orient_rotvec  # orientation target (rotvec)
+  action["ee.gripper_pos"] = (1 - closedness) * 100   # motor units; SO-101 calibrates 100 = open
+  ```
+
+  The gripper polarity (`100 = open, 0 = closed`) is a hardware‑calibration convention in the source — flip it there if the jaw opens when it should close.
+
+- `EEBoundsAndSafety` clamps the EE to a workspace and rate‑limits per‑frame jumps. The clutch's
+  no‑teleport keeps frames small, so `max_ee_step_m` mostly catches transient controller tracking
+  glitches. The z floor is `0.0` (the table plane) so a stray target cannot drive the EE below the
+  table; x/y stay at the loose `[-1, 1]` m box. Set `raise_on_jump=False` so an over‑limit frame is
+  **clamped and warned** instead of raising — a crash mid‑loop would leave the arm uncontrolled:
+
+  ```python
+  EEBoundsAndSafety(
+      end_effector_bounds={"min": [-1.0, -1.0, 0.0], "max": [1.0, 1.0, 1.0]},
+      max_ee_step_m=0.10,
+      raise_on_jump=False,
+  )
+  ```
+
+- `InverseKinematicsEEToJoints(initial_guess_current_joints=False, orientation_weight=0.01)` solves
+  closed‑loop Placo IK. SO‑101 is a 5‑DOF arm, so the IK is position‑dominant; the small
+  `orientation_weight` lets it softly track the orientation target carried in `ee.w*` so the wrist
+  follows the hand, while the under‑determined roll stays partial by design. There is **no**
+  `GripperVelocityToJoint`: the absolute `ee.gripper_pos` is passed straight to `gripper.pos`.
+  `initial_guess_current_joints=False` warm‑starts each solve from the **previous IK solution**
+  rather than re‑seeding from the measured joints, so the joint trajectory stays continuous
+  frame‑to‑frame. Tune `orientation_weight` on hardware — too high fights position tracking, too
+  low ignores the orientation command.
+
+The example also gates safety at the loop level: after the startup reset slew (on by default —
+pass `--reset_to_origin=false` to keep the arm where it is), it commands the robot **only while
+the clutch is engaged**, and re‑sends the measured joints while disengaged, so releasing the
+clutch freezes the arm in place.
+
+See the [Processors for Robots and Teleoperators](./processors_robots_teleop) guide for more on
+adapting the pipeline to other robots.
+
+## Troubleshooting
+
+- **`ModuleNotFoundError: isaacteleop`** — the `isaacteleop` package is not installed in the
+  active environment. Re-run the install command at the top of this guide:
+  `uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131"`.
+- **No controllers found** — make sure the CloudXR runtime is running, the firewall ports are
+  whitelisted, and the headset is connected (see
+  [Set up CloudXR and connect a headset](#set-up-cloudxr-and-connect-a-headset) and the Isaac
+  Teleop [Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html)).
+- **CloudXR auto-launch failed** — `connect()` raises a `RuntimeError` if the runtime does not
+  come up within its startup timeout. Check the launcher logs under `~/.cloudxr/logs`. Common
+  causes: the EULA was never accepted (run `python -m isaacteleop.cloudxr --accept-eula` once,
+  interactively — the auto-launch prompts on stdin and hangs headless), or the runtime is already
+  running externally (set `LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1` or `auto_launch_cloudxr=False` to
+  skip the auto-launch).
+- **Arm does not move** — the clutch is a deadman: you must hold the squeeze/grip past
+  `clutch_threshold`. Lower the threshold if your controller's squeeze is reported softly.
+- **Motion feels misaligned** — confirm the headset/play space orientation. The controller stream
+  is rebased into the robot base frame by the `base_T_anchor` transform on `XRControllerConfig`
+  (default: standard OpenXR → robot axis convention); adjust it if your anchor frame differs.
+
+## Learn more
+
+NVIDIA Isaac Teleop documentation ([docs home](https://nvidia.github.io/IsaacTeleop/),
+[GitHub](https://github.com/NVIDIA/IsaacTeleop)):
+
+- [Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html) —
+  install, run the CloudXR server, connect a headset, run a teleop example.
+- [TeleopSession](https://nvidia.github.io/IsaacTeleop/main/getting_started/teleop_session.html) —
+  the session API `XRController` wraps.
+- [Retargeting interface](https://nvidia.github.io/IsaacTeleop/main/references/retargeting/index.html)
+  and [architecture overview](https://nvidia.github.io/IsaacTeleop/main/overview/architecture.html) —
+  how source nodes and retargeters compose into a pipeline.
+- [Build from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html) —
+  build `isaacteleop` (and its C++ plugins, including the `so101_leader` plugin used above) from a
+  local checkout.
+- [System Requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html) and
+  the [CloudXR SDK docs](https://docs.nvidia.com/cloudxr-sdk) — supported platforms, GPUs,
+  CloudXR/OpenXR runtime versions, and headsets.

docs/source/policy_evo1_README.md

@@ -0,0 +1,18 @@
+# EVO1
+
+EVO1 is a Vision-Language-Action policy for robot control. The LeRobot
+integration uses an InternVL3 vision-language backbone with a flow-matching
+action head, and supports staged training through the standard LeRobot policy
+APIs.
+
+The upstream EVO1 project is available at
+[MINT-SJTU/Evo-1](https://github.com/MINT-SJTU/Evo-1).
+
+```bibtex
+@misc{evo1,
+  title = {EVO1},
+  author = {{MINT-SJTU}},
+  year = {2025},
+  howpublished = {\url{https://github.com/MINT-SJTU/Evo-1}},
+}
+```

docs/source/policy_groot_README.md

@@ -1,6 +1,13 @@
 ## Research Paper
 
-Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
+GR00T N1 technical report (covers the GR00T N1.x family, including N1.7): https://arxiv.org/abs/2503.14734
+
+GR00T N1.7 model card: https://huggingface.co/nvidia/GR00T-N1.7-3B
+
+GR00T N1.5 research page (earlier version): https://research.nvidia.com/labs/gear/gr00t-n1_5/
+
+> GR00T N1.5 support was removed from LeRobot; the last release supporting it is `lerobot==0.5.1`.
+> Current releases support GR00T N1.7 only.
 
 ## Repository
 
@@ -24,4 +31,108 @@ Code: https://github.com/NVIDIA/Isaac-GR00T
 
 Blog: https://developer.nvidia.com/isaac/gr00t
 
-Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B
+Hugging Face Models:
+
+- GR00T N1.7: https://huggingface.co/nvidia/GR00T-N1.7-3B
+- GR00T N1.7 LIBERO checkpoints: https://huggingface.co/nvidia/GR00T-N1.7-LIBERO
+
+<details>
+<summary><b>Original-vs-LeRobot parity test</b></summary>
+
+## Original-vs-LeRobot parity test
+
+`tests/policies/groot/test_groot_vs_original.py` verifies this LeRobot
+reimplementation of GR00T N1.7 (Qwen3-VL backbone + flow-matching action head)
+against NVIDIA's original `gr00t` package with two comparisons, each parametrized
+over every embodiment tag present in the checkpoint:
+
+1. **Model parity** — given byte-identical pre-processed inputs and the same
+   flow-matching seed (recorded in each artifact), both implementations must produce
+   the **same raw model output** (`get_action(...)["action_pred"]`, the normalized
+   flow-matching prediction). Output shapes must match exactly; any action-horizon
+   or action-dim mismatch fails the test.
+2. **Preprocessor parity** — given the identical raw observations (per-camera
+   frames, state vectors, language instruction), LeRobot's own preprocessor pipeline
+   (real Qwen3-VL chat template / tokenizer / image packing + checkpoint-driven
+   state normalization, no mocks) must produce the **same collated model inputs**
+   (`input_ids`, `attention_mask`, `pixel_values`, `image_grid_thw`, `state`,
+   `embodiment_id`) as the original package's processor.
+
+### Why two environments
+
+The original `gr00t` package pins `transformers==4.57.3` (Python 3.10); this
+integration requires `transformers>=5.x` (Qwen3-VL). Under 5.x, `PretrainedConfig`
+is itself a defaulted dataclass, so the original config dataclasses fail to import
+(`non-default argument follows default argument`). The two implementations therefore
+**cannot be imported in the same Python process**.
+
+So the test uses a **producer / consumer** split across two venvs:
+
+1. **Producer** — `tests/policies/groot/utils/dump_original_n1_7.py`, run in the _original_
+   gr00t venv. For each embodiment it builds dummy inputs generically from the
+   checkpoint metadata (state dims from `statistics.json`; camera/language keys from
+   the processor modality configs), runs the original model, and saves to one `.npz`
+   per tag: the raw observations (`raw::` keys), the exact collated inputs
+   (`in::` keys), the seed, and the raw `action_pred`.
+2. **Consumer** — the pytest above, run in the _LeRobot_ venv. It discovers every
+   `.npz`; the model-parity case replays the byte-identical collated inputs through
+   the LeRobot model with the recorded seed and asserts the outputs match, and the
+   preprocessor-parity case replays the raw observations through LeRobot's full
+   preprocessor pipeline and asserts the collated tensors match.
+
+> Artifacts generated by older versions of the dump script contain no `raw::`
+> fields; the preprocessor-parity case then **skips** with a regeneration hint.
+> Re-run the producer to refresh them.
+
+### Fairness controls
+
+- **Same pre-processed inputs (model parity)** — the original processor's `input_ids`,
+  `pixel_values`, `image_grid_thw`, `attention_mask`, `state`, `embodiment_id` are
+  fed verbatim to the LeRobot model (no re-tokenization / re-normalization), so the
+  model comparison isolates the model. LeRobot's own tokenization / image packing is
+  covered separately by the preprocessor-parity case, which compares its output
+  against those same collated tensors from identical raw observations.
+- **Same precision + attention kernel** — both sides run **fp32 + SDPA**. The
+  original defaults to `use_flash_attention=True` (flash_attention_2 + bf16); the
+  producer forces SDPA + fp32. (With the defaults the gap is ~3e-2 — pure
+  kernel/rounding noise, not an implementation difference.)
+- **Same flow-matching seed** — fixed right before sampling on both sides; the
+  producer records it in each artifact (`--seed`, default 42) and the consumer
+  replays the recorded value.
+
+### How to run
+
+```bash
+# Resolve a local checkpoint (GR00T-N1.7-LIBERO / libero_10)
+CKPT=$(python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+print(os.path.join(snapshot_download("nvidia/GR00T-N1.7-LIBERO",
+      allow_patterns=["libero_10/*"]), "libero_10"))
+PY
+)
+
+# 1) Produce the original-side artifacts for all embodiments (original gr00t venv, CUDA)
+CUDA_VISIBLE_DEVICES=0 /path/to/Isaac-GR00T/.venv-original/bin/python \
+    tests/policies/groot/utils/dump_original_n1_7.py \
+    --ckpt "$CKPT" --out-dir tests/policies/groot/artifacts --device cuda --seed 42
+
+# 2) Run the parity test (LeRobot venv) — one parametrized case per embodiment
+CUDA_VISIBLE_DEVICES=0 GROOT_PARITY_DEVICE=cuda \
+    uv run pytest tests/policies/groot/test_groot_vs_original.py -v -s
+```
+
+The `.npz` artifacts are local-only (gitignored, ~6–10 MB each) and are regenerated by
+the producer; they are never committed. The tests **skip** (do not fail) on CI or
+when the checkpoint / artifacts are absent.
+
+#### Env knobs (all optional)
+
+| Var                                       | Default                          | Purpose                               |
+| ----------------------------------------- | -------------------------------- | ------------------------------------- |
+| `GROOT_N1_7_PARITY_DIR`                   | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
+| `GROOT_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
+| `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
+| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |
+
+</details>

docs/source/recap_value_function_experiments.mdx

@@ -1,119 +0,0 @@
-# RECAP value-function experiments
-
-All variants use the same `mc_return`, `is_terminal`, 201-bin support, Dirac/HL-Gauss
-targets, metrics, and LeRobot training pipeline. Only the representation backbone changes.
-
-## Current RECAP Gemma3 baseline
-
-```bash
-lerobot-train \
-  --reward_model.type=distributional_value_function \
-  --reward_model.target_method=dirac_delta \
-  --reward_model.device=cuda \
-  --dataset.repo_id=<dataset_repo_id> \
-  --output_dir=outputs/vf_recap_gemma3 \
-  --steps=40000 \
-  --batch_size=8
-```
-
-This initializes SigLIP2 and Gemma3-270M from unimodal checkpoints and creates a
-fresh Gemma3 multimodal connector.
-
-## Temporal SigLIP2
-
-```bash
-lerobot-train \
-  --reward_model.type=temporal_siglip_value_function \
-  --reward_model.history_steps=6 \
-  --reward_model.frame_gap=30 \
-  --reward_model.target_method=dirac_delta \
-  --reward_model.device=cuda \
-  --dataset.repo_id=<dataset_repo_id> \
-  --output_dir=outputs/vf_temporal_siglip2 \
-  --steps=40000 \
-  --batch_size=16
-```
-
-The dataset factory supplies six past-only frames for every observation key.
-The model requires `observation.state` and all configured camera streams.
-
-## nanoVLM-460M
-
-Run a frozen-backbone probe first:
-
-```bash
-lerobot-train \
-  --reward_model.type=nanovlm_value_function \
-  --reward_model.nanovlm_pretrained_path=lusxvr/nanoVLM-460M-8k \
-  --reward_model.freeze_vision_encoder=true \
-  --reward_model.freeze_multimodal_projector=true \
-  --reward_model.freeze_language_model=true \
-  --reward_model.device=cuda \
-  --dataset.repo_id=<dataset_repo_id> \
-  --output_dir=outputs/vf_nanovlm_probe \
-  --steps=5000 \
-  --batch_size=1
-```
-
-The released checkpoint's native preprocessing resizes the long image side to
-2048 and creates 512px global/split views. A 480x640 camera therefore produces
-13 vision inputs and roughly 832 image placeholders; use batch size 1 initially
-for a three-camera setup.
-
-Then load the probe checkpoint and selectively fine-tune the projector/decoder
-at a lower learning rate.
-
-## Standalone Gemma3 VLM alignment
-
-The VLM trainer is intentionally separate from LeRobot:
-
-```bash
-cd third_party/nanoVLM
-
-# Projector warmup
-torchrun --standalone --nproc_per_node=4 train_recap_gemma3.py \
-  --output_dir=checkpoints/recap_vlm_warmup \
-  --steps=8000 \
-  --freeze_language_model
-
-# Full multimodal alignment
-torchrun --standalone --nproc_per_node=4 train_recap_gemma3.py \
-  --resume_from_checkpoint=checkpoints/recap_vlm_warmup/final \
-  --output_dir=checkpoints/recap_vlm_aligned \
-  --steps=50000
-```
-
-The output is a standard Hugging Face `Gemma3ForConditionalGeneration`
-checkpoint.
-
-## Aligned RECAP value function (run last)
-
-```bash
-lerobot-train \
-  --reward_model.type=distributional_value_function \
-  --reward_model.vlm_pretrained_path=third_party/nanoVLM/checkpoints/recap_vlm_aligned/final \
-  --reward_model.freeze_vision_encoder=true \
-  --reward_model.device=cuda \
-  --dataset.repo_id=<dataset_repo_id> \
-  --output_dir=outputs/vf_recap_aligned \
-  --steps=40000 \
-  --batch_size=8
-```
-
-## Small-batch verification
-
-Before each full run:
-
-```bash
-uv run python scripts/overfit_vf_variant.py \
-  --dataset_repo_id=<dataset_repo_id> \
-  --reward_type=<distributional_value_function|temporal_siglip_value_function|nanovlm_value_function> \
-  --num_samples=16 \
-  --steps=500
-```
-
-For `nanovlm_value_function`, start with `--num_samples=2` because all overfit
-samples are held in one batch and native image tiling is memory intensive.
-
-Compare runs using held-out episode NLL/MAE, per-episode return rank correlation,
-terminal success/failure separation, and the matched-versus-shuffled image loss gap.

docs/source/video_encoding_parameters.mdx

@@ -6,12 +6,11 @@ Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel
 
 You can set these parameters from the CLI with `--dataset.rgb_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
 
-<Tip>
-  Video storage must be on for `rgb_encoder` to have any effect —
-  `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
-  recording default). With video off, inputs stay as images and `rgb_encoder` is
-  ignored.
-</Tip>
+> [!TIP]
+> Video storage must be on for `rgb_encoder` to have any effect —
+> `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
+> recording default). With video off, inputs stay as images and `rgb_encoder` is
+> ignored.
 
 For details on **when** frames are written vs. encoded (streaming vs. post-episode), queues, and other top-level `--dataset.*` switches, see [Streaming Video Encoding](./streaming_video_encoding). For an encoding-parameter comparison and experiments, see the [video-benchmark Space](https://huggingface.co/spaces/lerobot/video-benchmark).
 
@@ -43,12 +42,10 @@ lerobot-record \
 
 ## Tuning parameters
 
-<Tip warning={true}>
-The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
-
-Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.
-
-</Tip>
+> [!WARNING]
+> The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
+>
+> Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.
 
 All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
 
@@ -69,25 +66,92 @@ All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
 
 Depth maps (Intel RealSense, Reachy 2) are stored as their **own video streams** alongside the RGB streams. Raw depth (`uint16` millimetres or `float32` metres) can't survive an 8-bit codec, so LeRobot **quantizes** each map to a 12-bit code (`[0, 4095]`) — logarithmically by default, to match the `1/depth` error profile of depth sensors — then packs it into a high-bit-depth pixel format (`gray12le`) and encodes it with a 12-bit codec.
 
-```mermaid
-flowchart LR
-    A["Raw depth (uint16 mm / float32 m)"] --> B["Clip to depth_min, depth_max"]
-    B --> C["Quantize to 12-bit code 0–4095 (log or linear)"]
-    C --> D["Pack into gray12le"]
-    D --> E["Encode video (hevc Main 12)"]
-    E --> F[("MP4 + metadata: depth_min/max, shift, use_log")]
-    F -. "load time (depth_output_unit)" .-> G["Dequantize to mm or m"]
-
-    classDef input fill:#e3f2fd,stroke:#1565c0,color:#0d47a1;
-    classDef encode fill:#ede7f6,stroke:#5e35b1,color:#311b92;
-    classDef store fill:#fff8e1,stroke:#f9a825,color:#e65100;
-    classDef load fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20;
-
-    class A input;
-    class B,C,D,E encode;
-    class F store;
-    class G load;
-```
+<div style="margin:28px 0;padding:14px 0;">
+  <div style="margin:0 auto;display:flex;flex-wrap:wrap;justify-content:center;align-items:stretch;gap:6px;font-family:'Source Sans 3',ui-sans-serif,system-ui,sans-serif;font-size:14px;font-weight:600;color:#1B1B1D;">
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#DBEAFE;color:#1D4ED8;border-radius:9px;padding:8px 12px;">
+      <span>Raw depth</span>
+      <span style="font-size:11px;font-weight:400;color:#3B6FD4;white-space:nowrap;">
+        uint16 mm
+        <br />
+        float32 m
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <div style="border:2px dashed #C4B5FD;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:stretch;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#7E22CE;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Record time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Clip</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          to [depth_min,
+          <br />
+          depth_max]
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Quantize</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          12-bit codes 0–4095
+          <br />
+          log (default) or linear
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Pack</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          into gray12le
+          <br />
+          plane
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Encode</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          HEVC
+          <br />
+          Main 12
+        </span>
+      </span>
+    </div>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#FEF3C7;color:#B45309;border-radius:9px;padding:8px 12px;">
+      <span>MP4</span>
+      <span style="font-size:11px;font-weight:400;color:#C77D18;white-space:nowrap;">
+        stored
+        <br />
+        stream
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#34A06B;">
+      →
+    </span>
+    <div style="border:2px dashed #6EE7B7;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:center;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#047857;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Load time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#D1FAE5;color:#047857;border-radius:9px;padding:8px 12px;">
+        <span>Dequantize</span>
+        <span style="font-size:11px;font-weight:400;color:#059669;white-space:nowrap;">
+          to mm / m
+        </span>
+      </span>
+    </div>
+  </div>
+</div>
 
 Configure the depth pipeline through a parallel **`depth_encoder`** block (`DepthEncoderConfig`). It shares every `RGBEncoderConfig` field (`vcodec`, `pix_fmt`, `crf`, …) and adds four quantizer knobs, set via `--dataset.depth_encoder.<field>`:
 
@@ -168,15 +232,16 @@ After the first episode of a video stream is encoded, the encoder configuration
 
 Two sources contribute to the `info` block:
 
-- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, plus `audio.*` if an audio stream is present.
-- **Encoder-derived** (taken from `RGBEncoderConfig` or `DepthEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
+| Source              | Where it comes from                                   | Fields                                                                                                                  |
+| ------------------- | ----------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+| **Stream-derived**  | Read back from the encoded MP4 with PyAV.             | `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, `audio.*` |
+| **Encoder-derived** | Taken from `RGBEncoderConfig` / `DepthEncoderConfig`. | `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`               |
 
-<Tip>
-  This block is populated **once**, from the **first** episode. It assumes every
-  episode in the dataset was encoded with the same `rgb_encoder`. Changing
-  encoder settings partway through a recording is not supported — the
-  `info.json` will only reflect the parameters used for the first episode.
-</Tip>
+> [!IMPORTANT]
+> This block is populated **once**, from the **first** episode. It assumes every
+> episode in the dataset was encoded with the same `rgb_encoder`. Changing
+> encoder settings partway through a recording is not supported — the
+> `info.json` will only reflect the parameters used for the first episode.
 
 ---
 
@@ -184,5 +249,7 @@ Two sources contribute to the `info` block:
 
 When aggregating datasets with `merge_datasets`, video files are concatenated as-is (no re-encoding), and encoder fields in `info.json` are merged per-key:
 
-- **Stream-derived fields must match** across sources: `video.codec`, `video.pix_fmt`, `video.height`, `video.width`, `video.fps`. Otherwise FFmpeg's concat demuxer fails.
-- **Encoder-tuning fields are merged loosely**: `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.extra_options`. If every source agrees, the value is kept; if not, it's set to `null` (or `{}` for `video.extra_options`) and a warning is logged.
+| Merge rule         | Fields                                                                             | Behaviour                                                                                                                                                 |
+| ------------------ | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Must match**     | `video.codec`, `video.pix_fmt`, `video.height`, `video.width`, `video.fps`         | Stream-derived fields must match across sources, otherwise FFmpeg's concat demuxer fails.                                                                 |
+| **Merged loosely** | `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.extra_options` | Encoder-tuning fields. If every source agrees, the value is kept; if not, it's set to `null` (or `{}` for `video.extra_options`) and a warning is logged. |

examples/isaac_teleop_to_so101/README.md

@@ -0,0 +1,131 @@
+# Isaac Teleop → SO-101
+
+Teleoperate an SO-101/SO-100 follower arm — and record LeRobot datasets — with NVIDIA
+[Isaac Teleop](https://github.com/NVIDIA/IsaacTeleop). Two input devices ship today:
+
+- **XR (VR) controller** (`--teleop.type=xr_controller`) — the controller's grip pose drives the
+  end-effector through a squeeze-to-engage clutch and LeRobot's Cartesian IK pipeline; the analog
+  trigger drives the gripper.
+- **SO-101 leader arm** (`--teleop.type=so101_leader`) — a back-drivable leader arm mirrored 1:1
+  onto the follower via Isaac Teleop's native `so101_leader` plugin (no clutch, no IK).
+
+The full narrative guide (how the clutch works, CloudXR setup, headset pairing, tuning, and
+troubleshooting) is in the [LeRobot docs](https://huggingface.co/docs/lerobot/isaac_teleop)
+(source: `docs/source/isaac_teleop.mdx`). This README is the canonical install and usage
+reference.
+
+## Requirements
+
+- Linux workstation (see NVIDIA's
+  [system requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html)
+  for supported OS/GPU/headset combinations; `isaacteleop` publishes Linux wheels only).
+- An SO-101 (or SO-100) follower arm, calibrated with `lerobot-calibrate`.
+- For the XR device: a CloudXR-capable headset (e.g. Quest 3, Pico 4, Apple Vision Pro) on the
+  same network.
+- For the leader device: a second, back-drivable SO-101 leader arm and the `so101_leader` plugin
+  binary built from the Isaac Teleop source tree (see
+  [Build from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html)).
+
+## Installation
+
+This example lives in the LeRobot repository and is not part of the `lerobot` pip package, so
+work from a source checkout. From the repo root:
+
+```bash
+# LeRobot with the extras this example uses:
+#   feetech    - SO-101 serial motor bus
+#   kinematics - Placo IK solver (XR controller path)
+#   dataset    - dataset recording (record.py)
+# huggingface_hub >= 1.5 is needed by the automatic URDF fetch (Buckets API).
+uv pip install -e ".[feetech,kinematics,dataset]" "huggingface_hub>=1.5"
+
+# Isaac Teleop from public PyPI. `cloudxr` brings the CloudXR runtime bindings;
+# `retargeters-lite` is the scipy-based retargeter path that resolves on both
+# x86_64 and ARM (the full `retargeters` extra does not resolve on aarch64).
+uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131" "scipy>=1.14"
+
+# Optional, x86_64 only: the full retargeter stack.
+uv pip install "isaacteleop[retargeters]~=1.3.131"
+```
+
+One-time CloudXR EULA (the auto-launch prompts on stdin and would hang on a headless machine):
+
+```bash
+python -m isaacteleop.cloudxr --accept-eula
+```
+
+## Usage
+
+Run everything from the repo root with `python -m` so the `examples` package resolves.
+
+### Teleoperate — XR controller
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm \
+    --teleop.type=xr_controller
+```
+
+On startup the script launches the CloudXR runtime (~30 s), prints the workstation IP to enter in
+the headset's CloudXR web client, waits for the controllers to stream, slews the arm to a reset
+pose (`--reset_to_origin=false` to skip), and then: **hold the squeeze/grip** to engage, move the
+controller to drive the arm, pull the trigger to close the gripper. Releasing the squeeze freezes
+the arm. The SO-101 URDF is fetched automatically from the `lerobot/robot-urdfs` Hugging Face
+bucket into the LeRobot cache on first run.
+
+To customize the reset pose: back-drive the arm to the pose you want, then
+
+```bash
+python -m examples.isaac_teleop_to_so101.override_reset_pose --port /dev/ttyACM0 --id so101_follower_arm
+```
+
+which writes it to `HF_LEROBOT_HOME/reset_poses/<robot.name>/<robot.id>.json`; runs with the same
+`--robot.id` use it automatically.
+
+### Teleoperate — SO-101 leader arm
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate \
+    --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \
+    --teleop.type=so101_leader --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+    --launch_plugin=/path/to/IsaacTeleop/install/plugins/so101_leader/so101_leader_plugin
+```
+
+The follower is first slewed to the leader's pose over `--align_duration` seconds
+(`--align=false` to skip), then mirrors it 1:1. The plugin reuses the serial leader's calibration
+(`HF_LEROBOT_CALIBRATION/teleoperators/so_leader/<teleop.id>.json`).
+
+### Record a dataset
+
+`record.py` takes the same `--robot.*`/`--teleop.*`/loop flags plus `lerobot-record`-style
+`--dataset.*` flags:
+
+```bash
+python -m examples.isaac_teleop_to_so101.record \
+    --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \
+    --teleop.type=xr_controller \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --dataset.repo_id=<hf_user>/<dataset_name> \
+    --dataset.single_task="Pick up the cube" \
+    --dataset.num_episodes=3 --dataset.episode_time_s=20 --dataset.reset_time_s=5
+```
+
+Keyboard shortcuts (terminal-first, so they work over SSH): **Right/n** end episode early,
+**Left/r** re-record, **Esc/q** stop after the current episode.
+
+Run either script with `--help` for all flags.
+
+## Layout
+
+```
+isaac_teleop/            device library: session lifecycle (base.py), XRController,
+                         SO101LeaderArm, Clutch, configs, and the XR→IK processor step
+common.py                shared loop infra: device bundles, clutch/IK pipeline wiring,
+                         reset/align slews, URDF fetch, keyboard listener
+teleoperate.py           teleoperation CLI (device selected via --teleop.type)
+record.py                dataset-recording CLI (same device selection + --dataset.*)
+override_reset_pose.py   save the current joints as the per-arm reset pose
+default.env              CloudXR device-profile overrides passed to the launcher
+```

examples/isaac_teleop_to_so101/__init__.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Isaac Teleop -> SO-101 example package."""

examples/isaac_teleop_to_so101/common.py

@@ -0,0 +1,650 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shared device + control-loop infrastructure for the Isaac Teleop -> SO-101 examples.
+
+Consumed by ``teleoperate.py`` and ``record.py``, which both build a per-device
+:class:`Device` bundle and run the same loop: read -> (maybe command) -> hold-when-idle ->
+sleep. A :class:`Device` bundles three closures: ``compute(obs) -> RobotAction | None``
+(``None`` = hold at the measured pose while idle), ``startup``, and ``cleanup``. The devices:
+
+* ``xr_controller`` — a thin :class:`XRController` whose raw grip pose an in-loop
+  :class:`Clutch` turns into an EE target for LeRobot's Cartesian IK pipeline.
+* ``so101_leader`` — a back-drivable leader arm mirrored 1:1 into the follower.
+
+Requires the ``isaacteleop`` package and an OpenXR runtime (install instructions in this
+folder's ``README.md``). User-facing guide: ``docs/source/isaac_teleop.mdx``.
+"""
+
+import json
+import logging
+import socket
+import subprocess
+import sys
+import time
+from collections.abc import Callable
+from contextlib import suppress
+from dataclasses import dataclass
+from importlib.resources import files
+from pathlib import Path
+from typing import Protocol
+
+import numpy as np
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    robot_action_observation_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots import RobotConfig, make_robot_from_config
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    EEBoundsAndSafety,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, HF_LEROBOT_HOME, TELEOPERATORS
+from lerobot.utils.robot_utils import precise_sleep
+
+from .isaac_teleop import (
+    Clutch,
+    IsaacTeleopConfig,
+    MapXRControllerActionToRobotAction,
+    SO101LeaderArm,
+    SO101LeaderArmConfig,
+    XRController,
+)
+
+# Fixed rate [Hz] for the teleoperate loop and the pre-loop slews / connect-wait poll sleeps.
+FPS = 30
+
+# CloudXR device-profile env file passed to the launcher (see default.env in this package).
+CLOUDXR_ENV_FILE = str(files(__package__) / "default.env")
+
+
+class LoopConfig(Protocol):
+    """Structural type for the loop/launch knobs ``build_device`` and the ``setup_*`` read.
+
+    Both ``TeleoperateConfig`` and ``RecordConfig`` satisfy it, keeping ``common`` decoupled
+    from either entry point's concrete config.
+    """
+
+    teleop: IsaacTeleopConfig
+    robot: RobotConfig
+    launch_plugin: str | None
+    reset_to_origin: bool
+    reset_duration: float
+    align: bool
+    align_duration: float
+
+
+# Per-device bundle consumed by the shared loop. ``compute`` returns None to mean
+# "idle -> hold at the measured pose"; ``startup`` warms up; ``cleanup`` reaps/disconnects.
+@dataclass(frozen=True)
+class Device:
+    compute: Callable[[RobotObservation | None], RobotAction | None]
+    startup: Callable[[], None]
+    cleanup: Callable[[], None]
+
+
+def hold_action(obs: RobotObservation, motor_names: list[str]) -> dict[str, float]:
+    """Re-send the measured joints — the explicit hold when a device is idle."""
+    return {f"{name}.pos": float(obs[f"{name}.pos"]) for name in motor_names}
+
+
+class HoldLatch:
+    """Resolve the per-frame action, holding one LATCHED pose while the device is idle.
+
+    Re-sending the freshly measured joints on every idle frame would ratchet the arm
+    downward: under gravity the P-only servo settles below its goal by a steady-state
+    error, so each re-command of the measurement lowers the goal by that error again.
+    Latching the target once on the active->idle transition holds a fixed pose instead.
+    """
+
+    def __init__(self, motor_names: list[str]):
+        self._motor_names = motor_names
+        self._held: dict[str, float] | None = None
+
+    def resolve(self, action: RobotAction | None, obs: RobotObservation) -> RobotAction:
+        """Pass through an active action (clearing the latch); latch + hold when idle."""
+        if action is not None:
+            self._held = None
+            return action
+        if self._held is None:
+            self._held = hold_action(obs, self._motor_names)
+        return self._held
+
+
+def slew(
+    robot,
+    motor_names: list[str],
+    target_fn: Callable[[], dict[str, float]],
+    duration_s: float,
+) -> None:
+    """Linearly slew all joints from their current measured pose toward a target.
+
+    ``target_fn`` is called EACH step, so the leader can pass a live re-read (landing on its
+    current pose at ``alpha == 1`` for a continuous handoff) while XR passes a constant.
+    """
+    obs = robot.get_observation()
+    start = {name: float(obs[f"{name}.pos"]) for name in motor_names}
+    n_steps = max(1, int(duration_s * FPS))
+    for step in range(1, n_steps + 1):
+        alpha = step / n_steps
+        target = target_fn()
+        action = {f"{name}.pos": start[name] + alpha * (target[name] - start[name]) for name in motor_names}
+        robot.send_action(action)
+        precise_sleep(1.0 / FPS)
+
+
+# ============================================================================
+# XR controller device
+# ============================================================================
+
+# Per-frame EE rate limit [m]. With raise_on_jump=False, EEBoundsAndSafety clamps an
+# over-limit step instead of raising, absorbing a tracking glitch as one slow frame. At
+# FPS=30, 0.1 m/frame caps EE speed at ~3 m/s. (end_effector_bounds clips the absolute target.)
+MAX_EE_STEP_M = 0.1
+
+# Soft-orientation IK weight: small but nonzero so the wrist follows the hand while position
+# dominates (the 5-DOF SO-101 cannot realize an arbitrary orientation). 0.0 = position-only.
+IK_ORIENTATION_WEIGHT = 0.01
+
+
+def _ensure_so101_urdf() -> str:
+    """Return the cached SO-101 URDF path, fetching the ``so101`` folder (URDF + meshes) from
+    the public ``lerobot/robot-urdfs`` HF bucket into the LeRobot cache on first use."""
+    dest_dir = HF_LEROBOT_HOME / "robot-urdfs" / "so101"
+    urdf_path = dest_dir / "so101_new_calib.urdf"
+    # Completeness marker written only after a FULL sync: the URDF file alone is not a
+    # completeness signal (an interrupted first sync can leave the meshes it references
+    # missing, which the URDF's mere existence would then hide forever). Re-syncing is
+    # idempotent and repairs a partial cache; delete the folder to force a re-download.
+    marker = dest_dir / ".sync_complete"
+    if not marker.exists():
+        from huggingface_hub import sync_bucket
+
+        sync_bucket("hf://buckets/lerobot/robot-urdfs/so101", str(dest_dir), quiet=True)
+        marker.touch()
+    return str(urdf_path)
+
+
+# Default duration [s] for the startup reset-to-origin slew.
+RESET_DURATION_S = 5.0
+
+# Optional cached file written by override_reset_pose.py. When present it takes priority over RESET_ORIGIN_DEG.
+RESET_POSE_FILE = str(HF_LEROBOT_HOME / "reset_poses" / "{robot_name}" / "{robot_id}.json")
+
+# Reset target in each motor's native units (arm joints in degrees, gripper RANGE_0_100,
+# 100 = open). An empirically comfortable pose (elbow/wrist bent) avoiding the singularity of
+# a fully-extended arm; assumes standard calibration. Override per-arm via override_reset_pose.py.
+RESET_ORIGIN_DEG: dict[str, float] = {
+    "shoulder_pan": -4.0,
+    "shoulder_lift": -103.0,
+    "elbow_flex": 97.0,
+    "wrist_flex": 78.0,
+    "wrist_roll": -65.0,
+    "gripper": 0.0,
+}
+
+
+def _load_reset_target(reset_pose_file: Path, motor_names: list[str]) -> dict[str, float]:
+    """Return reset targets: the saved reset pose if present, else RESET_ORIGIN_DEG."""
+    if reset_pose_file.exists():
+        saved = json.loads(reset_pose_file.read_text())
+        # Fill any missing motors from the fallback dict.
+        return {name: float(saved.get(name, RESET_ORIGIN_DEG.get(name, 0.0))) for name in motor_names}
+    return {name: RESET_ORIGIN_DEG.get(name, 0.0) for name in motor_names}
+
+
+# CloudXR web client URL opened in the headset (Isaac Teleop quick start, step 5).
+_CLOUDXR_WEB_CLIENT_URL = "https://nvidia.github.io/IsaacTeleop/client"
+# WSS-proxy / self-signed-cert port the operator accepts in-browser before connecting.
+_CLOUDXR_WSS_PORT = 48322
+# How often to re-print the connection hint while waiting for the headset [s].
+_XR_CONNECT_REMINDER_S = 15.0
+# Virtual / bridge / USB-gadget interfaces a headset can't reach over the network — skip
+# by name prefix (``docker0``, compose ``br-*``, ``veth*``, libvirt ``virbr*``, and the
+# Tegra USB device-mode bridge ``l4tbr0``).
+_SKIP_IFACE_PREFIXES = ("docker", "br-", "veth", "virbr", "l4tbr")
+
+
+def _primary_ipv4() -> str | None:
+    """The workstation's primary outbound IPv4, via the UDP-socket trick (``connect()`` on a
+    datagram socket selects the egress interface without sending packets)."""
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        try:
+            s.connect(("8.8.8.8", 80))
+            return s.getsockname()[0]
+        except OSError:
+            return None
+
+
+def _candidate_ipv4s() -> list[tuple[str, str]]:
+    """Return ``[(interface, ipv4), ...]`` the headset might reach this workstation at.
+
+    Lists each interface's IPv4 via ``psutil`` (dropping loopback, link-local, and the
+    virtual/bridge interfaces in ``_SKIP_IFACE_PREFIXES``), primary outbound first. Falls
+    back to just the primary IP when ``psutil`` is unavailable.
+    """
+    primary = _primary_ipv4()
+    found: list[tuple[str, str]] = []
+    try:
+        import psutil
+
+        for iface, addrs in psutil.net_if_addrs().items():
+            if iface.startswith(_SKIP_IFACE_PREFIXES):
+                continue
+            for addr in addrs:
+                if addr.family != socket.AF_INET:
+                    continue
+                ip = addr.address
+                if ip.startswith("127.") or ip.startswith("169.254."):
+                    continue
+                found.append((iface, ip))
+    except Exception:
+        if primary:
+            found.append(("default", primary))
+    found.sort(key=lambda t: t[1] != primary)  # primary outbound interface first
+    return found
+
+
+def _print_xr_connect_help() -> None:
+    """Print how to connect the headset to this workstation over CloudXR."""
+    ips = _candidate_ipv4s()
+    print("\n" + "=" * 76)
+    print("Connect your XR headset to this workstation over NVIDIA CloudXR:")
+    print(f"  1. In the headset, open the CloudXR web client:  {_CLOUDXR_WEB_CLIENT_URL}")
+    print("  2. Enter this workstation's IP address:")
+    if ips:
+        for iface, ip in ips:
+            print(f"        {ip:<15}  ({iface})")
+        if len(ips) > 1:
+            print("     (use the address on the same network as your headset)")
+    else:
+        print("        <could not determine — check `hostname -I` / `ip addr`>")
+    print(f"  3. Accept the self-signed cert at https://<that-ip>:{_CLOUDXR_WSS_PORT}/ , then Connect.")
+    print("=" * 76 + "\n")
+
+
+def _wait_for_xr_controller(teleop_device: XRController) -> None:
+    """Block until the XR controller is tracked, polling ``get_action()`` and re-printing a
+    reminder every ``_XR_CONNECT_REMINDER_S``. User-paced; ``Ctrl-C`` aborts (no hard timeout).
+    """
+    _print_xr_connect_help()
+    print("Waiting for the headset controllers to start streaming…  (Ctrl-C to abort)")
+    last_reminder = time.time()
+    while True:
+        teleop_device.get_action()  # steps the session; updates is_tracking
+        if teleop_device.is_tracking:
+            print("Headset connected — controllers are streaming.")
+            return
+        if time.time() - last_reminder >= _XR_CONNECT_REMINDER_S:
+            print("…still waiting for the headset to connect (Ctrl-C to abort).")
+            last_reminder = time.time()
+        time.sleep(1.0 / FPS)
+
+
+def setup_xr(cfg: LoopConfig, robot, motor_names: list[str]) -> Device:
+    """Build the XR controller device bundle (clutch + soft-orientation IK pipeline)."""
+    kinematics_solver = RobotKinematics(
+        urdf_path=_ensure_so101_urdf(),
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    teleop_config = cfg.teleop  # XRControllerConfig (selected via --teleop.type=xr_controller)
+    teleop_device = XRController(teleop_config)
+
+    # The clutch (below) turns the raw grip pose into an absolute base-frame ee_pose; this
+    # pipeline maps it to joint targets: rename -> bounds/rate-limit -> IK.
+    xr_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            MapXRControllerActionToRobotAction(),
+            # raise_on_jump=False: an over-limit step (e.g. a tracking glitch) is clamped +
+            # warned instead of raised, since a crash mid-loop would leave the arm uncontrolled.
+            # z floor 0.0 keeps a stray target above the table; x/y stay at a loose [-1,1]m box.
+            EEBoundsAndSafety(
+                end_effector_bounds={"min": [-1.0, -1.0, 0.0], "max": [1.0, 1.0, 1.0]},
+                max_ee_step_m=MAX_EE_STEP_M,
+                raise_on_jump=False,
+            ),
+            # initial_guess_current_joints=False: warm-start from the previous IK solution so
+            # the joint trajectory stays continuous frame-to-frame.
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=False,
+                orientation_weight=IK_ORIENTATION_WEIGHT,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # The clutch is built in startup() (after the optional reset slew, seeded from the
+    # post-slew MEASURED pose) and shared with compute() via nonlocal.
+    clutch: Clutch | None = None
+    prev_enabled = False
+
+    def startup() -> None:
+        nonlocal clutch
+        # Connect and wait for the operator to don the headset BEFORE moving the arm, so the
+        # reset slew happens while they are watching in VR.
+        teleop_device.connect()
+        if not teleop_device.is_connected:
+            raise ValueError("Teleop is not connected!")
+        _wait_for_xr_controller(teleop_device)
+
+        if cfg.reset_to_origin:
+            reset_pose_file = Path(RESET_POSE_FILE.format(robot_name=robot.name, robot_id=robot.id))
+            target = _load_reset_target(reset_pose_file, motor_names)
+            source = str(reset_pose_file) if reset_pose_file.exists() else "hardcoded defaults"
+            print(f"Reset target source: {source}")
+            print(f"Resetting to origin over {cfg.reset_duration:.1f} s…")
+            slew(robot, motor_names, lambda: target, cfg.reset_duration)
+            print("Reset complete.")
+
+        # Seed the clutch home from the arm's measured pose (FK of the current joints) so the
+        # first engage is jump-free, whether or not a reset slew ran.
+        obs0 = robot.get_observation()
+        q_measured_deg = np.array([float(obs0[f"{name}.pos"]) for name in motor_names], dtype=float)
+        home_base_T_ee = kinematics_solver.forward_kinematics(q_measured_deg)  # noqa: N806
+        clutch = Clutch(home_base_T_ee)
+
+        print("Starting teleop loop. Squeeze and move the controller to teleoperate the robot...")
+
+    def compute(robot_obs: RobotObservation | None) -> RobotAction | None:
+        nonlocal prev_enabled
+        if clutch is None:  # set in startup(), which runs before compute()
+            raise RuntimeError("compute() called before startup(); the clutch is not initialized")
+        xr_action = teleop_device.get_action()
+        grip_pos = np.asarray(xr_action["grip_pos"], dtype=float)
+        grip_quat = np.asarray(xr_action["grip_quat"], dtype=float)
+        squeeze = float(xr_action["squeeze"])
+        trigger = float(xr_action["trigger"])
+        enabled = squeeze > teleop_config.clutch_threshold
+
+        # On the engage edge, latch the clutch home at the arm's MEASURED EE pose (FK of
+        # the live joints) and the controller origin so the per-frame delta starts at zero.
+        # Latching the last commanded pose instead would snap the arm back to it at full
+        # servo speed if the arm moved while disengaged (gravity sag, external contact).
+        is_engage_frame = enabled and not prev_enabled
+        if is_engage_frame:
+            q_measured = np.array([float(robot_obs[f"{name}.pos"]) for name in motor_names], dtype=float)
+            measured_base_T_ee = kinematics_solver.forward_kinematics(q_measured)  # noqa: N806
+            clutch.engage(grip_pos, grip_quat, measured_base_T_ee=measured_base_T_ee)
+            # Re-anchor the pipeline state at the measured pose as well: EEBoundsAndSafety's
+            # rate limiter and the IK warm start otherwise still reference the stale
+            # pre-disengage command and would fight the fresh home for several frames.
+            xr_to_robot_joints_processor.reset()
+        prev_enabled = enabled
+
+        # SAFETY GATE: command the robot ONLY while the clutch is engaged; otherwise return
+        # None so the loop holds the measured joints (releasing the clutch freezes the arm).
+        if not enabled:
+            return None
+
+        # Rebase the raw grip pose onto the EE, then run the pipeline. closedness = trigger.
+        ee_pos, ee_quat = clutch.rebase(grip_pos, grip_quat)
+        ee_action = {
+            "ee_pose": np.concatenate([ee_pos, ee_quat]).astype(np.float32),
+            "closedness": trigger,
+        }
+        return xr_to_robot_joints_processor((ee_action, robot_obs))
+
+    return Device(compute=compute, startup=startup, cleanup=teleop_device.disconnect)
+
+
+# ============================================================================
+# SO-101 leader arm device
+# ============================================================================
+
+# Default duration [s] for the startup alignment slew (follower current -> leader first pose).
+ALIGN_DURATION_S = 3.0
+
+# How long to wait for the leader plugin to start streaming before aligning / looping.
+LEADER_WARMUP_TIMEOUT_S = 20.0
+
+# The plugin converts the leader's servo ticks to radians, so it reuses the serial SO-101
+# leader's calibration, stored by lerobot-calibrate under SO101Leader.name == "so_leader".
+SO_LEADER_CALIBRATION_NAME = "so_leader"
+
+
+def _leader_calibration_path(cfg: LoopConfig) -> Path | None:
+    """Infer the calibration JSON the launched plugin should read, or None.
+
+    Path convention: ``HF_LEROBOT_CALIBRATION / teleoperators / so_leader / {--teleop.id}.json``
+    (or ``--teleop.calibration_dir`` if set). Returns None (plugin falls back to defaults) when
+    it does not exist, warning if an id was given, or when no ``--teleop.id`` is set.
+    """
+    if not cfg.teleop.id:
+        return None
+    calib_dir = cfg.teleop.calibration_dir or (
+        HF_LEROBOT_CALIBRATION / TELEOPERATORS / SO_LEADER_CALIBRATION_NAME
+    )
+    calib_path = Path(calib_dir) / f"{cfg.teleop.id}.json"
+    if calib_path.is_file():
+        return calib_path
+    print(
+        f"WARNING: no leader calibration at {calib_path}; the plugin will use built-in defaults. "
+        f"Calibrate with the serial leader (`lerobot-calibrate --teleop.type=so101_leader "
+        f"--teleop.id={cfg.teleop.id}`) or the plugin's `calibrate` subcommand."
+    )
+    return None
+
+
+def _wait_for_leader(teleop: SO101LeaderArm, timeout_s: float) -> dict[str, float]:
+    """Poll the leader until it streams a live frame; return that frame's ``{joint}.pos``.
+
+    Raises ``SystemExit`` if no live frame arrives within ``timeout_s`` (plugin not pushing,
+    wrong ``--teleop.collection_id``, or CloudXR not up).
+    """
+    print(f"Waiting up to {timeout_s:.0f}s for the so101_leader plugin to stream…")
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        action = teleop.get_action()
+        if teleop.is_tracking:
+            print("Leader is streaming.")
+            return action
+        time.sleep(1.0 / FPS)
+    raise SystemExit(
+        f"FAILED: leader did not stream within {timeout_s:.0f}s. Is the so101_leader plugin "
+        "running and pushing (check --teleop.collection_id)? Is CloudXR up?"
+    )
+
+
+def _maybe_launch_plugin(cfg: LoopConfig) -> subprocess.Popen | None:
+    """Spawn the so101_leader plugin if ``--launch_plugin <path>`` was given (after connect())."""
+    if cfg.launch_plugin is None:
+        return None
+    if not Path(cfg.launch_plugin).exists():
+        raise SystemExit(
+            f"plugin binary not found: {cfg.launch_plugin} (build it in the IsaacTeleop repo first)"
+        )
+    leader_port = cfg.teleop.port  # SO101LeaderArmConfig.port, forwarded to the plugin
+    backend = f"leader on {leader_port}" if leader_port else "synthetic trajectory"
+    print(f"launching plugin: {cfg.launch_plugin} ({backend})")
+    # Positional args: [device_path] [collection_id] [calibration_file]. Empty device_path ->
+    # synthetic backend. Calibration (only real hardware needs it) is appended when a port is set.
+    argv = [cfg.launch_plugin, leader_port, cfg.teleop.collection_id]
+    if leader_port:
+        calib_path = _leader_calibration_path(cfg)
+        if calib_path is not None:
+            argv.append(str(calib_path))
+            print(f"  leader calibration: {calib_path}")
+    # Spawned after connect() so it inherits the CloudXR runtime env (XR_RUNTIME_JSON, ...).
+    proc = subprocess.Popen(argv)
+    time.sleep(1.5)  # let it create its OpenXR session and start pushing
+    return proc
+
+
+def setup_leader(cfg: LoopConfig, robot, motor_names: list[str]) -> Device:
+    """Build the SO-101 leader arm device bundle (1:1 joint mirror)."""
+    teleop_config = cfg.teleop  # SO101LeaderArmConfig (selected via --teleop.type=so101_leader)
+    teleop = SO101LeaderArm(teleop_config)
+
+    plugin_proc: subprocess.Popen | None = None
+
+    def startup() -> None:
+        nonlocal plugin_proc
+        # connect() auto-launches CloudXR (unless opted out); spawn the plugin AFTER so it
+        # inherits the runtime env. The plugin is reaped in cleanup().
+        teleop.connect()
+        plugin_proc = _maybe_launch_plugin(cfg)
+
+        if not teleop.is_connected:
+            raise ValueError("Teleop is not connected!")
+
+        # Block until the leader streams a live frame (clear error if it never does).
+        _wait_for_leader(teleop, LEADER_WARMUP_TIMEOUT_S)
+
+        if cfg.align:
+            print(f"Aligning follower to leader over {cfg.align_duration:.1f}s…")
+
+            # Re-read the live leader pose once per step so alpha=1 lands on its current pose
+            # from a single coherent frame.
+            def _leader_target() -> dict[str, float]:
+                leader_now = teleop.get_action()
+                return {name: float(leader_now[f"{name}.pos"]) for name in motor_names}
+
+            slew(robot, motor_names, _leader_target, cfg.align_duration)
+            print("Alignment complete.")
+
+        print(
+            "Starting joint-mirror loop. Back-drive the leader to teleoperate the follower… (Ctrl-C to stop)"
+        )
+
+    def compute(robot_obs: RobotObservation | None) -> RobotAction | None:
+        leader_action = teleop.get_action()
+        # Hold the follower at its measured pose when the leader drops out (stale stream)
+        # rather than commanding a possibly-old target.
+        if not teleop.is_tracking:
+            return None
+        return leader_action
+
+    def cleanup() -> None:
+        # A plugin-reaping failure must not skip the session disconnect (and vice versa
+        # the disconnect runs after the plugin stops pushing on it).
+        try:
+            if plugin_proc is not None:
+                plugin_proc.terminate()
+                try:
+                    plugin_proc.wait(timeout=5)
+                except subprocess.TimeoutExpired:
+                    plugin_proc.kill()
+        finally:
+            teleop.disconnect()
+
+    return Device(compute=compute, startup=startup, cleanup=cleanup)
+
+
+# ============================================================================
+# Shared setup
+# ============================================================================
+
+
+def build_device(cfg: LoopConfig) -> tuple:
+    """Connect the follower, build the selected Isaac device, and run its pre-loop startup.
+
+    Connects the follower FIRST (so the startup slew / clutch-home seed can read live joints),
+    dispatches on ``--teleop.type``, then runs ``device.startup()`` before returning. On any
+    failure after ``connect()`` the follower is disconnected so the connection never leaks.
+
+    Returns ``(robot, device, motor_names)``.
+    """
+    # Default the CloudXR input profile to this example's default.env unless the user overrode
+    # it via --teleop.cloudxr_env_file.
+    if cfg.teleop.cloudxr_env_file is None:
+        cfg.teleop.cloudxr_env_file = CLOUDXR_ENV_FILE
+
+    # SO-101/SO-100 only (both share the SO-101 URDF), reject other followers.
+    supported_robots = {"so101_follower", "so100_follower"}
+    if cfg.robot.type not in supported_robots:
+        raise ValueError(
+            f"This example only supports SO-101/SO-100 followers ({sorted(supported_robots)}), "
+            f"but got --robot.type={cfg.robot.type}."
+        )
+
+    # The degree-based pipeline relies on --robot.use_degrees (default True).
+    robot = make_robot_from_config(cfg.robot)
+    # Connect FIRST so the startup slew and clutch-home seed can read live joints.
+    robot.connect()
+    # Everything after connect() can fail; this runs outside the callers' try/finally, so
+    # disconnect the follower on any failure to avoid leaking the connection.
+    device: Device | None = None
+    try:
+        # Joint names in action order, read from {name}.pos action features (robot-agnostic).
+        motor_names = [key.removesuffix(".pos") for key in robot.action_features if key.endswith(".pos")]
+
+        if isinstance(cfg.teleop, SO101LeaderArmConfig):
+            device = setup_leader(cfg, robot, motor_names)
+        else:
+            device = setup_xr(cfg, robot, motor_names)
+
+        device.startup()
+    except BaseException:
+        # Reap a partially-started device, then always disconnect the follower.
+        if device is not None:
+            with suppress(Exception):
+                device.cleanup()
+        robot.disconnect()
+        raise
+
+    return robot, device, motor_names
+
+
+# ============================================================================
+# Keyboard control
+# ============================================================================
+
+
+def init_keyboard_listener():
+    """Recording shortcuts, terminal-first so they work over SSH.
+
+    Whenever stdin is a TTY we use the stdlib :class:`TerminalKeyListener` directly rather
+    than upstream's pynput-first :func:`init_keyboard_listener`, whose global listener would
+    capture the workstation console instead of this (often SSH) terminal. With no TTY we defer
+    to upstream (pynput on a GUI, else headless no-op).
+    """
+    if not (sys.stdin is not None and sys.stdin.isatty()):
+        from lerobot.utils.keyboard_input import init_keyboard_listener as _upstream
+
+        return _upstream()
+
+    from lerobot.utils.keyboard_input import TerminalKeyListener, apply_recording_control
+
+    events = {"exit_early": False, "rerecord_episode": False, "stop_recording": False}
+
+    # n/r/q are the arrow/Esc equivalents that survive escape-sequence splitting over laggy
+    # SSH/VNC links. Case-insensitive so Shift+letter still works.
+    def on_key(name: str) -> None:
+        key = name.lower()
+        if key in ("right", "n"):
+            apply_recording_control("right", events)
+        elif key in ("left", "r"):
+            apply_recording_control("left", events)
+        elif key in ("esc", "q"):
+            apply_recording_control("esc", events)
+
+    listener = TerminalKeyListener(on_key)
+    listener.start()
+    logging.info(
+        "Keyboard control via terminal — keep this terminal focused: "
+        "Right/n = end episode early, Left/r = re-record, Esc/q = stop."
+    )
+    return listener, events

examples/isaac_teleop_to_so101/default.env

@@ -0,0 +1,21 @@
+# CloudXR device-profile overrides for the Isaac Teleop XR -> SO-101 example.
+#
+# Passed to isaacteleop's CloudXRLauncher as `env_config` (via
+# XRControllerConfig.cloudxr_env_file). Format: KEY=value, one per line; `#`
+# comments and blank lines ignored; $VARS / ~ expanded. See
+# isaacteleop/cloudxr/env_config.py::_load_env_file.
+#
+# Runtime-resolved keys (XR_RUNTIME_JSON, XRT_NO_STDIN, NV_CXR_RUNTIME_DIR,
+# NV_CXR_OUTPUT_DIR) are reserved and ignored if set here.
+
+# Transport profile the runtime advertises (CloudXR default: auto-webrtc).
+# "Quest3" also covers the Pico 4. Other values: auto-native, AppleVisionPro.
+NV_DEVICE_PROFILE=Quest3
+
+# Input device discovery channels (both default to true; pinned for clarity).
+NV_CXR_ENABLE_PUSH_DEVICES=true
+NV_CXR_ENABLE_TENSOR_DATA=true
+
+# Runtime logs to ~/.cloudxr/logs — helps debug connection issues
+# (e.g. "Failed to get OpenXR system: -35").
+NV_CXR_FILE_LOGGING=true

examples/isaac_teleop_to_so101/isaac_teleop/__init__.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""NVIDIA Isaac Teleop teleoperators for LeRobot.
+
+Each input device is an :class:`IsaacTeleopTeleoperator` subclass: :class:`XRController`
+(XR/VR controller) and :class:`SO101LeaderArm` (back-drivable SO-101 leader arm) ship today.
+"""
+
+from .base import IsaacTeleopTeleoperator
+from .clutch import Clutch
+from .config_isaac_teleop import IsaacTeleopConfig, SO101LeaderArmConfig, XRControllerConfig
+from .teleop_so101_leader_arm import SO101LeaderArm, leader_joints_to_robot_action
+from .teleop_xr_controller import XRController
+from .xr_controller_processor import MapXRControllerActionToRobotAction
+
+__all__ = [
+    "Clutch",
+    "IsaacTeleopConfig",
+    "IsaacTeleopTeleoperator",
+    "MapXRControllerActionToRobotAction",
+    "SO101LeaderArm",
+    "SO101LeaderArmConfig",
+    "XRController",
+    "XRControllerConfig",
+    "leader_joints_to_robot_action",
+]

examples/isaac_teleop_to_so101/isaac_teleop/base.py

@@ -0,0 +1,282 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shared base for NVIDIA Isaac Teleop-backed LeRobot teleoperators.
+
+Isaac Teleop is a multi-modal framework: a single ``TeleopSession`` can be driven by
+XR controllers, hand tracking, Manus gloves, etc. Each modality is a
+:class:`Teleoperator` subclass in its own ``teleop_<device>.py``.
+
+:class:`IsaacTeleopTeleoperator` owns what those devices share — the session
+lifecycle, the per-step staleness/worker-health guard, and the no-op calibration
+tracking devices need. A concrete device implements :meth:`_build_pipeline` (its
+retargeting graph) and :meth:`get_action` (usually via :meth:`_step`).
+
+``isaacteleop`` is an optional NVIDIA dependency (install instructions in the example's
+``README.md``); its imports are guarded behind an availability check at module top, so this
+module imports without it and constructing a device fails fast with install instructions.
+"""
+
+from __future__ import annotations
+
+import abc
+import logging
+import os
+from collections.abc import Mapping
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from lerobot.teleoperators.teleoperator import Teleoperator
+from lerobot.utils.import_utils import is_package_available
+
+from .config_isaac_teleop import IsaacTeleopConfig
+
+_isaacteleop_available = is_package_available("isaacteleop")
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.cloudxr import CloudXRLauncher
+    from isaacteleop.retargeting_engine.interface import (
+        ExecutionEvents,
+        ExecutionState,
+        GraphExecutable,
+        RetargeterIO,
+    )
+    from isaacteleop.teleop_session_manager import TeleopSession, TeleopSessionConfig
+else:
+    CloudXRLauncher = None
+    ExecutionEvents = None
+    ExecutionState = None
+    GraphExecutable = None
+    RetargeterIO = None
+    TeleopSession = None
+    TeleopSessionConfig = None
+
+logger = logging.getLogger(__name__)
+
+# Gripper closedness [0, 1] -> SO-101 follower motor units [0, 100] (RANGE_0_100, 100 = OPEN).
+# Shared by the XR processor and leader device, which invert via ``pos = (1 - c) * SCALE``.
+_GRIPPER_MOTOR_SCALE = 100.0
+
+
+def _require_isaacteleop() -> None:
+    """Fail fast with install pointers when the optional ``isaacteleop`` package is missing."""
+    if not _isaacteleop_available:
+        raise ImportError(
+            "The 'isaacteleop' package is required for Isaac Teleop devices but is not "
+            "installed. See examples/isaac_teleop_to_so101/README.md for install instructions."
+        )
+
+
+class IsaacTeleopTeleoperator(Teleoperator):
+    """Abstract base for teleoperators backed by an Isaac Teleop ``TeleopSession``.
+
+    Owns the session lifecycle and the per-step health guard; subclasses supply
+    :meth:`_build_pipeline` and :meth:`get_action`.
+    """
+
+    config_class = IsaacTeleopConfig
+
+    def __init__(self, config: IsaacTeleopConfig):
+        _require_isaacteleop()
+        super().__init__(config)
+        self.config: IsaacTeleopConfig = config
+        self._session: TeleopSession | None = None
+        self._cloudxr_launcher: CloudXRLauncher | None = None
+
+    # ------------------------------------------------------------------
+    # Pipeline construction (device override point)
+    # ------------------------------------------------------------------
+
+    @abc.abstractmethod
+    def _build_pipeline(self) -> GraphExecutable:
+        """Build this device's retargeting pipeline (the ``GraphExecutable`` for
+        ``TeleopSessionConfig.pipeline``). Called once in :meth:`connect`; its output
+        keys must match what :meth:`get_action` unpacks.
+        """
+        raise NotImplementedError
+
+    # ------------------------------------------------------------------
+    # Lifecycle (shared)
+    # ------------------------------------------------------------------
+
+    @property
+    def is_connected(self) -> bool:
+        return self._session is not None
+
+    @property
+    def is_calibrated(self) -> bool:
+        return True  # Tracking devices are self-calibrating.
+
+    def calibrate(self) -> None:
+        pass
+
+    def configure(self) -> None:
+        pass
+
+    def connect(self, calibrate: bool = True) -> None:
+        """Auto-launch the CloudXR runtime (unless opted out) and open the session.
+
+        The CloudXR launch blocks ~30s and, on the first run, prompts on stdin for the
+        EULA (accept once via ``python -m isaacteleop.cloudxr --accept-eula``). Opt out
+        when CloudXR runs externally via ``config.auto_launch_cloudxr=False`` or
+        ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1`` (env var wins).
+        """
+        if self._session is not None:
+            raise RuntimeError("Already connected. Call disconnect() first.")
+
+        self._ensure_cloudxr_runtime()
+
+        try:
+            pipeline = self._build_pipeline()
+            session_config = TeleopSessionConfig(app_name=self.config.app_name, pipeline=pipeline)
+            self._session = TeleopSession(session_config)
+            self._session.__enter__()
+        except Exception:
+            self._session = None
+            try:
+                self._stop_cloudxr_runtime()
+            except Exception:
+                logger.exception("Failed to stop CloudXR runtime during connect() rollback")
+            raise
+        logger.info("Isaac Teleop session started: %s", self.config.app_name)
+
+    def disconnect(self) -> None:
+        try:
+            if self._session is not None:
+                # Null the handle BEFORE __exit__: even a failed session teardown must not
+                # wedge the device as is_connected (blocking every later connect/disconnect).
+                session = self._session
+                self._session = None
+                session.__exit__(None, None, None)
+                logger.info("Isaac Teleop session ended")
+        finally:
+            # Reap the CloudXR runtime even if session teardown raised, and even if no
+            # session was ever established (e.g. the launcher came up but session creation
+            # failed before this point); a no-op when we never launched CloudXR (opt-out /
+            # externally-owned runtime), so we never stop a runtime we don't own.
+            self._stop_cloudxr_runtime()
+
+    # ------------------------------------------------------------------
+    # CloudXR runtime (shared)
+    # ------------------------------------------------------------------
+
+    def _ensure_cloudxr_runtime(self) -> None:
+        """Auto-launch the CloudXR runtime once, unless opted out.
+
+        Idempotent (no-op once the launcher is up). ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH``
+        is checked first and wins over ``config.auto_launch_cloudxr``. Constructing
+        :class:`CloudXRLauncher` mutates the process env (``XR_RUNTIME_JSON`` etc.) and
+        blocks until the runtime is ready or raises :class:`RuntimeError`.
+        """
+        if self._cloudxr_launcher is not None:
+            return
+
+        if os.environ.get("LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH", "").strip() == "1":
+            logger.info(
+                "LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1 set; skipping CloudXR auto-launch "
+                "(assuming CloudXR is already running externally)"
+            )
+            return
+
+        if not self.config.auto_launch_cloudxr:
+            logger.info(
+                "config.auto_launch_cloudxr is False; skipping CloudXR auto-launch "
+                "(assuming CloudXR is already running externally)"
+            )
+            return
+
+        logger.info("Launching CloudXR runtime (first run may prompt for EULA and take ~30s)...")
+
+        self._cloudxr_launcher = CloudXRLauncher(
+            install_dir=str(Path.home() / ".cloudxr"),
+            env_config=self.config.cloudxr_env_file,
+            accept_eula=False,
+        )
+
+    def _stop_cloudxr_runtime(self) -> None:
+        """Stop the auto-launched CloudXR runtime, if any.
+
+        Clean stop nulls the handle. On :class:`RuntimeError` the handle is RETAINED so
+        the launcher's ``atexit`` hook owns the retry — a later :meth:`connect` then
+        treats the retained runtime as still up and will not relaunch.
+        """
+        if self._cloudxr_launcher is None:
+            return
+        try:
+            self._cloudxr_launcher.stop()
+        except RuntimeError:
+            logger.warning("CloudXR runtime could not be terminated; handle retained for atexit cleanup")
+        else:
+            self._cloudxr_launcher = None
+            logger.info("CloudXR runtime stopped")
+
+    def send_feedback(self, feedback: dict[str, Any]) -> None:
+        pass  # Haptic feedback not yet implemented.
+
+    # ------------------------------------------------------------------
+    # Stepping (shared)
+    # ------------------------------------------------------------------
+
+    def _running_events(self) -> ExecutionEvents:
+        """Constant ``RUNNING`` ``ExecutionEvents`` for a device with no clutch lifecycle.
+
+        Keeps the stream flowing; ``reset`` stays ``False``. A clutched device that needs
+        a real lifecycle should build its own ``ExecutionEvents`` instead.
+        """
+        return ExecutionEvents(execution_state=ExecutionState.RUNNING, reset=False)
+
+    def _step(
+        self,
+        *,
+        execution_events: ExecutionEvents | None = None,
+        external_inputs: Mapping[str, Any] | None = None,
+    ) -> RetargeterIO:
+        """Step the session once and return the raw pipeline outputs.
+
+        Applies the shared guard: re-raises a retargeting-worker exception and warns on a
+        stale frame. Subclasses call this from :meth:`get_action`.
+
+        Args:
+            execution_events: The ``ExecutionEvents`` driving the session this frame.
+                Devices with a lifecycle (clutch) MUST pass this every frame — when
+                ``None``, ``TeleopSession.step`` auto-fires ``RUNNING`` (the clutch would
+                latch immediately and never stop).
+            external_inputs: Per-step inputs (e.g. a static ``base_T_anchor``) in the
+                ``{leaf_node_name: {output_port_name: TensorGroup}}`` shape ``step`` expects.
+
+        Raises:
+            RuntimeError: If not connected, or if the retargeting worker raised.
+        """
+        if self._session is None:
+            raise RuntimeError("Not connected. Call connect() first.")
+
+        result = self._session.step(
+            execution_events=execution_events,
+            external_inputs=external_inputs,
+        )
+
+        info = self._session.last_step_info
+        if info is not None:
+            if info.worker_exception is not None:
+                raise RuntimeError(
+                    "Isaac Teleop retargeting worker raised an exception"
+                ) from info.worker_exception
+            if info.frame_deadline_miss:
+                logger.warning(
+                    "Isaac Teleop frame deadline miss (returned_age_frames=%s)",
+                    info.returned_age_frames,
+                )
+        return result

examples/isaac_teleop_to_so101/isaac_teleop/clutch.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Engage-relative clutch for the XR -> SO-101 teleop loop.
+
+Turns the raw controller grip pose into an absolute base-frame EE target, so the XR
+device can stay a thin raw-pose reader. Pure numpy + the local ``Rotation`` helper (no
+``isaacteleop``), so it is unit-testable without the XR runtime.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from lerobot.utils.rotation import Rotation
+
+
+class Clutch:
+    """Engage-relative clutch for both position AND orientation.
+
+    Latch an origin on engage, then track the base-frame delta from it, applied
+    independently to position and orientation. State:
+
+    - ``_last_commanded_pos`` / ``_last_commanded_rot``: last commanded EE pose; held
+      while disengaged so the arm freezes where it was left.
+    - ``_home_pos`` / ``_home_rot``: latched on engage — the EE pose the delta applies to.
+      The position comes from the arm's MEASURED pose when the caller provides it (so an
+      arm that moved while disengaged is not snapped back to a stale command); the
+      orientation always comes from the last commanded rotation (see NOTE below).
+    - ``_origin_pos`` / ``_origin_rot``: latched on engage — the controller pose the delta
+      is measured against.
+
+    Each engaged frame :meth:`rebase` returns::
+
+        pos = home_pos + (grip_pos - origin_pos)  # 1:1 controller -> EE translation
+        rot = (R_ctrl @ R_origin ^ -1) @ R_home  # base-frame delta, left-composed
+
+    On the engage edge the output is exactly the home pose (no teleport). The orientation
+    delta is left-composed (base frame), so hand rotation about base Z maps to EE rotation
+    about base Z. A re-clutch latches a fresh home/origin.
+
+    NOTE: ``_home_rot`` is the last *commanded* orientation even when the measured pose is
+    supplied: the 5-DOF SO-101 tracks orientation only softly, so its measured wrist
+    orientation persistently differs from the command, and latching the measurement would
+    inject that offset into the commanded signal on every re-clutch. Position has no such
+    tracking gap, and there latching the measurement is what prevents the snap-back.
+    """
+
+    def __init__(self, home_base_T_ee: np.ndarray):  # noqa: N803
+        # Seed the held pose from the arm's measured startup EE pose so the first
+        # engage latches home there (no jump on the first squeeze).
+        home = np.asarray(home_base_T_ee, dtype=float)
+        self._last_commanded_pos = home[:3, 3].copy()
+        self._last_commanded_rot = Rotation.from_matrix(home[:3, :3])
+        self._home_pos = self._last_commanded_pos.copy()
+        self._home_rot = self._last_commanded_rot
+        self._origin_pos = np.zeros(3, dtype=float)
+        self._origin_rot = Rotation.from_quat(np.array([0.0, 0.0, 0.0, 1.0]))
+
+    def engage(
+        self,
+        grip_pos: np.ndarray,
+        grip_quat: np.ndarray,
+        measured_base_T_ee: np.ndarray | None = None,  # noqa: N803
+    ) -> None:
+        """Latch the engage home (where the arm is now) and controller origin.
+
+        Pass ``measured_base_T_ee`` (FK of the measured joints) so the home POSITION is
+        where the arm physically is — if the arm moved while disengaged (gravity sag,
+        external contact), latching the stale last-commanded position would make the
+        first engaged frame command a full-speed jump back to it. The home ORIENTATION
+        always stays the last commanded one (see the class NOTE).
+        """
+        if measured_base_T_ee is not None:
+            self._home_pos = np.asarray(measured_base_T_ee, dtype=float)[:3, 3].copy()
+        else:
+            self._home_pos = self._last_commanded_pos.copy()
+        self._home_rot = self._last_commanded_rot
+        self._origin_pos = np.asarray(grip_pos, dtype=float).copy()
+        self._origin_rot = Rotation.from_quat(np.asarray(grip_quat, dtype=float))
+
+    def rebase(self, grip_pos: np.ndarray, grip_quat: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """Return the absolute base-frame EE target ``(pos [m], quat [xyzw])`` for this frame."""
+        pos = self._home_pos + (np.asarray(grip_pos, dtype=float) - self._origin_pos)
+        rot_ctrl = Rotation.from_quat(np.asarray(grip_quat, dtype=float))
+        rot = (rot_ctrl * self._origin_rot.inv()) * self._home_rot
+        self._last_commanded_pos = pos.copy()
+        self._last_commanded_rot = rot
+        return pos, rot.as_quat()

examples/isaac_teleop_to_so101/isaac_teleop/config_isaac_teleop.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Configuration dataclasses for NVIDIA Isaac Teleop-backed teleoperators.
+
+:class:`IsaacTeleopConfig` holds the shared fields; each device adds its own subclass
+(e.g. :class:`XRControllerConfig`, :class:`SO101LeaderArmConfig`).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import ClassVar
+
+from lerobot.teleoperators.config import TeleoperatorConfig
+
+
+@dataclass(kw_only=True)
+class IsaacTeleopConfig(TeleoperatorConfig):
+    """Shared config for all Isaac Teleop-backed teleoperators.
+
+    Uses its own draccus ``_choice_registry`` (decoupled from the global
+    :class:`TeleoperatorConfig` one) so ``--teleop.type`` on a field typed
+    ``IsaacTeleopConfig`` resolves against ONLY the Isaac devices — letting them claim
+    short names (``xr_controller``, ``so101_leader``) without colliding with the global
+    registry. These devices are selected by the example scripts, not routed through
+    ``make_teleoperator_from_config``.
+    """
+
+    _choice_registry: ClassVar[dict] = {}
+
+    app_name: str = "LeTeleop"
+    """Application name for the OpenXR / Isaac Teleop session."""
+
+    auto_launch_cloudxr: bool = True
+    """Auto-launch the CloudXR runtime on :meth:`connect`. Set ``False`` (or export
+    ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1``, which wins) when CloudXR runs externally.
+    """
+
+    cloudxr_env_file: str | None = None
+    """Optional CloudXR device-profile ``.env`` (an INPUT profile selecting the headset
+    transport) passed to ``CloudXRLauncher``. ``None`` keeps the default auto-WebRTC profile.
+    """
+
+
+# Static rebase from the OpenXR controller anchor frame (X=Right, Y=Up, Z=Backward) into the
+# robot base frame (X=Forward, Y=Left, Z=Up). A proper rotation (det=+1): controller motion
+# forward -> robot +X, right -> robot -Y (i.e. rightward), up -> robot +Z.
+_DEFAULT_BASE_T_ANCHOR: list[list[float]] = [
+    [0.0, 0.0, -1.0, 0.0],
+    [-1.0, 0.0, 0.0, 0.0],
+    [0.0, 1.0, 0.0, 0.0],
+    [0.0, 0.0, 0.0, 1.0],
+]
+
+
+@IsaacTeleopConfig.register_subclass("xr_controller")
+@dataclass(kw_only=True)
+class XRControllerConfig(IsaacTeleopConfig):
+    """Config for Isaac Teleop XR (VR) controller teleoperation.
+
+    Exposes the raw base-frame grip pose, squeeze, and trigger via ``ControllersSource``.
+    No retargeters: the clutch and gripper mapping live in the owning loop.
+    """
+
+    hand_side: str = "right"
+    """Which controller hand to use: ``"left"`` or ``"right"``. A plain ``str`` (validated in
+    ``__post_init__``) because draccus cannot decode ``Literal``-typed fields from the CLI."""
+
+    clutch_threshold: float = 0.5
+    """Squeeze value above which the owning loop's clutch engages (held-to-enable). The
+    device reports only the raw squeeze; the threshold is applied by the loop."""
+
+    base_T_anchor: list[list[float]] = field(  # noqa: N815  (frameA_T_frameB transform-matrix convention)
+        # Fresh copy per instance: returning the module-level list itself would alias one
+        # mutable matrix across every config.
+        default_factory=lambda: [row.copy() for row in _DEFAULT_BASE_T_ANCHOR]
+    )
+    """Static 4x4 [row-major] transform rebasing the OpenXR controller anchor frame into
+    the robot base frame. Defaults to OpenXR (X=Right, Y=Up, Z=Backward) -> robot
+    (X=Forward, Y=Left, Z=Up). Plain nested lists so the config stays serializable.
+    """
+
+    def __post_init__(self):
+        if self.hand_side not in ("left", "right"):
+            raise ValueError(f"hand_side must be 'left' or 'right', got {self.hand_side!r}")
+
+
+# Provisional gripper open/close endpoints [rad], normalizing the streamed gripper angle
+# into the follower's RANGE_0_100 jaw target. Derived from the so101_leader plugin README's
+# example calibration (home_ticks=2048, range 2000..3000; angle = (ticks-home)*2*pi/4096).
+_DEFAULT_GRIPPER_OPEN_RAD = -0.074
+_DEFAULT_GRIPPER_CLOSE_RAD = 1.460
+
+
+@IsaacTeleopConfig.register_subclass("so101_leader")
+@dataclass(kw_only=True)
+class SO101LeaderArmConfig(IsaacTeleopConfig):
+    """Config for an Isaac Teleop SO-101 *leader arm* (generic joint-space device).
+
+    Mirrors the leader's joint angles 1:1 onto a follower SO-101. The leader state is
+    streamed in radians by the native ``so101_leader`` plugin and read via a
+    ``JointStateSource``; the device converts arm joints to degrees and the gripper to the
+    follower's RANGE_0_100 jaw target (no IK/clutch/retargeter on the LeRobot side).
+    """
+
+    port: str = ""
+    """Serial port of the physical LEADER arm (e.g. ``/dev/ttyACM1``), forwarded to the
+    plugin (which reads the servos) when the example launches it. Empty -> the plugin runs
+    its synthetic trajectory."""
+
+    collection_id: str = "so101_leader"
+    """Tensor collection id the leader plugin pushes on; must match the running
+    ``so101_leader`` plugin (its second positional arg, default ``"so101_leader"``)."""
+
+    gripper_open_rad: float = _DEFAULT_GRIPPER_OPEN_RAD
+    """Leader gripper angle [rad] at fully OPEN -> follower jaw 100. Provisional default;
+    set from the plugin's ``calibrate`` subcommand. See ``_DEFAULT_GRIPPER_OPEN_RAD``."""
+
+    gripper_close_rad: float = _DEFAULT_GRIPPER_CLOSE_RAD
+    """Leader gripper angle [rad] at fully CLOSED -> follower jaw 0. Provisional default;
+    set from the plugin's ``calibrate`` subcommand. See ``_DEFAULT_GRIPPER_CLOSE_RAD``."""

examples/isaac_teleop_to_so101/isaac_teleop/teleop_so101_leader_arm.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""SO-101 leader-arm device for NVIDIA Isaac Teleop, exposed to LeRobot.
+
+The leader is a back-drivable SO-101 whose six joint angles are streamed (in radians) by
+the native ``so101_leader`` plugin; this device reads them via a ``JointStateSource`` and
+converts them into follower-ready ``{joint}.pos``. Same kinematics as the follower, so it
+needs no retargeting — a 1:1 joint mirror, direct joint drive.
+
+Units (converted in the device so the output is always follower-valid):
+
+* arm joints: ``rad2deg`` — correct only if the leader's calibrated zero and the follower's
+  homing map to the same physical zero (the standard same-hardware assumption).
+* gripper: normalized from ``[gripper_open_rad, gripper_close_rad]`` to RANGE_0_100.
+
+``isaacteleop`` imports are guarded behind the availability flag so this module — and the
+pure :func:`leader_joints_to_robot_action` converter — import without it (construction
+fails fast via the base class).
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from lerobot.types import RobotAction
+
+from .base import _GRIPPER_MOTOR_SCALE, IsaacTeleopTeleoperator, _isaacteleop_available
+from .config_isaac_teleop import SO101LeaderArmConfig
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.retargeting_engine.deviceio_source_nodes import JointStateSource
+    from isaacteleop.retargeting_engine.interface import OutputCombiner
+else:
+    JointStateSource = None
+    OutputCombiner = None
+
+# Canonical SO-101 DOF names and order — matches the plugin stream and the follower's motor
+# order. Passed to the ``JointStateSource`` as its output layout; the source maps by name and
+# :func:`_joints_group_to_rad` reads back by name, so a layout mismatch can't mislabel a DOF.
+SO101_LEADER_JOINTS = [
+    "shoulder_pan",
+    "shoulder_lift",
+    "elbow_flex",
+    "wrist_flex",
+    "wrist_roll",
+    "gripper",
+]
+
+
+def leader_joints_to_robot_action(
+    joints_rad: dict[str, float],
+    *,
+    gripper_joint: str,
+    gripper_open_rad: float,
+    gripper_close_rad: float,
+) -> RobotAction:
+    """Convert streamed leader joint angles [rad] to follower-ready ``{joint}.pos``.
+
+    Pure (no ``isaacteleop``, no I/O). Iteration follows ``joints_rad`` insertion order, so
+    pass it in :data:`SO101_LEADER_JOINTS` order for a stable layout. Arm joints are
+    converted ``rad2deg``; ``gripper_joint`` is normalized from
+    ``[gripper_open_rad, gripper_close_rad]`` to RANGE_0_100 (clipped).
+    """
+    action: RobotAction = {}
+    span = gripper_close_rad - gripper_open_rad
+    for name, rad in joints_rad.items():
+        if name == gripper_joint:
+            # Closedness c=0 at open, c=1 at closed; invert to the follower's 100=open jaw.
+            closedness = 0.0 if span == 0.0 else (rad - gripper_open_rad) / span
+            closedness = min(1.0, max(0.0, closedness))
+            action[f"{name}.pos"] = (1.0 - closedness) * _GRIPPER_MOTOR_SCALE
+        else:
+            action[f"{name}.pos"] = float(np.rad2deg(rad))
+    return action
+
+
+def _joints_group_to_rad(joints) -> dict[str, float]:
+    """Read a ``JointStateSource`` output group into ``{joint_name: angle [rad]}``.
+
+    Pure (duck-typed on the group). The group is positional but each slot carries its joint
+    name in ``group.group_type.types``; we key off those names (not a positional index) so a
+    layout mismatch surfaces as a wrong/missing key here rather than a mislabeled DOF.
+    """
+    names = [t.name for t in joints.group_type.types]
+    return {name: float(joints[i]) for i, name in enumerate(names)}
+
+
+class SO101LeaderArm(IsaacTeleopTeleoperator):
+    """SO-101 leader-arm teleoperator (joint-space), direct joint mirror to the follower.
+
+    Reads the six joint angles off a single ``JointStateSource`` each frame; no retargeter,
+    no clutch. When the leader is not streaming, :meth:`get_action` returns the held-last
+    joints and :attr:`is_tracking` is ``False`` so the owning loop can hold the follower.
+    """
+
+    config_class = SO101LeaderArmConfig
+    name = "isaac_teleop_so101_leader"
+
+    def __init__(self, config: SO101LeaderArmConfig):
+        super().__init__(config)
+        self.config: SO101LeaderArmConfig = config
+        # Held-last joint angles [rad], seeded at zero (URDF/home pose) so the first frames
+        # before the plugin starts pushing read as the home pose, not garbage.
+        self._last_joints_rad: dict[str, float] = dict.fromkeys(SO101_LEADER_JOINTS, 0.0)
+        # Whether the most recent get_action() read live leader data (vs held-last).
+        self._is_tracking = False
+
+    # ------------------------------------------------------------------
+    # Pipeline construction
+    # ------------------------------------------------------------------
+
+    def _build_pipeline(self) -> OutputCombiner:
+        """Build the joint-mirror pipeline: a single ``JointStateSource`` leaf that converts
+        the raw stream into a name-keyed joint group. No retargeter (shared kinematics)."""
+        source = JointStateSource(
+            name="so101_leader",
+            collection_id=self.config.collection_id,
+            joint_names=SO101_LEADER_JOINTS,
+        )
+        return OutputCombiner({"joints": source.output(JointStateSource.JOINTS)})
+
+    # ------------------------------------------------------------------
+    # Action features
+    # ------------------------------------------------------------------
+
+    @property
+    def action_features(self) -> dict[str, type]:
+        # Matches the serial SOLeader's action features so this is a drop-in joint-space
+        # leader: one float `{joint}.pos` per DOF, sendable straight to an SO-101 follower.
+        return {f"{name}.pos": float for name in SO101_LEADER_JOINTS}
+
+    @property
+    def feedback_features(self) -> dict[str, type]:
+        return {}
+
+    @property
+    def is_tracking(self) -> bool:
+        """Whether the last :meth:`get_action` read live leader data (vs held-last)."""
+        return self._is_tracking
+
+    # ------------------------------------------------------------------
+    # Action extraction
+    # ------------------------------------------------------------------
+
+    def get_action(self) -> RobotAction:
+        """Step the session and return the leader joints as follower-ready ``{joint}.pos``.
+
+        When the leader is streaming, the live angles are cached and converted; otherwise the
+        held-last angles are reused and :attr:`is_tracking` is set ``False``.
+        """
+        result = self._step(execution_events=self._running_events())
+
+        joints = result["joints"]
+        # The JointStateSource output is Optional: absent (is_none) when the device is
+        # inactive. Treat that as "not tracking" and reuse the held-last angles.
+        self._is_tracking = not getattr(joints, "is_none", False)
+        if self._is_tracking:
+            try:
+                self._last_joints_rad = _joints_group_to_rad(joints)
+            except (AttributeError, IndexError, KeyError, TypeError, ValueError):
+                # A partially-populated / malformed group on an odd frame: keep held-last, but
+                # report it as not-tracking so the loop holds the follower rather than trusting it.
+                self._is_tracking = False
+
+        return leader_joints_to_robot_action(
+            self._last_joints_rad,
+            gripper_joint="gripper",
+            gripper_open_rad=self.config.gripper_open_rad,
+            gripper_close_rad=self.config.gripper_close_rad,
+        )

examples/isaac_teleop_to_so101/isaac_teleop/teleop_xr_controller.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""XR (VR) controller device for NVIDIA Isaac Teleop, exposed to LeRobot.
+
+A deliberately thin reader: exposes the raw controller grip pose off
+``ControllersSource`` (statically rebased into the robot base frame by
+``ControllerTransform``), plus squeeze and trigger. No retargeters and no clutch —
+the clutch rebasing and gripper mapping live downstream in the owning loop, so this
+device is stateless across frames.
+
+``isaacteleop`` imports are guarded behind the availability flag so this module imports
+without it (construction fails fast via the base class).
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from lerobot.types import RobotAction
+
+from .base import IsaacTeleopTeleoperator, _isaacteleop_available
+from .config_isaac_teleop import XRControllerConfig
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.retargeting_engine.deviceio_source_nodes import ControllersSource
+    from isaacteleop.retargeting_engine.interface import OutputCombiner, TensorGroup, ValueInput
+    from isaacteleop.retargeting_engine.tensor_types import TransformMatrix
+    from isaacteleop.retargeting_engine.tensor_types.indices import ControllerInputIndex
+else:
+    ControllersSource = None
+    OutputCombiner = None
+    TensorGroup = None
+    ValueInput = None
+    TransformMatrix = None
+    ControllerInputIndex = None
+
+# Source-node name for the static base_T_anchor rebase input fed via
+# ``TeleopSession.step(external_inputs=...)`` each frame.
+_BASE_T_ANCHOR_INPUT = "base_T_anchor"
+
+
+class XRController(IsaacTeleopTeleoperator):
+    """Raw XR controller grip-pose teleoperator (base-frame), no retargeters.
+
+    Reads the raw grip pose + squeeze + trigger off a ``ControllersSource`` rebased into
+    the robot base frame. :meth:`get_action` returns the absolute base-frame grip pose
+    untouched; the owning loop owns the clutch and gripper mapping.
+    """
+
+    config_class = XRControllerConfig
+    name = "isaac_teleop_controller"
+
+    def __init__(self, config: XRControllerConfig):
+        super().__init__(config)
+        self.config: XRControllerConfig = config
+
+        # Constant base_T_anchor input, built once in connect() (a TensorGroup is heavy and
+        # isaacteleop-backed) and reused every step.
+        self._external_inputs: dict[str, Any] | None = None
+        # Whether the last get_action() read a tracked controller; the owning loop polls this
+        # to wait for the operator to connect before driving the arm.
+        self._is_tracking = False
+
+    # ------------------------------------------------------------------
+    # Pipeline construction
+    # ------------------------------------------------------------------
+
+    def _build_pipeline(self) -> OutputCombiner:
+        """Build the raw-grip-pose pipeline: a ``ControllersSource`` rebased into the base
+        frame by ``ControllerTransform``, exposed verbatim as ``"controller"``. No retargeters.
+        """
+        side = self.config.hand_side
+        controller_key = f"controller_{side}"
+
+        controllers = ControllersSource(name="controllers")
+        # Static base_T_anchor rebase fed via external_inputs each step.
+        xform = ValueInput(_BASE_T_ANCHOR_INPUT, TransformMatrix())
+        transformed = controllers.transformed(xform.output("value"))
+        ctrl = transformed.output(controller_key)
+
+        return OutputCombiner({"controller": ctrl})
+
+    def _build_external_inputs(self) -> dict[str, Any]:
+        """Materialize the constant ``base_T_anchor`` external input (once, in connect)."""
+        tg = TensorGroup(TransformMatrix())
+        tg[0] = np.asarray(self.config.base_T_anchor, dtype=np.float32)
+        return {_BASE_T_ANCHOR_INPUT: {"value": tg}}
+
+    def connect(self, calibrate: bool = True) -> None:
+        super().connect(calibrate=calibrate)
+        try:
+            self._external_inputs = self._build_external_inputs()
+        except Exception:
+            # Roll the session/runtime back so a failed connect() leaves no half-state
+            # (a live session behind a raised connect would leak the CloudXR runtime).
+            self.disconnect()
+            raise
+
+    # ------------------------------------------------------------------
+    # Action features
+    # ------------------------------------------------------------------
+
+    @property
+    def action_features(self) -> dict:
+        return {
+            "grip_pos": {
+                "dtype": "float32",
+                "shape": (3,),
+                "names": {"x": 0, "y": 1, "z": 2},
+            },
+            "grip_quat": {
+                "dtype": "float32",
+                "shape": (4,),
+                "names": {"qx": 0, "qy": 1, "qz": 2, "qw": 3},
+            },
+            # ``get_action`` returns scalars for these two, so the advertised
+            # shape is () (0-d) to stay consistent with the returned values.
+            "squeeze": {
+                "dtype": "float32",
+                "shape": (),
+                "names": None,
+            },
+            "trigger": {
+                "dtype": "float32",
+                "shape": (),
+                "names": None,
+            },
+        }
+
+    @property
+    def feedback_features(self) -> dict:
+        return {}
+
+    @property
+    def is_tracking(self) -> bool:
+        """Whether the last :meth:`get_action` read a tracked controller. ``False`` until the
+        headset is connected over CloudXR and its controllers are live; the owning loop polls
+        it to wait for the operator before commanding the arm."""
+        return self._is_tracking
+
+    # ------------------------------------------------------------------
+    # Action extraction
+    # ------------------------------------------------------------------
+
+    def get_action(self) -> RobotAction:
+        """Step the session and return the raw base-frame grip pose.
+
+        Reads the grip pose + squeeze + trigger off the transformed controller stream (with
+        the constant ``base_T_anchor`` rebase). When the controller is not tracked, returns
+        identity pose and squeeze/trigger = 0.0 so the owning loop freezes the arm.
+
+        Returns:
+            ``{"grip_pos": (3,) [m], "grip_quat": (4,) [qx,qy,qz,qw], "squeeze": float,
+            "trigger": float}`` — pose in the robot base frame; squeeze/trigger in ``[0, 1]``.
+        """
+        result = self._step(execution_events=self._running_events(), external_inputs=self._external_inputs)
+
+        # Optional controller group is None until the headset is connected and its controllers
+        # are live; expose that as is_tracking so the loop can wait before driving the arm.
+        controller = result["controller"]
+        grip_pos = np.zeros(3, dtype=np.float32)
+        grip_quat = np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32)
+        squeeze = 0.0
+        trigger = 0.0
+        self._is_tracking = not getattr(controller, "is_none", False)
+        if self._is_tracking:
+            # Read ALL four fields into locals before committing any of them: a failure on a
+            # partially-populated frame must not mix live values with the safe defaults (a
+            # live squeeze paired with a defaulted trigger=0.0 would keep the clutch engaged
+            # while commanding the gripper fully open, dropping whatever is grasped). On
+            # failure the defaults stand untouched and the frame reports not-tracked.
+            try:
+                pos = np.asarray(controller[ControllerInputIndex.GRIP_POSITION], dtype=np.float32)
+                quat = np.asarray(controller[ControllerInputIndex.GRIP_ORIENTATION], dtype=np.float32)
+                squeeze_val = float(controller[ControllerInputIndex.SQUEEZE_VALUE])
+                trigger_val = float(controller[ControllerInputIndex.TRIGGER_VALUE])
+            except (IndexError, KeyError, TypeError, ValueError):
+                self._is_tracking = False
+            else:
+                grip_pos, grip_quat = pos, quat
+                squeeze, trigger = squeeze_val, trigger_val
+
+        return {
+            "grip_pos": grip_pos,
+            "grip_quat": grip_quat,
+            "squeeze": squeeze,
+            "trigger": trigger,
+        }

examples/isaac_teleop_to_so101/isaac_teleop/xr_controller_processor.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Processor step that maps XR controller actions to robot EE targets.
+
+Analogous to ``MapPhoneActionToRobotAction``, this bridges the clutch-rebased EE pose to
+the IK pipeline's input contract (``EEBoundsAndSafety`` -> ``InverseKinematicsEEToJoints``).
+Pure (no ``isaacteleop``), so it is unit-testable without the XR runtime.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.processor import ProcessorStepRegistry, RobotActionProcessorStep
+from lerobot.types import RobotAction
+from lerobot.utils.rotation import Rotation
+
+from .base import _GRIPPER_MOTOR_SCALE
+
+
+@ProcessorStepRegistry.register("map_xr_controller_action_to_robot_action")
+@dataclass
+class MapXRControllerActionToRobotAction(RobotActionProcessorStep):
+    """Maps an absolute base-frame EE pose + gripper closedness to the IK input contract.
+
+    Pure, stateless rename (the owning loop's clutch already produced the absolute base-frame
+    target). Each frame it writes:
+
+    - ``ee.x/y/z`` = ``ee_pose[:3]`` (position [m]);
+    - ``ee.wx/wy/wz`` = rotvec of ``ee_pose[3:7]`` (orientation; the IK tracks it softly at a
+      small ``orientation_weight`` on the 5-DOF SO-101);
+    - ``ee.gripper_pos`` = ``(1 - closedness) * _GRIPPER_MOTOR_SCALE`` (jaw target [0, 100],
+      RANGE_0_100 where 100 = open, so closedness is inverted).
+
+    Input keys: ``ee_pose`` ``(7,)`` ``[x,y,z,qx,qy,qz,qw]``, ``closedness`` float in [0, 1].
+    """
+
+    def action(self, action: RobotAction) -> RobotAction:
+        ee_pose = action.pop("ee_pose")
+        closedness = float(action.pop("closedness"))
+
+        action["ee.x"] = float(ee_pose[0])
+        action["ee.y"] = float(ee_pose[1])
+        action["ee.z"] = float(ee_pose[2])
+        # Orientation target as a rotvec (quat [qx,qy,qz,qw] -> axis-angle); the IK
+        # consumes ee.w* as a rotvec and tracks it with orientation_weight.
+        rotvec = Rotation.from_quat(ee_pose[3:7]).as_rotvec()
+        action["ee.wx"] = float(rotvec[0])
+        action["ee.wy"] = float(rotvec[1])
+        action["ee.wz"] = float(rotvec[2])
+        # Inverted: closedness c=1 (closed) -> 0, c=0 (open) -> 100 (SO-101 calibration).
+        action["ee.gripper_pos"] = (1.0 - closedness) * _GRIPPER_MOTOR_SCALE
+        return action
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        for feat in ["ee_pose", "closedness"]:
+            features[PipelineFeatureType.ACTION].pop(feat, None)
+
+        for feat in [
+            "ee.x",
+            "ee.y",
+            "ee.z",
+            "ee.wx",
+            "ee.wy",
+            "ee.wz",
+            "ee.gripper_pos",
+        ]:
+            features[PipelineFeatureType.ACTION][feat] = PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+
+        return features

examples/isaac_teleop_to_so101/override_reset_pose.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Save the current SO-101 joint positions as the reset-origin pose (override).
+
+Move the arm to the desired reset pose by hand (torque off), then run this script to write
+those joints to a per-arm file in the LeRobot cache. ``teleoperate.py`` / ``record.py`` load
+it on startup (matched by ``--robot.id``) as the reset target instead of the defaults.
+
+Usage::
+
+    # 1. Move arm to desired reset pose by hand
+    python -m examples.isaac_teleop_to_so101.override_reset_pose [--port /dev/ttyACM0] [--id so101_follower_arm]
+
+    # 2. Launch teleop with the SAME --robot.id — it will now reset to this pose on startup
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=xr_controller
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+
+from .common import RESET_POSE_FILE
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--port", type=str, default="/dev/ttyACM0")
+    parser.add_argument("--id", type=str, default="so101_follower_arm")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    robot = SO100Follower(SO100FollowerConfig(port=args.port, id=args.id, use_degrees=True))
+    robot.connect()
+    # Always disconnect the follower so a failure never leaks the serial connection.
+    try:
+        obs = robot.get_observation()
+        motor_names = list(robot.bus.motors.keys())
+        pose = {name: float(obs[f"{name}.pos"]) for name in motor_names}
+    finally:
+        robot.disconnect()
+
+    print("Current joint positions:")
+    for name, val in pose.items():
+        print(f"  {name:20s}: {val:.2f}")
+
+    reset_pose_file = Path(RESET_POSE_FILE.format(robot_name=robot.name, robot_id=robot.id))
+    reset_pose_file.parent.mkdir(parents=True, exist_ok=True)
+    reset_pose_file.write_text(json.dumps(pose, indent=2))
+    print(f"\nSaved to {reset_pose_file}")
+
+
+if __name__ == "__main__":
+    main()

examples/isaac_teleop_to_so101/record.py

@@ -0,0 +1,321 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Record a LeRobot dataset via NVIDIA Isaac Teleop -> SO-101.
+
+Runs ``teleoperate.py``'s control loop while also saving each frame to a LeRobot dataset.
+``--teleop.type`` selects the device (``xr_controller`` | ``so101_leader``) as in
+``teleoperate.py``.
+
+Usage::
+
+    # XR (VR) controller: clutch + soft-orientation IK
+    python -m examples.isaac_teleop_to_so101.record \\
+        --robot.type=so101_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --robot.id=so101_follower_arm \\
+        --teleop.type=xr_controller \\
+        --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \\
+        --dataset.repo_id=<hf_user>/<dataset_name> \\
+        --dataset.single_task="Pick up vial from rack on the left side" \\
+        --dataset.num_episodes=3 \\
+        --dataset.episode_time_s=20 \\
+        --dataset.reset_time_s=5
+
+    # SO-101 leader arm: 1:1 joint mirror (real leader on /dev/ttyACM1)
+    python -m examples.isaac_teleop_to_so101.record \\
+        --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \\
+        --teleop.type=so101_leader --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \\
+        --launch_plugin=/path/to/IsaacTeleop/install/plugins/so101_leader/so101_leader_plugin \\
+        --dataset.repo_id=<hf_user>/<dataset_name> --dataset.single_task="Pick up the cube" \\
+        --dataset.num_episodes=3 --dataset.episode_time_s=20 --dataset.reset_time_s=5
+
+The loop/launch knobs mirror ``teleoperate.py`` (tagged ``[xr]`` / ``[leader]`` below).
+
+Keyboard shortcuts: Right/n = end episode early and save, Left/r = discard + re-record,
+Esc/q = stop after the current episode. All frames are recorded (including hold frames).
+"""
+
+import logging
+import time
+from dataclasses import asdict, dataclass
+from pprint import pformat
+
+from lerobot.cameras import CameraConfig  # noqa: F401
+from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.common.control_utils import sanity_check_dataset_robot_compatibility
+from lerobot.configs import parser
+from lerobot.configs.dataset import DatasetRecordConfig
+from lerobot.datasets import (
+    LeRobotDataset,
+    VideoEncodingManager,
+    aggregate_pipeline_dataset_features,
+    create_initial_features,
+    safe_stop_image_writer,
+)
+from lerobot.processor import make_default_processors
+from lerobot.robots import RobotConfig
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import init_logging
+
+from .common import (
+    ALIGN_DURATION_S,
+    RESET_DURATION_S,
+    Device,
+    HoldLatch,
+    build_device,
+    init_keyboard_listener,
+)
+from .isaac_teleop import IsaacTeleopConfig
+
+
+@dataclass
+class RecordConfig:
+    """CLI config for Isaac Teleop -> SO-101 dataset recording.
+
+    ``--robot.*`` / ``--teleop.*`` / ``--dataset.*`` configure the follower, device, and
+    recording; the loop/launch knobs below carry the same ``[xr]`` / ``[leader]`` tags as
+    ``teleoperate.py``. Use ``--flag=false`` for booleans (draccus style).
+    """
+
+    robot: RobotConfig
+    # --teleop.type=xr_controller|so101_leader, resolved against IsaacTeleopConfig's registry.
+    teleop: IsaacTeleopConfig
+    dataset: DatasetRecordConfig
+
+    # [leader] Path to the so101_leader plugin binary to spawn after CloudXR is up (it then
+    # inherits the runtime env). None (default) -> assume the plugin already runs externally.
+    launch_plugin: str | None = None
+
+    # [xr] Slew all joints to the reset pose before the first episode (--reset_to_origin=false to
+    # keep the arm where it is). After the slew the clutch seeds its home from the measured pose.
+    reset_to_origin: bool = True
+    # [xr] Duration [s] of the reset-to-origin slew (passed through to setup_xr).
+    reset_duration: float = RESET_DURATION_S
+
+    # [leader] Slew the follower to the leader's first pose before mirroring (--align=false to
+    # begin the 1:1 mirror immediately; the follower may snap).
+    align: bool = True
+    # [leader] Duration [s] of the startup alignment slew.
+    align_duration: float = ALIGN_DURATION_S
+
+    # Resume recording on an existing (previously interrupted) dataset.
+    resume: bool = False
+
+
+@safe_stop_image_writer
+def _record_loop(
+    robot,
+    device: Device,
+    motor_names: list[str],
+    events: dict,
+    fps: int,
+    dataset: LeRobotDataset | None = None,
+    control_time_s: float = 0.0,
+    single_task: str | None = None,
+) -> None:
+    """Run one episode (or reset phase) of the control loop.
+
+    When ``dataset`` is None the loop still controls the robot (so the operator
+    can reposition the arm during the reset window) but does not record frames.
+    """
+    control_interval = 1.0 / fps
+    timestamp = 0.0
+    start_t = time.perf_counter()
+    record_frames = dataset is not None
+    hold = HoldLatch(motor_names)
+
+    while timestamp < control_time_s:
+        loop_start = time.perf_counter()
+
+        if events["exit_early"]:
+            events["exit_early"] = False
+            break
+
+        obs = robot.get_observation()
+
+        if record_frames:
+            observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
+
+        # Device idle (XR clutch disengaged, or leader stream stale) -> hold the pose
+        # latched on the active->idle edge.
+        action = hold.resolve(device.compute(obs), obs)
+
+        robot.send_action(action)
+
+        if record_frames:
+            action_frame = build_dataset_frame(dataset.features, action, prefix=ACTION)
+            dataset.add_frame({**observation_frame, **action_frame, "task": single_task})
+
+        dt_s = time.perf_counter() - loop_start
+        precise_sleep(max(control_interval - dt_s, 0.0))
+        timestamp = time.perf_counter() - start_t
+
+
+@parser.wrap()
+def record(cfg: RecordConfig) -> LeRobotDataset:
+    init_logging()
+    logging.info(pformat(asdict(cfg)))
+
+    # Connect the follower, build the selected Isaac device, and run its pre-loop startup
+    # (reset slew / leader align) — shared with teleoperate.py.
+    robot, device, motor_names = build_device(cfg)
+
+    # Build dataset feature spec.  The IK pipeline lives inside device.compute(), so the
+    # action features are exactly robot.action_features (joint positions in degrees).
+    teleop_proc, _, obs_proc = make_default_processors()
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_proc,
+            initial_features=create_initial_features(action=robot.action_features),
+            use_videos=cfg.dataset.video,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=obs_proc,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=cfg.dataset.video,
+        ),
+    )
+
+    num_cameras = len(robot.cameras) if hasattr(robot, "cameras") else 0
+    image_writer_threads = cfg.dataset.num_image_writer_threads_per_camera * num_cameras
+
+    dataset: LeRobotDataset | None = None
+    listener = None
+    try:
+        if cfg.resume:
+            dataset = LeRobotDataset.resume(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                rgb_encoder=cfg.dataset.rgb_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
+                encoder_threads=cfg.dataset.encoder_threads,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                image_writer_processes=cfg.dataset.num_image_writer_processes if num_cameras > 0 else 0,
+                image_writer_threads=image_writer_threads if num_cameras > 0 else 0,
+            )
+            sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
+        else:
+            cfg.dataset.stamp_repo_id()
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=image_writer_threads,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                rgb_encoder=cfg.dataset.rgb_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
+                encoder_threads=cfg.dataset.encoder_threads,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+            )
+
+        listener, events = init_keyboard_listener()
+
+        loop_kwargs = {
+            "robot": robot,
+            "device": device,
+            "motor_names": motor_names,
+            "events": events,
+            "fps": cfg.dataset.fps,
+            "single_task": cfg.dataset.single_task,
+        }
+
+        with VideoEncodingManager(dataset):
+            recorded_episodes = 0
+            while recorded_episodes < cfg.dataset.num_episodes and not events["stop_recording"]:
+                logging.info(f"Recording episode {dataset.num_episodes}")
+                _record_loop(
+                    **loop_kwargs,
+                    dataset=dataset,
+                    control_time_s=cfg.dataset.episode_time_s,
+                )
+
+                # Reset window: give the operator time to reposition the scene.
+                # Skipped for the last episode (or if stop_recording was set).
+                if not events["stop_recording"] and (
+                    recorded_episodes < cfg.dataset.num_episodes - 1 or events["rerecord_episode"]
+                ):
+                    logging.info("Reset the environment")
+                    _record_loop(
+                        **loop_kwargs,
+                        dataset=None,
+                        control_time_s=cfg.dataset.reset_time_s,
+                    )
+
+                if events["rerecord_episode"]:
+                    logging.info("Re-record episode")
+                    events["rerecord_episode"] = False
+                    events["exit_early"] = False
+                    dataset.clear_episode_buffer()
+                    continue
+
+                dataset.save_episode()
+                recorded_episodes += 1
+
+    finally:
+        logging.info("Stop recording")
+
+        # Hardware teardown FIRST, each step guarded: the arm must be freed promptly (not
+        # after a potentially long finalize/encode), a cleanup failure must not skip the
+        # follower disconnect (which is what disables torque), and neither must prevent
+        # the dataset from being finalized below.
+        try:
+            device.cleanup()
+        except Exception:
+            logging.exception("Device cleanup failed")
+        try:
+            if robot.is_connected:
+                robot.disconnect()
+        except Exception:
+            logging.exception("Robot disconnect failed")
+
+        # Restore the terminal before the (potentially long) finalize/encode.
+        if listener is not None:
+            try:
+                listener.stop()
+            except Exception:
+                logging.exception("Keyboard listener stop failed")
+
+        if dataset is not None:
+            dataset.finalize()
+
+        if cfg.dataset.push_to_hub:
+            if dataset is not None and dataset.num_episodes > 0:
+                dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            else:
+                logging.warning("No episodes saved — skipping push to hub")
+
+        logging.info("Exiting")
+
+    return dataset
+
+
+def main():
+    record()
+
+
+if __name__ == "__main__":
+    main()

examples/isaac_teleop_to_so101/teleoperate.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Teleoperate an SO-101 follower arm via NVIDIA Isaac Teleop.
+
+``lerobot-teleoperate``-style CLI (draccus): ``--teleop.type`` selects the Isaac device
+(``xr_controller`` | ``so101_leader``), ``--robot.*`` the follower::
+
+    # XR (VR) controller: clutch + soft-orientation IK
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower \
+        --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=xr_controller
+
+    # SO-101 leader arm: 1:1 joint mirror (real leader on /dev/ttyACM1)
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower \
+        --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=so101_leader \
+        --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+        --launch_plugin=/code/Teleop/install/plugins/so101_leader/so101_leader_plugin
+
+``--teleop.type`` resolves against the Isaac device registry (see :class:`IsaacTeleopConfig`),
+distinct from the serial ``so101_leader``. The pipelines, clutch/IK/align internals, and
+reset-pose behavior live in ``common.py``. Requires the ``isaacteleop`` package and an OpenXR
+runtime (install instructions in this folder's ``README.md``).
+"""
+
+import time
+from dataclasses import dataclass
+
+from lerobot.configs import parser
+from lerobot.robots import RobotConfig
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.utils.robot_utils import precise_sleep
+
+from .common import (
+    ALIGN_DURATION_S,
+    FPS,
+    RESET_DURATION_S,
+    HoldLatch,
+    build_device,
+)
+from .isaac_teleop import IsaacTeleopConfig
+
+
+@dataclass
+class TeleoperateConfig:
+    """``lerobot-teleoperate``-style CLI for the Isaac Teleop -> SO-101 example.
+
+    The fields below are the loop/launch knobs (not part of either device's config); the
+    ``[xr]`` / ``[leader]`` tags mark which device a knob applies to. Use ``--flag=false``
+    for booleans (draccus style).
+    """
+
+    # Isaac Teleop input device + its knobs (--teleop.type=xr_controller|so101_leader,
+    # then --teleop.<field>=...). Resolved against IsaacTeleopConfig's own choice registry.
+    teleop: IsaacTeleopConfig
+    # SO-101 FOLLOWER arm (--robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=...).
+    robot: RobotConfig
+
+    # [leader] Path to the so101_leader plugin binary to spawn AFTER CloudXR is up (it then
+    # inherits the runtime env). None (default) -> assume the plugin already runs externally.
+    # The leader's serial port is --teleop.port (forwarded to the plugin; empty -> synthetic).
+    launch_plugin: str | None = None
+
+    # [xr] Slew all joints to a default reset pose before the loop (--reset_to_origin=false to
+    # keep the arm where it is). After the slew the clutch seeds its home from the measured pose.
+    reset_to_origin: bool = True
+    # [xr] Duration [s] of the reset-to-origin slew.
+    reset_duration: float = RESET_DURATION_S
+
+    # [leader] Slew the follower to the leader's first pose before mirroring (--align=false to
+    # begin the 1:1 mirror immediately; the follower may snap).
+    align: bool = True
+    # [leader] Duration [s] of the startup alignment slew.
+    align_duration: float = ALIGN_DURATION_S
+
+
+@parser.wrap()
+def teleoperate(cfg: TeleoperateConfig):
+    robot, device, motor_names = build_device(cfg)
+    hold = HoldLatch(motor_names)
+    try:
+        while True:
+            t0 = time.perf_counter()
+            obs = robot.get_observation()
+            # Idle (compute() -> None) holds the pose latched on the active->idle edge.
+            action = hold.resolve(device.compute(obs), obs)
+            robot.send_action(action)
+            precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+    except KeyboardInterrupt:
+        pass
+    finally:
+        # A failing device cleanup must not skip the follower disconnect (which is what
+        # disables torque on the arm).
+        try:
+            device.cleanup()
+        finally:
+            robot.disconnect()
+
+
+def main():
+    teleoperate()
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.5.2"
+version = "0.6.1"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
@@ -164,6 +164,7 @@ pynput-dep = ["pynput>=1.7.8,<1.9.0"]
 pyzmq-dep = ["pyzmq>=26.2.1,<28.0.0"]
 motorbridge-dep = ["motorbridge>=0.3.2,<0.4.0"]
 motorbridge-smart-servo-dep = ["motorbridge-smart-servo>=0.0.4,<0.1.0"]
+timm-dep = ["timm>=1.0.0,<1.1.0"]
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0", "lerobot[pyserial-dep]", "lerobot[deepdiff-dep]"]
@@ -219,22 +220,21 @@ groot = [
     "lerobot[transformers-dep]",
     "lerobot[peft-dep]",
     "lerobot[diffusers-dep]",
+    "lerobot[dataset]", # NOTE: processor_groot builds a LeRobotDataset for relative-action training stats
     "dm-tree>=0.1.8,<1.0.0",
-    "timm>=1.0.0,<1.1.0",
+    "lerobot[timm-dep]",
     "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
-    "ninja>=1.11.1,<2.0.0",
-    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
 sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot[peft-dep]"]
 topreward = ["lerobot[transformers-dep]"]
-recap = ["lerobot[transformers-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 eo1 = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]"]
 fastwam = [
     "lerobot[transformers-dep]",
     "lerobot[diffusers-dep]",
 ]
+evo1 = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.14,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]
 lingbot_va = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[accelerate-dep]"]
@@ -316,8 +316,9 @@ all = [
     "lerobot[molmoact2]",
     "lerobot[smolvla]",
     "lerobot[fastwam]",
-    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
+    "lerobot[groot]",
     "lerobot[xvla]",
+    "lerobot[evo1]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",
     "lerobot[lingbot_va]",
@@ -333,7 +334,6 @@ all = [
     "lerobot[sarm]",
     "lerobot[robometer]",
     "lerobot[topreward]",
-    "lerobot[recap]",
     "lerobot[peft]",
     # "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
 ]
@@ -357,8 +357,6 @@ lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
 lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
 lerobot-annotate="lerobot.scripts.lerobot_annotate:main"
 lerobot-rollout="lerobot.scripts.lerobot_rollout:main"
-lerobot-compute-returns="lerobot.scripts.lerobot_compute_returns:main"
-lerobot-eval-reward-model="lerobot.scripts.lerobot_eval_reward_model:main"
 
 # ---------------- Tool Configurations ----------------
 

requirements-macos.txt

@@ -1,729 +0,0 @@
-#
-# This file is autogenerated by pip-compile with Python 3.12
-# by the following command:
-#
-#    pip-compile --output-file=requirements-macos.txt requirements.in
-#
--e .[all]
-    # via -[all]
-absl-py==2.4.0
-    # via
-    #   dm-control
-    #   dm-env
-    #   dm-tree
-    #   labmaze
-    #   mujoco
-accelerate==1.13.0
-    # via
-    #   lerobot
-    #   peft
-aiohappyeyeballs==2.6.1
-    # via aiohttp
-aiohttp==3.13.3
-    # via fsspec
-aiosignal==1.4.0
-    # via aiohttp
-annotated-doc==0.0.4
-    # via
-    #   fastapi
-    #   typer
-annotated-types==0.7.0
-    # via pydantic
-anyio==4.12.1
-    # via
-    #   httpx
-    #   starlette
-    #   watchfiles
-asttokens==3.0.1
-    # via stack-data
-attrs==25.4.0
-    # via
-    #   aiohttp
-    #   dm-tree
-    #   jsonlines
-    #   rerun-sdk
-av==15.1.0
-    # via
-    #   lerobot
-    #   qwen-vl-utils
-certifi==2026.2.25
-    # via
-    #   httpcore
-    #   httpx
-    #   requests
-    #   sentry-sdk
-cffi==2.0.0
-    # via pymunk
-cfgv==3.5.0
-    # via pre-commit
-charset-normalizer==3.4.5
-    # via requests
-click==8.3.1
-    # via
-    #   typer
-    #   uvicorn
-    #   wandb
-cloudpickle==3.1.2
-    # via gymnasium
-cmake==4.1.3
-    # via lerobot
-cmeel==0.59.0
-    # via
-    #   cmeel-assimp
-    #   cmeel-boost
-    #   cmeel-console-bridge
-    #   cmeel-octomap
-    #   cmeel-qhull
-    #   cmeel-tinyxml2
-    #   cmeel-urdfdom
-    #   cmeel-zlib
-    #   coal-library
-    #   eigenpy
-    #   eiquadprog
-    #   pin
-    #   placo
-    #   rhoban-cmeel-jsoncpp
-cmeel-assimp==5.4.3.1
-    # via coal-library
-cmeel-boost==1.87.0.1
-    # via
-    #   coal-library
-    #   eigenpy
-    #   eiquadprog
-    #   pin
-cmeel-console-bridge==1.0.2.3
-    # via cmeel-urdfdom
-cmeel-octomap==1.10.0
-    # via coal-library
-cmeel-qhull==8.0.2.1
-    # via coal-library
-cmeel-tinyxml2==10.0.0
-    # via cmeel-urdfdom
-cmeel-urdfdom==4.0.1
-    # via pin
-cmeel-zlib==1.3.1
-    # via cmeel-assimp
-coal-library==3.0.1
-    # via pin
-contourpy==1.3.3
-    # via
-    #   lerobot
-    #   matplotlib
-coverage[toml]==7.13.4
-    # via pytest-cov
-cycler==0.12.1
-    # via matplotlib
-datasets==4.6.1
-    # via lerobot
-debugpy==1.8.20
-    # via lerobot
-decorator==5.2.1
-    # via ipython
-deepdiff==8.6.1
-    # via lerobot
-diffusers==0.35.2
-    # via lerobot
-dill==0.4.0
-    # via
-    #   datasets
-    #   multiprocess
-distlib==0.4.0
-    # via virtualenv
-dm-control==1.0.37
-    # via gym-aloha
-dm-env==1.6
-    # via dm-control
-dm-tree==0.1.9
-    # via
-    #   dm-control
-    #   dm-env
-docopt==0.6.2
-    # via num2words
-draccus==0.10.0
-    # via lerobot
-dynamixel-sdk==3.8.4
-    # via lerobot
-eigenpy==3.10.3
-    # via coal-library
-einops==0.8.2
-    # via lerobot
-eiquadprog==1.2.9
-    # via placo
-etils[epath,epy]==1.14.0
-    # via mujoco
-executing==2.2.1
-    # via stack-data
-faker==34.0.2
-    # via lerobot
-farama-notifications==0.0.4
-    # via gymnasium
-fastapi==0.135.1
-    # via
-    #   lerobot
-    #   teleop
-feetech-servo-sdk==1.0.0
-    # via lerobot
-filelock==3.25.0
-    # via
-    #   datasets
-    #   diffusers
-    #   huggingface-hub
-    #   python-discovery
-    #   torch
-    #   virtualenv
-fonttools==4.61.1
-    # via matplotlib
-frozenlist==1.8.0
-    # via
-    #   aiohttp
-    #   aiosignal
-fsspec[http]==2026.2.0
-    # via
-    #   datasets
-    #   etils
-    #   huggingface-hub
-    #   torch
-gitdb==4.0.12
-    # via gitpython
-gitpython==3.1.46
-    # via wandb
-glfw==2.10.0
-    # via
-    #   dm-control
-    #   mujoco
-grpcio==1.73.1
-    # via
-    #   grpcio-tools
-    #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-grpcio-tools==1.73.1
-    # via
-    #   lerobot
-    #   reachy2-sdk-api
-gym-aloha==0.1.3
-    # via lerobot
-gym-hil==0.1.13
-    # via lerobot
-gym-pusht==0.1.6
-    # via lerobot
-gymnasium==1.2.3
-    # via
-    #   gym-aloha
-    #   gym-hil
-    #   gym-pusht
-    #   lerobot
-    #   metaworld
-h11==0.16.0
-    # via
-    #   httpcore
-    #   uvicorn
-hebi-py==2.11.0
-    # via lerobot
-hf-xet==1.3.2
-    # via huggingface-hub
-hidapi==0.14.0.post4
-    # via
-    #   gym-hil
-    #   lerobot
-httpcore==1.0.9
-    # via httpx
-httptools==0.7.1
-    # via uvicorn
-httpx==0.28.1
-    # via
-    #   datasets
-    #   huggingface-hub
-huggingface-hub==1.6.0
-    # via
-    #   accelerate
-    #   datasets
-    #   diffusers
-    #   lerobot
-    #   peft
-    #   tokenizers
-    #   transformers
-identify==2.6.17
-    # via pre-commit
-idna==3.11
-    # via
-    #   anyio
-    #   httpx
-    #   requests
-    #   yarl
-imageio[ffmpeg]==2.37.2
-    # via
-    #   gym-aloha
-    #   gym-hil
-    #   lerobot
-    #   metaworld
-    #   scikit-image
-imageio-ffmpeg==0.6.0
-    # via imageio
-importlib-metadata==8.7.1
-    # via diffusers
-iniconfig==2.3.0
-    # via pytest
-ipython==9.11.0
-    # via meshcat
-ipython-pygments-lexers==1.1.1
-    # via ipython
-ischedule==1.2.7
-    # via placo
-jedi==0.19.2
-    # via ipython
-jinja2==3.1.6
-    # via torch
-jsonlines==4.0.0
-    # via lerobot
-kiwisolver==1.4.9
-    # via matplotlib
-labmaze==1.0.6
-    # via dm-control
-lazy-loader==0.5
-    # via scikit-image
-librt==0.8.1
-    # via mypy
-lxml==6.0.2
-    # via dm-control
-markdown-it-py==4.0.0
-    # via rich
-markupsafe==3.0.3
-    # via jinja2
-matplotlib==3.10.8
-    # via lerobot
-matplotlib-inline==0.2.1
-    # via ipython
-mdurl==0.1.2
-    # via markdown-it-py
-mergedeep==1.3.4
-    # via draccus
-meshcat==0.3.2
-    # via placo
-metaworld==3.0.0
-    # via lerobot
-mock-serial==0.0.1
-    # via lerobot
-mpmath==1.3.0
-    # via sympy
-mujoco==3.5.0
-    # via
-    #   dm-control
-    #   gym-aloha
-    #   gym-hil
-    #   metaworld
-multidict==6.7.1
-    # via
-    #   aiohttp
-    #   yarl
-multiprocess==0.70.18
-    # via datasets
-mypy==1.19.1
-    # via lerobot
-mypy-extensions==1.1.0
-    # via
-    #   mypy
-    #   typing-inspect
-networkx==3.6.1
-    # via
-    #   scikit-image
-    #   torch
-nodeenv==1.10.0
-    # via pre-commit
-num2words==0.5.14
-    # via lerobot
-numpy==2.2.6
-    # via
-    #   accelerate
-    #   cmeel-boost
-    #   contourpy
-    #   datasets
-    #   diffusers
-    #   dm-control
-    #   dm-env
-    #   dm-tree
-    #   gymnasium
-    #   hebi-py
-    #   imageio
-    #   labmaze
-    #   lerobot
-    #   matplotlib
-    #   meshcat
-    #   metaworld
-    #   mujoco
-    #   opencv-python
-    #   opencv-python-headless
-    #   pandas
-    #   peft
-    #   pyquaternion
-    #   reachy2-sdk
-    #   rerun-sdk
-    #   scikit-image
-    #   scipy
-    #   shapely
-    #   teleop
-    #   tifffile
-    #   torchvision
-    #   transformers
-    #   transforms3d
-opencv-python==4.13.0.92
-    # via
-    #   gym-pusht
-    #   reachy2-sdk
-opencv-python-headless==4.12.0.88
-    # via lerobot
-orderly-set==5.5.0
-    # via deepdiff
-packaging==25.0
-    # via
-    #   accelerate
-    #   datasets
-    #   huggingface-hub
-    #   lazy-loader
-    #   lerobot
-    #   matplotlib
-    #   peft
-    #   pytest
-    #   qwen-vl-utils
-    #   reachy2-sdk
-    #   scikit-image
-    #   transformers
-    #   wandb
-pandas==2.3.3
-    # via
-    #   datasets
-    #   lerobot
-parso==0.8.6
-    # via jedi
-pathspec==1.0.4
-    # via mypy
-peft==0.18.1
-    # via lerobot
-pexpect==4.9.0
-    # via ipython
-pillow==12.1.1
-    # via
-    #   diffusers
-    #   imageio
-    #   matplotlib
-    #   meshcat
-    #   qwen-vl-utils
-    #   rerun-sdk
-    #   scikit-image
-    #   torchvision
-pin==3.4.0
-    # via placo
-placo==0.9.16
-    # via lerobot
-platformdirs==4.9.4
-    # via
-    #   python-discovery
-    #   virtualenv
-    #   wandb
-pluggy==1.6.0
-    # via
-    #   pytest
-    #   pytest-cov
-pre-commit==4.5.1
-    # via lerobot
-prompt-toolkit==3.0.52
-    # via ipython
-propcache==0.4.1
-    # via
-    #   aiohttp
-    #   yarl
-protobuf==6.31.1
-    # via
-    #   dm-control
-    #   grpcio-tools
-    #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   wandb
-psutil==7.2.2
-    # via
-    #   accelerate
-    #   imageio
-    #   peft
-ptyprocess==0.7.0
-    # via pexpect
-pure-eval==0.2.3
-    # via stack-data
-pyarrow==23.0.1
-    # via
-    #   datasets
-    #   rerun-sdk
-pycparser==3.0
-    # via cffi
-pydantic==2.12.5
-    # via
-    #   fastapi
-    #   wandb
-pydantic-core==2.41.5
-    # via pydantic
-pygame==2.6.1
-    # via
-    #   gym-hil
-    #   gym-pusht
-    #   lerobot
-pygments==2.19.2
-    # via
-    #   ipython
-    #   ipython-pygments-lexers
-    #   pytest
-    #   rich
-pymunk==6.11.1
-    # via
-    #   gym-pusht
-    #   lerobot
-pyngrok==7.5.1
-    # via meshcat
-pynput==1.8.1
-    # via
-    #   gym-hil
-    #   lerobot
-pyobjc-core==12.1
-    # via
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-cocoa
-    #   pyobjc-framework-coretext
-    #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.1
-    # via pynput
-pyobjc-framework-cocoa==12.1
-    # via
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-coretext
-    #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.1
-    # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.1
-    # via
-    #   pynput
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-coretext
-pyopengl==3.1.10
-    # via
-    #   dm-control
-    #   mujoco
-pyparsing==3.3.2
-    # via
-    #   dm-control
-    #   matplotlib
-pyquaternion==0.9.9
-    # via reachy2-sdk
-pyrealsense2-macosx==2.56.5
-    # via lerobot
-pyserial==3.5
-    # via
-    #   dynamixel-sdk
-    #   feetech-servo-sdk
-    #   lerobot
-pytest==8.4.2
-    # via
-    #   lerobot
-    #   pytest-cov
-    #   pytest-timeout
-    #   teleop
-pytest-cov==7.0.0
-    # via lerobot
-pytest-timeout==2.4.0
-    # via lerobot
-python-dateutil==2.9.0.post0
-    # via
-    #   faker
-    #   matplotlib
-    #   pandas
-python-discovery==1.1.1
-    # via virtualenv
-python-dotenv==1.2.2
-    # via uvicorn
-pytz==2026.1.post1
-    # via pandas
-pyyaml==6.0.3
-    # via
-    #   accelerate
-    #   datasets
-    #   draccus
-    #   hebi-py
-    #   huggingface-hub
-    #   peft
-    #   pre-commit
-    #   pyngrok
-    #   pyyaml-include
-    #   transformers
-    #   uvicorn
-    #   wandb
-pyyaml-include==1.4.1
-    # via draccus
-pyzmq==27.1.0
-    # via
-    #   lerobot
-    #   meshcat
-qwen-vl-utils==0.0.14
-    # via lerobot
-reachy2-sdk==1.0.15
-    # via lerobot
-reachy2-sdk-api==1.0.21
-    # via reachy2-sdk
-regex==2026.2.28
-    # via
-    #   diffusers
-    #   transformers
-requests==2.32.5
-    # via
-    #   datasets
-    #   diffusers
-    #   dm-control
-    #   qwen-vl-utils
-    #   teleop
-    #   wandb
-rerun-sdk==0.26.2
-    # via lerobot
-rhoban-cmeel-jsoncpp==1.9.4.9
-    # via placo
-rich==14.3.3
-    # via typer
-safetensors==0.7.0
-    # via
-    #   accelerate
-    #   diffusers
-    #   lerobot
-    #   peft
-    #   transformers
-scikit-image==0.25.2
-    # via
-    #   gym-pusht
-    #   lerobot
-scipy==1.17.1
-    # via
-    #   dm-control
-    #   lerobot
-    #   metaworld
-    #   scikit-image
-    #   torchdiffeq
-sentry-sdk==2.54.0
-    # via wandb
-shapely==2.1.2
-    # via gym-pusht
-shellingham==1.5.4
-    # via typer
-six==1.17.0
-    # via
-    #   pynput
-    #   python-dateutil
-smmap==5.0.3
-    # via gitdb
-stack-data==0.6.3
-    # via ipython
-starlette==0.52.1
-    # via fastapi
-sympy==1.14.0
-    # via torch
-teleop==0.1.4
-    # via lerobot
-termcolor==3.3.0
-    # via lerobot
-tifffile==2026.3.3
-    # via scikit-image
-tokenizers==0.22.2
-    # via transformers
-toml==0.10.2
-    # via draccus
-torch==2.10.0
-    # via
-    #   accelerate
-    #   lerobot
-    #   peft
-    #   torchdiffeq
-    #   torchvision
-torchcodec==0.10.0
-    # via lerobot
-torchdiffeq==0.2.5
-    # via lerobot
-torchvision==0.25.0
-    # via lerobot
-tornado==6.5.4
-    # via meshcat
-tqdm==4.67.3
-    # via
-    #   datasets
-    #   dm-control
-    #   huggingface-hub
-    #   peft
-    #   transformers
-traitlets==5.14.3
-    # via
-    #   ipython
-    #   matplotlib-inline
-transformers==5.3.0
-    # via
-    #   lerobot
-    #   peft
-transforms3d==0.4.2
-    # via teleop
-typer==0.24.1
-    # via
-    #   huggingface-hub
-    #   transformers
-typing-extensions==4.15.0
-    # via
-    #   aiosignal
-    #   anyio
-    #   etils
-    #   faker
-    #   fastapi
-    #   gymnasium
-    #   huggingface-hub
-    #   mypy
-    #   pydantic
-    #   pydantic-core
-    #   rerun-sdk
-    #   starlette
-    #   torch
-    #   typing-inspect
-    #   typing-inspection
-    #   wandb
-typing-inspect==0.9.0
-    # via draccus
-typing-inspection==0.4.2
-    # via
-    #   fastapi
-    #   pydantic
-tzdata==2025.3
-    # via pandas
-u-msgpack-python==2.8.0
-    # via meshcat
-urllib3==2.6.3
-    # via
-    #   requests
-    #   sentry-sdk
-uvicorn[standard]==0.41.0
-    # via teleop
-uvloop==0.22.1
-    # via uvicorn
-virtualenv==21.1.0
-    # via pre-commit
-wandb==0.24.2
-    # via lerobot
-watchfiles==1.1.1
-    # via uvicorn
-wcwidth==0.6.0
-    # via prompt-toolkit
-websocket-client==1.9.0
-    # via teleop
-websockets==16.0
-    # via uvicorn
-wrapt==2.1.2
-    # via dm-tree
-xxhash==3.6.0
-    # via datasets
-yarl==1.23.0
-    # via aiohttp
-zipp==3.23.0
-    # via
-    #   etils
-    #   importlib-metadata
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools

requirements-ubuntu.txt

@@ -1,882 +0,0 @@
-#
-# This file is autogenerated by pip-compile with Python 3.12
-# by the following command:
-#
-#    pip-compile --output-file=requirements-ubuntu.txt requirements.in
-#
--e .[all]
-    # via -[all]
-absl-py==2.4.0
-    # via
-    #   dm-control
-    #   dm-env
-    #   dm-tree
-    #   labmaze
-    #   mujoco
-    #   tensorboard
-accelerate==1.13.0
-    # via
-    #   lerobot
-    #   peft
-aiohappyeyeballs==2.6.1
-    # via aiohttp
-aiohttp==3.13.3
-    # via fsspec
-aiosignal==1.4.0
-    # via aiohttp
-annotated-doc==0.0.4
-    # via
-    #   fastapi
-    #   typer
-annotated-types==0.7.0
-    # via pydantic
-antlr4-python3-runtime==4.9.3
-    # via
-    #   hydra-core
-    #   omegaconf
-anyio==4.12.1
-    # via
-    #   httpx
-    #   starlette
-    #   watchfiles
-asttokens==3.0.1
-    # via stack-data
-attrs==25.4.0
-    # via
-    #   aiohttp
-    #   dm-tree
-    #   jsonlines
-    #   jsonschema
-    #   referencing
-    #   rerun-sdk
-av==15.1.0
-    # via
-    #   lerobot
-    #   qwen-vl-utils
-bddl==1.0.1
-    # via hf-libero
-certifi==2026.2.25
-    # via
-    #   httpcore
-    #   httpx
-    #   requests
-    #   sentry-sdk
-cffi==2.0.0
-    # via pymunk
-cfgv==3.5.0
-    # via pre-commit
-charset-normalizer==3.4.5
-    # via requests
-click==8.3.1
-    # via
-    #   typer
-    #   uvicorn
-    #   wandb
-cloudpickle==3.1.2
-    # via
-    #   gymnasium
-    #   hf-libero
-cmake==4.1.3
-    # via lerobot
-cmeel==0.59.0
-    # via
-    #   cmeel-assimp
-    #   cmeel-boost
-    #   cmeel-console-bridge
-    #   cmeel-octomap
-    #   cmeel-qhull
-    #   cmeel-tinyxml2
-    #   cmeel-urdfdom
-    #   cmeel-zlib
-    #   coal-library
-    #   eigenpy
-    #   eiquadprog
-    #   pin
-    #   placo
-    #   rhoban-cmeel-jsoncpp
-cmeel-assimp==5.4.3.1
-    # via coal-library
-cmeel-boost==1.87.0.1
-    # via
-    #   coal-library
-    #   eigenpy
-    #   eiquadprog
-    #   pin
-cmeel-console-bridge==1.0.2.3
-    # via cmeel-urdfdom
-cmeel-octomap==1.10.0
-    # via coal-library
-cmeel-qhull==8.0.2.1
-    # via coal-library
-cmeel-tinyxml2==10.0.0
-    # via cmeel-urdfdom
-cmeel-urdfdom==4.0.1
-    # via pin
-cmeel-zlib==1.3.1
-    # via cmeel-assimp
-coal-library==3.0.1
-    # via pin
-contourpy==1.3.3
-    # via
-    #   lerobot
-    #   matplotlib
-coverage[toml]==7.13.4
-    # via pytest-cov
-cuda-bindings==12.9.4
-    # via torch
-cuda-pathfinder==1.4.1
-    # via cuda-bindings
-cycler==0.12.1
-    # via matplotlib
-datasets==4.6.1
-    # via lerobot
-debugpy==1.8.20
-    # via lerobot
-decorator==5.2.1
-    # via ipython
-deepdiff==8.6.1
-    # via lerobot
-diffusers==0.35.2
-    # via lerobot
-dill==0.4.0
-    # via
-    #   datasets
-    #   multiprocess
-distlib==0.4.0
-    # via virtualenv
-dm-control==1.0.37
-    # via gym-aloha
-dm-env==1.6
-    # via dm-control
-dm-tree==0.1.9
-    # via
-    #   dm-control
-    #   dm-env
-docopt==0.6.2
-    # via num2words
-draccus==0.10.0
-    # via lerobot
-dynamixel-sdk==3.8.4
-    # via lerobot
-easydict==1.13
-    # via hf-libero
-egl-probe==1.0.2
-    # via robomimic
-eigenpy==3.10.3
-    # via coal-library
-einops==0.8.2
-    # via
-    #   hf-libero
-    #   lerobot
-eiquadprog==1.2.9
-    # via placo
-etils[epath,epy]==1.14.0
-    # via mujoco
-evdev==1.9.3
-    # via pynput
-executing==2.2.1
-    # via stack-data
-faker==34.0.2
-    # via lerobot
-farama-notifications==0.0.4
-    # via gymnasium
-fastapi==0.135.1
-    # via
-    #   lerobot
-    #   teleop
-fastjsonschema==2.21.2
-    # via nbformat
-feetech-servo-sdk==1.0.0
-    # via lerobot
-filelock==3.25.0
-    # via
-    #   datasets
-    #   diffusers
-    #   huggingface-hub
-    #   python-discovery
-    #   torch
-    #   virtualenv
-fonttools==4.61.1
-    # via matplotlib
-frozenlist==1.8.0
-    # via
-    #   aiohttp
-    #   aiosignal
-fsspec[http]==2026.2.0
-    # via
-    #   datasets
-    #   etils
-    #   huggingface-hub
-    #   torch
-future==1.0.0
-    # via hf-libero
-gitdb==4.0.12
-    # via gitpython
-gitpython==3.1.46
-    # via wandb
-glfw==2.10.0
-    # via
-    #   dm-control
-    #   mujoco
-grpcio==1.73.1
-    # via
-    #   grpcio-tools
-    #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
-grpcio-tools==1.73.1
-    # via
-    #   lerobot
-    #   reachy2-sdk-api
-gym-aloha==0.1.3
-    # via lerobot
-gym-hil==0.1.13
-    # via lerobot
-gym-pusht==0.1.6
-    # via lerobot
-gymnasium==1.2.3
-    # via
-    #   gym-aloha
-    #   gym-hil
-    #   gym-pusht
-    #   hf-libero
-    #   lerobot
-    #   metaworld
-h11==0.16.0
-    # via
-    #   httpcore
-    #   uvicorn
-h5py==3.16.0
-    # via robomimic
-hebi-py==2.11.0
-    # via lerobot
-hf-egl-probe==1.0.2
-    # via hf-libero
-hf-libero==0.1.3
-    # via lerobot
-hf-xet==1.3.2
-    # via huggingface-hub
-hidapi==0.14.0.post4
-    # via
-    #   gym-hil
-    #   lerobot
-httpcore==1.0.9
-    # via httpx
-httptools==0.7.1
-    # via uvicorn
-httpx==0.28.1
-    # via
-    #   datasets
-    #   huggingface-hub
-huggingface-hub==1.6.0
-    # via
-    #   accelerate
-    #   datasets
-    #   diffusers
-    #   lerobot
-    #   peft
-    #   tokenizers
-    #   transformers
-hydra-core==1.3.2
-    # via hf-libero
-identify==2.6.17
-    # via pre-commit
-idna==3.11
-    # via
-    #   anyio
-    #   httpx
-    #   requests
-    #   yarl
-imageio[ffmpeg]==2.37.2
-    # via
-    #   gym-aloha
-    #   gym-hil
-    #   lerobot
-    #   metaworld
-    #   robomimic
-    #   scikit-image
-imageio-ffmpeg==0.6.0
-    # via
-    #   imageio
-    #   robomimic
-importlib-metadata==8.7.1
-    # via diffusers
-iniconfig==2.3.0
-    # via pytest
-ipython==9.11.0
-    # via meshcat
-ipython-pygments-lexers==1.1.1
-    # via ipython
-ischedule==1.2.7
-    # via placo
-jedi==0.19.2
-    # via ipython
-jinja2==3.1.6
-    # via torch
-jsonlines==4.0.0
-    # via lerobot
-jsonschema==4.26.0
-    # via nbformat
-jsonschema-specifications==2025.9.1
-    # via jsonschema
-jupyter-core==5.9.1
-    # via nbformat
-jupytext==1.19.1
-    # via bddl
-kiwisolver==1.4.9
-    # via matplotlib
-labmaze==1.0.6
-    # via dm-control
-lazy-loader==0.5
-    # via scikit-image
-librt==0.8.1
-    # via mypy
-llvmlite==0.46.0
-    # via numba
-lxml==6.0.2
-    # via dm-control
-markdown==3.10.2
-    # via tensorboard
-markdown-it-py==4.0.0
-    # via
-    #   jupytext
-    #   mdit-py-plugins
-    #   rich
-markupsafe==3.0.3
-    # via
-    #   jinja2
-    #   werkzeug
-matplotlib==3.10.8
-    # via
-    #   hf-libero
-    #   lerobot
-matplotlib-inline==0.2.1
-    # via ipython
-mdit-py-plugins==0.5.0
-    # via jupytext
-mdurl==0.1.2
-    # via markdown-it-py
-mergedeep==1.3.4
-    # via draccus
-meshcat==0.3.2
-    # via placo
-metaworld==3.0.0
-    # via lerobot
-mock-serial==0.0.1
-    # via lerobot
-mpmath==1.3.0
-    # via sympy
-mujoco==3.5.0
-    # via
-    #   dm-control
-    #   gym-aloha
-    #   gym-hil
-    #   hf-libero
-    #   metaworld
-    #   robosuite
-multidict==6.7.1
-    # via
-    #   aiohttp
-    #   yarl
-multiprocess==0.70.18
-    # via datasets
-mypy==1.19.1
-    # via lerobot
-mypy-extensions==1.1.0
-    # via
-    #   mypy
-    #   typing-inspect
-nbformat==5.10.4
-    # via jupytext
-networkx==3.6.1
-    # via
-    #   bddl
-    #   scikit-image
-    #   torch
-nodeenv==1.10.0
-    # via pre-commit
-num2words==0.5.14
-    # via lerobot
-numba==0.64.0
-    # via robosuite
-numpy==2.2.6
-    # via
-    #   accelerate
-    #   bddl
-    #   cmeel-boost
-    #   contourpy
-    #   datasets
-    #   diffusers
-    #   dm-control
-    #   dm-env
-    #   dm-tree
-    #   gymnasium
-    #   h5py
-    #   hebi-py
-    #   hf-libero
-    #   imageio
-    #   labmaze
-    #   lerobot
-    #   matplotlib
-    #   meshcat
-    #   metaworld
-    #   mujoco
-    #   numba
-    #   opencv-python
-    #   opencv-python-headless
-    #   pandas
-    #   peft
-    #   pyquaternion
-    #   reachy2-sdk
-    #   rerun-sdk
-    #   robomimic
-    #   robosuite
-    #   scikit-image
-    #   scipy
-    #   shapely
-    #   teleop
-    #   tensorboard
-    #   tensorboardx
-    #   tifffile
-    #   torchvision
-    #   transformers
-    #   transforms3d
-nvidia-cublas-cu12==12.8.4.1
-    # via
-    #   nvidia-cudnn-cu12
-    #   nvidia-cusolver-cu12
-    #   torch
-nvidia-cuda-cupti-cu12==12.8.90
-    # via torch
-nvidia-cuda-nvrtc-cu12==12.8.93
-    # via torch
-nvidia-cuda-runtime-cu12==12.8.90
-    # via torch
-nvidia-cudnn-cu12==9.10.2.21
-    # via torch
-nvidia-cufft-cu12==11.3.3.83
-    # via torch
-nvidia-cufile-cu12==1.13.1.3
-    # via torch
-nvidia-curand-cu12==10.3.9.90
-    # via torch
-nvidia-cusolver-cu12==11.7.3.90
-    # via torch
-nvidia-cusparse-cu12==12.5.8.93
-    # via
-    #   nvidia-cusolver-cu12
-    #   torch
-nvidia-cusparselt-cu12==0.7.1
-    # via torch
-nvidia-nccl-cu12==2.27.5
-    # via torch
-nvidia-nvjitlink-cu12==12.8.93
-    # via
-    #   nvidia-cufft-cu12
-    #   nvidia-cusolver-cu12
-    #   nvidia-cusparse-cu12
-    #   torch
-nvidia-nvshmem-cu12==3.4.5
-    # via torch
-nvidia-nvtx-cu12==12.8.90
-    # via torch
-omegaconf==2.3.0
-    # via hydra-core
-opencv-python==4.13.0.92
-    # via
-    #   gym-pusht
-    #   hf-libero
-    #   reachy2-sdk
-    #   robosuite
-opencv-python-headless==4.12.0.88
-    # via lerobot
-orderly-set==5.5.0
-    # via deepdiff
-packaging==25.0
-    # via
-    #   accelerate
-    #   datasets
-    #   huggingface-hub
-    #   hydra-core
-    #   jupytext
-    #   lazy-loader
-    #   lerobot
-    #   matplotlib
-    #   peft
-    #   pytest
-    #   qwen-vl-utils
-    #   reachy2-sdk
-    #   scikit-image
-    #   tensorboard
-    #   tensorboardx
-    #   transformers
-    #   wandb
-pandas==2.3.3
-    # via
-    #   datasets
-    #   lerobot
-parso==0.8.6
-    # via jedi
-pathspec==1.0.4
-    # via mypy
-peft==0.18.1
-    # via lerobot
-pexpect==4.9.0
-    # via ipython
-pillow==12.1.1
-    # via
-    #   diffusers
-    #   imageio
-    #   matplotlib
-    #   meshcat
-    #   qwen-vl-utils
-    #   rerun-sdk
-    #   robosuite
-    #   scikit-image
-    #   tensorboard
-    #   torchvision
-pin==3.4.0
-    # via placo
-placo==0.9.16
-    # via lerobot
-platformdirs==4.9.4
-    # via
-    #   jupyter-core
-    #   python-discovery
-    #   virtualenv
-    #   wandb
-pluggy==1.6.0
-    # via
-    #   pytest
-    #   pytest-cov
-pre-commit==4.5.1
-    # via lerobot
-prompt-toolkit==3.0.52
-    # via ipython
-propcache==0.4.1
-    # via
-    #   aiohttp
-    #   yarl
-protobuf==6.31.1
-    # via
-    #   dm-control
-    #   grpcio-tools
-    #   lerobot
-    #   reachy2-sdk
-    #   reachy2-sdk-api
-    #   tensorboard
-    #   tensorboardx
-    #   wandb
-psutil==7.2.2
-    # via
-    #   accelerate
-    #   imageio
-    #   peft
-    #   robomimic
-ptyprocess==0.7.0
-    # via pexpect
-pure-eval==0.2.3
-    # via stack-data
-pyarrow==23.0.1
-    # via
-    #   datasets
-    #   rerun-sdk
-pycparser==3.0
-    # via cffi
-pydantic==2.12.5
-    # via
-    #   fastapi
-    #   wandb
-pydantic-core==2.41.5
-    # via pydantic
-pygame==2.6.1
-    # via
-    #   gym-hil
-    #   gym-pusht
-    #   lerobot
-pygments==2.19.2
-    # via
-    #   ipython
-    #   ipython-pygments-lexers
-    #   pytest
-    #   rich
-pymunk==6.11.1
-    # via
-    #   gym-pusht
-    #   lerobot
-pyngrok==7.5.1
-    # via meshcat
-pynput==1.8.1
-    # via
-    #   gym-hil
-    #   lerobot
-pyopengl==3.1.10
-    # via
-    #   dm-control
-    #   mujoco
-pyparsing==3.3.2
-    # via
-    #   dm-control
-    #   matplotlib
-pyquaternion==0.9.9
-    # via reachy2-sdk
-pyrealsense2==2.56.5.9235
-    # via lerobot
-pyserial==3.5
-    # via
-    #   dynamixel-sdk
-    #   feetech-servo-sdk
-    #   lerobot
-pytest==8.4.2
-    # via
-    #   bddl
-    #   lerobot
-    #   pytest-cov
-    #   pytest-timeout
-    #   teleop
-pytest-cov==7.0.0
-    # via lerobot
-pytest-timeout==2.4.0
-    # via lerobot
-python-dateutil==2.9.0.post0
-    # via
-    #   faker
-    #   matplotlib
-    #   pandas
-python-discovery==1.1.1
-    # via virtualenv
-python-dotenv==1.2.2
-    # via uvicorn
-python-xlib==0.33
-    # via pynput
-pytz==2026.1.post1
-    # via pandas
-pyyaml==6.0.3
-    # via
-    #   accelerate
-    #   datasets
-    #   draccus
-    #   hebi-py
-    #   huggingface-hub
-    #   jupytext
-    #   omegaconf
-    #   peft
-    #   pre-commit
-    #   pyngrok
-    #   pyyaml-include
-    #   transformers
-    #   uvicorn
-    #   wandb
-pyyaml-include==1.4.1
-    # via draccus
-pyzmq==27.1.0
-    # via
-    #   lerobot
-    #   meshcat
-qwen-vl-utils==0.0.14
-    # via lerobot
-reachy2-sdk==1.0.15
-    # via lerobot
-reachy2-sdk-api==1.0.21
-    # via reachy2-sdk
-referencing==0.37.0
-    # via
-    #   jsonschema
-    #   jsonschema-specifications
-regex==2026.2.28
-    # via
-    #   diffusers
-    #   transformers
-requests==2.32.5
-    # via
-    #   datasets
-    #   diffusers
-    #   dm-control
-    #   qwen-vl-utils
-    #   teleop
-    #   wandb
-rerun-sdk==0.26.2
-    # via lerobot
-rhoban-cmeel-jsoncpp==1.9.4.9
-    # via placo
-rich==14.3.3
-    # via typer
-robomimic==0.2.0
-    # via hf-libero
-robosuite==1.4.0
-    # via hf-libero
-rpds-py==0.30.0
-    # via
-    #   jsonschema
-    #   referencing
-safetensors==0.7.0
-    # via
-    #   accelerate
-    #   diffusers
-    #   lerobot
-    #   peft
-    #   transformers
-scikit-image==0.25.2
-    # via
-    #   gym-pusht
-    #   lerobot
-scipy==1.17.1
-    # via
-    #   dm-control
-    #   lerobot
-    #   metaworld
-    #   robosuite
-    #   scikit-image
-    #   torchdiffeq
-sentry-sdk==2.54.0
-    # via wandb
-shapely==2.1.2
-    # via gym-pusht
-shellingham==1.5.4
-    # via typer
-six==1.17.0
-    # via
-    #   pynput
-    #   python-dateutil
-    #   python-xlib
-smmap==5.0.3
-    # via gitdb
-stack-data==0.6.3
-    # via ipython
-starlette==0.52.1
-    # via fastapi
-sympy==1.14.0
-    # via torch
-teleop==0.1.4
-    # via lerobot
-tensorboard==2.20.0
-    # via robomimic
-tensorboard-data-server==0.7.2
-    # via tensorboard
-tensorboardx==2.6.4
-    # via robomimic
-termcolor==3.3.0
-    # via
-    #   lerobot
-    #   robomimic
-thop==0.1.1.post2209072238
-    # via hf-libero
-tifffile==2026.3.3
-    # via scikit-image
-tokenizers==0.22.2
-    # via transformers
-toml==0.10.2
-    # via draccus
-torch==2.10.0
-    # via
-    #   accelerate
-    #   lerobot
-    #   peft
-    #   robomimic
-    #   thop
-    #   torchdiffeq
-    #   torchvision
-torchcodec==0.10.0
-    # via lerobot
-torchdiffeq==0.2.5
-    # via lerobot
-torchvision==0.25.0
-    # via
-    #   lerobot
-    #   robomimic
-tornado==6.5.4
-    # via meshcat
-tqdm==4.67.3
-    # via
-    #   datasets
-    #   dm-control
-    #   huggingface-hub
-    #   peft
-    #   robomimic
-    #   transformers
-traitlets==5.14.3
-    # via
-    #   ipython
-    #   jupyter-core
-    #   matplotlib-inline
-    #   nbformat
-transformers==5.3.0
-    # via
-    #   hf-libero
-    #   lerobot
-    #   peft
-transforms3d==0.4.2
-    # via teleop
-triton==3.6.0
-    # via torch
-typer==0.24.1
-    # via
-    #   huggingface-hub
-    #   transformers
-typing-extensions==4.15.0
-    # via
-    #   aiosignal
-    #   anyio
-    #   etils
-    #   faker
-    #   fastapi
-    #   gymnasium
-    #   huggingface-hub
-    #   mypy
-    #   pydantic
-    #   pydantic-core
-    #   referencing
-    #   rerun-sdk
-    #   starlette
-    #   torch
-    #   typing-inspect
-    #   typing-inspection
-    #   wandb
-typing-inspect==0.9.0
-    # via draccus
-typing-inspection==0.4.2
-    # via
-    #   fastapi
-    #   pydantic
-tzdata==2025.3
-    # via pandas
-u-msgpack-python==2.8.0
-    # via meshcat
-urllib3==2.6.3
-    # via
-    #   requests
-    #   sentry-sdk
-uvicorn[standard]==0.41.0
-    # via teleop
-uvloop==0.22.1
-    # via uvicorn
-virtualenv==21.1.0
-    # via pre-commit
-wandb==0.24.2
-    # via
-    #   hf-libero
-    #   lerobot
-watchfiles==1.1.1
-    # via uvicorn
-wcwidth==0.6.0
-    # via prompt-toolkit
-websocket-client==1.9.0
-    # via teleop
-websockets==16.0
-    # via uvicorn
-werkzeug==3.1.6
-    # via tensorboard
-wrapt==2.1.2
-    # via dm-tree
-xxhash==3.6.0
-    # via datasets
-yarl==1.23.0
-    # via aiohttp
-zipp==3.23.0
-    # via
-    #   etils
-    #   importlib-metadata
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools

requirements.in

@@ -1,9 +0,0 @@
-# requirements.in
-
-# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
-# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
-
-# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
-# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
-
--e .[all]

scripts/overfit_vf_variant.py

@@ -1,199 +0,0 @@
-"""Overfit any distributional VF architecture on a small real-data batch."""
-
-from __future__ import annotations
-
-import argparse
-
-import numpy as np
-import torch
-
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.datasets.factory import resolve_delta_timestamps
-from lerobot.rewards.distributional_value_function.configuration_distributional_value_function import (
-    DistributionalVFConfig,
-)
-from lerobot.rewards.factory import make_reward_model, make_reward_pre_post_processors
-from lerobot.rewards.nanovlm_value_function.configuration_nanovlm_value_function import (
-    NanoVLMVFConfig,
-)
-from lerobot.rewards.nanovlm_value_function.processor_nanovlm_value_function import (
-    NANOVLM_IMAGES,
-)
-from lerobot.rewards.temporal_siglip_value_function.configuration_temporal_siglip_value_function import (
-    TemporalSiglipVFConfig,
-)
-from lerobot.utils.collate import lerobot_collate_fn
-from lerobot.utils.constants import OBS_STATE
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset_repo_id", required=True)
-    parser.add_argument("--root", default=None)
-    parser.add_argument(
-        "--reward_type",
-        choices=(
-            "distributional_value_function",
-            "temporal_siglip_value_function",
-            "nanovlm_value_function",
-        ),
-        required=True,
-    )
-    parser.add_argument("--vlm_pretrained_path", default=None)
-    parser.add_argument("--nanovlm_pretrained_path", default="lusxvr/nanoVLM-460M-8k")
-    parser.add_argument("--num_samples", type=int, default=16)
-    parser.add_argument("--steps", type=int, default=500)
-    parser.add_argument("--lr_head", type=float, default=1e-3)
-    parser.add_argument("--lr_backbone", type=float, default=1e-5)
-    parser.add_argument("--history_steps", type=int, default=6)
-    parser.add_argument("--history_frame_gap", type=int, default=30)
-    parser.add_argument("--log_every", type=int, default=25)
-    args = parser.parse_args()
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    metadata = LeRobotDatasetMetadata(args.dataset_repo_id, root=args.root)
-    input_features = {
-        key: PolicyFeature(type=FeatureType.VISUAL, shape=tuple(metadata.features[key]["shape"]))
-        for key in metadata.camera_keys
-    }
-    if OBS_STATE in metadata.features:
-        input_features[OBS_STATE] = PolicyFeature(
-            type=FeatureType.STATE,
-            shape=tuple(metadata.features[OBS_STATE]["shape"]),
-        )
-    common = {"input_features": input_features, "device": str(device), "target_method": "dirac_delta"}
-    if args.reward_type == "distributional_value_function":
-        config = DistributionalVFConfig(
-            **common,
-            vlm_pretrained_path=args.vlm_pretrained_path,
-            freeze_vision_encoder=True,
-        )
-    elif args.reward_type == "temporal_siglip_value_function":
-        config = TemporalSiglipVFConfig(
-            **common,
-            history_steps=args.history_steps,
-            frame_gap=args.history_frame_gap,
-        )
-        config.normalization_mapping = {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-        }
-    else:
-        config = NanoVLMVFConfig(
-            **common,
-            nanovlm_pretrained_path=args.nanovlm_pretrained_path,
-        )
-
-    delta_timestamps = resolve_delta_timestamps(config, metadata)
-    dataset = LeRobotDataset(
-        args.dataset_repo_id,
-        root=args.root,
-        delta_timestamps=delta_timestamps,
-        video_backend="pyav",
-    )
-    indices = np.linspace(0, len(dataset) - 1, args.num_samples, dtype=int).tolist()
-    preprocessor, _ = make_reward_pre_post_processors(
-        config,
-        dataset_stats=metadata.stats,
-    )
-
-    samples = []
-    returns = []
-    terminals = []
-    for index in indices:
-        sample = dataset[index]
-        returns.append(torch.as_tensor(sample["mc_return"]).reshape(-1)[0])
-        terminals.append(torch.as_tensor(sample["is_terminal"]).reshape(-1)[0])
-        samples.append(sample)
-    raw_batch = lerobot_collate_fn(samples)
-    if raw_batch is None:
-        raise ValueError("The selected overfit samples produced an empty batch")
-    batch = preprocessor(raw_batch)
-    batch["mc_return"] = torch.stack(returns).to(device)
-    batch["is_terminal"] = torch.stack(terminals).bool().to(device)
-
-    model = make_reward_model(config).to(device)
-    _run_stage(
-        model,
-        batch,
-        steps=args.steps // 2,
-        learning_rate=args.lr_head,
-        head_only=True,
-        log_every=args.log_every,
-        label="head probe",
-    )
-    _run_stage(
-        model,
-        batch,
-        steps=args.steps - args.steps // 2,
-        learning_rate=args.lr_backbone,
-        head_only=False,
-        log_every=args.log_every,
-        label="fine-tune",
-    )
-    _image_shuffle_diagnostic(model, batch, metadata.camera_keys)
-
-
-def _set_trainable(model, *, head_only: bool):
-    for param in model.parameters():
-        param.requires_grad = False
-    for param in model.value_head.parameters():
-        param.requires_grad = True
-    if hasattr(model, "value_query"):
-        for param in model.value_query.parameters():
-            param.requires_grad = True
-    if head_only:
-        return
-
-    if hasattr(model, "multi_modal_projector"):
-        model.multi_modal_projector.requires_grad_(True)
-        model.language_model.requires_grad_(True)
-    elif hasattr(model, "temporal_transformer"):
-        for name, param in model.named_parameters():
-            if not name.startswith("siglip."):
-                param.requires_grad = True
-    else:
-        model.nanovlm.MP.requires_grad_(True)
-        model.nanovlm.decoder.requires_grad_(True)
-
-
-def _run_stage(model, batch, *, steps, learning_rate, head_only, log_every, label):
-    _set_trainable(model, head_only=head_only)
-    model.train()
-    params = [param for param in model.parameters() if param.requires_grad]
-    optimizer = torch.optim.AdamW(params, lr=learning_rate)
-    print(f"\n{label}: {sum(param.numel() for param in params):,} trainable parameters")
-    for step in range(steps + 1):
-        optimizer.zero_grad(set_to_none=True)
-        loss, metrics = model(batch)
-        loss.backward()
-        optimizer.step()
-        if step % log_every == 0 or step == steps:
-            print(
-                f"step={step:04d} loss={metrics['loss']:.4f} "
-                f"mae={metrics['mae']:.4f} acc={metrics['acc_neighbor']:.3f}"
-            )
-
-
-@torch.no_grad()
-def _image_shuffle_diagnostic(model, batch, camera_keys):
-    model.eval()
-    matched_loss, _ = model(batch)
-    shuffled = dict(batch)
-    permutation = torch.roll(torch.arange(batch["mc_return"].shape[0], device=matched_loss.device), 1)
-    if NANOVLM_IMAGES in batch:
-        shuffled[NANOVLM_IMAGES] = [batch[NANOVLM_IMAGES][index] for index in permutation.cpu().tolist()]
-    for key in camera_keys:
-        if key in batch:
-            shuffled[key] = batch[key][permutation]
-    shuffled_loss, _ = model(shuffled)
-    print(
-        f"\nvisual dependence: matched_loss={matched_loss.item():.4f} "
-        f"shuffled_loss={shuffled_loss.item():.4f} "
-        f"gap={shuffled_loss.item() - matched_loss.item():+.4f}"
-    )
-
-
-if __name__ == "__main__":
-    main()

src/lerobot/annotations/steerable_pipeline/config.py

@@ -169,56 +169,6 @@ class ExecutorConfig:
     episode_parallelism: int = 16
 
 
-@dataclass
-class AdvantageConfig:
-    """``advantage`` module: RECAP advantage scoring via frozen value function."""
-
-    enabled: bool = True
-
-    # Constant advantage label for all frames (e.g. "positive" for SFT iteration 0).
-    # Skips VF inference.
-    constant_value: str | None = None
-
-    # Trained value function checkpoint (local path or Hub repo ID).
-    # Ignored when constant_value is set.
-    value_function_path: str = ""
-
-    # Optional CSV from ``lerobot-eval-reward-model``. When set, annotation
-    # consumes its per-frame predictions instead of rerunning VF inference.
-    # This is the recommended path for temporal and multi-camera value models.
-    predictions_path: str = ""
-
-    # Device to run the value function on.
-    device: str = "cuda"
-
-    # N-step lookahead for advantage estimation.
-    # None = MC (N=T): A_t = R_t - V(s_t), using mc_return from dataset.
-    # 50 = fine-tuning mode: A_t = Σ r_{t:t+N} + V(s_{t+N}) - V(s_t).
-    n_step: int | None = None
-
-    # Percentile for binarization threshold ε_ℓ. Appendix F uses a threshold
-    # yielding about 40% positive rollout actions during post-training, i.e.
-    # the 60th percentile. Pre-training uses 0.7 (~30% positive), while the
-    # slow-but-successful laundry setting uses 0.9 (~10% positive).
-    threshold_percentile: float = 0.6
-
-    # When True, compute a single global threshold across all episodes (paper behavior).
-    # When False, compute threshold per-episode (faster but less accurate).
-    global_threshold: bool = True
-
-    # Force I_t = True for frames marked as human interventions.
-    force_positive_on_intervention: bool = True
-
-    # Column name in dataset for intervention flag.
-    intervention_key: str = "intervention"
-
-    # Column name for pre-computed MC returns (from lerobot-compute-returns).
-    mc_return_key: str = "mc_return"
-
-    # Batch size for value function inference.
-    batch_size: int = 32
-
-
 @dataclass
 class AnnotationPipelineConfig:
     """Top-level config for ``lerobot-annotate`` (rewrites data shards in place)."""
@@ -240,7 +190,6 @@ class AnnotationPipelineConfig:
     plan: PlanConfig = field(default_factory=PlanConfig)
     interjections: InterjectionsConfig = field(default_factory=InterjectionsConfig)
     vqa: VqaConfig = field(default_factory=VqaConfig)
-    advantage: AdvantageConfig = field(default_factory=AdvantageConfig)
 
     vlm: VlmConfig = field(default_factory=VlmConfig)
     executor: ExecutorConfig = field(default_factory=ExecutorConfig)

src/lerobot/annotations/steerable_pipeline/executor.py

@@ -15,24 +15,20 @@
 # limitations under the License.
 """In-process executor that runs the annotation phases.
 
-The executor runs **seven phases** in dependency order:
+The executor runs **six phases** in dependency order:
 
     phase 1: ``plan`` module (plan + subtasks + memory)
     phase 2: ``interjections`` module (interjections + speech)
     phase 3: ``plan`` plan-update pass — re-runs plan emission at every
              interjection timestamp produced by phase 2
     phase 4: ``vqa`` module (VQA)
-    phase 5: ``advantage`` module (advantage scoring via frozen VF)
-    phase 6: validator
-    phase 7: writer
+    phase 5: validator
+    phase 6: writer
 
 Phase 3 is why the ``plan`` module must be re-entered after the
 ``interjections`` module — to refresh ``plan`` rows at interjection
 timestamps.
 
-Phase 5 (advantage) does not depend on the VLM modules, it uses a frozen
-distributional value function to compute per-frame advantage indicators.
-
 Distributed execution is provided by Hugging Face Jobs (see
 ``examples/annotations/run_hf_job.py``); the runner inside the job
 invokes ``lerobot-annotate`` which uses this in-process executor.
@@ -78,7 +74,7 @@ class PipelineRunSummary:
 
 @dataclass
 class Executor:
-    """Run all seven phases over a dataset root in-process.
+    """Run all six phases over a dataset root in-process.
 
     Episode-level concurrency comes from ``ExecutorConfig.episode_parallelism``
     (a thread pool); cluster-level concurrency comes from running this
@@ -90,7 +86,6 @@ class Executor:
     plan: Any  # PlanSubtasksMemoryModule
     interjections: Any  # InterjectionsAndSpeechModule
     vqa: Any  # GeneralVqaModule
-    advantage: Any  # AdvantageModule
     writer: LanguageColumnsWriter
     validator: StagingValidator
 
@@ -117,12 +112,6 @@ class Executor:
         phases.append(self._run_plan_update_phase(records, staging_dir))
         # Phase 4: ``vqa`` module (VQA)
         phases.append(self._run_module_phase("vqa", records, staging_dir, self.vqa))
-        # Phase 5: ``advantage`` module (advantage scoring via frozen VF)
-        # Two-pass global threshold: compute advantages across all episodes first,
-        # then apply the single threshold uniformly (matches paper Section V-D).
-        if self.advantage.enabled and self.advantage.config.global_threshold:
-            self.advantage.precompute_global_threshold(records)
-        phases.append(self._run_module_phase("advantage", records, staging_dir, self.advantage))
 
         print("[annotate] running validator...", flush=True)
         report = self.validator.validate(records, staging_dir)
@@ -190,7 +179,7 @@ class Executor:
         staging_dir: Path,
         module: Any,
     ) -> PhaseResult:
-        if module is None or not module.enabled:
+        if not module.enabled:
             print(f"[annotate] phase={name} skipped (module disabled)", flush=True)
             return PhaseResult(name=name, episodes_processed=0, episodes_skipped=len(records))
         n = len(records)
@@ -242,7 +231,7 @@ class Executor:
         ``plan`` module with the interjection timestamps so its existing
         prompt path is reused.
         """
-        if not self.plan or not self.plan.enabled or not self.interjections or not self.interjections.enabled:
+        if not self.plan.enabled or not self.interjections.enabled:
             return PhaseResult(name="plan_update", episodes_processed=0, episodes_skipped=len(records))
         processed = 0
         for record in records:

src/lerobot/annotations/steerable_pipeline/modules/__init__.py

@@ -14,13 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .advantage import AdvantageModule
 from .general_vqa import GeneralVqaModule
 from .interjections_and_speech import InterjectionsAndSpeechModule
 from .plan_subtasks_memory import PlanSubtasksMemoryModule
 
 __all__ = [
-    "AdvantageModule",
     "GeneralVqaModule",
     "InterjectionsAndSpeechModule",
     "PlanSubtasksMemoryModule",

src/lerobot/annotations/steerable_pipeline/modules/advantage.py

@@ -1,429 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Advantage scoring module for RECAP.
-
-Computes per-frame advantage values using a frozen distributional value function,
-binarizes them into improvement indicators (I_t), and emits ``style="advantage"``
-persistent rows for policy conditioning.
-
-Paper reference: pi*0.6, Section IV-B and Appendix F.
-"""
-
-from __future__ import annotations
-
-import csv
-import logging
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-
-from ..config import AdvantageConfig
-from ..frames import VideoFrameProvider, null_provider
-from ..reader import EpisodeRecord
-from ..staging import EpisodeStaging
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class AdvantageModule:
-    """Compute advantage indicators and emit persistent annotation rows.
-
-    The module loads a frozen distributional value function and scores each
-    frame in an episode. Advantages are binarized into ``positive``/``negative``
-    indicators using a per-task threshold, then written as ``style="advantage"``
-    persistent rows into the staging area.
-
-    Requires ``mc_return`` column in the dataset (from lerobot-compute-returns).
-    """
-
-    config: AdvantageConfig
-    frame_provider: Any = None
-    _model: Any = field(default=None, init=False, repr=False)
-    _preprocessor: Any = field(default=None, init=False, repr=False)
-    _threshold: float | None = field(default=None, init=False, repr=False)
-    _cache: dict = field(default_factory=dict, init=False, repr=False)
-    _prediction_lookup: dict[tuple[int, int], float] | None = field(default=None, init=False, repr=False)
-
-    @property
-    def enabled(self) -> bool:
-        return self.config.enabled
-
-    def _ensure_model_loaded(self) -> None:
-        """Lazy-load the frozen value function on first use."""
-        if self._model is not None:
-            return
-
-        from lerobot.rewards import (
-            make_reward_model,
-            make_reward_model_config,
-            make_reward_pre_post_processors,
-        )
-
-        cfg = make_reward_model_config(
-            "distributional_value_function",
-            pretrained_path=self.config.value_function_path,
-            device=self.config.device,
-        )
-        self._model = make_reward_model(cfg)
-        self._model.eval()
-        for p in self._model.parameters():
-            p.requires_grad_(False)
-
-        self._preprocessor, _ = make_reward_pre_post_processors(cfg)
-        logger.info("Loaded frozen VF from %s on %s", self.config.value_function_path, self.config.device)
-
-    def compute_advantages_for_episode(self, record: EpisodeRecord) -> tuple[np.ndarray, np.ndarray]:
-        """Compute raw advantage values for all frames in an episode.
-
-        Returns:
-            (advantages, intervention_mask) both shape [num_frames].
-            advantages[t] = A_t, intervention_mask[t] = True if frame is intervention.
-        """
-        if self.config.predictions_path:
-            self._ensure_predictions_loaded()
-        else:
-            self._ensure_model_loaded()
-
-        df = record.frames_df()
-        num_frames = len(df)
-
-        mc_return_key = self.config.mc_return_key
-        if mc_return_key not in df.columns:
-            raise KeyError(
-                f"Column '{mc_return_key}' not found in episode {record.episode_index}. "
-                "Run lerobot-compute-returns first."
-            )
-
-        mc_returns = df[mc_return_key].values.astype(np.float32)
-
-        intervention_mask = np.zeros(num_frames, dtype=bool)
-        if self.config.intervention_key in df.columns:
-            intervention_mask = df[self.config.intervention_key].values.astype(bool)
-
-        # Skip VF inference on intervention frames — they're always "positive"
-        # regardless of advantage value, so V(s_t) is never used for them.
-        skip_mask = intervention_mask if self.config.force_positive_on_intervention else None
-        values = self._compute_values(record, skip_mask=skip_mask)
-
-        if self.config.n_step is None:
-            advantages = mc_returns - values
-        else:
-            advantages = self._compute_n_step_advantages(mc_returns, values, record, n=self.config.n_step)
-
-        return advantages, intervention_mask
-
-    def _compute_values(self, record: EpisodeRecord, skip_mask: np.ndarray | None = None) -> np.ndarray:
-        """Run frozen VF over all frames to get V(s_t) predictions.
-
-        Supports both image datasets (columns in parquet) and video datasets
-        (frames decoded from .mp4 via the shared VideoFrameProvider).
-
-        Args:
-            record: Episode data.
-            skip_mask: Optional boolean mask [num_frames]. Frames where True are
-                skipped (left as 0.0) to avoid unnecessary inference.
-        """
-        if self.config.predictions_path:
-            self._ensure_predictions_loaded()
-            assert self._prediction_lookup is not None
-            missing = [
-                frame_index
-                for frame_index in record.frame_indices
-                if (record.episode_index, frame_index) not in self._prediction_lookup
-            ]
-            if missing:
-                raise KeyError(
-                    f"Predictions CSV is missing {len(missing)} frame(s) from episode "
-                    f"{record.episode_index}; first missing frame_index={missing[0]}"
-                )
-            return np.asarray(
-                [
-                    self._prediction_lookup[(record.episode_index, frame_index)]
-                    for frame_index in record.frame_indices
-                ],
-                dtype=np.float32,
-            )
-
-        df = record.frames_df()
-        num_frames = len(df)
-        values = np.zeros(num_frames, dtype=np.float32)
-
-        # Determine which frame indices actually need inference
-        infer_indices = np.where(~skip_mask)[0] if skip_mask is not None else np.arange(num_frames)
-        if len(infer_indices) == 0:
-            return values
-
-        # Try parquet image columns first, fall back to video decoding
-        image_key = self._resolve_image_key(df)
-        video_frames = None
-
-        if image_key is None:
-            image_key, video_frames = self._decode_video_frames(record, infer_indices)
-            if image_key is None:
-                logger.warning(
-                    "No image/video key found for episode %d; returning zero values.", record.episode_index
-                )
-                return values
-
-        task_text = record.episode_task
-
-        for batch_start in range(0, len(infer_indices), self.config.batch_size):
-            batch_end = min(batch_start + self.config.batch_size, len(infer_indices))
-            batch_indices = infer_indices[batch_start:batch_end]
-            batch_images = []
-
-            for local_i in range(len(batch_indices)):
-                if video_frames is not None:
-                    img_tensor = video_frames[batch_start + local_i].float()
-                else:
-                    idx = batch_indices[local_i]
-                    img_val = df.iloc[idx][image_key]
-                    if isinstance(img_val, np.ndarray):
-                        img_tensor = torch.from_numpy(img_val).float()
-                    elif isinstance(img_val, torch.Tensor):
-                        img_tensor = img_val.float()
-                    else:
-                        img_tensor = torch.zeros(3, 224, 224)
-                batch_images.append(img_tensor)
-
-            batch_images_tensor = torch.stack(batch_images)
-            batch_size = batch_images_tensor.shape[0]
-
-            raw_batch = {
-                image_key: batch_images_tensor,
-                "task": [task_text] * batch_size,
-            }
-
-            processed = self._preprocessor(raw_batch)
-
-            with torch.no_grad():
-                v_values = self._model.compute_reward(processed)
-
-            values[batch_indices] = v_values.cpu().numpy()
-
-        return values
-
-    def _ensure_predictions_loaded(self) -> None:
-        if self._prediction_lookup is not None:
-            return
-        path = Path(self.config.predictions_path)
-        if not path.is_file():
-            raise FileNotFoundError(f"Advantage predictions CSV not found: {path}")
-        lookup: dict[tuple[int, int], float] = {}
-        with path.open(newline="") as handle:
-            reader = csv.DictReader(handle)
-            required = {"episode_index", "frame_index", "predicted_value"}
-            missing_columns = required.difference(reader.fieldnames or ())
-            if missing_columns:
-                raise ValueError(f"Predictions CSV is missing required columns: {sorted(missing_columns)}")
-            for row in reader:
-                key = (int(row["episode_index"]), int(row["frame_index"]))
-                if key in lookup:
-                    raise ValueError(f"Duplicate prediction for episode/frame {key}")
-                lookup[key] = float(row["predicted_value"])
-        self._prediction_lookup = lookup
-        logger.info("Loaded %d per-frame value predictions from %s", len(lookup), path)
-
-    def _decode_video_frames(
-        self, record: EpisodeRecord, infer_indices: np.ndarray
-    ) -> tuple[str | None, torch.Tensor | None]:
-        """Decode video frames using the existing VideoFrameProvider infrastructure.
-
-        Returns (image_key, decoded_frames_tensor) or (None, None) on failure.
-        """
-        dataset_root = record.data_path.parent.parent.parent
-
-        if not hasattr(self, "_frame_provider") or self._frame_provider is None:
-            try:
-                self._frame_provider = VideoFrameProvider(root=dataset_root)
-            except Exception:
-                self._frame_provider = null_provider()
-
-        if not self._frame_provider.camera_keys:
-            return None, None
-
-        camera_key = self._frame_provider.camera_keys[0]
-        timestamps = [float(record.frame_timestamps[i]) for i in infer_indices]
-
-        frames = self._frame_provider.frames_at(record, timestamps, camera_key=camera_key)
-        if not frames:
-            return None, None
-
-        frames_tensor = torch.stack(frames)
-        return camera_key, frames_tensor
-
-    def _compute_n_step_advantages(
-        self, mc_returns: np.ndarray, values: np.ndarray, record: EpisodeRecord, n: int
-    ) -> np.ndarray:
-        """Compute N-step advantage: A_t = Σ r_{t:t+N-1} + V(s_{t+N}) - V(s_t).
-
-        When t+N exceeds episode length, truncates to MC (uses mc_return directly).
-        """
-        num_frames = len(values)
-        advantages = np.zeros(num_frames, dtype=np.float32)
-
-        for t in range(num_frames):
-            if t + n >= num_frames:
-                advantages[t] = mc_returns[t] - values[t]
-            else:
-                n_step_return = mc_returns[t] - mc_returns[t + n]
-                advantages[t] = n_step_return + values[t + n] - values[t]
-
-        return advantages
-
-    def _resolve_image_key(self, df) -> str | None:
-        """Find the first image observation key in the dataframe columns."""
-        for col in df.columns:
-            if col.startswith("observation.images."):
-                return col
-        return None
-
-    def precompute_global_threshold(self, records: list[EpisodeRecord]) -> None:
-        """Two-pass: compute advantages for all episodes and set a single global threshold.
-
-        This matches the paper (pi*0.6, Section V-D / Appendix F):
-        'We set ε_ℓ to the Nth percentile of values predicted by the value function for the task ℓ.'
-
-        The threshold is computed across ALL non-intervention frames in the dataset,
-        so successful episodes naturally get more 'positive' labels and failed episodes
-        get more 'negative' labels.
-        """
-        if self.config.constant_value:
-            return
-
-        if not self.config.value_function_path and not self.config.predictions_path:
-            return
-
-        logger.info("Computing global advantage threshold (two-pass mode)...")
-        all_advantages: list[float] = []
-
-        for record in records:
-            advantages, intervention_mask = self.compute_advantages_for_episode(record)
-            self._cache[record.episode_index] = (advantages, intervention_mask)
-            non_intervention = advantages[~intervention_mask] if intervention_mask.any() else advantages
-            all_advantages.extend(non_intervention.tolist())
-
-        if not all_advantages:
-            self._threshold = 0.0
-        else:
-            self._threshold = float(np.percentile(all_advantages, self.config.threshold_percentile * 100))
-
-        num_positive = sum(1 for a in all_advantages if a > self._threshold)
-        logger.info(
-            "Global threshold: %.4f (percentile=%.0f%%, %d/%d frames positive = %.1f%%)",
-            self._threshold,
-            self.config.threshold_percentile * 100,
-            num_positive,
-            len(all_advantages),
-            100 * num_positive / max(len(all_advantages), 1),
-        )
-
-    def run_episode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
-        """Score one episode and write advantage rows to staging."""
-        if self.config.constant_value:
-            self._run_constant_mode(record, staging)
-            return
-
-        if not self.config.value_function_path and not self.config.predictions_path:
-            logger.warning(
-                "No value_function_path, predictions_path, or constant_value configured; "
-                "skipping advantage scoring."
-            )
-            return
-
-        if record.episode_index in self._cache:
-            advantages, intervention_mask = self._cache.pop(record.episode_index)
-        else:
-            advantages, intervention_mask = self.compute_advantages_for_episode(record)
-        num_frames = len(advantages)
-
-        if self._threshold is not None:
-            threshold = self._threshold
-        else:
-            threshold = self._compute_threshold(advantages, intervention_mask)
-
-        rows: list[dict[str, Any]] = []
-        for t in range(num_frames):
-            if (
-                self.config.force_positive_on_intervention
-                and intervention_mask[t]
-                or advantages[t] > threshold
-            ):
-                indicator = "positive"
-            else:
-                indicator = "negative"
-
-            timestamp = float(record.frame_timestamps[t]) if t < len(record.frame_timestamps) else 0.0
-
-            rows.append(
-                {
-                    "role": "user",
-                    "content": indicator,
-                    "style": "advantage",
-                    "timestamp": timestamp,
-                    "camera": None,
-                    "tool_calls": None,
-                }
-            )
-
-        staging.write("advantage", rows)
-        logger.debug(
-            "Episode %d: %d/%d frames scored (threshold=%.4f, %d positive, %d negative)",
-            record.episode_index,
-            len(rows),
-            num_frames,
-            threshold,
-            sum(1 for r in rows if r["content"] == "positive"),
-            sum(1 for r in rows if r["content"] == "negative"),
-        )
-
-    def _run_constant_mode(self, record: EpisodeRecord, staging: EpisodeStaging) -> None:
-        """Emit a fixed advantage value for every frame."""
-        num_frames = len(record.frame_timestamps)
-
-        rows: list[dict[str, Any]] = []
-        for t in range(num_frames):
-            rows.append(
-                {
-                    "role": "user",
-                    "content": self.config.constant_value,
-                    "style": "advantage",
-                    "timestamp": float(record.frame_timestamps[t]),
-                    "camera": None,
-                    "tool_calls": None,
-                }
-            )
-
-        staging.write("advantage", rows)
-        logger.debug(
-            "Episode %d: %d/%d frames labeled constant '%s'",
-            record.episode_index,
-            len(rows),
-            num_frames,
-            self.config.constant_value,
-        )
-
-    def _compute_threshold(self, advantages: np.ndarray, intervention_mask: np.ndarray) -> float:
-        """Compute the binarization threshold as the configured percentile of advantages."""
-        non_intervention = advantages[~intervention_mask] if intervention_mask.any() else advantages
-        if len(non_intervention) == 0:
-            return 0.0
-        return float(np.percentile(non_intervention, self.config.threshold_percentile * 100))

src/lerobot/annotations/steerable_pipeline/reader.py

@@ -31,7 +31,6 @@ rows into memory at once.
 
 from __future__ import annotations
 
-import functools
 from collections.abc import Iterator, Sequence
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -43,18 +42,6 @@ from lerobot.datasets.io_utils import load_tasks
 from lerobot.datasets.utils import DEFAULT_TASKS_PATH
 
 
-@functools.lru_cache(maxsize=8)
-def _read_parquet_as_pandas(path: Path):  # type: ignore[no-untyped-def]
-    """Read a parquet shard once and cache the pandas DataFrame.
-
-    Multiple EpisodeRecords from the same shard share this single read.
-    The LRU cache (keyed by path) avoids re-reading the same file
-    across 100+ episodes that all live in one chunk.
-    """
-
-    return pq.read_table(path).to_pandas()
-
-
 @dataclass
 class EpisodeRecord:
     """Per-episode record yielded by the reader."""
@@ -74,7 +61,10 @@ class EpisodeRecord:
     def frames_df(self):  # type: ignore[no-untyped-def]
         """Lazy-load the pandas slice for this episode (memoized)."""
         if self._frames_df_cache is None:
-            df = _read_parquet_as_pandas(self.data_path)
+            import pandas as pd  # noqa: PLC0415  - deferred for optional dataset extra
+
+            table = pq.read_table(self.data_path)
+            df: pd.DataFrame = table.to_pandas()
             self._frames_df_cache = df.iloc[self.row_offset : self.row_offset + self.row_count].reset_index(
                 drop=True
             )

src/lerobot/annotations/steerable_pipeline/staging.py

@@ -39,7 +39,6 @@ _MODULES: tuple[ModuleName, ...] = (
     "plan",
     "interjections",
     "vqa",
-    "advantage",
 )
 
 

src/lerobot/configs/recipe.py

@@ -32,7 +32,6 @@ DEFAULT_BINDINGS = {
     "interjection": "emitted_at(t, style=interjection)",
     "vqa": "emitted_at(t, style=vqa, role=assistant)",
     "vqa_query": "emitted_at(t, style=vqa, role=user)",
-    "advantage": "active_at(t, style=advantage)",
 }
 
 PLACEHOLDER_RE = re.compile(r"\$\{([A-Za-z_][A-Za-z0-9_]*)\}")

src/lerobot/configs/recipes/recap_advantage.yaml

@@ -1,30 +0,0 @@
-# RECAP advantage-conditioned recipe.
-#
-# Composes task + advantage indicator into the prompt for conditional SFT.
-# The advantage binding resolves to "positive" or "negative" from the
-# language_persistent column (written by lerobot-annotate --advantage).
-# When advantage is absent (30% dropout), the advantage turn is skipped
-# entirely via if_present, training the unconditional branch for CFG.
-#
-# This recipe is policy-agnostic: any VLA that consumes chat-style messages
-# can use it. Override bindings or add blend components for task-specific needs.
-#
-# Paper: pi*0.6, Section IV-B (conditional policy training with I_t).
-
-bindings:
-  advantage: "active_at(t, style=advantage)"
-
-messages:
-  - role: user
-    content: "${task}"
-    stream: high_level
-
-  - role: user
-    content: "Advantage: ${advantage}"
-    stream: high_level
-    if_present: advantage
-
-  - role: assistant
-    content: "${subtask}"
-    stream: low_level
-    target: true

src/lerobot/configs/recipes/recap_advantage_blend.yaml

@@ -1,41 +0,0 @@
-# RECAP full recipe with advantage conditioning and subtask blending.
-#
-# Blend of two training modes:
-# 1. advantage_conditioned (70%): Task + advantage indicator → action
-# 2. unconditional (30%): Task only → action (no advantage, trains CFG baseline)
-#
-# This achieves the same effect as per-frame dropout in the annotation module
-# but at the recipe level, giving explicit control over the conditioning ratio.
-# Use this instead of annotation-level dropout if you want a fixed split.
-#
-# Paper: pi*0.6, Appendix E (classifier-free guidance requires both branches).
-
-blend:
-  advantage_conditioned:
-    weight: 0.7
-    messages:
-      - role: user
-        content: "${task}\nAdvantage: ${advantage}"
-        stream: high_level
-        if_present: advantage
-
-      - role: user
-        content: "${task}"
-        stream: high_level
-
-      - role: assistant
-        content: "${subtask}"
-        stream: low_level
-        target: true
-
-  unconditional:
-    weight: 0.3
-    messages:
-      - role: user
-        content: "${task}"
-        stream: high_level
-
-      - role: assistant
-        content: "${subtask}"
-        stream: low_level
-        target: true

src/lerobot/configs/recipes/recap_advantage_molmoact2.yaml

@@ -1,28 +0,0 @@
-# RECAP advantage recipe for MolmoAct2.
-#
-# Renders task + advantage into the task field as "<task> Advantage: <value>".
-# MolmoAct2PackInputsProcessorStep parses this, extracts the advantage value,
-# and places it AFTER the full user prompt but BEFORE action tokens — matching
-# the RECAP paper (Section V-B): "The advantage indicator appears in the training
-# sequence after ˆℓ but before the actions, such that only the action
-# log-likelihoods are affected."
-#
-# Final prompt layout:
-#   <images><|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\nAdvantage: positive. <action_output>...
-#
-# When advantage is absent (CFG dropout), if_present guard skips this message
-# and RenderedMessagesToTaskStep leaves the task unchanged — no advantage clause.
-
-bindings:
-  advantage: "active_at(t, style=advantage)"
-
-messages:
-  - role: user
-    content: "${task} Advantage: ${advantage}"
-    stream: high_level
-    if_present: advantage
-
-  - role: assistant
-    content: ""
-    stream: low_level
-    target: true

src/lerobot/configs/recipes/recap_advantage_molmoact2_blend.yaml

@@ -1,43 +0,0 @@
-# RECAP advantage recipe for MolmoAct2 with CFG blend (training-time dropout).
-#
-# Two components selected per sample:
-# 1. advantage_conditioned (70%): Task + advantage indicator → action
-# 2. unconditional (30%): Task only → action (no advantage, trains CFG baseline)
-#
-# At inference, classifier-free guidance combines both:
-#   action = action_uncond + w * (action_cond - action_uncond)
-#
-# Paper: pi*0.6, Appendix E & F.
-
-bindings:
-  advantage: "active_at(t, style=advantage)"
-
-blend:
-  advantage_conditioned:
-    weight: 0.7
-    messages:
-      - role: user
-        content: "${task} Advantage: ${advantage}"
-        stream: high_level
-        if_present: advantage
-
-      - role: user
-        content: "${task}"
-        stream: high_level
-
-      - role: assistant
-        content: ""
-        stream: low_level
-        target: true
-
-  unconditional:
-    weight: 0.3
-    messages:
-      - role: user
-        content: "${task}"
-        stream: high_level
-
-      - role: assistant
-        content: ""
-        stream: low_level
-        target: true

src/lerobot/datasets/aggregate.py

@@ -92,7 +92,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
     return merged_info
 
 
-def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata], lenient: bool = False):
+def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
     """Validates that all dataset metadata have consistent properties.
 
     Ensures all datasets have the same fps, robot_type, and features to guarantee
@@ -101,16 +101,13 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata], lenient: b
 
     Args:
         all_metadata: List of LeRobotDatasetMetadata objects to validate.
-        lenient: If True, allow feature mismatches and return the union of all features.
-            Missing columns will be filled with default values during aggregation.
 
     Returns:
-        tuple: A tuple containing (fps, robot_type, features) from the first metadata
-            (or union of features if lenient=True).
+        tuple: A tuple containing (fps, robot_type, features) from the first metadata.
 
     Raises:
         ValueError: If any metadata has different fps, robot_type, or features
-                   than the first metadata in the list (unless lenient=True for features).
+                   than the first metadata in the list.
     """
 
     fps = all_metadata[0].fps
@@ -125,15 +122,9 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata], lenient: b
                 f"Same robot_type is expected, but got robot_type={meta.robot_type} instead of {robot_type}."
             )
         if not features_equal_for_merge(features, meta.features):
-            if not lenient:
-                raise ValueError(
-                    f"Same features is expected, but got features={meta.features} instead of {features}."
-                )
-            # Union: add any features present in this dataset but not the first
-            for key, feat_def in meta.features.items():
-                if key not in features:
-                    features[key] = feat_def
-                    logging.info(f"Lenient merge: adding missing feature '{key}' from {meta.repo_id}")
+            raise ValueError(
+                f"Same features is expected, but got features={meta.features} instead of {features}."
+            )
 
     return fps, robot_type, features
 
@@ -298,7 +289,6 @@ def aggregate_datasets(
     chunk_size: int | None = None,
     concatenate_videos: bool = True,
     concatenate_data: bool = True,
-    lenient: bool = False,
 ):
     """Aggregates multiple LeRobot datasets into a single unified dataset.
 
@@ -335,17 +325,8 @@ def aggregate_datasets(
             LeRobotDatasetMetadata(repo_id, root=root) for repo_id, root in zip(repo_ids, roots, strict=False)
         ]
     )
-    fps, robot_type, union_features = validate_all_metadata(all_metadata, lenient=lenient)
-    if lenient:
-        # Use union features as the base, then merge video encoder info on top
-        features = copy.deepcopy(union_features)
-        video_keys_for_merge = [k for k in features if features[k].get("dtype") == "video"]
-        merged_video_info = merge_video_feature_info_for_aggregate(all_metadata)
-        for vk in video_keys_for_merge:
-            if vk in merged_video_info:
-                features[vk] = merged_video_info[vk]
-    else:
-        features = merge_video_feature_info_for_aggregate(all_metadata)
+    fps, robot_type, _ = validate_all_metadata(all_metadata)
+    features = merge_video_feature_info_for_aggregate(all_metadata)
     video_keys = [key for key in features if features[key]["dtype"] == "video"]
 
     dst_meta = LeRobotDatasetMetadata.create(
@@ -558,31 +539,6 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
             df = pd.read_parquet(src_path)
         df = update_data_df(df, src_meta, dst_meta)
 
-        # Fill missing columns with default values (for lenient merge)
-        for col_name, feat_def in dst_meta.features.items():
-            if col_name in df.columns:
-                continue
-            if col_name in ("index", "episode_index", "task_index"):
-                continue
-            dtype = feat_def.get("dtype", "float32")
-            # Video/image features are stored as separate files, not in parquet
-            if dtype in ("video", "image"):
-                continue
-            n_rows = len(df)
-            if dtype == "language":
-                df[col_name] = [[] for _ in range(n_rows)]
-            elif dtype == "bool":
-                df[col_name] = False
-            elif dtype in ("float32", "float64"):
-                df[col_name] = 0.0
-            elif dtype in ("int32", "int64"):
-                df[col_name] = 0
-            elif dtype == "string":
-                df[col_name] = ""
-            else:
-                df[col_name] = 0.0
-            logging.info(f"Filled missing column '{col_name}' with default for {n_rows} rows")
-
         # Write data and get the actual destination file it was written to
         # This avoids duplicating the rotation logic here
         data_idx, (dst_chunk, dst_file) = append_or_create_parquet_file(

src/lerobot/datasets/dataset_tools.py

@@ -274,7 +274,6 @@ def merge_datasets(
     output_dir: str | Path | None = None,
     concatenate_videos: bool = True,
     concatenate_data: bool = True,
-    lenient: bool = False,
 ) -> LeRobotDataset:
     """Merge multiple LeRobotDatasets into a single dataset.
 
@@ -286,7 +285,6 @@ def merge_datasets(
         output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
         concatenate_videos: When False, keep one mp4 per source file instead of packing into shards.
         concatenate_data: When False, keep one parquet per source file instead of packing into shards.
-        lenient: Allow merging datasets with different feature sets (union + fill defaults).
     """
     if not datasets:
         raise ValueError("No datasets to merge")
@@ -303,7 +301,6 @@ def merge_datasets(
         aggr_root=output_dir,
         concatenate_videos=concatenate_videos,
         concatenate_data=concatenate_data,
-        lenient=lenient,
     )
 
     merged_dataset = LeRobotDataset(

src/lerobot/datasets/language.py

@@ -43,10 +43,10 @@ CORE_STYLES = {
 # validation. Empty by default — populate from a downstream module that
 # also extends ``PERSISTENT_STYLES`` or ``EVENT_ONLY_STYLES`` to declare
 # the new style's column.
-EXTENDED_STYLES: set[str] = {"advantage"}
+EXTENDED_STYLES: set[str] = set()
 STYLE_REGISTRY = CORE_STYLES | EXTENDED_STYLES
 
-PERSISTENT_STYLES = {"subtask", "plan", "memory", "motion", "task_aug", "advantage"}
+PERSISTENT_STYLES = {"subtask", "plan", "memory", "motion", "task_aug"}
 EVENT_ONLY_STYLES = {"interjection", "vqa", "trace"}
 
 # Styles whose ``content`` is grounded in a specific camera view. Rows of these

src/lerobot/optim/schedulers.py

@@ -105,6 +105,28 @@ class ConstantWithWarmupSchedulerConfig(LRSchedulerConfig):
         return LambdaLR(optimizer, lr_lambda, -1)
 
 
+@LRSchedulerConfig.register_subclass("cosine_annealing_with_warmup")
+@dataclass
+class CosineAnnealingWithWarmupSchedulerConfig(LRSchedulerConfig):
+    """Linear warmup followed by cosine annealing from the peak LR to zero.
+
+    Used by EVO1; the annealing phase always spans the remaining training steps.
+    """
+
+    num_warmup_steps: int
+
+    def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
+        def lr_lambda(current_step: int) -> float:
+            if current_step < self.num_warmup_steps:
+                return current_step / max(1, self.num_warmup_steps)
+            progress = (current_step - self.num_warmup_steps) / max(
+                1, num_training_steps - self.num_warmup_steps
+            )
+            return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
+
+        return LambdaLR(optimizer, lr_lambda, -1)
+
+
 @LRSchedulerConfig.register_subclass("cosine_decay_with_warmup")
 @dataclass
 class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):

src/lerobot/policies/__init__.py

@@ -17,6 +17,7 @@ from lerobot.utils.action_interpolator import ActionInterpolator as ActionInterp
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config as EO1Config
+from .evo1.configuration_evo1 import Evo1Config as Evo1Config
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
 from .fastwam.configuration_fastwam import FastWAMConfig as FastWAMConfig
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig as GaussianActorConfig
@@ -46,6 +47,7 @@ __all__ = [
     "EO1Config",
     "FastWAMConfig",
     "GaussianActorConfig",
+    "Evo1Config",
     "GrootConfig",
     "LingBotVAConfig",
     "MolmoAct2Config",

src/lerobot/policies/evo1/README.md

@@ -0,0 +1 @@
+../../../../docs/source/policy_evo1_README.md

src/lerobot/policies/evo1/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,12 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .configuration_distributional_value_function import DistributionalVFConfig
-from .modeling_distributional_value_function import DistributionalVFRewardModel
-from .processor_distributional_value_function import make_distributional_vf_pre_post_processors
+from .configuration_evo1 import Evo1Config
+from .modeling_evo1 import Evo1Policy
+from .processor_evo1 import make_evo1_pre_post_processors
 
-__all__ = [
-    "DistributionalVFConfig",
-    "DistributionalVFRewardModel",
-    "make_distributional_vf_pre_post_processors",
-]
+__all__ = ["Evo1Config", "Evo1Policy", "make_evo1_pre_post_processors"]

src/lerobot/policies/evo1/configuration_evo1.py

@@ -0,0 +1,252 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineAnnealingWithWarmupSchedulerConfig
+from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
+
+from ..rtc.configuration_rtc import RTCConfig
+
+logger = logging.getLogger(__name__)
+
+
+@PreTrainedConfig.register_subclass("evo1")
+@dataclass
+class Evo1Config(PreTrainedConfig):
+    training_stage: str = "stage1"
+    # When True and the policy runs on CUDA, EVO1 wraps its own forward passes (training and
+    # inference) in a bfloat16 autocast block, so its numerics do not depend on the dtype of any
+    # outer autocast context opened by lerobot-train/lerobot-eval.
+    use_amp: bool = True
+
+    n_obs_steps: int = 1
+    chunk_size: int = 50
+    n_action_steps: int = 50
+
+    max_state_dim: int = 24
+    max_action_dim: int = 24
+    max_views: int = 3
+    image_resolution: tuple[int, int] = (448, 448)
+    empty_cameras: int = 0
+    postprocess_action_dim: int | None = None
+    binarize_gripper: bool = False
+    gripper_index: int = 6
+    gripper_threshold: float = 0.5
+    gripper_below_threshold_value: float = 1.0
+    gripper_above_threshold_value: float = -1.0
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MIN_MAX,
+            "ACTION": NormalizationMode.MIN_MAX,
+        }
+    )
+
+    vlm_model_name: str = "OpenGVLab/InternVL3-1B-hf"
+    vlm_num_layers: int | None = 14
+    vlm_dtype: str = "bfloat16"
+    # Max token length for tokenizing the (image placeholders + instruction) prompt. Prompts longer
+    # than this are right-truncated, so raise it for tasks with long language instructions or many views.
+    max_text_length: int = 1024
+    use_flash_attn: bool = True
+    action_head: str = "flowmatching"
+    embed_dim: int = 896
+    hidden_dim: int = 1024
+    state_hidden_dim: int = 1024
+    num_heads: int = 8
+    num_layers: int = 8
+    dropout: float = 0.0
+    num_inference_timesteps: int = 32
+    num_categories: int = 1
+    # When True, the action head is conditioned on a single pooled VL token (the last non-padding
+    # token of the causal decoder) instead of the full fused token sequence.
+    return_cls_only: bool = False
+    enable_gradient_checkpointing: bool = True
+    gradient_checkpointing_use_reentrant: bool = False
+
+    finetune_vlm: bool | None = None
+    finetune_language_model: bool | None = None
+    finetune_vision_model: bool | None = None
+    finetune_action_head: bool | None = None
+    # Reapply stage defaults after loading checkpoint configs so stage2 cannot
+    # accidentally inherit the frozen VLM flags stored by a stage1 checkpoint.
+    apply_training_stage_defaults: bool = True
+
+    task_field: str = "task"
+    embodiment_id_field: str | None = None
+    default_embodiment_id: int = 0
+
+    # Real-Time Chunking guidance for asynchronous inference (lerobot-rollout --inference.type=rtc
+    # sets this and calls init_rtc_processor()); None disables RTC.
+    rtc_config: RTCConfig | None = None
+
+    optimizer_lr: float = 1e-5
+    optimizer_betas: tuple[float, float] = (0.9, 0.999)
+    optimizer_eps: float = 1e-8
+    optimizer_weight_decay: float = 1e-5
+    optimizer_grad_clip_norm: float = 1.0
+
+    scheduler_warmup_steps: int = 300
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.training_stage not in {"stage1", "stage2"}:
+            raise ValueError(
+                f"Unsupported EVO1 training_stage '{self.training_stage}', expected 'stage1' or 'stage2'"
+            )
+
+        if self.apply_training_stage_defaults:
+            stage_defaults = {
+                "stage1": {
+                    "finetune_vlm": False,
+                    "finetune_language_model": False,
+                    "finetune_vision_model": False,
+                    "finetune_action_head": True,
+                },
+                "stage2": {
+                    "finetune_vlm": True,
+                    "finetune_language_model": True,
+                    "finetune_vision_model": True,
+                    "finetune_action_head": True,
+                },
+            }[self.training_stage]
+            for flag_name, default_value in stage_defaults.items():
+                current_value = getattr(self, flag_name)
+                if current_value is not None and current_value != default_value:
+                    logger.warning(
+                        "EVO1 %s=%s is overridden by training_stage=%s default %s. "
+                        "Set apply_training_stage_defaults=false to keep explicit finetuning flags.",
+                        flag_name,
+                        current_value,
+                        self.training_stage,
+                        default_value,
+                    )
+                setattr(self, flag_name, default_value)
+        elif self.training_stage == "stage1":
+            if self.finetune_vlm is None:
+                self.finetune_vlm = False
+            if self.finetune_language_model is None:
+                self.finetune_language_model = False
+            if self.finetune_vision_model is None:
+                self.finetune_vision_model = False
+            if self.finetune_action_head is None:
+                self.finetune_action_head = True
+        elif self.training_stage == "stage2":
+            has_explicit_branch_flags = any(
+                flag is not None for flag in (self.finetune_language_model, self.finetune_vision_model)
+            )
+            if not has_explicit_branch_flags:
+                # An explicit finetune_vlm decides both branches; otherwise stage2 defaults to a
+                # full-VLM finetune.
+                vlm_finetune = self.finetune_vlm if self.finetune_vlm is not None else True
+                self.finetune_vlm = vlm_finetune
+                self.finetune_language_model = vlm_finetune
+                self.finetune_vision_model = vlm_finetune
+            elif self.finetune_vlm is None:
+                self.finetune_vlm = bool(self.finetune_language_model or self.finetune_vision_model)
+            if self.finetune_action_head is None:
+                self.finetune_action_head = True
+
+        if self.finetune_vlm is None:
+            self.finetune_vlm = False
+        if self.finetune_language_model is None:
+            self.finetune_language_model = False
+        if self.finetune_vision_model is None:
+            self.finetune_vision_model = False
+        if self.finetune_action_head is None:
+            self.finetune_action_head = False
+
+        branch_vlm = self.finetune_language_model or self.finetune_vision_model
+        if self.finetune_vlm != branch_vlm:
+            raise ValueError(
+                "Inconsistent EVO1 finetune config: "
+                f"finetune_vlm={self.finetune_vlm} but "
+                f"(finetune_language_model or finetune_vision_model)={branch_vlm}. "
+                "When branch-level flags are used, finetune_vlm must match their effective union."
+            )
+
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"n_action_steps ({self.n_action_steps}) must be <= chunk_size ({self.chunk_size})"
+            )
+        if len(self.image_resolution) != 2 or self.image_resolution[0] != self.image_resolution[1]:
+            raise ValueError(
+                "EVO1 currently expects a square image_resolution because InternVL3 preprocessing "
+                f"uses a scalar image_size, got {self.image_resolution}."
+            )
+        if not 0 <= self.default_embodiment_id < self.num_categories:
+            raise ValueError(
+                f"default_embodiment_id ({self.default_embodiment_id}) must be in "
+                f"[0, num_categories={self.num_categories})"
+            )
+
+    def validate_features(self) -> None:
+        if self.input_features is None:
+            self.input_features = {}
+        if self.output_features is None:
+            self.output_features = {}
+
+        for i in range(self.empty_cameras):
+            key = OBS_IMAGES + f".empty_camera_{i}"
+            if key not in self.input_features:
+                self.input_features[key] = PolicyFeature(
+                    type=FeatureType.VISUAL,
+                    shape=(3, *self.image_resolution),
+                )
+
+        if OBS_STATE not in self.input_features:
+            self.input_features[OBS_STATE] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(self.max_state_dim,),
+            )
+
+        if ACTION not in self.output_features:
+            self.output_features[ACTION] = PolicyFeature(
+                type=FeatureType.ACTION,
+                shape=(self.max_action_dim,),
+            )
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self):
+        return CosineAnnealingWithWarmupSchedulerConfig(
+            num_warmup_steps=self.scheduler_warmup_steps,
+        )
+
+    @property
+    def observation_delta_indices(self) -> list[int]:
+        return [0]
+
+    @property
+    def action_delta_indices(self) -> list[int]:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None

src/lerobot/policies/evo1/evo1_model.py

@@ -0,0 +1,210 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+from .configuration_evo1 import Evo1Config
+from .flow_matching import FlowmatchingActionHead
+from .internvl3_embedder import InternVL3Embedder
+
+
+class Evo1Model(nn.Module):
+    def __init__(self, config: Evo1Config, vlm_hub_kwargs: dict | None = None):
+        super().__init__()
+        self.config = config
+        self._device = config.device
+        self.return_cls_only = config.return_cls_only
+        # Set by Evo1Policy.init_rtc_processor() when config.rtc_config is provided.
+        self.rtc_processor = None
+
+        # Gradient checkpointing only pays off when the VLM is actually being trained; keep it off
+        # whenever every VLM branch is frozen so the frozen forward stays cheap.
+        tracks_vlm_gradients = bool(
+            config.finetune_vlm or config.finetune_language_model or config.finetune_vision_model
+        )
+        enable_gradient_checkpointing = config.enable_gradient_checkpointing and tracks_vlm_gradients
+
+        self.embedder = InternVL3Embedder(
+            model_name=config.vlm_model_name,
+            image_size=int(config.image_resolution[0]),
+            device=self._device,
+            num_language_layers=config.vlm_num_layers,
+            model_dtype=config.vlm_dtype,
+            use_flash_attn=config.use_flash_attn,
+            max_text_length=config.max_text_length,
+            enable_gradient_checkpointing=enable_gradient_checkpointing,
+            gradient_checkpointing_use_reentrant=config.gradient_checkpointing_use_reentrant,
+            hub_kwargs=vlm_hub_kwargs,
+        )
+
+        action_head_type = config.action_head.lower()
+        if action_head_type != "flowmatching":
+            raise NotImplementedError(f"Unknown action_head: {action_head_type}")
+
+        horizon = config.chunk_size
+        per_action_dim = config.max_action_dim
+        action_dim = horizon * per_action_dim
+
+        self.horizon = horizon
+        self.per_action_dim = per_action_dim
+        self.action_head = FlowmatchingActionHead(
+            embed_dim=config.embed_dim,
+            hidden_dim=config.hidden_dim,
+            action_dim=action_dim,
+            horizon=horizon,
+            per_action_dim=per_action_dim,
+            num_heads=config.num_heads,
+            num_layers=config.num_layers,
+            dropout=config.dropout,
+            num_inference_timesteps=config.num_inference_timesteps,
+            num_categories=config.num_categories,
+            state_dim=config.max_state_dim,
+            state_hidden_dim=config.state_hidden_dim,
+        ).to(self._device)
+
+    def get_vl_embeddings(
+        self,
+        images: list[torch.Tensor],
+        image_mask: torch.Tensor,
+        prompt: str | list[str] | None = None,
+        return_cls_only: bool | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Fused VL embeddings from per-camera image batches.
+
+        Args:
+            images: list of per-camera tensors, each shaped ``(B, C, H, W)`` with values in ``[0, 1]``.
+            image_mask: bool tensor ``(B, max_views)`` marking present views.
+
+        Returns:
+            ``(embeddings, valid_mask)``: the fused tokens and the bool mask of attendable context
+            positions (None when a single pooled token is returned).
+        """
+        if return_cls_only is None:
+            return_cls_only = self.return_cls_only
+        if not images:
+            raise ValueError("EVO1 expects at least one image per sample.")
+
+        batch_size = images[0].shape[0]
+        if prompt is None:
+            prompts = [""] * batch_size
+        elif isinstance(prompt, str):
+            prompts = [prompt] * batch_size
+        else:
+            prompts = [str(p) for p in prompt]
+            if len(prompts) != batch_size:
+                raise ValueError(
+                    f"Prompt batch size {len(prompts)} does not match image batch size {batch_size}"
+                )
+
+        if image_mask.dim() == 1:
+            image_mask = image_mask.unsqueeze(0)
+        if image_mask.shape[0] != batch_size:
+            raise ValueError(
+                f"image_mask batch size {image_mask.shape[0]} does not match image batch size {batch_size}"
+            )
+
+        return self.embedder.get_fused_image_text_embedding_batched(
+            camera_images=images,
+            image_masks=image_mask,
+            text_prompts=prompts,
+            return_cls_only=return_cls_only,
+        )
+
+    def predict_action(
+        self,
+        fused_tokens: torch.Tensor,
+        state: torch.Tensor,
+        actions_gt: torch.Tensor | None = None,
+        action_mask: torch.Tensor | None = None,
+        embodiment_ids: torch.Tensor | None = None,
+        context_mask: torch.Tensor | None = None,
+        inference_delay: int | None = None,
+        prev_chunk_left_over: torch.Tensor | None = None,
+        execution_horizon: int | None = None,
+    ):
+        if actions_gt is None:
+            return self.action_head.get_action(
+                fused_tokens,
+                state=state,
+                action_mask=action_mask,
+                embodiment_id=embodiment_ids,
+                context_mask=context_mask,
+                inference_delay=inference_delay,
+                prev_chunk_left_over=prev_chunk_left_over,
+                execution_horizon=execution_horizon,
+                rtc_processor=self.rtc_processor,
+            )
+        return self.action_head(
+            fused_tokens,
+            state=state,
+            actions_gt=actions_gt,
+            action_mask=action_mask,
+            embodiment_id=embodiment_ids,
+            context_mask=context_mask,
+        )
+
+    def forward(
+        self,
+        fused_tokens: torch.Tensor,
+        state: torch.Tensor | None = None,
+        actions_gt: torch.Tensor | None = None,
+        action_mask: torch.Tensor | None = None,
+        embodiment_ids: torch.Tensor | None = None,
+        context_mask: torch.Tensor | None = None,
+        inference_delay: int | None = None,
+        prev_chunk_left_over: torch.Tensor | None = None,
+        execution_horizon: int | None = None,
+    ):
+        return self.predict_action(
+            fused_tokens,
+            state,
+            actions_gt,
+            action_mask,
+            embodiment_ids,
+            context_mask,
+            inference_delay,
+            prev_chunk_left_over,
+            execution_horizon,
+        )
+
+    def _set_module_trainable(self, module: nn.Module, trainable: bool):
+        for param in module.parameters():
+            param.requires_grad = trainable
+
+    def _vlm_submodule(self, name: str) -> nn.Module:
+        module = getattr(self.embedder.model, name, None)
+        if not isinstance(module, nn.Module):
+            raise AttributeError(
+                f"InternVL model {type(self.embedder.model).__name__} has no '{name}' submodule; "
+                "the native HF InternVL layout (language_model / vision_tower / "
+                "multi_modal_projector) is required to apply the EVO1 finetune flags."
+            )
+        return module
+
+    def set_finetune_flags(self):
+        # __post_init__ resolves every finetune flag to a concrete boolean, so branch-level flags
+        # are authoritative here. Freeze everything first, then re-enable the requested branches.
+        self._set_module_trainable(self.embedder, False)
+        self._set_module_trainable(
+            self._vlm_submodule("language_model"), bool(self.config.finetune_language_model)
+        )
+        finetune_vision = bool(self.config.finetune_vision_model)
+        self._set_module_trainable(self._vlm_submodule("vision_tower"), finetune_vision)
+        self._set_module_trainable(self._vlm_submodule("multi_modal_projector"), finetune_vision)
+
+        if not self.config.finetune_action_head:
+            self._set_module_trainable(self.action_head, False)

src/lerobot/policies/evo1/flow_matching.py

@@ -0,0 +1,483 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+import math
+
+import torch
+import torch.nn as nn
+
+logger = logging.getLogger(__name__)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    def __init__(self, dim: int, max_len: int = 1000):
+        super().__init__()
+        pe = torch.zeros(max_len, dim)
+        position = torch.arange(0, max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, dim, 2) * -(math.log(10000.0) / dim))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer("pe", pe)
+
+    def forward(self, seq_len: int):
+        if seq_len > self.pe.size(1):
+            self._extend_pe(seq_len)
+        return self.pe[:, :seq_len, :]
+
+    def _extend_pe(self, new_max_len):
+        old_max_len, dim = self.pe.size(1), self.pe.size(2)
+        if new_max_len <= old_max_len:
+            return
+        extra_positions = torch.arange(old_max_len, new_max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, dim, 2, dtype=torch.float) * -(math.log(10000.0) / dim))
+        extra_pe = torch.zeros(new_max_len - old_max_len, dim)
+        extra_pe[:, 0::2] = torch.sin(extra_positions * div_term)
+        extra_pe[:, 1::2] = torch.cos(extra_positions * div_term)
+        extra_pe = extra_pe.unsqueeze(0)
+        new_pe = torch.cat([self.pe, extra_pe.to(self.pe.device)], dim=1)
+        self.pe = new_pe
+
+
+class CategorySpecificLinear(nn.Module):
+    def __init__(self, in_dim: int, out_dim: int, num_categories: int = 1):
+        super().__init__()
+        self.num_categories = num_categories
+        if num_categories <= 1:
+            self.linear = nn.Linear(in_dim, out_dim)
+        else:
+            self.weight = nn.Parameter(torch.empty(num_categories, in_dim, out_dim))
+            self.bias = nn.Parameter(torch.zeros(num_categories, out_dim))
+            # Initialize each per-category (in_dim, out_dim) matrix separately: xavier on the full
+            # 3D tensor would compute fan_in = in_dim * out_dim and badly under-scale the weights.
+            for category in range(num_categories):
+                nn.init.xavier_uniform_(self.weight[category])
+
+    def forward(self, x: torch.Tensor, category_id: torch.LongTensor):
+        if self.num_categories <= 1:
+            if x.dtype != self.linear.weight.dtype:
+                x = x.to(dtype=self.linear.weight.dtype)
+            return self.linear(x)
+
+        if x.dtype != self.weight.dtype:
+            x = x.to(dtype=self.weight.dtype)
+
+        orig_shape = x.shape
+        x_flat = x.reshape(-1, orig_shape[-1])
+        if category_id.dim() == 0:
+            cid = category_id.item()
+            out = x_flat @ self.weight[cid] + self.bias[cid]
+        else:
+            category_id = category_id.reshape(-1)
+            if category_id.numel() != x_flat.size(0):
+                raise ValueError(
+                    f"category_id length {category_id.numel()} does not match flattened batch {x_flat.size(0)}"
+                )
+            weight_selected = self.weight[category_id]
+            bias_selected = self.bias[category_id]
+            out = torch.bmm(x_flat.unsqueeze(1), weight_selected).squeeze(1) + bias_selected
+        out_shape = orig_shape[:-1] + (out.shape[-1],)
+        return out.view(out_shape)
+
+
+class CategorySpecificMLP(nn.Module):
+    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_categories: int = 1):
+        super().__init__()
+        self.fc1 = CategorySpecificLinear(input_dim, hidden_dim, num_categories)
+        self.fc2 = CategorySpecificLinear(hidden_dim, output_dim, num_categories)
+        self.activation = nn.ReLU(inplace=True)
+
+    def forward(self, x: torch.Tensor, category_id: torch.LongTensor):
+        out = self.activation(self.fc1(x, category_id))
+        out = self.fc2(out, category_id)
+        return out
+
+
+class MultiEmbodimentActionEncoder(nn.Module):
+    def __init__(
+        self, action_dim: int, embed_dim: int, hidden_dim: int, horizon: int, num_categories: int = 1
+    ):
+        super().__init__()
+        self.horizon = horizon
+        self.embed_dim = embed_dim
+        self.num_categories = num_categories
+
+        self.W1 = CategorySpecificLinear(action_dim, hidden_dim, num_categories)
+        self.W2 = CategorySpecificLinear(hidden_dim, hidden_dim, num_categories)
+        self.W3 = CategorySpecificLinear(hidden_dim, embed_dim, num_categories)
+
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_dim, max_len=horizon)
+        self.activation = nn.ReLU(inplace=True)
+
+    def forward(self, action_seq: torch.Tensor, category_id: torch.LongTensor):
+        batch_size, horizon, action_dim = action_seq.shape
+        if self.horizon != horizon:
+            raise ValueError(
+                f"Action sequence length must match horizon: got {horizon}, expected {self.horizon}."
+            )
+
+        x = action_seq.reshape(batch_size * horizon, action_dim)
+        if category_id.dim() == 0:
+            cat_ids = category_id.expand(horizon * batch_size)
+        else:
+            cat_ids = category_id.unsqueeze(1).expand(batch_size, horizon).reshape(batch_size * horizon)
+
+        out = self.activation(self.W1(x, cat_ids))
+        pos_enc = self.pos_encoding(horizon).to(device=out.device, dtype=out.dtype)
+        out = out.view(batch_size, horizon, -1) + pos_enc
+        out = out.view(batch_size * horizon, -1)
+        out = self.activation(self.W2(out, cat_ids))
+        out = self.W3(out, cat_ids)
+        return out.view(batch_size, horizon, self.embed_dim)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, embed_dim: int, num_heads: int, hidden_dim: int, dropout: float = 0.0):
+        super().__init__()
+        self.attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, batch_first=True)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.norm2 = nn.LayerNorm(embed_dim)
+        self.ff = nn.Sequential(nn.Linear(embed_dim, hidden_dim), nn.GELU(), nn.Linear(hidden_dim, embed_dim))
+
+    def forward(
+        self,
+        action_tokens: torch.Tensor,
+        context_tokens: torch.Tensor,
+        time_emb: torch.Tensor,
+        context_key_padding_mask: torch.Tensor | None = None,
+    ):
+        x = self.norm1(action_tokens)
+        attn_out, _ = self.attn(x, context_tokens, context_tokens, key_padding_mask=context_key_padding_mask)
+        x = action_tokens + attn_out
+        x2 = self.norm2(x)
+        if time_emb is not None:
+            x2 = x2 + time_emb.unsqueeze(1)
+        ff_out = self.ff(x2)
+        return x + ff_out
+
+
+class FlowmatchingActionHead(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int = 896,
+        hidden_dim: int = 1024,
+        action_dim: int = 16 * 7,
+        horizon: int = 16,
+        per_action_dim: int = 7,
+        num_heads: int = 8,
+        num_layers: int = 8,
+        dropout: float = 0.0,
+        num_inference_timesteps: int = 20,
+        num_categories: int = 1,
+        state_dim: int | None = None,
+        state_hidden_dim: int | None = None,
+    ):
+        super().__init__()
+
+        logger.info("FlowmatchingActionHead num_inference_timesteps=%s", num_inference_timesteps)
+        self.embed_dim = embed_dim
+        self.horizon = horizon
+        self.per_action_dim = per_action_dim
+        self.action_dim = action_dim
+        self.num_inference_timesteps = num_inference_timesteps
+        self.num_categories = num_categories
+
+        self.time_pos_enc = SinusoidalPositionalEncoding(embed_dim, max_len=1000)
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    embed_dim=embed_dim,
+                    num_heads=num_heads,
+                    hidden_dim=embed_dim * 4,
+                    dropout=dropout,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm_out = nn.LayerNorm(embed_dim)
+        self.seq_pool_proj = nn.Linear(self.horizon * self.embed_dim, self.embed_dim)
+        self.mlp_head = CategorySpecificMLP(
+            input_dim=embed_dim,
+            hidden_dim=hidden_dim,
+            output_dim=action_dim,
+            num_categories=num_categories,
+        )
+
+        self.state_encoder = None
+        if state_dim is not None:
+            state_hidden = state_hidden_dim if state_hidden_dim is not None else embed_dim
+            self.state_encoder = CategorySpecificMLP(
+                input_dim=state_dim,
+                hidden_dim=state_hidden,
+                output_dim=embed_dim,
+                num_categories=num_categories,
+            )
+
+        if horizon > 1:
+            self.action_encoder = MultiEmbodimentActionEncoder(
+                action_dim=self.per_action_dim,
+                embed_dim=embed_dim,
+                hidden_dim=embed_dim,
+                horizon=horizon,
+                num_categories=num_categories,
+            )
+            self.single_action_proj = None
+        else:
+            self.action_encoder = None
+            self.single_action_proj = nn.Linear(self.per_action_dim, self.embed_dim)
+
+    def _project_actions(self, action_seq: torch.Tensor, embodiment_id: torch.LongTensor) -> torch.Tensor:
+        if self.horizon > 1 and self.action_encoder is not None:
+            return self.action_encoder(action_seq, embodiment_id)
+        if self.single_action_proj is None:
+            raise RuntimeError("single_action_proj is not initialized for horizon <= 1.")
+        return self.single_action_proj(action_seq)
+
+    def _expand_action_mask(
+        self,
+        action_mask: torch.Tensor,
+        batch_size: int,
+        per_action_dim: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> torch.Tensor:
+        if action_mask is None:
+            raise ValueError("action_mask must be provided for flow matching inference.")
+
+        if action_mask.dim() == 2:
+            expected_last_dim = self.horizon * per_action_dim
+            if action_mask.shape == (batch_size, expected_last_dim):
+                expanded_mask = action_mask.reshape(batch_size, self.horizon, per_action_dim)
+            elif action_mask.shape == (batch_size, per_action_dim):
+                expanded_mask = action_mask.unsqueeze(1).expand(batch_size, self.horizon, per_action_dim)
+            else:
+                raise ValueError(
+                    f"Expected action_mask shape {(batch_size, expected_last_dim)} or "
+                    f"{(batch_size, per_action_dim)}, got {tuple(action_mask.shape)}"
+                )
+        elif action_mask.dim() == 3:
+            expected_shape = (batch_size, self.horizon, per_action_dim)
+            if tuple(action_mask.shape) != expected_shape:
+                raise ValueError(
+                    f"Expected action_mask shape {expected_shape}, got {tuple(action_mask.shape)}"
+                )
+            expanded_mask = action_mask
+        else:
+            raise ValueError(f"Unsupported action_mask rank: {action_mask.dim()}")
+
+        return expanded_mask.to(device=device, dtype=dtype)
+
+    def _prepare_context(
+        self,
+        fused_tokens: torch.Tensor,
+        state: torch.Tensor | None,
+        embodiment_id: torch.LongTensor | None,
+        context_mask: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, torch.LongTensor]:
+        """Normalize the VL context and embodiment ids shared by training and inference.
+
+        Returns the context tokens ``(B, S, E)``, a key_padding_mask for
+        ``nn.MultiheadAttention`` (True = ignore) or None, and the resolved embodiment ids.
+        """
+        batch_size = fused_tokens.size(0)
+        device = fused_tokens.device
+        if embodiment_id is None:
+            embodiment_id = torch.zeros(batch_size, dtype=torch.long, device=device)
+        elif self.num_categories > 1 and (
+            int(embodiment_id.min()) < 0 or int(embodiment_id.max()) >= self.num_categories
+        ):
+            raise ValueError(
+                f"embodiment ids must be in [0, num_categories={self.num_categories}), "
+                f"got range [{int(embodiment_id.min())}, {int(embodiment_id.max())}]"
+            )
+
+        context_tokens = fused_tokens
+        if context_tokens.dim() == 2:
+            # A single pooled VL token (return_cls_only): give it a sequence dim of 1.
+            context_tokens = context_tokens.unsqueeze(1)
+            context_mask = None
+        if state is not None and self.state_encoder is not None:
+            state_emb = self.state_encoder(state, embodiment_id).unsqueeze(1)
+            context_tokens = torch.cat([context_tokens, state_emb], dim=1)
+            if context_mask is not None:
+                state_valid = torch.ones(batch_size, 1, dtype=torch.bool, device=context_mask.device)
+                context_mask = torch.cat([context_mask.to(torch.bool), state_valid], dim=1)
+
+        key_padding_mask = None if context_mask is None else ~context_mask.to(torch.bool)
+        return context_tokens, key_padding_mask, embodiment_id
+
+    def forward(
+        self,
+        fused_tokens: torch.Tensor,
+        state: torch.Tensor = None,
+        actions_gt: torch.Tensor = None,
+        embodiment_id: torch.LongTensor = None,
+        action_mask: torch.Tensor = None,
+        context_mask: torch.Tensor = None,
+    ):
+        if actions_gt is None:
+            return self.get_action(
+                fused_tokens,
+                state=state,
+                embodiment_id=embodiment_id,
+                action_mask=action_mask,
+                context_mask=context_mask,
+            )
+
+        batch_size = fused_tokens.size(0)
+        device = fused_tokens.device
+        context_tokens, key_padding_mask, embodiment_id = self._prepare_context(
+            fused_tokens, state, embodiment_id, context_mask
+        )
+
+        t = (
+            torch.distributions.Beta(2, 2)
+            .sample((batch_size,))
+            .clamp(0.02, 0.98)
+            .to(device)
+            .to(dtype=self.dtype)
+        )
+        time_index = (t * 999).long().clamp_(0, 999)
+        time_emb = self.time_pos_enc(1000)[:, time_index, :].squeeze(0).to(dtype=context_tokens.dtype)
+
+        actions_gt_seq = actions_gt
+        noise = torch.rand_like(actions_gt) * 2 - 1
+        if action_mask is not None:
+            action_mask = action_mask.to(dtype=noise.dtype, device=noise.device)
+            if action_mask.shape != noise.shape:
+                raise ValueError(f"action_mask shape {action_mask.shape} != noise shape {noise.shape}")
+            actions_gt_seq = actions_gt_seq * action_mask
+            noise = noise * action_mask
+
+        if self.horizon > 1:
+            noise_seq = noise.view(batch_size, self.horizon, self.per_action_dim)
+        else:
+            noise_seq = noise if noise.dim() == 3 else noise.unsqueeze(1)
+        t_broadcast = t.view(batch_size, 1, 1)
+        action_intermediate_seq = (1 - t_broadcast) * noise_seq + t_broadcast * actions_gt_seq
+
+        action_tokens = self._project_actions(action_intermediate_seq, embodiment_id)
+        target_dtype = self.dtype
+        action_tokens = action_tokens.to(dtype=target_dtype)
+        context_tokens = context_tokens.to(dtype=target_dtype)
+        time_emb = time_emb.to(dtype=target_dtype)
+
+        x = action_tokens
+        for block in self.transformer_blocks:
+            x = block(x, context_tokens, time_emb, key_padding_mask)
+        x = self.norm_out(x)
+
+        if self.horizon > 1:
+            x_flat = x.reshape(batch_size, -1)
+            x_pooled = self.seq_pool_proj(x_flat)
+        else:
+            x_pooled = x.squeeze(1)
+
+        pred_velocity = self.mlp_head(x_pooled, embodiment_id)
+        return pred_velocity, noise
+
+    def get_action(
+        self,
+        fused_tokens: torch.Tensor,
+        state: torch.Tensor = None,
+        embodiment_id: torch.LongTensor = None,
+        action_mask: torch.Tensor = None,
+        context_mask: torch.Tensor = None,
+        inference_delay: int | None = None,
+        prev_chunk_left_over: torch.Tensor | None = None,
+        execution_horizon: int | None = None,
+        rtc_processor=None,
+    ):
+        batch_size = fused_tokens.size(0)
+        device = fused_tokens.device
+        context_tokens, key_padding_mask, embodiment_id = self._prepare_context(
+            fused_tokens, state, embodiment_id, context_mask
+        )
+
+        action_dim_total = self.action_dim
+        per_action_dim = self.per_action_dim
+
+        action = torch.rand(batch_size, action_dim_total, device=device, dtype=context_tokens.dtype) * 2 - 1
+        action_seq = action.view(batch_size, self.horizon, per_action_dim)
+        action_mask = self._expand_action_mask(
+            action_mask,
+            batch_size=batch_size,
+            per_action_dim=per_action_dim,
+            device=action_seq.device,
+            dtype=action_seq.dtype,
+        )
+        action_seq = action_seq * action_mask
+
+        target_dtype = self.dtype
+        context_tokens = context_tokens.to(dtype=target_dtype)
+
+        num_steps = int(self.num_inference_timesteps)
+        if num_steps <= 0:
+            raise ValueError(f"num_inference_timesteps must be positive, got {num_steps}")
+        dt = 1.0 / num_steps
+
+        use_rtc = rtc_processor is not None and (
+            inference_delay is not None or prev_chunk_left_over is not None
+        )
+
+        def predict_velocity(seq: torch.Tensor, step_time_emb: torch.Tensor) -> torch.Tensor:
+            """Predict the masked flow velocity (x1 - x0 convention) for one integration step."""
+            seq = seq * action_mask
+            action_tokens = self._project_actions(seq, embodiment_id).to(dtype=target_dtype)
+            x = action_tokens
+            for block in self.transformer_blocks:
+                x = block(x, context_tokens, step_time_emb, key_padding_mask)
+            x = self.norm_out(x)
+            x_pooled = self.seq_pool_proj(x.reshape(batch_size, -1)) if self.horizon > 1 else x.squeeze(1)
+            pred = self.mlp_head(x_pooled, embodiment_id)
+            return pred.view(batch_size, self.horizon, per_action_dim) * action_mask
+
+        for i in range(num_steps):
+            t = i / num_steps
+            time_index = min(int(t * 999), 999)
+            time_emb = self.time_pos_enc(1000)[:, time_index, :].to(device).squeeze(0).to(dtype=target_dtype)
+            time_emb = time_emb.unsqueeze(0).repeat(batch_size, 1)
+
+            if use_rtc:
+                # RTCProcessor assumes the pi0 flow convention: its `time` runs 1 -> 0 and the
+                # clean-action estimate is x1 = x_t - time * v. EVO1 integrates t: 0 -> 1 with
+                # velocity v = x1 - x0 (so x1 = x_t + (1 - t) * v); passing time = 1 - t and
+                # flipping the velocity sign in both directions maps one convention onto the other.
+                guided = rtc_processor.denoise_step(
+                    x_t=action_seq,
+                    prev_chunk_left_over=prev_chunk_left_over,
+                    inference_delay=inference_delay,
+                    time=1.0 - t,
+                    original_denoise_step_partial=lambda seq, emb=time_emb: -predict_velocity(seq, emb),
+                    execution_horizon=execution_horizon,
+                )
+                velocity = -guided
+            else:
+                velocity = predict_velocity(action_seq, time_emb)
+
+            action_seq = action_seq + dt * velocity
+
+        action_seq = action_seq * action_mask
+        return action_seq.reshape(batch_size, -1)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype

src/lerobot/policies/evo1/internvl3_embedder.py

@@ -0,0 +1,369 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+import torch
+import torch.nn as nn
+import torchvision.transforms.functional as tvf
+from torchvision.transforms.functional import InterpolationMode
+
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoModel, AutoTokenizer
+else:
+    AutoModel = None
+    AutoTokenizer = None
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+IMG_CONTEXT_TOKEN = "<IMG_CONTEXT>"  # nosec B105
+IMG_START_TOKEN = "<img>"  # nosec B105
+IMG_END_TOKEN = "</img>"  # nosec B105
+
+logger = logging.getLogger(__name__)
+
+
+def _batched_resize_01(images: torch.Tensor, image_size: int) -> torch.Tensor:
+    """Resize a batch of ``[0, 1]`` images to ``(image_size, image_size)`` on-device.
+
+    Numerically mirrors InternVL3's reference PIL preprocessing
+    (``to_pil_image`` -> ``Image.resize`` -> ``to_tensor``): the float input is quantized to uint8
+    exactly as ``to_pil_image`` does, then resized with bicubic interpolation and antialiasing,
+    which matches PIL's default resampler. Matching the reference pixel-for-pixel keeps the policy
+    interchangeable with checkpoints produced by the upstream EVO1 preprocessing.
+
+    Args:
+        images: float tensor of shape ``(N, C, H, W)`` with values in ``[0, 1]``.
+
+    Returns:
+        float32 tensor of shape ``(N, C, image_size, image_size)`` with values in ``[0, 1]``.
+    """
+    # to_pil_image() quantizes float [0, 1] to uint8 (x * 255, truncated); replicate that so the
+    # bicubic resample sees the same integer pixels PIL would.
+    pixels_u8 = (images * 255.0).clamp(0, 255).to(torch.uint8)
+    resized = tvf.resize(
+        pixels_u8, [image_size, image_size], interpolation=InterpolationMode.BICUBIC, antialias=True
+    )
+    return resized.to(torch.float32) / 255.0
+
+
+def _batched_pixel_values(
+    camera_images: Sequence[torch.Tensor],
+    max_views: int,
+    image_size: int,
+    mean: torch.Tensor,
+    std: torch.Tensor,
+    dtype: torch.dtype,
+    device: torch.device | str,
+) -> torch.Tensor:
+    """Build InternVL3 ``pixel_values`` from per-camera ``[0, 1]`` image batches without leaving the device.
+
+    Each image is resized, converted to ``dtype``, and ImageNet-normalized (a single tile per
+    image), batched across the whole minibatch. Absent views (fewer cameras than ``max_views``)
+    are filled with zero images; their placeholder tokens are masked out of attention downstream
+    via ``_mask_absent_image_tokens``.
+
+    Returns:
+        ``pixel_values`` of shape ``(B * max_views, C, image_size, image_size)``, ordered row-major
+        over ``(sample, view)`` to line up with the per-view image placeholders in the prompt.
+    """
+    resized: list[torch.Tensor] = []
+    for image in camera_images:
+        resized.append(_batched_resize_01(image.to(device=device), image_size).to(dtype))
+
+    batch_size = resized[0].shape[0]
+    channels = resized[0].shape[1]
+    while len(resized) < max_views:
+        resized.append(torch.zeros(batch_size, channels, image_size, image_size, dtype=dtype, device=device))
+
+    stacked = torch.stack(resized[:max_views], dim=1)  # (B, V, C, H, W)
+    mean = mean.to(device=device, dtype=dtype).view(1, 1, -1, 1, 1)
+    std = std.to(device=device, dtype=dtype).view(1, 1, -1, 1, 1)
+    normalized = (stacked - mean) / std
+    return normalized.reshape(batch_size * max_views, channels, image_size, image_size)
+
+
+class InternVL3Embedder(nn.Module):
+    """Vision-language embedder using the native HF InternVL3 model (no trust_remote_code)."""
+
+    def __init__(
+        self,
+        model_name="OpenGVLab/InternVL3-1B-hf",
+        image_size=448,
+        device="cuda",
+        num_language_layers: int | None = 14,
+        model_dtype: str | torch.dtype = "bfloat16",
+        use_flash_attn: bool = True,
+        max_text_length: int = 1024,
+        enable_gradient_checkpointing: bool = True,
+        gradient_checkpointing_use_reentrant: bool = False,
+        hub_kwargs: dict | None = None,
+    ):
+        super().__init__()
+        self._requested_device = device
+        self.image_size = image_size
+        self.num_language_layers = num_language_layers
+        self.max_text_length = max_text_length
+        self.enable_gradient_checkpointing = bool(enable_gradient_checkpointing)
+        self.gradient_checkpointing_use_reentrant = bool(gradient_checkpointing_use_reentrant)
+        hub_kwargs = hub_kwargs or {}
+
+        require_package("transformers", extra="evo1")
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, **hub_kwargs)
+        if isinstance(model_dtype, str):
+            try:
+                model_dtype = getattr(torch, model_dtype)
+            except AttributeError as exc:
+                raise ValueError(f"Unsupported EVO1 vlm_dtype '{model_dtype}'") from exc
+        self.model_dtype = model_dtype
+
+        attn_implementation = "flash_attention_2" if (use_flash_attn and _flash_attn_available()) else "eager"
+        if use_flash_attn and attn_implementation == "eager":
+            logger.warning("flash_attn is not installed. Falling back to eager attention.")
+
+        self.model = AutoModel.from_pretrained(
+            model_name,
+            torch_dtype=model_dtype,
+            attn_implementation=attn_implementation,
+            low_cpu_mem_usage=True,
+            **hub_kwargs,
+        ).to(self._requested_device)
+
+        checkpoint_image_size = getattr(self.model.config.vision_config, "image_size", None)
+        if isinstance(checkpoint_image_size, (list, tuple)):
+            checkpoint_image_size = checkpoint_image_size[0]
+        if checkpoint_image_size is not None and int(checkpoint_image_size) != int(image_size):
+            raise ValueError(
+                f"EVO1 image_resolution ({image_size}) must match the InternVL checkpoint's native "
+                f"image size ({checkpoint_image_size}): the checkpoint's image_seq_length assumes "
+                "its native resolution, so other sizes would desync the image placeholder tokens "
+                "from the vision features."
+            )
+
+        self.num_image_token = self.model.config.image_seq_length
+
+        # Truncate language model to the requested number of layers
+        layers = self.model.language_model.layers
+        if self.num_language_layers is not None:
+            layers = layers[: self.num_language_layers]
+        self.model.language_model.layers = torch.nn.ModuleList(layers)
+
+        self._configure_memory_features()
+        self.img_context_token_id = self.tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+
+    def _configure_memory_features(self) -> None:
+        checkpoint_kwargs = {"use_reentrant": self.gradient_checkpointing_use_reentrant}
+
+        if not self.enable_gradient_checkpointing:
+            language_model = self.model.language_model
+            if hasattr(language_model, "gradient_checkpointing_disable"):
+                language_model.gradient_checkpointing_disable()
+            vision_tower = getattr(self.model, "vision_tower", None)
+            if vision_tower is not None and hasattr(vision_tower, "encoder"):
+                vision_tower.encoder.gradient_checkpointing = False
+            return
+
+        def _enable_ckpt(module: nn.Module | None) -> bool:
+            if module is None:
+                return False
+            if hasattr(module, "gradient_checkpointing_enable"):
+                try:
+                    module.gradient_checkpointing_enable(gradient_checkpointing_kwargs=checkpoint_kwargs)
+                except TypeError:
+                    module.gradient_checkpointing_enable()
+                return True
+            if hasattr(module, "gradient_checkpointing"):
+                module.gradient_checkpointing = True
+                return True
+            return False
+
+        enabled_any = _enable_ckpt(self.model)
+
+        vision_tower = getattr(self.model, "vision_tower", None)
+        if vision_tower is not None:
+            enabled_any = _enable_ckpt(vision_tower) or enabled_any
+
+        language_model = self.model.language_model
+        enabled_any = _enable_ckpt(language_model) or enabled_any
+        if hasattr(language_model, "config"):
+            language_model.config.use_cache = False
+
+        if hasattr(self.model, "config"):
+            self.model.config.use_cache = False
+        if hasattr(self.model, "enable_input_require_grads"):
+            self.model.enable_input_require_grads()
+
+        if enabled_any:
+            logger.info("Gradient checkpointing enabled for InternVL3 embedder.")
+        else:
+            logger.warning(
+                "Requested gradient checkpointing, but model does not expose checkpointing controls."
+            )
+
+    def _build_multimodal_prompts(
+        self,
+        batch_num_tiles_list: list[list[int]],
+        text_prompts: Sequence[str],
+    ) -> list[str]:
+        prompts = []
+        for num_tiles_list, text_prompt in zip(batch_num_tiles_list, text_prompts, strict=True):
+            prompt_segments = []
+            for i, tile_count in enumerate(num_tiles_list):
+                token_count = self.num_image_token * tile_count
+                image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * token_count + IMG_END_TOKEN
+                prompt_segments.append(f"Image-{i + 1}: {image_tokens}\n")
+            prompts.append("".join(prompt_segments) + text_prompt.strip())
+        return prompts
+
+    def get_fused_image_text_embedding_batched(
+        self,
+        camera_images: Sequence[torch.Tensor],
+        image_masks: torch.Tensor,
+        text_prompts: Sequence[str],
+        return_cls_only: bool = True,
+    ):
+        """Fused VL embedding from per-camera ``[0, 1]`` image batches (no PIL, no host round-trip).
+
+        Args:
+            camera_images: list of per-camera tensors, each shaped ``(B, C, H, W)`` in ``[0, 1]``.
+            image_masks: bool tensor ``(B, max_views)`` marking present views.
+
+        Returns:
+            A ``(embeddings, valid_mask)`` tuple. With ``return_cls_only=False``, ``embeddings`` is
+            ``(B, L, H)`` and ``valid_mask`` is a ``(B, L)`` bool tensor marking tokens downstream
+            attention may attend to (padding and absent-view tokens are False). With
+            ``return_cls_only=True``, ``embeddings`` is the pooled ``(B, H)`` last-valid-token state
+            and ``valid_mask`` is None.
+        """
+        max_views = int(image_masks.shape[1])
+        batch_size = int(image_masks.shape[0])
+        mean = torch.tensor(IMAGENET_MEAN, device=self.device, dtype=self.model_dtype)
+        std = torch.tensor(IMAGENET_STD, device=self.device, dtype=self.model_dtype)
+        pixel_values = _batched_pixel_values(
+            camera_images, max_views, self.image_size, mean, std, self.model_dtype, self.device
+        )
+        # InternVL3 preprocessing uses a single tile per image (max_num=1).
+        batch_num_tiles_list = [[1] * max_views for _ in range(batch_size)]
+        return self._forward_vlm(
+            pixel_values, batch_num_tiles_list, image_masks, text_prompts, return_cls_only
+        )
+
+    def _mask_absent_image_tokens(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        image_masks: torch.Tensor,
+        batch_num_tiles_list: list[list[int]],
+    ) -> torch.Tensor:
+        """Zero attention over the image-context tokens of absent (zero-padded) views.
+
+        Fully vectorized: runs without any host<->device synchronization.
+        """
+        # A single tile per image (max_num=1), so every image occupies the same number of
+        # context tokens.
+        tiles_per_image = (
+            batch_num_tiles_list[0][0] if batch_num_tiles_list and batch_num_tiles_list[0] else 1
+        )
+        tokens_per_image = self.num_image_token * tiles_per_image
+
+        image_masks = image_masks.to(device=input_ids.device).bool()
+        img_token_mask = input_ids == self.img_context_token_id  # (B, L)
+        # keep[b, k] tells whether the k-th image-context token (ordered view0, view1, ...) survives.
+        per_token_keep = image_masks.repeat_interleave(tokens_per_image, dim=1)  # (B, V * tokens_per_image)
+        # Rank each context token by its running position among the row's context tokens.
+        ctx_index = img_token_mask.to(torch.long).cumsum(dim=1) - 1
+        ctx_index = ctx_index.clamp(min=0, max=per_token_keep.shape[1] - 1)
+        keep_here = torch.gather(per_token_keep, 1, ctx_index)  # (B, L)
+        drop = img_token_mask & ~keep_here
+        return attention_mask.masked_fill(drop, 0)
+
+    def _forward_vlm(
+        self,
+        pixel_values: torch.Tensor,
+        batch_num_tiles_list: list[list[int]],
+        image_masks: torch.Tensor,
+        text_prompts: Sequence[str],
+        return_cls_only: bool,
+    ):
+        if pixel_values.shape[0] == 0:
+            logger.warning("InternVL3 received an empty image batch after preprocessing.")
+            hidden_size = getattr(self.model.config, "hidden_size", None)
+            if hidden_size is None:
+                hidden_size = getattr(self.model.config.text_config, "hidden_size", None)
+            if hidden_size is None:
+                raise RuntimeError("Unable to infer hidden size for empty InternVL3 batch.")
+            return torch.empty(0, hidden_size, device=self.device, dtype=torch.float32), None
+
+        prompts = self._build_multimodal_prompts(batch_num_tiles_list, text_prompts)
+
+        model_inputs = self.tokenizer(
+            list(prompts),
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=self.max_text_length,
+        ).to(self.device)
+        input_ids = model_inputs["input_ids"]
+        if input_ids.shape[1] >= self.max_text_length:
+            # Truncation cuts from the right, so text is dropped before image placeholders — but a
+            # large max_views * image_seq_length budget can still eat into them. Fail loudly instead
+            # of letting the VLM crash on a placeholder/vision-feature count mismatch.
+            expected_image_tokens = self.num_image_token * sum(batch_num_tiles_list[0])
+            image_token_counts = (input_ids == self.img_context_token_id).sum(dim=1)
+            if not bool((image_token_counts == expected_image_tokens).all()):
+                raise ValueError(
+                    f"Prompt truncation at max_text_length={self.max_text_length} cut into the "
+                    f"image placeholder tokens ({expected_image_tokens} expected per sample). "
+                    "Increase max_text_length or reduce max_views."
+                )
+        attention_mask = self._mask_absent_image_tokens(
+            input_ids, model_inputs["attention_mask"], image_masks, batch_num_tiles_list
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        fused_hidden = outputs.hidden_states[-1].to(torch.float32)
+        valid_mask = attention_mask.to(torch.bool)
+        if return_cls_only:
+            # Right-padded causal decoder: the last valid token is the only one that has attended
+            # to the full image + text prompt.
+            positions = torch.arange(valid_mask.shape[1], device=valid_mask.device)
+            last_valid = (valid_mask.long() * positions).argmax(dim=1)
+            batch_index = torch.arange(fused_hidden.shape[0], device=fused_hidden.device)
+            return fused_hidden[batch_index, last_valid], None
+        return fused_hidden, valid_mask
+
+    @property
+    def device(self) -> torch.device:
+        return next(self.model.parameters()).device
+
+
+def _flash_attn_available() -> bool:
+    try:
+        import flash_attn  # noqa: F401
+    except ModuleNotFoundError:
+        return False
+    return True

src/lerobot/policies/evo1/modeling_evo1.py

@@ -0,0 +1,532 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import builtins
+from collections import deque
+from contextlib import nullcontext
+from pathlib import Path
+from typing import TypedDict, Unpack
+
+import torch
+from torch import Tensor
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.policies.pretrained import PreTrainedPolicy, T
+from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
+
+from ..rtc.modeling_rtc import RTCProcessor
+from .configuration_evo1 import Evo1Config
+from .evo1_model import Evo1Model
+
+
+class ActionSelectKwargs(TypedDict, total=False):
+    inference_delay: int | None
+    prev_chunk_left_over: Tensor | None
+    execution_horizon: int | None
+
+
+class Evo1Policy(PreTrainedPolicy):
+    config_class = Evo1Config
+    name = "evo1"
+
+    def __init__(self, config: Evo1Config, *, vlm_hub_kwargs: dict | None = None, **kwargs):
+        super().__init__(config)
+        config.validate_features()
+
+        if len(config.image_features) > config.max_views:
+            raise ValueError(
+                f"EVO1 supports at most {config.max_views} camera streams, got {len(config.image_features)}"
+            )
+
+        self.config = config
+        self.model = Evo1Model(config, vlm_hub_kwargs=vlm_hub_kwargs)
+        self.model.set_finetune_flags()
+        self._keep_frozen_embedder_eval()
+        self.init_rtc_processor()
+        self.reset()
+
+    def init_rtc_processor(self):
+        """Create the RTC processor when config.rtc_config is set.
+
+        The RTC rollout backend assigns config.rtc_config after loading the policy and re-invokes
+        this method.
+        """
+        self.rtc_processor = None
+        if self.config.rtc_config is not None:
+            self.rtc_processor = RTCProcessor(self.config.rtc_config)
+        model = getattr(self, "model", None)
+        if model is not None:
+            model.rtc_processor = self.rtc_processor
+
+    def _rtc_enabled(self) -> bool:
+        return self.config.rtc_config is not None and self.config.rtc_config.enabled
+
+    @classmethod
+    def from_pretrained(
+        cls: builtins.type[T],
+        pretrained_name_or_path: str | Path,
+        *,
+        config: PreTrainedConfig | None = None,
+        force_download: bool = False,
+        resume_download: bool | None = None,
+        proxies: dict | None = None,
+        token: str | bool | None = None,
+        cache_dir: str | Path | None = None,
+        local_files_only: bool = False,
+        revision: str | None = None,
+        strict: bool | None = None,
+        **kwargs,
+    ) -> T:
+        if strict is None:
+            strict = True
+        vlm_hub_kwargs = kwargs.pop("vlm_hub_kwargs", None)
+        if config is None:
+            config = PreTrainedConfig.from_pretrained(
+                pretrained_name_or_path=pretrained_name_or_path,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                token=token,
+                cache_dir=cache_dir,
+                local_files_only=local_files_only,
+                revision=revision,
+                **kwargs,
+            )
+        if vlm_hub_kwargs is None:
+            # Forward the hub download options to the base-VLM download as well; `revision` is not
+            # forwarded because it identifies the policy repo, not the VLM repo.
+            vlm_hub_kwargs = {
+                key: value
+                for key, value in (
+                    ("token", token),
+                    ("cache_dir", cache_dir),
+                    ("local_files_only", local_files_only),
+                    ("proxies", proxies),
+                )
+                if value not in (None, False)
+            }
+        kwargs["vlm_hub_kwargs"] = vlm_hub_kwargs
+        return super().from_pretrained(
+            pretrained_name_or_path=pretrained_name_or_path,
+            config=config,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            token=token,
+            cache_dir=cache_dir,
+            local_files_only=local_files_only,
+            revision=revision,
+            strict=strict,
+            **kwargs,
+        )
+
+    @property
+    def _camera_keys(self) -> list[str]:
+        return list(self.config.image_features)
+
+    @property
+    def _env_action_dim(self) -> int:
+        action_feature = self.config.action_feature
+        if action_feature is None:
+            return self.config.max_action_dim
+        return int(action_feature.shape[0])
+
+    @property
+    def _compute_dtype(self) -> torch.dtype:
+        return next(self.model.action_head.parameters()).dtype
+
+    @property
+    def _device(self) -> torch.device:
+        # The device the policy actually lives on. Derived from the parameters rather than
+        # config.device so the policy keeps working after accelerate (or a plain .to()) moves it.
+        return next(self.model.action_head.parameters()).device
+
+    @property
+    def _amp_enabled(self) -> bool:
+        return bool(self.config.use_amp) and self._device.type == "cuda"
+
+    def _maybe_autocast(self):
+        # EVO1 manages its own mixed precision: an explicit bf16 autocast that also overrides any
+        # outer autocast context (e.g. lerobot-eval's fp16 default), keeping train and eval
+        # numerics identical.
+        if self._amp_enabled:
+            return torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+        return nullcontext()
+
+    def get_optim_params(self) -> list[dict]:
+        decay, no_decay = [], []
+        for name, param in self.named_parameters():
+            if not param.requires_grad:
+                continue
+            is_bias = name.endswith("bias") or ".bias" in name
+            is_norm = param.dim() == 1 or "norm" in name.lower()
+            if is_bias or is_norm:
+                no_decay.append(param)
+            else:
+                decay.append(param)
+        return [
+            {"params": decay, "weight_decay": self.config.optimizer_weight_decay},
+            {"params": no_decay, "weight_decay": 0.0},
+        ]
+
+    def reset(self):
+        self._action_queue = deque([], maxlen=self.config.n_action_steps)
+
+    def _normalize_task_batch(self, batch: dict[str, Tensor | list[str] | str]) -> list[str]:
+        prompts = batch.get(self.config.task_field)
+        if prompts is None and self.config.task_field != "task":
+            prompts = batch.get("task")
+        if prompts is None:
+            raise ValueError(f"EVO1 expects a '{self.config.task_field}' text field in the batch.")
+        if isinstance(prompts, str):
+            return [prompts]
+        if isinstance(prompts, (list, tuple)):
+            return [str(prompt) for prompt in prompts]
+        raise TypeError(f"Unsupported prompt batch type: {type(prompts)}")
+
+    def _prepare_state(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
+        if OBS_STATE not in batch:
+            raise ValueError(f"EVO1 requires '{OBS_STATE}' in the batch.")
+        state = batch[OBS_STATE]
+        if state.dim() == 1:
+            state = state.unsqueeze(0)
+        elif state.dim() == 3:
+            state = state[:, -1]
+        elif state.dim() != 2:
+            raise ValueError(f"Unsupported state tensor shape for EVO1: {tuple(state.shape)}")
+        batch_size, state_dim = state.shape
+        if state_dim > self.config.max_state_dim:
+            raise ValueError(
+                f"State dim {state_dim} exceeds configured max_state_dim {self.config.max_state_dim}"
+            )
+        explicit_mask = batch.get("state_mask")
+        if explicit_mask is not None:
+            if explicit_mask.dim() == 1:
+                explicit_mask = explicit_mask.unsqueeze(0)
+            elif explicit_mask.dim() == 3:
+                explicit_mask = explicit_mask[:, -1]
+            elif explicit_mask.dim() != 2:
+                raise ValueError(
+                    f"Unsupported state_mask tensor shape for EVO1: {tuple(explicit_mask.shape)}"
+                )
+            if explicit_mask.shape != (batch_size, state_dim):
+                raise ValueError(
+                    f"state_mask shape {tuple(explicit_mask.shape)} does not match state shape {(batch_size, state_dim)}"
+                )
+        device = self._device
+        padded = torch.zeros(
+            batch_size,
+            self.config.max_state_dim,
+            dtype=state.dtype,
+            device=device,
+        )
+        padded[:, :state_dim] = state.to(device=device)
+        mask = torch.zeros(
+            batch_size,
+            self.config.max_state_dim,
+            dtype=torch.bool,
+            device=device,
+        )
+        if explicit_mask is None:
+            mask[:, :state_dim] = True
+        else:
+            mask[:, :state_dim] = explicit_mask.to(device=device, dtype=torch.bool)
+        # Zero out masked state dims so an explicit state_mask actually affects the model input
+        # (the state encoder has no mask argument of its own).
+        padded = padded * mask.to(dtype=padded.dtype)
+        return padded.to(dtype=self._compute_dtype), mask
+
+    def _prepare_actions(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
+        if ACTION not in batch:
+            raise ValueError(f"EVO1 requires '{ACTION}' in the batch for training.")
+        action = batch[ACTION]
+        if action.dim() == 2:
+            action = action.unsqueeze(1)
+        batch_size, horizon, action_dim = action.shape
+        if horizon != self.config.chunk_size:
+            raise ValueError(
+                f"EVO1 expects chunk_size={self.config.chunk_size}, got action horizon {horizon}"
+            )
+        if action_dim > self.config.max_action_dim:
+            raise ValueError(
+                f"Action dim {action_dim} exceeds configured max_action_dim {self.config.max_action_dim}"
+            )
+        explicit_mask = batch.get("action_mask")
+        if explicit_mask is not None:
+            if explicit_mask.dim() == 2:
+                if horizon == 1:
+                    explicit_mask = explicit_mask.unsqueeze(1)
+                else:
+                    raise ValueError(
+                        f"2D action_mask is only supported when chunk_size=1, got action horizon {horizon}"
+                    )
+            elif explicit_mask.dim() != 3:
+                raise ValueError(
+                    f"Unsupported action_mask tensor shape for EVO1: {tuple(explicit_mask.shape)}"
+                )
+            if explicit_mask.shape != (batch_size, horizon, action_dim):
+                raise ValueError(
+                    "action_mask shape "
+                    f"{tuple(explicit_mask.shape)} does not match action shape {(batch_size, horizon, action_dim)}"
+                )
+        device = self._device
+        padded = torch.zeros(
+            batch_size,
+            horizon,
+            self.config.max_action_dim,
+            dtype=action.dtype,
+            device=device,
+        )
+        padded[:, :, :action_dim] = action.to(device=device)
+        mask = torch.zeros(
+            batch_size,
+            horizon,
+            self.config.max_action_dim,
+            dtype=torch.bool,
+            device=device,
+        )
+        if explicit_mask is None:
+            mask[:, :, :action_dim] = True
+        else:
+            mask[:, :, :action_dim] = explicit_mask.to(device=device, dtype=torch.bool)
+
+        # Timesteps beyond the episode end hold fabricated (repeated) actions; exclude them from
+        # the loss like the other chunked policies do.
+        action_is_pad = batch.get("action_is_pad")
+        if action_is_pad is not None:
+            if action_is_pad.shape != (batch_size, horizon):
+                raise ValueError(
+                    f"action_is_pad shape {tuple(action_is_pad.shape)} does not match "
+                    f"(batch_size, chunk_size)={(batch_size, horizon)}"
+                )
+            in_episode = ~action_is_pad.to(device=device, dtype=torch.bool)
+            mask = mask & in_episode.unsqueeze(-1)
+        return padded.to(dtype=self._compute_dtype), mask
+
+    def _prepare_inference_action_mask(self, batch_size: int) -> Tensor:
+        mask = torch.zeros(
+            batch_size,
+            self.config.max_action_dim,
+            dtype=torch.bool,
+            device=self._device,
+        )
+        mask[:, : self._env_action_dim] = True
+        return mask
+
+    def _get_embodiment_ids(self, batch: dict[str, Tensor], batch_size: int) -> Tensor:
+        embodiment_ids = batch.get("embodiment_id")
+        if embodiment_ids is None and self.config.embodiment_id_field:
+            embodiment_ids = batch.get(self.config.embodiment_id_field)
+        if embodiment_ids is None:
+            return torch.full(
+                (batch_size,),
+                self.config.default_embodiment_id,
+                dtype=torch.long,
+                device=self._device,
+            )
+        if embodiment_ids.dim() == 0:
+            embodiment_ids = embodiment_ids.unsqueeze(0)
+        elif embodiment_ids.dim() > 1:
+            embodiment_ids = embodiment_ids[:, -1]
+        return embodiment_ids.to(device=self._device, dtype=torch.long)
+
+    @property
+    def _tracks_vlm_gradients(self) -> bool:
+        return bool(
+            self.config.finetune_vlm
+            or self.config.finetune_language_model
+            or self.config.finetune_vision_model
+        )
+
+    def _keep_frozen_embedder_eval(self) -> None:
+        if self._tracks_vlm_gradients:
+            return
+        embedder = getattr(self.model, "embedder", None)
+        if embedder is not None:
+            embedder.eval()
+
+    def train(self, mode: bool = True):
+        super().train(mode)
+        self._keep_frozen_embedder_eval()
+        return self
+
+    def _collect_image_batches(self, batch: dict[str, Tensor]) -> tuple[list[Tensor], Tensor]:
+        camera_keys = self._camera_keys or sorted(key for key in batch if key.startswith(f"{OBS_IMAGES}."))
+        if not camera_keys:
+            raise ValueError("EVO1 requires at least one visual observation feature.")
+        camera_keys = list(camera_keys)[: self.config.max_views]
+
+        # Configured cameras may be absent from the batch up to the empty_cameras budget (e.g. the
+        # placeholder features added by validate_features); they become masked-out views that the
+        # embedder zero-pads. Any other absent camera is an error.
+        present_keys = [key for key in camera_keys if key in batch]
+        missing_keys = [key for key in camera_keys if key not in batch]
+        if len(missing_keys) > self.config.empty_cameras:
+            raise ValueError(
+                f"Missing camera features {missing_keys} in batch; at most "
+                f"empty_cameras={self.config.empty_cameras} may be absent."
+            )
+        if not present_keys:
+            raise ValueError("EVO1 requires at least one visual observation in the batch.")
+
+        # Keep each present camera as a batched (B, C, H, W) tensor on its current (GPU) device.
+        # Resizing/normalization and zero-padding of absent views happen batched inside the
+        # embedder, so images never leave the device here.
+        camera_images: list[Tensor] = []
+        for camera_key in present_keys:
+            image = batch[camera_key]
+            if image.dim() == 3:
+                # Promote an unbatched (C, H, W) frame so batch_size is read from a real batch dim.
+                image = image.unsqueeze(0)
+            elif image.dim() == 5:
+                image = image[:, -1]
+            elif image.dim() != 4:
+                raise ValueError(
+                    f"Unsupported image tensor shape for EVO1: key={camera_key} shape={tuple(image.shape)}"
+                )
+            camera_images.append(image)
+
+        batch_size = camera_images[0].shape[0]
+        n_present = len(camera_images)
+        image_masks = torch.zeros(
+            batch_size, self.config.max_views, dtype=torch.bool, device=camera_images[0].device
+        )
+        image_masks[:, :n_present] = True
+
+        return camera_images, image_masks
+
+    def _compute_fused_tokens(
+        self,
+        prompts: list[str],
+        image_batches: list[Tensor],
+        image_masks: Tensor,
+    ) -> tuple[Tensor, Tensor | None]:
+        track_vlm_gradients = self._tracks_vlm_gradients
+        grad_context = nullcontext() if track_vlm_gradients else torch.no_grad()
+        with grad_context:
+            fused_tokens, context_mask = self.model.get_vl_embeddings(
+                images=image_batches,
+                image_mask=image_masks,
+                prompt=prompts,
+                return_cls_only=self.config.return_cls_only,
+            )
+
+        if not track_vlm_gradients:
+            fused_tokens = fused_tokens.detach()
+        fused_tokens = fused_tokens.to(device=self._device, dtype=self._compute_dtype)
+        if context_mask is not None:
+            context_mask = context_mask.to(device=self._device)
+        return fused_tokens, context_mask
+
+    def _compute_masked_loss(
+        self,
+        pred_velocity: Tensor,
+        target_velocity: Tensor,
+        action_mask: Tensor,
+        reduction: str,
+    ) -> Tensor:
+        flat_mask = action_mask.view(action_mask.shape[0], -1).to(dtype=pred_velocity.dtype)
+        sq_error = ((pred_velocity - target_velocity) * flat_mask).pow(2)
+        active = flat_mask.sum(dim=1).clamp_min(1.0)
+        per_sample_loss = sq_error.sum(dim=1) / active
+        if reduction == "none":
+            return per_sample_loss
+        if reduction != "mean":
+            raise ValueError(f"Unsupported reduction '{reduction}'")
+        return sq_error.sum() / active.sum()
+
+    def forward(self, batch: dict[str, Tensor], reduction: str = "mean") -> tuple[Tensor, dict]:
+        prompts = self._normalize_task_batch(batch)
+        image_batches, image_masks = self._collect_image_batches(batch)
+        states, _state_mask = self._prepare_state(batch)
+        actions_gt, action_mask = self._prepare_actions(batch)
+        embodiment_ids = self._get_embodiment_ids(batch, states.shape[0])
+
+        with self._maybe_autocast():
+            fused_tokens, context_mask = self._compute_fused_tokens(prompts, image_batches, image_masks)
+            pred_velocity, noise = self.model(
+                fused_tokens,
+                state=states,
+                actions_gt=actions_gt,
+                action_mask=action_mask.to(device=self._device, dtype=self._compute_dtype),
+                embodiment_ids=embodiment_ids,
+                context_mask=context_mask,
+            )
+
+        # Compute the flow-matching regression loss in fp32, outside the autocast block.
+        pred_velocity = pred_velocity.float()
+        noise = noise.float()
+        flat_action_mask = action_mask.view(action_mask.shape[0], -1).to(dtype=torch.float32)
+        # Flow-matching velocity target. Padded (masked-out) action dims are already zero on both sides
+        # here (`actions_gt` is zero-padded in `_prepare_actions`, and `noise` is masked inside the head),
+        # and the whole difference is multiplied by `flat_action_mask`, so padded dims contribute nothing.
+        target_velocity = (actions_gt.float() - noise).view(actions_gt.shape[0], -1) * flat_action_mask
+        loss = self._compute_masked_loss(pred_velocity, target_velocity, action_mask, reduction)
+        loss_mean = loss.mean().item() if loss.ndim > 0 else loss.item()
+        return loss, {
+            "loss": loss_mean,
+            "active_action_dims": float(action_mask.sum(dim=(1, 2)).float().mean().item()),
+        }
+
+    @torch.no_grad()
+    def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: Unpack[ActionSelectKwargs]) -> Tensor:
+        inference_delay = kwargs.get("inference_delay")
+        prev_chunk_left_over = kwargs.get("prev_chunk_left_over")
+        execution_horizon = kwargs.get("execution_horizon")
+        if (inference_delay is not None or prev_chunk_left_over is not None) and not self._rtc_enabled():
+            raise RuntimeError(
+                "Received RTC arguments but RTC is not configured for this EVO1 policy: set "
+                "config.rtc_config and call init_rtc_processor() (lerobot-rollout does this for "
+                "--inference.type=rtc)."
+            )
+        self.eval()
+
+        prompts = self._normalize_task_batch(batch)
+        image_batches, image_masks = self._collect_image_batches(batch)
+        states, _state_mask = self._prepare_state(batch)
+        embodiment_ids = self._get_embodiment_ids(batch, states.shape[0])
+        action_mask = self._prepare_inference_action_mask(states.shape[0])
+        if prev_chunk_left_over is not None:
+            prev_chunk_left_over = prev_chunk_left_over.to(device=self._device)
+
+        with self._maybe_autocast():
+            fused_tokens, context_mask = self._compute_fused_tokens(prompts, image_batches, image_masks)
+            actions = self.model(
+                fused_tokens,
+                state=states,
+                action_mask=action_mask,
+                embodiment_ids=embodiment_ids,
+                context_mask=context_mask,
+                inference_delay=inference_delay,
+                prev_chunk_left_over=prev_chunk_left_over,
+                execution_horizon=execution_horizon,
+            )
+        actions = actions.view(states.shape[0], self.config.chunk_size, self.config.max_action_dim)
+        return actions.to(dtype=torch.float32)
+
+    @torch.no_grad()
+    def select_action(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
+        assert not self._rtc_enabled(), (
+            "RTC is not supported for select_action, use it with predict_action_chunk"
+        )
+        self.eval()
+        if len(self._action_queue) == 0:
+            action_chunk = self.predict_action_chunk(batch)[:, : self.config.n_action_steps]
+            self._action_queue.extend(action_chunk.transpose(0, 1))
+        # Returns one step of shape (B, max_action_dim): actions are emitted at the padded max_action_dim
+        # width and cropped to the real action dim downstream by the postprocessor (Evo1ActionProcessorStep).
+        # Callers that bypass the postprocessor receive the padded width.
+        return self._action_queue.popleft()

src/lerobot/policies/evo1/processor_evo1.py

@@ -0,0 +1,400 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    NormalizerProcessorStep,
+    ObservationProcessorStep,
+    PolicyAction,
+    PolicyActionProcessorStep,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    RenameObservationsProcessorStep,
+    UnnormalizerProcessorStep,
+)
+from lerobot.processor.converters import (
+    batch_to_transition,
+    create_transition,
+    policy_action_to_transition,
+    transition_to_policy_action,
+)
+from lerobot.types import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    ACTION,
+    DONE,
+    INFO,
+    OBS_PREFIX,
+    OBS_STATE,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+    REWARD,
+    TRUNCATED,
+)
+
+from .configuration_evo1 import Evo1Config
+
+
+def evo1_batch_to_transition(batch: dict[str, Any]):
+    transition = batch_to_transition(batch)
+    complementary_data = dict(transition.get("complementary_data") or {})
+    reserved = {ACTION, REWARD, DONE, TRUNCATED, INFO}
+    for key, value in batch.items():
+        if key in reserved or key.startswith(OBS_PREFIX):
+            continue
+        complementary_data.setdefault(key, value)
+    return create_transition(
+        observation=transition.get("observation"),
+        action=transition.get("action"),
+        reward=transition.get("reward", 0.0),
+        done=transition.get("done", False),
+        truncated=transition.get("truncated", False),
+        info=transition.get("info", {}),
+        complementary_data=complementary_data,
+    )
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="evo1_pad_state_processor")
+class Evo1PadStateProcessorStep(ObservationProcessorStep):
+    """Pad policy observations to EVO1's fixed state width before normalization."""
+
+    max_state_dim: int = 24
+
+    def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
+        if OBS_STATE not in observation:
+            return observation
+
+        state = observation[OBS_STATE]
+        state_dim = state.shape[-1]
+        if state_dim > self.max_state_dim:
+            raise ValueError(
+                f"EVO1 state has {state_dim} dims, which exceeds max_state_dim={self.max_state_dim}."
+            )
+        if state_dim < self.max_state_dim:
+            observation = observation.copy()
+            observation[OBS_STATE] = torch.nn.functional.pad(state, (0, self.max_state_dim - state_dim))
+        return observation
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        new_features = {ft: feats.copy() for ft, feats in features.items()}
+        obs_feats = new_features.setdefault(PipelineFeatureType.OBSERVATION, {})
+        if OBS_STATE in obs_feats:
+            obs_feats[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(self.max_state_dim,))
+        return new_features
+
+    def get_config(self) -> dict[str, Any]:
+        return {"max_state_dim": self.max_state_dim}
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="evo1_pad_action_processor")
+class Evo1PadActionProcessorStep(ProcessorStep):
+    """Pad training actions and preserve the active action dimensions with action_mask."""
+
+    max_action_dim: int = 24
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition.get(TransitionKey.ACTION)
+        if action is None:
+            return transition
+        if not isinstance(action, PolicyAction):
+            raise ValueError(f"EVO1 action should be a PolicyAction tensor, but got {type(action)}.")
+
+        action_dim = action.shape[-1]
+        if action_dim > self.max_action_dim:
+            raise ValueError(
+                f"EVO1 action has {action_dim} dims, which exceeds max_action_dim={self.max_action_dim}."
+            )
+
+        new_transition = transition.copy()
+        new_action = action
+        if action_dim < self.max_action_dim:
+            new_action = torch.nn.functional.pad(action, (0, self.max_action_dim - action_dim))
+
+        complementary_data = dict(new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {})
+        action_mask = complementary_data.get("action_mask")
+        if action_mask is None:
+            action_mask = torch.ones(action.shape, dtype=torch.bool, device=action.device)
+        else:
+            action_mask = torch.as_tensor(action_mask, dtype=torch.bool, device=action.device)
+            if action_mask.shape != action.shape:
+                raise ValueError(
+                    f"action_mask shape {tuple(action_mask.shape)} does not match action shape {tuple(action.shape)}."
+                )
+        if action_dim < self.max_action_dim:
+            action_mask = torch.nn.functional.pad(action_mask, (0, self.max_action_dim - action_dim))
+
+        complementary_data["action_mask"] = action_mask
+        new_transition[TransitionKey.ACTION] = new_action
+        new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
+        return new_transition
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        new_features = {ft: feats.copy() for ft, feats in features.items()}
+        action_feats = new_features.setdefault(PipelineFeatureType.ACTION, {})
+        action_feats[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(self.max_action_dim,))
+        return new_features
+
+    def get_config(self) -> dict[str, Any]:
+        return {"max_action_dim": self.max_action_dim}
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="evo1_action_processor")
+class Evo1ActionProcessorStep(PolicyActionProcessorStep):
+    """Crop padded EVO1 actions and optionally binarize the LIBERO gripper channel."""
+
+    action_dim: int
+    binarize_gripper: bool = False
+    gripper_index: int = 6
+    gripper_threshold: float = 0.5
+    gripper_below_threshold_value: float = 1.0
+    gripper_above_threshold_value: float = -1.0
+
+    def action(self, action: PolicyAction) -> PolicyAction:
+        if action.shape[-1] < self.action_dim:
+            raise ValueError(
+                f"EVO1 action has {action.shape[-1]} dims, which is smaller than action_dim={self.action_dim}."
+            )
+
+        action = action[..., : self.action_dim]
+        if not self.binarize_gripper:
+            return action
+
+        if not 0 <= self.gripper_index < self.action_dim:
+            raise ValueError(
+                f"gripper_index={self.gripper_index} must be within action_dim={self.action_dim}."
+            )
+
+        action = action.clone()
+        below = torch.as_tensor(
+            self.gripper_below_threshold_value,
+            dtype=action.dtype,
+            device=action.device,
+        )
+        above = torch.as_tensor(
+            self.gripper_above_threshold_value,
+            dtype=action.dtype,
+            device=action.device,
+        )
+        action[..., self.gripper_index] = torch.where(
+            action[..., self.gripper_index] > self.gripper_threshold,
+            above,
+            below,
+        )
+        return action
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        new_features = {ft: feats.copy() for ft, feats in features.items()}
+        action_feats = new_features.setdefault(PipelineFeatureType.ACTION, {})
+        action_feats[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(self.action_dim,))
+        return new_features
+
+    def get_config(self) -> dict[str, Any]:
+        return {
+            "action_dim": self.action_dim,
+            "binarize_gripper": self.binarize_gripper,
+            "gripper_index": self.gripper_index,
+            "gripper_threshold": self.gripper_threshold,
+            "gripper_below_threshold_value": self.gripper_below_threshold_value,
+            "gripper_above_threshold_value": self.gripper_above_threshold_value,
+        }
+
+
+def _evo1_action_dim(config: Evo1Config) -> int:
+    if config.postprocess_action_dim is not None:
+        return config.postprocess_action_dim
+    action_feature = config.action_feature
+    if action_feature is None:
+        return config.max_action_dim
+    return int(action_feature.shape[0])
+
+
+def _evo1_normalization_features(config: Evo1Config) -> dict[str, PolicyFeature]:
+    features = {**config.input_features, **config.output_features}
+    features[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(config.max_state_dim,))
+    features[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(config.max_action_dim,))
+    return features
+
+
+def _evo1_action_features(config: Evo1Config) -> dict[str, PolicyFeature]:
+    return {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(config.max_action_dim,))}
+
+
+_STAT_PAD_VALUES = {
+    "mean": 0.0,
+    "std": 1.0,
+    "min": -1.0,
+    "max": 1.0,
+    "q01": -1.0,
+    "q99": 1.0,
+    "q10": -1.0,
+    "q90": 1.0,
+}
+
+
+def _pad_stat_value(value: Any, target_dim: int, stat_name: str) -> torch.Tensor:
+    tensor = torch.as_tensor(value)
+    if not tensor.is_floating_point():
+        tensor = tensor.to(dtype=torch.float32)
+    if tensor.ndim == 0 or tensor.shape[-1] >= target_dim:
+        return tensor
+
+    pad_shape = (*tensor.shape[:-1], target_dim - tensor.shape[-1])
+    pad_value = _STAT_PAD_VALUES.get(stat_name, 0.0)
+    padding = torch.full(pad_shape, pad_value, dtype=tensor.dtype, device=tensor.device)
+    return torch.cat([tensor, padding], dim=-1)
+
+
+def _pad_feature_stats(
+    stats: dict[str, dict[str, Any]],
+    feature_key: str,
+    target_dim: int,
+) -> None:
+    if feature_key not in stats:
+        return
+    stats[feature_key] = {
+        stat_name: _pad_stat_value(stat_value, target_dim, stat_name)
+        for stat_name, stat_value in stats[feature_key].items()
+    }
+
+
+def _pad_evo1_stats(
+    config: Evo1Config,
+    stats: dict[str, dict[str, Any]] | None,
+) -> dict[str, dict[str, Any]] | None:
+    if stats is None:
+        return None
+
+    padded_stats = deepcopy(stats)
+    # Added dimensions represent zero-padding inside EVO1. These neutral stats keep
+    # padded observations at normalized zero and only provide shape compatibility.
+    _pad_feature_stats(padded_stats, OBS_STATE, config.max_state_dim)
+    _pad_feature_stats(padded_stats, ACTION, config.max_action_dim)
+    return padded_stats
+
+
+def reconcile_evo1_processors(
+    config: Evo1Config,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
+    """Reconcile checkpoint-loaded pipelines with the current EVO1 config.
+
+    Two things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
+    (converters are plain functions and are never serialized), and eval-time CLI overrides of the
+    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`). This
+    restores the converter and rebuilds the action step from the current config so those overrides
+    take effect.
+    """
+    # Pipelines reloaded from a checkpoint come back with the default batch converter, which drops
+    # non-observation extras (embodiment_id, state_mask, custom task fields) needed by EVO1.
+    preprocessor.to_transition = evo1_batch_to_transition
+
+    action_step = Evo1ActionProcessorStep(
+        action_dim=_evo1_action_dim(config),
+        binarize_gripper=config.binarize_gripper,
+        gripper_index=config.gripper_index,
+        gripper_threshold=config.gripper_threshold,
+        gripper_below_threshold_value=config.gripper_below_threshold_value,
+        gripper_above_threshold_value=config.gripper_above_threshold_value,
+    )
+    steps = list(postprocessor.steps)
+    action_step_idx = next(
+        (idx for idx, step in enumerate(steps) if isinstance(step, Evo1ActionProcessorStep)), None
+    )
+    if action_step_idx is None:
+        insert_idx = next(
+            (idx + 1 for idx, step in enumerate(steps) if isinstance(step, UnnormalizerProcessorStep)),
+            0,
+        )
+        steps.insert(insert_idx, action_step)
+    else:
+        steps[action_step_idx] = action_step
+    postprocessor.steps = steps
+
+    return preprocessor, postprocessor
+
+
+def make_evo1_pre_post_processors(
+    config: Evo1Config,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    normalization_features = _evo1_normalization_features(config)
+    action_features = _evo1_action_features(config)
+    normalization_stats = _pad_evo1_stats(config, dataset_stats)
+
+    input_steps = [
+        RenameObservationsProcessorStep(rename_map={}),
+        AddBatchDimensionProcessorStep(),
+        Evo1PadStateProcessorStep(max_state_dim=config.max_state_dim),
+        Evo1PadActionProcessorStep(max_action_dim=config.max_action_dim),
+        NormalizerProcessorStep(
+            features=normalization_features,
+            norm_map=config.normalization_mapping,
+            stats=normalization_stats,
+        ),
+        DeviceProcessorStep(device=config.device),
+    ]
+    output_steps = [
+        UnnormalizerProcessorStep(
+            features=action_features,
+            norm_map=config.normalization_mapping,
+            stats=normalization_stats,
+        ),
+        Evo1ActionProcessorStep(
+            action_dim=_evo1_action_dim(config),
+            binarize_gripper=config.binarize_gripper,
+            gripper_index=config.gripper_index,
+            gripper_threshold=config.gripper_threshold,
+            gripper_below_threshold_value=config.gripper_below_threshold_value,
+            gripper_above_threshold_value=config.gripper_above_threshold_value,
+        ),
+        # float32 so downstream numpy conversion works even when the policy computes in bf16.
+        DeviceProcessorStep(device="cpu", float_dtype="float32"),
+    ]
+
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=input_steps,
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+            to_transition=evo1_batch_to_transition,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=output_steps,
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )

src/lerobot/policies/factory.py

@@ -47,6 +47,7 @@ from lerobot.utils.feature_utils import dataset_to_policy_features
 from .act.configuration_act import ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config
+from .evo1.configuration_evo1 import Evo1Config
 from .fastwam.configuration_fastwam import FastWAMConfig
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
 from .groot.configuration_groot import GrootConfig
@@ -93,7 +94,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
             "multi_task_dit", "vqbet", "pi0", "pi05", "gaussian_actor", "smolvla", "wall_x",
-            "molmoact2".
+            "molmoact2", "eo1", "evo1".
     Returns:
         The policy class corresponding to the given name.
 
@@ -172,6 +173,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from .fastwam.modeling_fastwam import FastWAMPolicy
 
         return FastWAMPolicy
+    elif name == "evo1":
+        from .evo1.modeling_evo1 import Evo1Policy
+
+        return Evo1Policy
     else:
         try:
             return _get_policy_cls_from_policy_name(name=name)
@@ -189,7 +194,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
     Args:
         policy_type: The type of the policy. Supported types include "tdmpc",
                      "multi_task_dit", "diffusion", "act", "vqbet", "pi0", "pi05", "gaussian_actor",
-                     "smolvla", "wall_x", "molmoact2".
+                     "smolvla", "wall_x", "molmoact2", "eo1", "evo1".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -232,6 +237,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return LingBotVAConfig(**kwargs)
     elif policy_type == "fastwam":
         return FastWAMConfig(**kwargs)
+    elif policy_type == "evo1":
+        return Evo1Config(**kwargs)
     else:
         try:
             config_cls = PreTrainedConfig.get_choice_class(policy_type)
@@ -295,26 +302,23 @@ def make_pre_post_processors(
             policy configuration type.
     """
     if pretrained_path:
-        # TODO(Steven): Temporary patch, implement correctly the processors for Gr00t
         if isinstance(policy_cfg, GrootConfig):
-            # GROOT handles normalization in groot_pack_inputs_v3 step
-            # Need to override both stats AND normalize_min_max since saved config might be empty
-            preprocessor_overrides = {}
-            postprocessor_overrides = {}
-            preprocessor_overrides["groot_pack_inputs_v3"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-            }
+            from .groot.processor_groot import make_groot_pre_post_processors_from_pretrained
 
-            # Also ensure postprocessing slices to env action dim and unnormalizes with dataset stats
-            env_action_dim = policy_cfg.output_features[ACTION].shape[0]
-            postprocessor_overrides["groot_action_unpack_unnormalize_v1"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-                "env_action_dim": env_action_dim,
-            }
-            kwargs["preprocessor_overrides"] = preprocessor_overrides
-            kwargs["postprocessor_overrides"] = postprocessor_overrides
+            return make_groot_pre_post_processors_from_pretrained(
+                config=policy_cfg,
+                pretrained_path=pretrained_path,
+                dataset_stats=kwargs.get("dataset_stats"),
+                dataset_meta=kwargs.get("dataset_meta"),
+                preprocessor_overrides=kwargs.get("preprocessor_overrides"),
+                postprocessor_overrides=kwargs.get("postprocessor_overrides"),
+                preprocessor_config_filename=kwargs.get(
+                    "preprocessor_config_filename", f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json"
+                ),
+                postprocessor_config_filename=kwargs.get(
+                    "postprocessor_config_filename", f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json"
+                ),
+            )
 
         preprocessor = PolicyProcessorPipeline.from_pretrained(
             pretrained_model_name_or_path=pretrained_path,
@@ -337,6 +341,14 @@ def make_pre_post_processors(
             revision=pretrained_revision,
         )
         _reconnect_relative_absolute_steps(preprocessor, postprocessor)
+        if isinstance(policy_cfg, Evo1Config):
+            from .evo1.processor_evo1 import reconcile_evo1_processors
+
+            preprocessor, postprocessor = reconcile_evo1_processors(
+                policy_cfg,
+                preprocessor,
+                postprocessor,
+            )
         return preprocessor, postprocessor
 
     # Create a new processor based on policy type
@@ -420,6 +432,7 @@ def make_pre_post_processors(
         processors = make_groot_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
+            dataset_meta=kwargs.get("dataset_meta"),
         )
 
     elif isinstance(policy_cfg, XVLAConfig):
@@ -447,6 +460,13 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
+    elif isinstance(policy_cfg, Evo1Config):
+        from .evo1.processor_evo1 import make_evo1_pre_post_processors
+
+        processors = make_evo1_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
 
     elif isinstance(policy_cfg, MolmoAct2Config):
         from .molmoact2.processor_molmoact2 import make_molmoact2_pre_post_processors
@@ -570,6 +590,7 @@ def make_policy(
         set_dataset_feature_metadata = getattr(cfg, "set_dataset_feature_metadata", None)
         if callable(set_dataset_feature_metadata):
             set_dataset_feature_metadata(ds_meta.features)
+        cfg._runtime_dataset_meta = ds_meta
 
     kwargs["config"] = cfg
 

src/lerobot/policies/groot/action_head/action_encoder.py

@@ -1,54 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-import torch.nn as nn
-
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-class SinusoidalPositionalEncoding(nn.Module):
-    """
-    Produces a sinusoidal encoding of shape (B, T, w)
-    given timesteps of shape (B, T).
-    """
-
-    def __init__(self, embedding_dim):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-
-    def forward(self, timesteps):
-        # timesteps: shape (B, T)
-        # We'll compute sin/cos frequencies across dim T
-        timesteps = timesteps.float()  # ensure float
-
-        b, t = timesteps.shape
-        device = timesteps.device
-
-        half_dim = self.embedding_dim // 2
-        # typical log space frequencies for sinusoidal encoding
-        exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
-            torch.log(torch.tensor(10000.0)) / half_dim
-        )
-        # Expand timesteps to (B, T, 1) then multiply
-        freqs = timesteps.unsqueeze(-1) * exponent.exp()  # (B, T, half_dim)
-
-        sin = torch.sin(freqs)
-        cos = torch.cos(freqs)
-        enc = torch.cat([sin, cos], dim=-1)  # (B, T, w)
-
-        return enc

src/lerobot/policies/groot/action_head/cross_attention_dit.py

@@ -1,11 +1,12 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
+#!/usr/bin/env python
+
+# Copyright 2025 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-# http://www.apache.org/licenses/LICENSE-2.0
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -14,6 +15,7 @@
 # limitations under the License.
 
 
+import logging
 from typing import TYPE_CHECKING
 
 import torch
@@ -42,6 +44,9 @@ else:
     Timesteps = None
 
 
+logger = logging.getLogger(__name__)
+
+
 class TimestepEncoder(nn.Module):
     def __init__(self, embedding_dim, compute_dtype=torch.float32):
         require_package("diffusers", extra="groot")
@@ -181,8 +186,7 @@ class BasicTransformerBlock(nn.Module):
         attn_output = self.attn1(
             norm_hidden_states,
             encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            # encoder_attention_mask=encoder_attention_mask,
+            attention_mask=encoder_attention_mask if encoder_hidden_states is not None else attention_mask,
         )
         if self.final_dropout:
             attn_output = self.final_dropout(attn_output)
@@ -266,8 +270,8 @@ class DiT(ModelMixin, ConfigMixin):
         self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
         self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
         self.proj_out_2 = nn.Linear(self.inner_dim, self.config.output_dim)
-        print(
-            "Total number of DiT parameters: ",
+        logger.debug(
+            "Total number of DiT parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 
@@ -318,6 +322,71 @@ class DiT(ModelMixin, ConfigMixin):
             return self.proj_out_2(hidden_states)
 
 
+class AlternateVLDiT(DiT):
+    """N1.7 DiT variant that alternates cross-attention over image and text tokens."""
+
+    def __init__(self, *args, attend_text_every_n_blocks: int = 2, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.attend_text_every_n_blocks = attend_text_every_n_blocks
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.LongTensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        return_all_hidden_states: bool = False,
+        image_mask: torch.Tensor | None = None,
+        backbone_attention_mask: torch.Tensor | None = None,
+    ):
+        if image_mask is None:
+            raise ValueError("image_mask is required for AlternateVLDiT.")
+        if backbone_attention_mask is None:
+            raise ValueError("backbone_attention_mask is required for AlternateVLDiT.")
+
+        temb = self.timestep_encoder(timestep)
+        hidden_states = hidden_states.contiguous()
+        encoder_hidden_states = encoder_hidden_states.contiguous()
+
+        image_attention_mask = image_mask & backbone_attention_mask
+        non_image_attention_mask = (~image_mask) & backbone_attention_mask
+
+        all_hidden_states = [hidden_states]
+        if not self.config.interleave_self_attention:
+            raise ValueError("AlternateVLDiT requires interleave_self_attention=True.")
+
+        for idx, block in enumerate(self.transformer_blocks):
+            if idx % 2 == 1:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=None,
+                    encoder_attention_mask=None,
+                    temb=temb,
+                )
+            else:
+                curr_encoder_attention_mask = (
+                    non_image_attention_mask
+                    if idx % (2 * self.attend_text_every_n_blocks) == 0
+                    else image_attention_mask
+                )
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=curr_encoder_attention_mask,
+                    temb=temb,
+                )
+            all_hidden_states.append(hidden_states)
+
+        conditioning = temb
+        shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
+        if return_all_hidden_states:
+            return self.proj_out_2(hidden_states), all_hidden_states
+        return self.proj_out_2(hidden_states)
+
+
 class SelfAttentionTransformer(ModelMixin, ConfigMixin):
     _supports_gradient_checkpointing = True
 
@@ -362,8 +431,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                 for _ in range(self.config.num_layers)
             ]
         )
-        print(
-            "Total number of SelfAttentionTransformer parameters: ",
+        logger.debug(
+            "Total number of SelfAttentionTransformer parameters: %d",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 

src/lerobot/policies/groot/action_head/flow_matching_action_head.py

@@ -1,408 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import field
-from typing import TYPE_CHECKING
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import nn
-from torch.distributions import Beta
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from transformers import PretrainedConfig
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-    PretrainedConfig = object
-    BatchFeature = None
-
-from .action_encoder import (
-    SinusoidalPositionalEncoding,
-    swish,
-)
-from .cross_attention_dit import DiT, SelfAttentionTransformer
-
-
-class CategorySpecificLinear(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        # For each category, we have separate weights and biases.
-        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
-        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
-
-    def forward(self, x, cat_ids):
-        selected_w = self.W[cat_ids]
-        selected_b = self.b[cat_ids]
-        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
-
-
-class CategorySpecificMLP(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
-        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
-
-    def forward(self, x, cat_ids):
-        hidden = F.relu(self.layer1(x, cat_ids))
-        return self.layer2(hidden, cat_ids)
-
-
-class MultiEmbodimentActionEncoder(nn.Module):
-    def __init__(self, action_dim, hidden_size, num_embodiments):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_embodiments = num_embodiments
-
-        # W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
-        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)  # (d -> w)
-        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)  # (2w -> w)
-        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)  # (w -> w)
-        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
-
-    def forward(self, actions, timesteps, cat_ids):
-        """
-        actions:   shape (B, T, action_dim)
-        timesteps: shape (B,)  -- a single scalar per batch item
-        cat_ids:   shape (B,)
-        returns:   shape (B, T, hidden_size)
-        """
-        b, t, _ = actions.shape
-
-        # 1) Expand each batch's single scalar time 'tau' across all T steps
-        #    so that shape => (B, T)
-        #    e.g. if timesteps is (B,), replicate across T
-        if timesteps.dim() == 1 and timesteps.shape[0] == b:
-            # shape (B,) => (B,T)
-            timesteps = timesteps.unsqueeze(1).expand(-1, t)
-        else:
-            raise ValueError("Expected `timesteps` to have shape (B,) so we can replicate across T.")
-
-        # 2) Standard action MLP step for shape => (B, T, w)
-        a_emb = self.W1(actions, cat_ids)
-
-        # 3) Get the sinusoidal encoding (B, T, w)
-        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)
-
-        # 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
-        x = torch.cat([a_emb, tau_emb], dim=-1)
-        x = swish(self.W2(x, cat_ids))
-
-        # 5) Finally W3 => (B, T, w)
-        x = self.W3(x, cat_ids)
-        return x
-
-
-class FlowmatchingActionHeadConfig(PretrainedConfig):
-    """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""
-
-    add_pos_embed: bool = field(default=True, metadata={"help": "Whether to add positional embedding"})
-    model_dtype: str = field(default="float32", metadata={"help": "Model data type."})
-    diffusion_model_cfg: dict = field(default=None, metadata={"help": "Diffusion model configuration."})
-    input_embedding_dim: int = field(default=1536, metadata={"help": "Input embedding channel dimension."})
-    backbone_embedding_dim: int = field(
-        default=1536, metadata={"help": "Backbone embedding channel dimension."}
-    )
-
-    hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
-    max_seq_len: int = field(default=1024, metadata={"help": "Maximum Sequence Length"})
-    action_dim: int = field(default=None, metadata={"help": "Action dimension."})
-    action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
-    noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
-    noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
-    noise_s: float = field(default=0.999, metadata={"help": "Flow matching noise Beta distribution s."})
-    num_timestep_buckets: int = field(
-        default=1000, metadata={"help": "Number of timestep discretization buckets."}
-    )
-    num_inference_timesteps: int = field(
-        default=None,
-        metadata={"help": "Number of inference steps for noise diffusion."},
-    )
-    max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
-    tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
-    tune_diffusion_model: bool = field(
-        default=True, metadata={"help": "Whether to tune the diffusion model."}
-    )
-    load_pretrained_det_decode_layer_path: str = field(
-        default=None, metadata={"help": "Path to pretrained detection model."}
-    )
-    detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})
-
-    freeze_decode_layer: bool = field(default=False)
-    expand_batch: int = field(default=None)
-    use_vlln: bool = field(default=True)
-
-    vl_self_attention_cfg: dict = field(default=None)
-    num_target_vision_tokens: int = field(default=32, metadata={"help": "Number of target vision tokens."})
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-
-class FlowmatchingActionHead(nn.Module):
-    config_class = FlowmatchingActionHeadConfig
-    supports_gradient_checkpointing = True
-
-    def __init__(
-        self,
-        config: FlowmatchingActionHeadConfig,
-    ):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.input_embedding_dim = config.input_embedding_dim
-
-        self.model = DiT(**config.diffusion_model_cfg)
-        self.action_dim = config.action_dim
-        self.action_horizon = config.action_horizon
-        self.num_inference_timesteps = config.num_inference_timesteps
-
-        self.state_encoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=config.max_state_dim,
-            hidden_dim=self.hidden_size,
-            output_dim=self.input_embedding_dim,
-        )
-        self.action_encoder = MultiEmbodimentActionEncoder(
-            action_dim=config.action_dim,
-            hidden_size=self.input_embedding_dim,
-            num_embodiments=config.max_num_embodiments,
-        )
-        self.action_decoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=self.hidden_size,
-            hidden_dim=self.hidden_size,
-            output_dim=self.action_dim,
-        )
-        self.future_tokens = nn.Embedding(config.num_target_vision_tokens, self.input_embedding_dim)
-        nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)
-
-        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
-        self.vl_self_attention = (
-            SelfAttentionTransformer(**config.vl_self_attention_cfg) if config.use_vlln else nn.Identity()
-        )
-
-        if config.add_pos_embed:
-            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
-            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
-
-        self._noise_beta_alpha = config.noise_beta_alpha
-        self._noise_beta_beta = config.noise_beta_beta
-        self._beta_dist = None
-        self.num_timestep_buckets = config.num_timestep_buckets
-        self.config = config
-        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model)
-
-    def set_trainable_parameters(self, tune_projector: bool, tune_diffusion_model: bool):
-        self.tune_projector = tune_projector
-        self.tune_diffusion_model = tune_diffusion_model
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_projector:
-            self.state_encoder.requires_grad_(False)
-            self.action_encoder.requires_grad_(False)
-            self.action_decoder.requires_grad_(False)
-            if self.config.add_pos_embed:
-                self.position_embedding.requires_grad_(False)
-        if not tune_diffusion_model:
-            self.model.requires_grad_(False)
-        print(f"Tune action head projector: {self.tune_projector}")
-        print(f"Tune action head diffusion model: {self.tune_diffusion_model}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_projector and not tune_diffusion_model:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Action head trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No action head trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if not self.tune_projector:
-                self.state_encoder.eval()
-                self.action_encoder.eval()
-                self.action_decoder.eval()
-                if self.config.add_pos_embed:
-                    self.position_embedding.eval()
-            if not self.tune_diffusion_model:
-                self.model.eval()
-
-    def sample_time(self, batch_size, device, dtype):
-        if self._beta_dist is None:
-            self._beta_dist = Beta(self._noise_beta_alpha, self._noise_beta_beta, validate_args=False)
-        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
-        return (self.config.noise_s - sample) / self.config.noise_s
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
-        backbone_features = backbone_output["backbone_features"]
-        backbone_features = self.vlln(backbone_features)
-        backbone_features = self.vl_self_attention(backbone_features)
-        backbone_output["backbone_features"] = backbone_features
-        return backbone_output
-
-    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        # Set frozen modules to eval
-        self.set_frozen_modules_to_eval_mode()
-
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        if self.config.expand_batch is not None:
-            for k, v in backbone_output.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                backbone_output[k] = expanded
-
-            for k, v in action_input.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                action_input[k] = expanded
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        device = vl_embs.device
-
-        # Get embodiment ID.
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Embed noised action trajectory.
-        actions = action_input.action
-        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
-        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
-        t = t[:, None, None]  # shape (B,1,1) for broadcast
-
-        noisy_trajectory = (1 - t) * noise + t * actions
-        velocity = actions - noise
-
-        # Convert (continuous) t -> discrete if needed
-        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
-        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
-
-        # Maybe add position embedding.
-        if self.config.add_pos_embed:
-            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-            pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-            action_features = action_features + pos_embs
-
-        # Join vision, language, state and action embedding along sequence dimension.
-        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-        sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-        vl_attn_mask = backbone_output.backbone_attention_mask
-
-        model_output = self.model(
-            hidden_states=sa_embs,
-            encoder_hidden_states=vl_embs,
-            encoder_attention_mask=vl_attn_mask,
-            timestep=t_discretized,
-            return_all_hidden_states=False,  # NOTE (YL): not using flare now
-        )
-        pred = self.action_decoder(model_output, embodiment_id)
-        pred_actions = pred[:, -actions.shape[1] :]
-
-        # Slice out only the action portion of pred and target.
-        action_mask = action_input.action_mask
-        loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
-        loss = loss.sum() / action_mask.sum()
-        output_dict = {
-            "loss": loss,
-        }
-        return BatchFeature(data=output_dict)
-
-    @torch.no_grad()
-    def get_action(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Set initial actions as the sampled noise.
-        batch_size = vl_embs.shape[0]
-        device = vl_embs.device
-        actions = torch.randn(
-            size=(batch_size, self.config.action_horizon, self.config.action_dim),
-            dtype=vl_embs.dtype,
-            device=device,
-        )
-
-        num_steps = self.num_inference_timesteps
-        dt = 1.0 / num_steps
-
-        # Run denoising steps.
-        for t in range(num_steps):
-            t_cont = t / float(num_steps)  # e.g. goes 0, 1/N, 2/N, ...
-            t_discretized = int(t_cont * self.num_timestep_buckets)
-
-            # Embed noised action trajectory.
-            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
-            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
-            # Maybe add position embedding.
-            if self.config.add_pos_embed:
-                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-                pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-                action_features = action_features + pos_embs
-
-            # Join vision, language, state and action embedding along sequence dimension.
-            future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-            sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-            # Run model forward.
-            model_output = self.model(
-                hidden_states=sa_embs,
-                encoder_hidden_states=vl_embs,
-                timestep=timesteps_tensor,
-            )
-            pred = self.action_decoder(model_output, embodiment_id)
-
-            pred_velocity = pred[:, -self.action_horizon :]
-
-            # Update actions using euler integration.
-            actions = actions + dt * pred_velocity
-        return BatchFeature(data={"action_pred": actions})
-
-    @property
-    def device(self):
-        return next(iter(self.parameters())).device
-
-    @property
-    def dtype(self):
-        return next(iter(self.parameters())).dtype

src/lerobot/policies/groot/configuration_groot.py

@@ -14,12 +14,229 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
+import math
 from dataclasses import dataclass, field
+from pathlib import Path
 
 from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTrainedConfig
-from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
+from lerobot.optim import AdamWConfig, DiffuserSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE
 
+from .utils import read_json
+
+logger = logging.getLogger(__name__)
+
+GROOT_N1_7 = "n1.7"
+# Legacy GR00T N1.5 identifier. N1.5 is NOT a supported model_version (it is
+# intentionally absent from _GROOT_MODEL_VERSION_ALIASES so normalize_groot_model_version
+# still rejects it). It is retained only so that infer_groot_model_version can recognise
+# an N1.5 base path/checkpoint and the N1.7 config/loader can reject the mismatch.
+GROOT_N1_5 = "n1.5"
+# Canonical guidance appended to every error raised when an N1.5 checkpoint, config,
+# or processor pipeline is detected. Keep this message in sync with docs/source/groot.mdx.
+GROOT_N1_5_REMOVAL_GUIDANCE = (
+    "GR00T N1.5 support was removed from LeRobot. "
+    "To keep using an N1.5 checkpoint, pin the last release that supports it: "
+    "`pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 "
+    "(model_version='n1.7', base model nvidia/GR00T-N1.7-3B)."
+)
+GROOT_N1_7_BASE_MODEL = "nvidia/GR00T-N1.7-3B"
+GROOT_N1_7_BACKBONE_MODEL = "nvidia/Cosmos-Reason2-2B"
+# Default GR00T N1.7 training resolution. Fallback if processor_config lacks sizing. Prevents mismatched
+# full-res patchification by forcing a resize. Mirrored by GR00T_N1_7_DEFAULTS in groot_n1_7.py.
+N1_7_DEFAULT_IMAGE_TARGET_SIZE = (256, 256)
+N1_7_DEFAULT_IMAGE_CROP_SIZE = (230, 230)
+GROOT_ACTION_DECODE_TRANSFORM_LIBERO = "libero"
+# Sentinel meaning "the user did not pick an action decode transform": __post_init__ resolves it
+# to the embodiment default ('libero' for 'libero_sim', otherwise None). It is distinct from an
+# explicit 'none' (resolved to None) so an opt-out survives a draccus save/load round-trip.
+GROOT_ACTION_DECODE_TRANSFORM_AUTO = "auto"
+
+_GROOT_MODEL_VERSION_ALIASES = {
+    "n1.7": GROOT_N1_7,
+    "n1_7": GROOT_N1_7,
+    "n1d7": GROOT_N1_7,
+    "n17": GROOT_N1_7,
+    "1.7": GROOT_N1_7,
+}
+
+# Legacy N1.5 spellings, kept ONLY so they can be detected and rejected with
+# GROOT_N1_5_REMOVAL_GUIDANCE (see GROOT_N1_5 above). Never map these to a supported version.
+_GROOT_N1_5_VERSION_ALIASES = {"n1.5", "n1_5", "n1d5", "n15", "1.5"}
+
+_GROOT_ACTION_DECODE_TRANSFORM_ALIASES = {
+    GROOT_ACTION_DECODE_TRANSFORM_AUTO: GROOT_ACTION_DECODE_TRANSFORM_AUTO,
+    "none": None,
+    "": None,
+    GROOT_ACTION_DECODE_TRANSFORM_LIBERO: GROOT_ACTION_DECODE_TRANSFORM_LIBERO,
+}
+
+
+def normalize_groot_model_version(model_version: str) -> str:
+    normalized = _GROOT_MODEL_VERSION_ALIASES.get(model_version.lower())
+    if normalized is None:
+        supported = GROOT_N1_7
+        message = f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+        raise ValueError(message)
+    return normalized
+
+
+def normalize_groot_action_decode_transform(transform: str | None) -> str | None:
+    if transform is None:
+        return None
+    normalized = _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.get(transform.lower())
+    if normalized is None and transform.lower() not in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES:
+        supported = ", ".join(
+            sorted(key for key, value in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.items() if value is not None)
+        )
+        raise ValueError(
+            f"Unsupported GR00T N1.7 action decode transform '{transform}'. "
+            f"Supported transforms: none, {supported}."
+        )
+    return normalized
+
+
+def infer_groot_model_version(model_path: str | None) -> str | None:
+    if not model_path:
+        return None
+    model_path_lower = model_path.lower()
+    if "gr00t-n1.7" in model_path_lower or "gr00t_n1.7" in model_path_lower:
+        return GROOT_N1_7
+    # Detect legacy N1.5 paths so the N1.7 config/loader can reject the mismatch.
+    # N1.5 is unsupported, but it must still be recognised here to fail loudly
+    # rather than silently treating an N1.5 checkpoint as N1.7.
+    if "gr00t-n1.5" in model_path_lower or "gr00t_n1.5" in model_path_lower:
+        return GROOT_N1_5
+    config_version = _infer_groot_model_version_from_local_config(model_path)
+    if config_version is not None:
+        return config_version
+    return None
+
+
+def is_raw_groot_n1_7_checkpoint(model_path: str | Path | None) -> bool:
+    if model_path is None:
+        return False
+
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return False
+
+    config = read_json(config_path)
+    return "type" not in config and _infer_groot_model_version_from_config(config) == GROOT_N1_7
+
+
+def infer_groot_n1_7_embodiment_tag(model_path: str | Path | None) -> str | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    processor_config = read_json(processor_config_path)
+
+    modality_configs = processor_config.get("processor_kwargs", {}).get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+    if "libero_sim" in modality_configs:
+        return "libero_sim"
+    if len(modality_configs) == 1:
+        return next(iter(modality_configs))
+    return None
+
+
+def infer_groot_n1_7_action_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    processor_config = read_json(processor_config_path)
+
+    processor_kwargs = processor_config.get("processor_kwargs", {})
+    if not isinstance(processor_kwargs, dict):
+        return None
+    modality_configs = processor_kwargs.get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag is None:
+        return None
+
+    embodiment_config = modality_configs.get(embodiment_tag, {})
+    if not isinstance(embodiment_config, dict):
+        return None
+    action_config = embodiment_config.get("action", {})
+    if not isinstance(action_config, dict):
+        return None
+    delta_indices = action_config.get("delta_indices", [])
+    if not isinstance(delta_indices, list):
+        return None
+    return len(delta_indices) or None
+
+
+def infer_groot_n1_7_action_execution_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    action_horizon = infer_groot_n1_7_action_horizon(model_path, embodiment_tag)
+    if action_horizon is None:
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag == "libero_sim":
+        # NVIDIA's N1.7 LIBERO rollout wrapper replans after 8 of the 16 decoded
+        # actions. Keeping that execution cadence avoids stale open-loop chunks.
+        return min(action_horizon, 8)
+    return action_horizon
+
+
+def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return None
+
+    return _infer_groot_model_version_from_config(read_json(config_path))
+
+
+def _infer_groot_model_version_from_config(config: dict) -> str | None:
+    model_version = config.get("model_version")
+    if isinstance(model_version, str):
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            return GROOT_N1_5
+        try:
+            return normalize_groot_model_version(model_version)
+        except ValueError:
+            return None
+
+    candidates = [config.get("model_type"), *(config.get("architectures") or [])]
+    for candidate in candidates:
+        if not isinstance(candidate, str):
+            continue
+        normalized = candidate.lower().replace("-", "_")
+        if normalized in {"gr00tn1d7", "gr00t_n1d7", "gr00t_n1_7"}:
+            return GROOT_N1_7
+        if normalized in {"gr00t_n1_5", "gr00tn1_5", "gr00t_n15", "gr00t_n1d5", "gr00tn1d5"}:
+            return GROOT_N1_5
+    if config.get("model_name") == GROOT_N1_7_BACKBONE_MODEL:
+        return GROOT_N1_7
+    # The Eagle VLM backbone is specific to pre-N1.7 GR00T checkpoints (N1.7 uses Cosmos/Qwen3-VL).
+    backbone_cfg = config.get("backbone_cfg")
+    if isinstance(backbone_cfg, dict) and "eagle_path" in backbone_cfg:
+        return GROOT_N1_5
+    return None
+
 
 @PreTrainedConfig.register_subclass("groot")
 @dataclass
@@ -28,35 +245,44 @@ class GrootConfig(PreTrainedConfig):
 
     # Basic policy settings
     n_obs_steps: int = 1
-    chunk_size: int = 50
-    n_action_steps: int = 50
+    chunk_size: int = 40
+    n_action_steps: int = 40
 
     # Dimension settings (must match pretrained GR00T model expectations)
     # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 64
+    max_state_dim: int = 132
 
     # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 32
+    max_action_dim: int = 132
 
-    # Normalization (start with identity, adjust as needed)
+    # GR00T normalizes state/action internally in its processor steps (min/max with
+    # q01/q99 percentiles, per embodiment), and the Qwen3-VL backbone's image processor
+    # handles image normalization. The policy therefore does NOT use LeRobot's
+    # NormalizerProcessorStep/UnnormalizerProcessorStep, so this mapping is intentionally
+    # IDENTITY for every feature and is not consulted by make_groot_pre_post_processors.
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
+            "STATE": NormalizationMode.IDENTITY,
+            "ACTION": NormalizationMode.IDENTITY,
         }
     )
 
-    # Image preprocessing (adjust to match Groot's expected input)
-    image_size: tuple[int, int] = (224, 224)
+    # Groot-specific model parameters
 
-    # Groot-specific model parameters (from groot_finetune_script.py)
+    # Path or HuggingFace model ID for the base GR00T N1.7 model whose backbone weights and
+    # checkpoint sidecars (statistics.json, processor_config.json, ...) are loaded. This is the
+    # model *source*, and is intentionally distinct from the inherited `pretrained_path`:
+    # `pretrained_path` (`--policy.path`) points at a saved LeRobot checkpoint directory whose
+    # `config.json` carries a `type` field, whereas a raw NVIDIA GR00T checkpoint has no such
+    # field and so can only be loaded through `base_model_path` (`--policy.base_model_path`).
+    # Defaults to GROOT_N1_7_BASE_MODEL when unset (resolved in __post_init__).
+    base_model_path: str | None = None
 
-    # Path or HuggingFace model ID for the base Groot model
-    base_model_path: str = "nvidia/GR00T-N1.5-3B"
-
-    # HF repo ID (or local path) that hosts vocab.json and merges.txt for Eagle tokenizer.
-    tokenizer_assets_repo: str = "lerobot/eagle2hg-processor-groot-n1p5"
+    # Optional named action transform applied after raw N1.7 checkpoint decoding and before env.step().
+    # 'auto' (default) resolves to the embodiment default ('libero' for 'libero_sim', otherwise no
+    # transform). Pass 'none' to explicitly disable the transform, including for 'libero_sim'.
+    action_decode_transform: str | None = GROOT_ACTION_DECODE_TRANSFORM_AUTO
 
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
@@ -75,38 +301,67 @@ class GrootConfig(PreTrainedConfig):
     # Whether to fine-tune the diffusion model
     tune_diffusion_model: bool = True
 
-    # LoRA parameters (from groot_finetune_script.py)
-    # Rank for the LORA model. If 0, no LORA will be used.
-    lora_rank: int = 0
+    # Whether to fine-tune the VL LayerNorm + VL self-attention projector in the action head.
+    tune_vlln: bool = True
 
-    # Alpha value for the LORA model
-    lora_alpha: int = 16
+    # Number of top LLM backbone layers to fine-tune (0 = none). Lets you adapt just the final
+    # language layers without unfreezing the whole backbone; independent of `tune_llm`, which tunes
+    # the entire LLM.
+    tune_top_llm_layers: int = 0
 
-    # Dropout rate for the LORA model
-    lora_dropout: float = 0.1
+    # Inference-time knob: Number of flow-matching denoising steps used to decode an action chunk.
+    # Trades inference latency for action quality.
+    # None keeps the checkpoint value (GR00T N1.7 default: 4).
+    num_inference_timesteps: int | None = None
 
-    # Whether to use the full model for LORA
-    lora_full_model: bool = False
+    # Inference-time knob: Real-Time Chunking (RTC) overlap-blend ramp rate, used when the RTC engine
+    # supplies a previous-chunk prefix. Higher values blend the overlapping prefix more aggressively.
+    # None keeps the checkpoint value (GR00T N1.7 default: 6.0).
+    rtc_ramp_rate: float | None = None
 
-    # Training parameters (matching groot_finetune_script.py)
+    # Inference-time knob: Whether to request the flash-attention-2 kernel for the Qwen3-VL backbone.
+    # flash-attn is an optional, user-managed optimization; when it is absent (the default),
+    # the backbone transparently falls back to SDPA, which is numerically equivalent.
+    # Set to True only after installing a flash-attn build matching your torch/CUDA env.
+    use_flash_attention: bool = False
+
+    # Enable GR00T-style state-relative action chunks (action chunk expressed relative to the current
+    # observation state).
+    use_relative_actions: bool = False
+
+    # relative_exclude_joints names the action dimensions that stay absolute; the
+    # match is substring/case-insensitive against the dataset action feature names. With the empty
+    # default every dimension is treated as relative, including the gripper -- set e.g. ["gripper"] to
+    # keep the gripper absolute, matching the Isaac-GR00T single-arm + absolute-gripper convention.
+    relative_exclude_joints: list[str] = field(default_factory=list)
+
+    # Training parameters
     optimizer_lr: float = 1e-4
-    optimizer_betas: tuple[float, float] = (0.95, 0.999)
+    # Isaac-GR00T N1.7 fine-tunes with AdamW betas (0.9, 0.999).
+    optimizer_betas: tuple[float, float] = (0.9, 0.999)
     optimizer_eps: float = 1e-8
     optimizer_weight_decay: float = 1e-5
     warmup_ratio: float = 0.05
     use_bf16: bool = True
+    # The native N1.7 fine-tuning recipe keeps model parameters in FP32 and computes under BF16 autocast.
+    model_params_fp32: bool = True
 
-    # Dataset parameters
-    # Video backend to use for training ('decord' or 'torchvision_av')
+    # TODO(Steven): Remove these deprecated fields in a future release.
+    # Deprecated Isaac-GR00T runner / GR00T N1.5 fields, plus the (never-wired) LoRA fields — all
+    # unused by the LeRobot N1.7 implementation except the `tokenizer_assets_repo` N1.5 tripwire and
+    # the `image_size` legacy remap in __post_init__. They are kept ONLY so a config.json saved by an
+    # earlier lerobot release (notably a GR00T N1.5 checkpoint) still parses under draccus — which
+    # rejects unknown fields — and is then rejected with a clear N1.5 removal message rather than an
+    # opaque draccus decoding error.
+    image_size: tuple[int, int] = (256, 256)  # image sizing is handled by the backbone's image processor.
+    tokenizer_assets_repo: str | None = None
+    lora_rank: int = 0
+    lora_alpha: int = 16
+    lora_dropout: float = 0.1
+    lora_full_model: bool = False
     video_backend: str = "decord"
-
-    # Whether to balance dataset weights in mixture datasets
     balance_dataset_weights: bool = True
-
-    # Whether to sample trajectories weighted by their length
     balance_trajectory_weights: bool = True
-
-    # Optional dataset paths for delegating training to Isaac-GR00T runner
     dataset_paths: list[str] | None = None
     output_dir: str = "./tmp/gr00t"
     save_steps: int = 1000
@@ -117,6 +372,65 @@ class GrootConfig(PreTrainedConfig):
     resume: bool = False
 
     def __post_init__(self):
+        if self.tokenizer_assets_repo is not None:
+            raise ValueError(
+                "Config sets 'tokenizer_assets_repo', which only existed for GR00T N1.5; this looks "
+                f"like a legacy GR00T N1.5 checkpoint or config. {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            )
+
+        self.action_decode_transform = normalize_groot_action_decode_transform(self.action_decode_transform)
+        if self.base_model_path is None:
+            self.base_model_path = GROOT_N1_7_BASE_MODEL
+
+        # The N1.7 LIBERO checkpoints emit a [0, 1] gripper action, but the LIBERO
+        # simulator expects the OpenVLA/[-1, 1] sign convention. NVIDIA's rollout
+        # wrapper applies this conversion; mirror it here so eval on the
+        # 'libero_sim' embodiment grasps correctly instead of scoring 0% success.
+        # This matches the embodiment-specific handling already done for the
+        # action execution horizon (see infer_groot_n1_7_action_execution_horizon).
+        # Only the 'auto' sentinel resolves to the embodiment default; an explicit
+        # 'none' (normalized to None above) keeps the transform disabled.
+        if self.action_decode_transform == GROOT_ACTION_DECODE_TRANSFORM_AUTO:
+            self.action_decode_transform = (
+                GROOT_ACTION_DECODE_TRANSFORM_LIBERO if self.embodiment_tag == "libero_sim" else None
+            )
+
+        # GR00T N1.5-era default values (e.g. --policy.chunk_size=50 from old commands or
+        # stale configs) are migrated to the values the N1.7 checkpoints expect, with a
+        # warning. The dataclass defaults are already the N1.7 values, so a plain
+        # GrootConfig() never triggers this.
+        legacy_default_remaps = (
+            ("max_state_dim", 64, 132),
+            ("max_action_dim", 32, 132),
+            ("chunk_size", 50, 40),
+            ("n_action_steps", 50, 40),
+            ("image_size", (224, 224), (256, 256)),
+        )
+        for field_name, legacy_value, n1_7_value in legacy_default_remaps:
+            current_value = getattr(self, field_name)
+            if isinstance(legacy_value, tuple):
+                current_value = tuple(current_value)
+            if current_value == legacy_value:
+                logger.warning(
+                    "GrootConfig.%s=%s matches a legacy GR00T N1.5-era default; remapping it to %s, "
+                    "the value expected by GR00T N1.7 checkpoints. Set a different value explicitly "
+                    "if this is not what you want.",
+                    field_name,
+                    legacy_value,
+                    n1_7_value,
+                )
+                setattr(self, field_name, n1_7_value)
+
+        inferred_version = infer_groot_model_version(self.base_model_path)
+        if inferred_version is not None and inferred_version != GROOT_N1_7:
+            message = (
+                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"'{self.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
+
         super().__post_init__()
 
         if self.n_action_steps > self.chunk_size:
@@ -124,9 +438,6 @@ class GrootConfig(PreTrainedConfig):
                 f"n_action_steps ({self.n_action_steps}) cannot exceed chunk_size ({self.chunk_size})"
             )
 
-        # groot_repo_path is now optional since we ported the components
-        # No validation needed
-
     def validate_features(self) -> None:
         """Validate and set up input/output features for Groot."""
         image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
@@ -173,15 +484,20 @@ class GrootConfig(PreTrainedConfig):
             betas=self.optimizer_betas,
             eps=self.optimizer_eps,
             weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=1.0,
         )
 
-    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
-        """Return scheduler configuration."""
-        return CosineDecayWithWarmupSchedulerConfig(
-            num_warmup_steps=int(10000 * self.warmup_ratio),  # 5% warmup by default
-            num_decay_steps=10000,  # Adjust based on training steps
-            peak_lr=self.optimizer_lr,
-            decay_lr=self.optimizer_lr * 0.1,
+    def get_scheduler_preset(self) -> DiffuserSchedulerConfig:
+        """Return scheduler configuration.
+
+        Isaac-GR00T uses the HF Trainer cosine schedule with ~5% warmup over the
+        actual training update count; DiffuserSchedulerConfig wraps the same
+        diffusers/transformers `get_scheduler("cosine")` implementation and
+        derives num_training_steps from the outer --steps value at runtime.
+        """
+        return DiffuserSchedulerConfig(
+            name="cosine",
+            num_warmup_steps=math.ceil(self.max_steps * self.warmup_ratio),
         )
 
     @property
@@ -192,7 +508,15 @@ class GrootConfig(PreTrainedConfig):
     @property
     def action_delta_indices(self) -> list[int]:
         """Return indices for delta actions."""
-        return list(range(min(self.chunk_size, 16)))
+        model_action_horizon = (
+            infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
+        )
+        return list(range(min(self.chunk_size, model_action_horizon)))
+
+    @property
+    def drop_n_last_frames(self) -> int:
+        """Exclude episode tails that cannot supply a complete N1.7 action chunk."""
+        return max(0, len(self.action_delta_indices) - 1)
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/groot/eagle2_hg_model/configuration_eagle2_5_vl.py

@@ -1,135 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import copy
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.models.llama.configuration_llama import LlamaConfig
-from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
-from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
-from transformers.models.siglip.configuration_siglip import SiglipVisionConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class Eagle25VLConfig(PretrainedConfig):
-    model_type = "eagle_2_5_vl"
-    is_composition = True
-    sub_configs = {"vision_config": SiglipVisionConfig, "text_config": Qwen2Config}
-
-    def __init__(
-        self,
-        vision_config=None,
-        text_config=None,
-        use_backbone_lora=0,
-        use_llm_lora=0,
-        pad2square=False,
-        select_layer=-4,
-        force_image_size=None,
-        downsample_ratio=0.5,
-        template=None,
-        dynamic_image_size=False,
-        use_thumbnail=False,
-        loss_version="v1",
-        min_dynamic_tiles=1,
-        max_dynamic_tiles=6,
-        mlp_checkpoint=False,
-        initializer_range=0.02,
-        _attn_implementation="flash_attention_2",
-        _attn_implementation_autoset=False,
-        llm_config=None,
-        image_token_index=None,
-        use_pixel_shuffle=True,
-        mlp_connector_layers=2,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        if vision_config is None:
-            vision_config = {"model_type": "siglip_vision_model"}
-            logger.info("vision_config is None. Initializing the InternVisionConfig with default values.")
-
-        if text_config is None:
-            text_config = {"architectures": ["Qwen2ForCausalLM"]}
-            logger.info(
-                "text_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
-            )
-
-        if vision_config["model_type"] == "siglip_vision_model":
-            self.vision_config = SiglipVisionConfig(**vision_config)
-        else:
-            raise ValueError("Unsupported model_type: {}".format(vision_config["model_type"]))
-
-        if text_config["architectures"][0] == "LlamaForCausalLM":
-            self.text_config = LlamaConfig(**text_config)
-        elif text_config["architectures"][0] == "Qwen2ForCausalLM":
-            self.text_config = Qwen2Config(**text_config)
-        elif text_config["architectures"][0] == "Qwen3ForCausalLM":
-            self.text_config = Qwen3Config(**text_config)
-        else:
-            raise ValueError("Unsupported architecture: {}".format(text_config["architectures"][0]))
-        self.use_backbone_lora = use_backbone_lora
-        self.use_llm_lora = use_llm_lora
-        self.mlp_checkpoint = mlp_checkpoint
-        self.pad2square = pad2square
-        self.select_layer = select_layer
-        self.force_image_size = force_image_size
-        self.downsample_ratio = downsample_ratio
-        self.template = template
-        self.dynamic_image_size = dynamic_image_size
-        self.use_thumbnail = use_thumbnail
-        self.loss_version = loss_version
-        self.initializer_range = initializer_range
-        self.min_dynamic_tiles = min_dynamic_tiles
-        self.max_dynamic_tiles = max_dynamic_tiles
-        self.tie_word_embeddings = self.text_config.tie_word_embeddings
-        self._attn_implementation = _attn_implementation
-        self._attn_implementation_autoset = _attn_implementation_autoset
-        self.image_token_index = image_token_index
-        self.use_pixel_shuffle = use_pixel_shuffle
-        self.mlp_connector_layers = mlp_connector_layers
-        logger.info(f"min_dynamic_tiles: {self.min_dynamic_tiles}")
-        logger.info(f"max_dynamic_tiles: {self.max_dynamic_tiles}")
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = copy.deepcopy(self.__dict__)
-        output["vision_config"] = self.vision_config.to_dict()
-        output["text_config"] = self.text_config.to_dict()
-        output["model_type"] = self.__class__.model_type
-        output["use_backbone_lora"] = self.use_backbone_lora
-        output["use_llm_lora"] = self.use_llm_lora
-        output["pad2square"] = self.pad2square
-        output["select_layer"] = self.select_layer
-        output["force_image_size"] = self.force_image_size
-        output["downsample_ratio"] = self.downsample_ratio
-        output["template"] = self.template
-        output["dynamic_image_size"] = self.dynamic_image_size
-        output["use_thumbnail"] = self.use_thumbnail
-        output["min_dynamic_tiles"] = self.min_dynamic_tiles
-        output["max_dynamic_tiles"] = self.max_dynamic_tiles
-        output["tie_word_embeddings"] = self.tie_word_embeddings
-        output["_attn_implementation"] = self._attn_implementation
-        output["_attn_implementation_autoset"] = self._attn_implementation_autoset
-        output["use_pixel_shuffle"] = self.use_pixel_shuffle
-        output["mlp_connector_layers"] = self.mlp_connector_layers
-        return output

src/lerobot/policies/groot/eagle2_hg_model/image_processing_eagle2_5_vl_fast.py

@@ -1,503 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-from __future__ import annotations
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
-from transformers.image_processing_utils import (
-    BatchFeature,
-    get_patch_output_size,
-)
-from transformers.image_processing_utils_fast import (
-    BaseImageProcessorFast,
-    ImagesKwargs,
-    group_images_by_shape,
-    reorder_images,
-)
-from transformers.image_utils import (
-    IMAGENET_STANDARD_MEAN,  # 0.5, 0.5, 0.5
-    IMAGENET_STANDARD_STD,  # 0.5, 0.5, 0.5
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    SizeDict,
-    get_image_size,
-    make_flat_list_of_images,
-    validate_kwargs,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    TensorType,
-    add_start_docstrings,
-    is_torch_available,
-    is_torchvision_v2_available,
-)
-from transformers.video_utils import VideoInput
-
-if is_torch_available():
-    import torch
-if is_torchvision_v2_available():
-    from torchvision.transforms.v2 import functional as F  # noqa: N812
-    from transformers.image_utils import pil_torch_interpolation_mapping
-else:
-    from torchvision.transforms import functional as F  # noqa: N812
-
-
-def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> torch.Tensor:
-    """Crop the given numpy array.
-
-    Args:
-        img (torch.Tensor): Image to be cropped. Format should be (C, H, W).
-        left (int): The left coordinate of the crop box.
-        top (int): The top coordinate of the crop box.
-        right (int): The right coordinate of the crop box.
-        bottom (int): The bottom coordinate of the crop box.
-
-    Returns:
-        torch.Tensor: Cropped image.
-    """
-    if not isinstance(img, torch.Tensor):
-        raise TypeError(f"img should be torch.Tensor. Got {type(img)}")
-
-    if img.ndim not in [2, 3]:
-        raise ValueError(f"Image should have 2 or 3 dimensions. Got {img.ndim}")
-
-    img_height = img.shape[1]
-    img_width = img.shape[2]
-    if top < 0 or left < 0 or bottom > img_height or right > img_width:
-        raise ValueError("Crop coordinates out of bounds")
-
-    if top >= bottom or left >= right:
-        raise ValueError("Invalid crop coordinates")
-
-    return img[:, top:bottom, left:right]
-
-
-class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
-    max_dynamic_tiles: int | None
-    min_dynamic_tiles: int | None
-    use_thumbnail: bool | None
-    pad_during_tiling: bool | None
-    do_pad: bool | None
-
-
-@add_start_docstrings(
-    "Constructs a fast ConvNeXT image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.",
-    # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, TODO: this was depreciated from transformers remove!
-    """
-        image_grid_pinpoints (`List[List[int]]`, *optional*):
-            A list of possible resolutions to use for processing high resolution images. The best resolution is selected
-            based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
-            method. Not used for processing videos.
-        do_pad (`bool`, *optional*):
-            Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-            number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-    """,
-)
-class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
-    resample = PILImageResampling.BICUBIC
-    image_mean = IMAGENET_STANDARD_MEAN
-    image_std = IMAGENET_STANDARD_STD
-    size = {"height": 448, "width": 448}
-    default_to_square = False
-    crop_size = None
-    do_resize = True
-    do_center_crop = None
-    do_rescale = True
-    do_normalize = True
-    do_convert_rgb = True
-    do_pad = True
-    max_dynamic_tiles = 12
-    min_dynamic_tiles = 1
-    use_thumbnail = True
-    pad_during_tiling = False
-    valid_kwargs = Eagle25VLFastImageProcessorKwargs
-    model_input_names = ["pixel_values_videos"]
-
-    def __init__(self, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]):
-        super().__init__(**kwargs)
-
-    @add_start_docstrings(
-        # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, TODO: this was depreciated from transformers remove!
-        """
-            max_dynamic_tiles (`int`, *optional*):
-                The maximum number of dynamic tiles to use for processing high resolution images.
-            min_dynamic_tiles (`int`, *optional*):
-                The minimum number of dynamic tiles to use for processing high resolution images.
-            use_thumbnail (`bool`, *optional*):
-                Whether to use a thumbnail for processing high resolution images.
-            pad_during_tiling (`bool`, *optional*):
-                Whether to pad the image during tiling.
-            do_pad (`bool`, *optional*):
-                    Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-                    number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-        """,
-    )
-
-    # NOTE(YL): we will overload the preprocess method to add the image_flags
-    # def preprocess(
-    #     self, images: ImageInput, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]
-    # ) -> BatchFeature:
-    #     return super().preprocess(images, **kwargs)
-
-    def _prepare_images_structure(
-        self,
-        images: ImageInput,
-        expected_ndims: int = 3,
-    ) -> ImageInput:
-        """
-        Prepare the images structure for processing.
-
-        Args:
-            images (`ImageInput`):
-                The input images to process.
-            expected_ndims (`int`, *optional*, defaults to 3):
-                Expected number of dimensions for the images (added for transformers >=4.53.0 compatibility).
-
-        Returns:
-            `ImageInput`: The images with a valid nesting.
-        """
-        return make_flat_list_of_images(images)
-
-    def _resize_for_patching(
-        self,
-        image: torch.Tensor,
-        target_resolution: tuple,
-        interpolation: F.InterpolationMode,
-        input_data_format: ChannelDimension,
-    ) -> torch.Tensor:
-        """
-        Resizes an image to a target resolution while maintaining aspect ratio.
-
-        Args:
-            image ("torch.Tensor"):
-                The input image.
-            target_resolution (tuple):
-                The target resolution (height, width) of the image.
-            interpolation (`InterpolationMode`):
-                Resampling filter to use if resizing the image.
-            input_data_format (`ChannelDimension` or `str`):
-                The channel dimension format of the input image.
-
-        Returns:
-            "torch.Tensor": The resized and padded image.
-        """
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        # Resize the image
-        resized_image = F.resize(image, (new_height, new_width), interpolation=interpolation)
-
-        return resized_image
-
-    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
-        """
-        previous version mainly focus on ratio.
-        We also consider area ratio here.
-        """
-        best_factor = float("-inf")
-        best_ratio = (1, 1)
-        area = width * height
-        for ratio in target_ratios:
-            target_aspect_ratio = ratio[0] / ratio[1]
-            # ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-            # area_ratio = (ratio[0] * ratio[1] * image_size * image_size) / area
-            """
-            new area > 60% of original image area is enough.
-            """
-            factor_based_on_area_n_ratio = min(
-                (ratio[0] * ratio[1] * image_size * image_size) / area, 0.6
-            ) * min(target_aspect_ratio / aspect_ratio, aspect_ratio / target_aspect_ratio)
-
-            if factor_based_on_area_n_ratio > best_factor:
-                best_factor = factor_based_on_area_n_ratio
-                best_ratio = ratio
-
-        return best_ratio
-
-    def _pad_for_patching(
-        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> torch.Tensor:
-        """
-        Pad an image to a target resolution while maintaining aspect ratio.
-        """
-        target_height, target_width = target_resolution
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        paste_x = (target_width - new_width) // 2
-        paste_y = (target_height - new_height) // 2
-
-        padded_image = F.pad(image, padding=[paste_x, paste_y, paste_x, paste_y])
-
-        return padded_image
-
-    def _get_image_patches(
-        self,
-        image: torch.Tensor,
-        min_num: int,
-        max_num: int,
-        size: tuple,
-        tile_size: int,
-        use_thumbnail: bool,
-        interpolation: F.InterpolationMode,
-        pad_during_tiling: bool,
-    ) -> list[torch.Tensor]:
-        image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-
-        # calculate the target width and height
-        target_width = tile_size * target_aspect_ratio[0]
-        target_height = tile_size * target_aspect_ratio[1]
-        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if pad_during_tiling:
-            resized_image = self._resize_for_patching(
-                image,
-                (target_height, target_width),
-                interpolation=interpolation,
-                input_data_format=ChannelDimension.FIRST,
-            )
-            padded_image = self._pad_for_patching(
-                resized_image,
-                (target_height, target_width),
-                input_data_format=ChannelDimension.FIRST,
-            )
-            image_used_to_split = padded_image
-        else:
-            image_used_to_split = F.resize(image, (target_height, target_width), interpolation=interpolation)
-
-        processed_tiles = []
-        for i in range(blocks):
-            box = (
-                (i % (target_width // tile_size)) * tile_size,
-                (i // (target_width // tile_size)) * tile_size,
-                ((i % (target_width // tile_size)) + 1) * tile_size,
-                ((i // (target_width // tile_size)) + 1) * tile_size,
-            )
-            # split the image
-            split_img = crop(image_used_to_split, box[0], box[1], box[2], box[3])
-            processed_tiles.append(split_img)
-        assert len(processed_tiles) == blocks
-
-        if use_thumbnail and len(processed_tiles) != 1:
-            thumbnail_img = F.resize(image, (tile_size, tile_size), interpolation=interpolation)
-            processed_tiles.append(thumbnail_img)
-
-        return processed_tiles
-
-    def _pad_for_batching(
-        self,
-        pixel_values: list[torch.Tensor],
-    ) -> list[torch.Tensor]:
-        """
-        Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
-
-        Args:
-            pixel_values (`List[torch.Tensor]`):
-                An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
-
-        Returns:
-            List[`torch.Tensor`]: The padded images.
-        """
-        max_patch = max(len(x) for x in pixel_values)
-        pixel_values = [
-            torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
-            for image in pixel_values
-        ]
-
-        return pixel_values
-
-    def _preprocess(
-        self,
-        images: list[torch.Tensor],
-        do_resize: bool,
-        size: SizeDict,
-        max_dynamic_tiles: int,
-        min_dynamic_tiles: int,
-        use_thumbnail: bool,
-        pad_during_tiling: bool,
-        interpolation: F.InterpolationMode | None,
-        do_center_crop: bool,
-        crop_size: SizeDict,
-        do_rescale: bool,
-        rescale_factor: float,
-        do_normalize: bool,
-        image_mean: float | list[float] | None,
-        image_std: float | list[float] | None,
-        do_pad: bool,
-        return_tensors: str | TensorType | None,
-        pad_size: SizeDict | None = None,  # Added for transformers >=4.53.0 compatibility
-        disable_grouping: bool | None = None,  # Added for transformers >=4.53.0 compatibility
-    ) -> BatchFeature:
-        processed_images = []
-        image_sizes = []
-        # Determine the size tuple
-        if size and size.height and size.width:
-            size_tuple = (size.height, size.width)
-        else:
-            size_tuple = (size.shortest_edge, size.shortest_edge)
-
-        # Determine the patch size
-        if crop_size and crop_size.height:
-            tile_size = crop_size.height
-        elif size and size.height:
-            tile_size = size.height
-        else:
-            tile_size = size.shortest_edge
-
-        for image in images:
-            image_patches = self._get_image_patches(
-                image,
-                min_num=min_dynamic_tiles,
-                max_num=max_dynamic_tiles,
-                size=size_tuple,
-                tile_size=tile_size,
-                use_thumbnail=use_thumbnail,
-                interpolation=interpolation,
-                pad_during_tiling=pad_during_tiling,
-            )
-
-            # Group images by size for batched processing
-            processed_image_patches_grouped = {}
-            # Added for transformers >=4.53.0 compatibility
-            grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
-                image_patches,
-                disable_grouping=disable_grouping,
-            )
-
-            for shape, stacked_image_patches in grouped_image_patches.items():
-                if do_resize:
-                    stacked_image_patches = self.resize(
-                        image=stacked_image_patches,
-                        size=size,
-                        interpolation=interpolation,
-                    )
-                if do_center_crop:
-                    stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
-                # Fused rescale and normalize
-                stacked_image_patches = self.rescale_and_normalize(
-                    stacked_image_patches,
-                    do_rescale,
-                    rescale_factor,
-                    do_normalize,
-                    image_mean,
-                    image_std,
-                )
-                processed_image_patches_grouped[shape] = stacked_image_patches
-            processed_image_patches = reorder_images(
-                processed_image_patches_grouped, grouped_image_patches_index
-            )
-            processed_image_patches = (
-                torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
-            )
-            processed_images.append(processed_image_patches)
-            image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
-
-        if do_pad:
-            processed_images = self._pad_for_batching(processed_images)
-
-        # processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(
-            data={"pixel_values": processed_images, "image_sizes": image_sizes},
-            tensor_type=return_tensors,
-        )
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs],
-    ) -> BatchFeature:
-        validate_kwargs(
-            captured_kwargs=kwargs.keys(),
-            valid_processor_keys=self.valid_kwargs.__annotations__.keys(),
-        )
-        # Set default kwargs from self. This ensures that if a kwarg is not provided
-        # by the user, it gets its default value from the instance, or is set to None.
-        for kwarg_name in self.valid_kwargs.__annotations__:
-            kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
-
-        # Extract parameters that are only used for preparing the input images
-        do_convert_rgb = kwargs.pop("do_convert_rgb")
-        input_data_format = kwargs.pop("input_data_format")
-        device = kwargs.pop("device")
-        # Prepare input images
-        # transformers >= 4.53.0: uses _prepare_image_like_inputs instead of _prepare_input_images
-        if images is not None:
-            images = self._prepare_image_like_inputs(
-                images=images,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        if videos is not None:
-            videos = self._prepare_image_like_inputs(
-                images=videos,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        # Update kwargs that need further processing before being validated
-        kwargs = self._further_process_kwargs(**kwargs)
-
-        # Validate kwargs
-        self._validate_preprocess_kwargs(**kwargs)
-
-        # torch resize uses interpolation instead of resample
-        # Added for transformers >=4.53.0 compatibility
-        resample = kwargs.pop("resample", self.resample)
-        kwargs["interpolation"] = (
-            pil_torch_interpolation_mapping[resample]
-            if isinstance(resample, PILImageResampling | int)
-            else resample
-        )
-
-        # Filter kwargs to only include those accepted by _preprocess
-        valid_preprocess_kwargs = {
-            "do_resize",
-            "size",
-            "max_dynamic_tiles",
-            "min_dynamic_tiles",
-            "use_thumbnail",
-            "pad_during_tiling",
-            "interpolation",
-            "do_center_crop",
-            "crop_size",
-            "do_rescale",
-            "rescale_factor",
-            "do_normalize",
-            "image_mean",
-            "image_std",
-            "do_pad",
-            "return_tensors",
-            "pad_size",
-            "disable_grouping",
-        }
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_preprocess_kwargs}
-        if images is not None:
-            return self._preprocess(images, **filtered_kwargs)
-        elif videos is not None:
-            return self._preprocess(videos, **filtered_kwargs)
-
-
-__all__ = ["Eagle25VLImageProcessorFast"]

src/lerobot/policies/groot/eagle2_hg_model/modeling_eagle2_5_vl.py

@@ -1,396 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import inspect
-
-import torch
-import torch.utils.checkpoint as cp
-from peft import LoraConfig, get_peft_model
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import GenerationConfig
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.models.llama.modeling_llama import LlamaForCausalLM
-from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
-from transformers.models.qwen3.modeling_qwen3 import Qwen3ForCausalLM
-from transformers.models.siglip.modeling_siglip import SiglipVisionModel
-from transformers.utils import add_start_docstrings, logging
-
-from .configuration_eagle2_5_vl import Eagle25VLConfig
-
-logger = logging.get_logger(__name__)
-
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/modeling_llava_onevision.py#L241C1-L280C1
-EAGLE2_5_VL_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`Eagle25VLConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare Eagle2_5_VL Model outputting raw hidden-states without any specific head on top.",
-    EAGLE2_5_VL_START_DOCSTRING,
-)
-class Eagle25VLPreTrainedModel(PreTrainedModel):
-    config_class = Eagle25VLConfig
-    base_model_prefix = "model"
-    main_input_name = "input_ids"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [
-        "Qwen2DecoderLayer",
-        "LlamaDecoderLayer",
-        "Siglip2EncoderLayer",
-        "SiglipEncoderLayer",
-    ]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn = True
-    _supports_flash_attn_2 = True
-    _supports_cache_class = True
-    _supports_static_cache = True
-    _supports_quantized_cache = True
-    _supports_sdpa = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear | nn.Conv2d):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-class Eagle25VLForConditionalGeneration(Eagle25VLPreTrainedModel, GenerationMixin):
-    config_class = Eagle25VLConfig
-
-    def __init__(self, config: Eagle25VLConfig, vision_model=None, language_model=None):
-        super().__init__(config)
-
-        image_size = config.force_image_size or config.vision_config.image_size
-        patch_size = config.vision_config.patch_size
-        self.patch_size = patch_size
-        if config.use_pixel_shuffle:
-            self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio**2))
-        else:
-            self.num_image_token = int((image_size // patch_size) ** 2)
-
-        self.select_layer = config.select_layer
-        self.downsample_ratio = config.downsample_ratio
-        self.loss_version = config.loss_version
-        self.mlp_checkpoint = config.mlp_checkpoint
-        self.use_pixel_shuffle = config.use_pixel_shuffle
-        self.mlp_connector_layers = config.mlp_connector_layers
-        logger.info(f"num_image_token: {self.num_image_token}")
-        logger.info(f"mlp_checkpoint: {self.mlp_checkpoint}")
-        if vision_model is not None:
-            self.vision_model = vision_model
-        else:
-            if config.vision_config.model_type == "siglip_vision_model":
-                config.vision_config._attn_implementation = "flash_attention_2"
-                self.vision_model = SiglipVisionModel(config.vision_config)
-            else:
-                raise NotImplementedError(f"{config.vision_config.model_type} is not implemented.")
-
-        if language_model is not None:
-            self.language_model = language_model
-        else:
-            if config.text_config.architectures[0] == "LlamaForCausalLM":
-                self.language_model = LlamaForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Phi3ForCausalLM":
-                raise NotImplementedError("Phi3 is not implemented.")
-                # self.language_model = Phi3ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen2ForCausalLM":
-                assert config.text_config._attn_implementation == "flash_attention_2", (
-                    f"Qwen2 must use flash_attention_2 but got {config.text_config._attn_implementation}"
-                )
-                self.language_model = Qwen2ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen3ForCausalLM":
-                self.language_model = Qwen3ForCausalLM(config.text_config)
-            else:
-                raise NotImplementedError(f"{config.text_config.architectures[0]} is not implemented.")
-
-        vit_hidden_size = config.vision_config.hidden_size
-        llm_hidden_size = config.text_config.hidden_size
-
-        if config.mlp_connector_layers == 2:
-            self.mlp1 = nn.Sequential(
-                nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-                nn.GELU(),
-                nn.Linear(llm_hidden_size, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and not config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size, llm_hidden_size),
-            )
-        else:
-            raise NotImplementedError(f"{config.mlp_connector_layers} is not implemented.")
-
-        self.image_token_index = config.image_token_index
-        self.neftune_alpha = None
-
-        if config.use_backbone_lora:
-            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
-
-        self.use_llm_lora = config.use_llm_lora
-        if config.use_llm_lora:
-            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
-
-        self.check_forward_kwargs()
-
-    def check_forward_kwargs(self):
-        # We intentionally avoid using **kwargs in forward because Hugging Face Transformers
-        # has special handling for functions with **kwargs parameters that would affect
-        # how our model is processed during training and inference.
-        forward_params = inspect.signature(self.forward).parameters
-        assert not any(k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values())
-
-    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.out_proj",
-                "mlp.fc1",
-                "mlp.fc2",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-        )
-        self.vision_model = get_peft_model(self.vision_model, lora_config)
-        self.vision_model.print_trainable_parameters()
-
-    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.o_proj",
-                "mlp.gate_proj",
-                "mlp.down_proj",
-                "mlp.up_proj",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-            task_type="CAUSAL_LM",
-        )
-        self.language_model = get_peft_model(self.language_model, lora_config)
-        self.language_model.enable_input_require_grads()
-        self.language_model.print_trainable_parameters()
-        self.use_llm_lora = True
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        input_ids: torch.LongTensor = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        image_flags: torch.LongTensor | None = None,
-        past_key_values: list[torch.FloatTensor] | None = None,
-        labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        num_tiles_list: list[torch.Tensor] | None = None,
-    ) -> tuple | CausalLMOutputWithPast:
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        vit_embeds = self.extract_feature(pixel_values)
-
-        if image_flags is not None:
-            image_flags = image_flags.view(-1)
-            vit_embeds = vit_embeds[image_flags == 1]
-
-        b, n, c = input_embeds.shape
-        input_embeds = input_embeds.reshape(b * n, c)
-
-        input_ids = input_ids.reshape(b * n)
-        selected = input_ids == self.image_token_index
-        try:
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, c)
-        except Exception as e:
-            vit_embeds = vit_embeds.reshape(-1, c)
-            print(
-                f"warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, "
-                f"vit_embeds.shape={vit_embeds.shape}"
-            )
-            n_token = selected.sum()
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
-
-        input_embeds = input_embeds.reshape(b, n, c)
-
-        outputs = self.language_model(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        logits = outputs.logits
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def pixel_shuffle(self, x, scale_factor=0.5):
-        n, w, h, c = x.size()
-        # N, W, H, C --> N, W, H * scale, C // scale
-        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
-        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
-        x = x.permute(0, 2, 1, 3).contiguous()
-        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
-        x = x.view(n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor)))
-
-        x = x.permute(0, 2, 1, 3).contiguous()
-        return x
-
-    def extract_feature(self, pixel_values):
-        if self.select_layer == -1:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
-            )
-            if hasattr(vit_embeds, "last_hidden_state"):
-                vit_embeds = vit_embeds.last_hidden_state
-
-        else:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
-            ).hidden_states[self.select_layer]
-
-        if self.use_pixel_shuffle:
-            h = w = int(vit_embeds.shape[1] ** 0.5)
-            vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
-            vit_embeds = self.pixel_shuffle(
-                vit_embeds, scale_factor=self.downsample_ratio
-            )  # torch.Size([B, 1024, 1024]) -> torch.Size([B, 16, 16, 4096])
-            vit_embeds = vit_embeds.reshape(
-                vit_embeds.shape[0], -1, vit_embeds.shape[-1]
-            )  # torch.Size([B, 16, 16, 4096]) -> torch.Size([B, 256, 4096])
-
-        if self.mlp_checkpoint and vit_embeds.requires_grad:
-            vit_embeds = cp.checkpoint(self.mlp1, vit_embeds)
-        else:
-            vit_embeds = self.mlp1(vit_embeds)
-
-        return vit_embeds
-
-    @torch.no_grad()
-    def generate(
-        self,
-        pixel_values: torch.FloatTensor | None = None,
-        input_ids: torch.FloatTensor | None = None,
-        attention_mask: torch.LongTensor | None = None,
-        visual_features: torch.FloatTensor | None = None,
-        generation_config: GenerationConfig | None = None,
-        output_hidden_states: bool | None = None,
-        image_sizes: list[tuple[int, int]] | None = None,
-        **generate_kwargs,
-    ) -> torch.LongTensor:
-        if pixel_values is not None:
-            if visual_features is not None:
-                vit_embeds = visual_features
-            else:
-                vit_embeds = self.extract_feature(pixel_values)
-
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-            b, n, c = input_embeds.shape
-            input_embeds = input_embeds.reshape(b * n, c)
-
-            input_ids = input_ids.reshape(b * n)
-            selected = input_ids == self.config.image_token_index
-            assert selected.sum() != 0
-            input_embeds[selected] = vit_embeds.reshape(-1, c).to(input_embeds.device)
-
-            input_embeds = input_embeds.reshape(b, n, c)
-        else:
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        if "use_cache" not in generate_kwargs:
-            generate_kwargs["use_cache"] = True
-
-        outputs = self.language_model.generate(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            generation_config=generation_config,
-            output_hidden_states=output_hidden_states,
-            **generate_kwargs,
-        )
-
-        return outputs
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.language_model.get_input_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.language_model.set_input_embeddings(value)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.language_model.get_output_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.language_model.set_output_embeddings(new_embeddings)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder
-    def set_decoder(self, decoder):
-        self.language_model.set_decoder(decoder)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder
-    def get_decoder(self):
-        return self.language_model.get_decoder()

src/lerobot/policies/groot/eagle2_hg_model/processing_eagle2_5_vl.py

@@ -1,541 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Processor class for Eagle25VL.
-copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/processing_llava_onevision.py
-"""
-
-import base64
-import os
-import re
-from io import BytesIO
-
-import requests
-import torch
-from PIL import Image
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_utils import ImageInput
-from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-from transformers.utils import logging
-from transformers.video_utils import VideoInput
-
-logger = logging.get_logger(__name__)
-
-
-FRAME_FACTOR = 2
-FPS = 2.0
-FPS_MIN_FRAMES = 4
-FPS_MAX_FRAMES = 256
-
-
-def to_rgb(pil_image: Image.Image) -> Image.Image:
-    if pil_image.mode == "RGBA":
-        white_background = Image.new("RGB", pil_image.size, (255, 255, 255))
-        white_background.paste(pil_image, mask=pil_image.split()[3])  # Use alpha channel as mask
-        return white_background
-    else:
-        return pil_image.convert("RGB")
-
-
-def fetch_image(ele: dict[str, str | Image.Image]) -> Image.Image:
-    image = ele["image"] if "image" in ele else ele["image_url"]
-    image_obj = None
-    if isinstance(image, Image.Image):
-        image_obj = image
-    elif image.startswith("http://") or image.startswith("https://"):
-        response = requests.get(image, stream=True, timeout=10)
-        image_obj = Image.open(BytesIO(response.content))
-    elif image.startswith("file://"):
-        image_obj = Image.open(image[7:])
-    elif image.startswith("data:image"):
-        if "base64," in image:
-            _, base64_data = image.split("base64,", 1)
-            data = base64.b64decode(base64_data)
-            image_obj = Image.open(BytesIO(data))
-    else:
-        image_obj = Image.open(image)
-    if image_obj is None:
-        raise ValueError(
-            f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}"
-        )
-    image = to_rgb(image_obj)
-    if "scale_factor" in ele:
-        scale_factor = ele["scale_factor"]
-        image = image.resize((image.width * scale_factor, image.height * scale_factor), Image.BILINEAR)
-    return image
-
-
-class Eagle25VLProcessorKwargs(ProcessingKwargs, total=False):
-    # see processing_utils.ProcessingKwargs documentation for usage.
-    _defaults = {
-        "text_kwargs": {
-            "padding": False,
-        },
-        "images_kwargs": {},
-        "videos_kwargs": {"max_dynamic_tiles": 1},
-    }
-
-
-class Eagle25VLProcessor(ProcessorMixin):
-    r"""
-    Constructs a Eagle25VL processor which wraps a Eagle25VL video processor, Eagle25VL image processor and a Eagle25VL tokenizer into a single processor.
-
-    [`Eagle25VLProcessor`] offers all the functionalities of [`Eagle25VLVideoProcessor`], [`Eagle25VLImageProcessor`] and [`Eagle25VLTokenizer`]. See the
-    [`~Eagle25VLVideoProcessor.__call__`], [`~Eagle25VLProcessor.__call__`] and [`~Eagle25VLProcessor.decode`] for more information.
-
-    Args:
-        image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
-            The image processor is a required input.
-        tokenizer ([`LlamaTokenizerFast`], *optional*):
-            The tokenizer is a required input.
-        num_image_tokens (`int`, *optional*):
-            Number of image tokens for one imagethat will be returned by vision tower.
-        vision_feature_select_strategy (`str`, *optional*):
-            The feature selection strategy used to select the vision feature from the vision backbone.
-            Should be same as in model's config
-        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
-            in a chat into a tokenizable string.
-        image_token (`str`, *optional*, defaults to `"<image>"`):
-            Special token used to denote image location.
-        video_token (`str`, *optional*, defaults to `"<video>"`):
-            Special token used to denote video location.
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    valid_kwargs = [
-        "chat_template",
-        "num_image_tokens",
-        "vision_feature_select_strategy",
-        "image_token",
-        "video_token",
-        "images_kwargs",
-        "videos_kwargs",
-        "text_kwargs",
-    ]
-    tokenizer_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        image_processor=None,
-        tokenizer=None,
-        vision_feature_select_strategy=None,
-        chat_template=None,
-        image_token="<IMG_CONTEXT>",  # nosec: B107
-        video_token="<IMG_CONTEXT>",  # nosec: B107
-        tokens_per_tile=256,
-        image_placeholder="image",
-        video_placeholder="video",
-        image_start_token="<img>",
-        image_end_token="</img>",
-        **kwargs,
-    ):
-        self.vision_feature_select_strategy = vision_feature_select_strategy
-        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
-        self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
-        self.image_token_id = (
-            tokenizer.image_token_id
-            if getattr(tokenizer, "image_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.image_token)
-        )
-        self.video_token_id = (
-            tokenizer.video_token_id
-            if getattr(tokenizer, "video_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.video_token)
-        )
-        self.image_placeholder = image_placeholder
-        self.video_placeholder = video_placeholder
-        self.tokens_per_tile = tokens_per_tile
-        self.image_start_token = image_start_token
-        self.image_end_token = image_end_token
-        if "auto_map" in kwargs:
-            self.auto_map = kwargs["auto_map"]
-        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-
-    def replace_media_placeholder(
-        self, text, image_list, video_list, timestamps_list, fps_list, **output_kwargs
-    ):
-        num_of_images_in_this_sample = 0
-        num_of_videos_in_this_sample = 0
-        # Regular expression pattern to match formats like <image-1> or <video-2>
-        pattern = re.compile(rf"<({self.image_placeholder}|{self.video_placeholder})-(\d+)>")
-        unified_frame_list = []
-
-        # image_min_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        # )
-        # image_max_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        # )
-        # image_use_thumbnail = output_kwargs["images_kwargs"].get(
-        #     "use_thumbnail", self.image_processor.use_thumbnail
-        # )
-        video_min_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        )
-        video_max_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        )
-        video_use_thumbnail = output_kwargs["videos_kwargs"].get(
-            "use_thumbnail", self.image_processor.use_thumbnail
-        )
-
-        tile_size = self.image_processor.size.get("height", 448)
-
-        # Function to replace tags in a single text
-        def replace_in_text(text):
-            # repl callback function for each match replacement operation
-            def repl(match):
-                nonlocal unified_frame_list
-                nonlocal num_of_images_in_this_sample
-                nonlocal num_of_videos_in_this_sample
-                media_type = match.group(1)  # 'image' or 'video'
-                idx_in_list = int(match.group(2)) - 1  # Convert to list index (0-based)
-                # Select the corresponding path based on media type
-                idx_mapper = {
-                    0: "first",
-                    1: "second",
-                    2: "third",
-                    3: "fourth",
-                    4: "fifth",
-                    5: "sixth",
-                    6: "seventh",
-                    7: "eighth",
-                    8: "ninth",
-                    9: "tenth",
-                }
-                if media_type == "image":
-                    image_inputs = self.image_processor(
-                        images=[image_list[idx_in_list]],
-                        videos=None,
-                        **output_kwargs["images_kwargs"],
-                    )
-                    if isinstance(image_inputs["pixel_values"], list):
-                        _pv = image_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        image_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = image_inputs["pixel_values"].shape[0]
-                    special_placeholder = f"<image {idx_in_list + 1}>{self.image_start_token}{self.image_token * num_all_tiles * self.tokens_per_tile}{self.image_end_token}"
-                    unified_frame_list.append(image_inputs)
-                    num_of_images_in_this_sample += 1
-
-                elif media_type == "video":
-                    video_inputs = self.image_processor(
-                        images=None,
-                        videos=[video_list[idx_in_list]],
-                        **output_kwargs["videos_kwargs"],
-                    )
-                    if isinstance(video_inputs["pixel_values"], list):
-                        _pv = video_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        video_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = video_inputs["pixel_values"].shape[0]
-                    image_sizes = video_inputs["image_sizes"]
-                    if timestamps_list is not None and -1 not in timestamps_list:
-                        frame_timestamps = timestamps_list[idx_in_list]
-                    else:
-                        frame_timestamps = None
-                    sampled_fps = fps_list[idx_in_list] if fps_list is not None else None
-
-                    num_of_tiles_each_frame = [
-                        self.get_number_tiles_based_on_image_size(
-                            image_size,
-                            video_min_dynamic_tiles,
-                            video_max_dynamic_tiles,
-                            video_use_thumbnail,
-                            tile_size,
-                        )
-                        for image_size in image_sizes
-                    ]
-                    assert sum(num_of_tiles_each_frame) == num_all_tiles, (
-                        f"The number of tiles in each frame is not equal to the total number of tiles: {sum(num_of_tiles_each_frame)} != {num_all_tiles}"
-                    )
-
-                    if frame_timestamps is not None:
-                        assert len(frame_timestamps) == len(num_of_tiles_each_frame), (
-                            f"The number of timestamps is not equal to the number of frames: {len(frame_timestamps)} != {len(num_of_tiles_each_frame)}"
-                        )
-                        special_placeholder = [
-                            f"Frame {i + 1} sample at {frame_timestamps[i]:.2f}s: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-                    else:
-                        special_placeholder = [
-                            f"Frame {i + 1}: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-
-                    if sampled_fps is not None:
-                        special_placeholder = (
-                            f"The {idx_mapper[idx_in_list]} video sampled with {sampled_fps:.2f} fps: "
-                            + "".join(special_placeholder)
-                        )
-                    else:
-                        special_placeholder = f"The {idx_mapper[idx_in_list]} video: " + "".join(
-                            special_placeholder
-                        )
-                    unified_frame_list.append(video_inputs)
-                    num_of_videos_in_this_sample += 1
-                else:
-                    raise ValueError(f"Unknown media type: {media_type}")
-                return special_placeholder
-
-            return pattern.sub(repl, text)
-
-        text = replace_in_text(text)
-        if len(unified_frame_list) > 0:
-
-            def _to_tensor(v):
-                if isinstance(v, torch.Tensor):
-                    return v
-                if isinstance(v, list):
-                    if v and isinstance(v[0], list):
-                        v = [t for sub in v for t in sub]
-                    return torch.stack([t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in v])
-                return torch.as_tensor(v)
-
-            pixel_values = torch.cat([_to_tensor(frame["pixel_values"]) for frame in unified_frame_list])
-            image_sizes = torch.cat([_to_tensor(frame["image_sizes"]) for frame in unified_frame_list])
-        else:
-            pixel_values = None
-            image_sizes = None
-        return (
-            text,
-            pixel_values,
-            image_sizes,
-            num_of_images_in_this_sample,
-            num_of_videos_in_this_sample,
-        )
-
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        audio=None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLProcessorKwargs],
-    ) -> BatchFeature:
-        """
-        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
-        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
-        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
-        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
-        of the above two methods for more information.
-
-        Args:
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. Both channels-first and channels-last formats are supported.
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
-            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
-
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
-            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
-        """
-
-        output_kwargs = self._merge_kwargs(
-            Eagle25VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if isinstance(text, str):
-            text_list = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-        elif isinstance(text, list) and isinstance(text[0], str):
-            text_list = text
-
-        if images is None:
-            images = []
-        if videos is None:
-            videos = []
-
-        pixel_values_list = []
-        image_sizes_list = []
-        new_sample_list = []
-        image_start_idx = 0
-        video_start_idx = 0
-        timestamps_batch = output_kwargs["videos_kwargs"].pop("timestamps", None)
-        fps_batch = output_kwargs["videos_kwargs"].pop("fps", None)
-        for sample in text_list:
-            timestamps_list = timestamps_batch[video_start_idx:] if timestamps_batch is not None else None
-            fps_list = fps_batch[video_start_idx:] if fps_batch is not None else None
-            (
-                sample,
-                pixel_values,
-                image_sizes,
-                num_of_images_in_this_sample,
-                num_of_videos_in_this_sample,
-            ) = self.replace_media_placeholder(
-                sample,
-                images[image_start_idx:],
-                videos[video_start_idx:],
-                timestamps_list,
-                fps_list,
-                **output_kwargs,
-            )
-            new_sample_list.append(sample)
-            if pixel_values is not None:
-                pixel_values_list.append(pixel_values)
-                image_sizes_list.append(image_sizes)
-            image_start_idx += num_of_images_in_this_sample
-            video_start_idx += num_of_videos_in_this_sample
-
-        if len(pixel_values_list) > 0:
-            image_inputs = {
-                "pixel_values": torch.cat(pixel_values_list),
-                "image_sizes": torch.cat(image_sizes_list),
-            }
-        else:
-            image_inputs = {}
-        video_inputs = {}
-        text_inputs = self.tokenizer(new_sample_list, **output_kwargs["text_kwargs"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs})
-
-    def get_number_tiles_based_on_image_size(
-        self, image_size: tuple, min_num: int, max_num: int, use_thumbnail: bool, tile_size: int
-    ) -> int:
-        """
-        Get the number of tiles based on the image size.
-        """
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.image_processor.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-        tiles_num = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if use_thumbnail and tiles_num > 1:
-            tiles_num += 1
-        return tiles_num
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
-        the docstring of this method for more information.
-        """
-        return self.tokenizer.decode(*args, **kwargs)
-
-    @property
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
-
-    # override to save video-config in a separate config file
-    def save_pretrained(self, save_directory, **kwargs):
-        if os.path.isfile(save_directory):
-            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
-        os.makedirs(save_directory, exist_ok=True)
-
-        outputs = super().save_pretrained(save_directory, **kwargs)
-        return outputs
-
-    # override to load video-config from a separate config file
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
-        if isinstance(processor, tuple):
-            processor = processor[0]
-        return processor
-
-    # Copy from https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
-    def process_vision_info(
-        self,
-        conversations: list[dict] | list[list[dict]],
-        return_video_kwargs: bool = False,
-    ) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None, dict | None]:
-        vision_infos = self.extract_vision_info(conversations)
-        ## Read images or videos
-        image_inputs = []
-        video_inputs = []
-        video_sample_fps_list = []
-        video_timestamps_list = []
-        for vision_info in vision_infos:
-            if "image" in vision_info or "image_url" in vision_info:
-                image_inputs.append(fetch_image(vision_info))
-            else:
-                raise ValueError("image, image_url or video should in content.")
-        if len(image_inputs) == 0:
-            image_inputs = None
-        if len(video_inputs) == 0:
-            video_inputs = None
-        if return_video_kwargs:
-            return (
-                image_inputs,
-                video_inputs,
-                {"fps": video_sample_fps_list, "timestamps": video_timestamps_list},
-            )
-        return image_inputs, video_inputs
-
-    def extract_vision_info(self, conversations: list[dict] | list[list[dict]]) -> list[dict]:
-        vision_infos = []
-        if isinstance(conversations[0], dict):
-            conversations = [conversations]
-        for conversation in conversations:
-            for message in conversation:
-                if isinstance(message["content"], list):
-                    for ele in message["content"]:
-                        if (
-                            "image" in ele
-                            or "image_url" in ele
-                            or "video" in ele
-                            or ele["type"] in ("image", "image_url", "video")
-                        ):
-                            vision_infos.append(ele)
-        return vision_infos
-
-
-__all__ = ["Eagle25VLProcessor"]

src/lerobot/policies/groot/groot_n1.py

@@ -1,380 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-import numpy as np
-import torch
-import torch.nn as nn
-from huggingface_hub import snapshot_download
-from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from huggingface_hub.dataclasses import strict
-    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-
-    def strict(cls):
-        return cls
-
-    AutoConfig = None
-    AutoModel = None
-    PretrainedConfig = object
-    PreTrainedModel = object
-    BatchFeature = None
-
-try:
-    import tree
-except ImportError:
-    tree = None
-
-from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME
-
-from .action_head.flow_matching_action_head import (
-    FlowmatchingActionHead,
-    FlowmatchingActionHeadConfig,
-)
-from .utils import ensure_eagle_cache_ready
-
-DEFAULT_VENDOR_EAGLE_PATH = str((Path(__file__).resolve().parent / "eagle2_hg_model").resolve())
-DEFAULT_TOKENIZER_ASSETS_REPO = "lerobot/eagle2hg-processor-groot-n1p5"
-
-
-class EagleBackbone(nn.Module):
-    def __init__(
-        self,
-        tune_llm: bool = False,
-        tune_visual: bool = False,
-        select_layer: int = -1,
-        reproject_vision: bool = False,
-        use_flash_attention: bool = False,
-        load_bf16: bool = False,
-        eagle_path: str = DEFAULT_VENDOR_EAGLE_PATH,
-        tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO,
-        project_to_dim: int = 1536,
-    ):
-        """
-        Args:
-            tune_llm: whether to tune the LLM model (default: True)
-            tune_visual: whether to tune the visual model (default: False)
-        """
-        super().__init__()
-        assert not reproject_vision, "Reproject vision is not implemented here, set to False"
-
-        # Prefer loading Eagle model config from the cache directory where vendor files were copied.
-        vendor_dir = DEFAULT_VENDOR_EAGLE_PATH
-        cache_dir = HF_LEROBOT_HOME / tokenizer_assets_repo
-        try:
-            ensure_eagle_cache_ready(vendor_dir, cache_dir, tokenizer_assets_repo)
-        except Exception as exc:  # nosec: B110
-            print(f"[GROOT] Warning: failed to prepare Eagle cache for backbone: {exc}")
-
-        config = AutoConfig.from_pretrained(str(cache_dir), trust_remote_code=True)
-        self.eagle_model = AutoModel.from_config(config, trust_remote_code=True)
-
-        if project_to_dim is not None:
-            self.eagle_linear = torch.nn.Linear(2048, project_to_dim)
-        else:
-            self.eagle_linear = torch.nn.Identity()
-
-        # needed since we don't use these layers. Also saves compute
-        while len(self.eagle_model.language_model.model.layers) > select_layer:
-            self.eagle_model.language_model.model.layers.pop(-1)
-
-        self.select_layer = select_layer
-        self.set_trainable_parameters(tune_llm, tune_visual)
-
-    def set_trainable_parameters(self, tune_llm: bool, tune_visual: bool):
-        self.tune_llm = tune_llm
-        self.tune_visual = tune_visual
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_llm:
-            self.eagle_model.language_model.requires_grad_(False)
-        if not tune_visual:
-            self.eagle_model.vision_model.requires_grad_(False)
-            self.eagle_model.mlp1.requires_grad_(False)
-        print(f"Tune backbone llm: {self.tune_llm}")
-        print(f"Tune backbone visual: {self.tune_visual}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_llm and not tune_visual:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Backbone trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No backbone trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if self.eagle_model.language_model and not self.tune_llm:
-                self.eagle_model.language_model.eval()
-            if self.eagle_model.vision_model and not self.tune_visual:
-                self.eagle_model.vision_model.eval()
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def forward_eagle(self, vl_input: BatchFeature) -> BatchFeature:
-        eagle_prefix = "eagle_"
-        eagle_input = {
-            k.removeprefix(eagle_prefix): v for k, v in vl_input.items() if k.startswith(eagle_prefix)
-        }
-        del eagle_input["image_sizes"]
-
-        eagle_output = self.eagle_model(**eagle_input, output_hidden_states=True, return_dict=True)
-        eagle_features = eagle_output.hidden_states[self.select_layer]
-
-        eagle_features = self.eagle_linear(eagle_features)
-        return eagle_features, eagle_input["attention_mask"]
-
-    def forward(self, vl_input: BatchFeature) -> BatchFeature:
-        self.set_frozen_modules_to_eval_mode()
-
-        eagle_embeds, eagle_mask = self.forward_eagle(vl_input)
-
-        # YL (TODO HACK): to resolve DDP issue when tune_visual=True
-        # Ensure all trainable parameters in vision_model are used in the forward pass for DDP compatibility
-        if self.training and self.tune_visual:
-            dummy_term = torch.tensor(
-                0.0, device=eagle_embeds.device, dtype=eagle_embeds.dtype, requires_grad=True
-            )
-            for param in self.eagle_model.vision_model.parameters():
-                if param.requires_grad:
-                    dummy_term = dummy_term + 0.0 * param.sum()
-            eagle_embeds = eagle_embeds + dummy_term
-
-        return BatchFeature(
-            data={"backbone_features": eagle_embeds, "backbone_attention_mask": eagle_mask}
-        )  # [B, T2, hidden_size]
-
-
-BACKBONE_FEATURE_KEY = "backbone_features"
-ACTION_KEY = "action_pred"
-LOSS_KEY = "loss"
-ERROR_MSG = "Error: unexpected input/output"
-N_COLOR_CHANNELS = 3
-
-
-# config
-@strict
-class GR00TN15Config(PretrainedConfig):
-    model_type = "gr00t_n1_5"
-
-    backbone_cfg: dict[str, Any] | None = None
-    action_head_cfg: dict[str, Any] | None = None
-    action_horizon: int = 0
-    action_dim: int = 0
-    compute_dtype: str = "float32"
-
-    def __post_init__(self, **kwargs):
-        self.backbone_cfg = {} if self.backbone_cfg is None else self.backbone_cfg
-        self.action_head_cfg = {} if self.action_head_cfg is None else self.action_head_cfg
-        super().__post_init__(**kwargs)
-
-
-# real model
-class GR00TN15(PreTrainedModel):
-    supports_gradient_checkpointing = True
-    config_class = GR00TN15Config
-    """
-    we expect the backbone output to have a key 'backbone_features' with shape (batch_size, n, hidden_size)
-    here n is variable and can be e.g. time, 1 or user specified
-    we expect the action head output to have a key 'action_pred' with shape (batch_size, time, action_dim) during inference time
-    we expect these to have type BatchFeature, and they can of course have many other user specified keys too
-    """
-
-    def __init__(
-        self,
-        config: GR00TN15Config,
-        local_model_path: str,
-    ):
-        assert isinstance(config.backbone_cfg, dict)
-        assert isinstance(config.action_head_cfg, dict)
-
-        super().__init__(config)
-        self.local_model_path = local_model_path
-
-        self.backbone = EagleBackbone(**config.backbone_cfg)
-        action_head_cfg = FlowmatchingActionHeadConfig(**config.action_head_cfg)
-        self.action_head = FlowmatchingActionHead(action_head_cfg)
-
-        self.action_horizon = config.action_horizon
-        self.action_dim = config.action_dim
-        self.compute_dtype = config.compute_dtype
-        self.post_init()
-
-    def validate_inputs(self, inputs):
-        # NOTE -- this should be handled internally by the model
-        # however, doing that will likely be breaking changes -- so we'll need to do it after the deadline
-
-        detected_error = False
-        error_msg = ERROR_MSG
-        if ACTION in inputs:
-            action = inputs[ACTION]
-            # In inference, action may be omitted or None; validate only when it's a tensor.
-            if action is None:
-                pass  # allow None during inference
-            elif isinstance(action, torch.Tensor):
-                shape_ok = (
-                    len(action.shape) == 3
-                    and action.shape[1] == self.action_horizon
-                    and action.shape[2] == self.action_dim
-                )
-                if not shape_ok:
-                    error_msg += f"\n{action.shape=}"
-                    detected_error = True
-            else:
-                # Unexpected non-tensor type provided for action
-                error_msg += f"\nInvalid type for action: {type(action)}"
-                detected_error = True
-
-        if "video" in inputs:
-            video = inputs["video"]
-            type_ok = isinstance(video, np.ndarray)
-            dtype_ok = video.dtype == np.uint8
-            shape_ok = len(video.shape) == 6 and video.shape[3] == N_COLOR_CHANNELS
-            if not type_ok:
-                error_msg += f"\n{type(video)=}"
-                detected_error = True
-            if not dtype_ok:
-                error_msg += f"\n{video.dtype=}"
-                detected_error = True
-            if not shape_ok:
-                error_msg += f"\n{video.shape=}"
-                detected_error = True
-
-        if detected_error:
-            raise ValueError(error_msg)
-
-    def validate_data(self, action_head_outputs, backbone_outputs, is_training):
-        fail_backbone = (
-            not isinstance(backbone_outputs, BatchFeature) or BACKBONE_FEATURE_KEY not in backbone_outputs
-        )
-
-        if fail_backbone:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(backbone_outputs, BatchFeature)=}"
-            error_msg += f"\n{BACKBONE_FEATURE_KEY in backbone_outputs=}"
-            error_msg += f"\n{backbone_outputs[BACKBONE_FEATURE_KEY].shape=}"
-            raise ValueError(error_msg)
-
-        fail_action_head = (not isinstance(action_head_outputs, BatchFeature)) or not (
-            (
-                LOSS_KEY in action_head_outputs and is_training
-            )  # there might not be an action prediction during training
-            or (
-                ACTION_KEY in action_head_outputs
-                and action_head_outputs[ACTION_KEY].shape[1] == self.action_horizon
-                and action_head_outputs[ACTION_KEY].shape[2] == self.action_dim
-            )
-        )
-
-        if fail_action_head:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(action_head_outputs, BatchFeature)=}"
-            error_msg += f"\n{LOSS_KEY in action_head_outputs=}"
-            error_msg += f"\n{action_head_outputs[ACTION_KEY].shape=}"
-            error_msg += f"\n{self.action_horizon=}"
-            error_msg += f"\n{self.action_dim=}"
-            raise ValueError(error_msg)
-
-    def forward(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=True)
-        return action_head_outputs
-
-    def get_action(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        # Because the behavior of backbones remains the same for training and inference, we can use `forward` for backbones.
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head.get_action(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=False)
-        return action_head_outputs
-
-    def prepare_input(self, inputs) -> tuple[BatchFeature, BatchFeature]:
-        self.validate_inputs(inputs)
-        backbone_inputs = self.backbone.prepare_input(inputs)
-        action_inputs = self.action_head.prepare_input(inputs)
-
-        def to_device_with_maybe_dtype(x):
-            # Cast floating tensors to a memory-efficient compute dtype when requested.
-            # Rationale: Upcasting backbone activations to fp32 significantly increases VRAM.
-            # When compute_dtype is bfloat16, prefer bf16 for activations to match AMP behavior.
-            if not isinstance(x, torch.Tensor):
-                return x
-            if torch.is_floating_point(x):
-                if getattr(self, "compute_dtype", None) == "bfloat16":
-                    return x.to(self.device, dtype=torch.bfloat16)
-                # Fallback: preserve previous behavior if not using bf16 compute
-                return x.to(self.device, dtype=self.action_head.dtype)
-            # Non-floating tensors: move device only
-            return x.to(self.device)
-
-        backbone_inputs = tree.map_structure(to_device_with_maybe_dtype, backbone_inputs)
-        action_inputs = tree.map_structure(to_device_with_maybe_dtype, action_inputs)
-        return backbone_inputs, action_inputs
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
-        tune_visual = kwargs.pop("tune_visual", True)
-        tune_llm = kwargs.pop("tune_llm", False)
-        tune_projector = kwargs.pop("tune_projector", True)
-        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
-
-        print(f"Loading pretrained dual brain from {pretrained_model_name_or_path}")
-        print(f"Tune backbone vision tower: {tune_visual}")
-        print(f"Tune backbone LLM: {tune_llm}")
-        print(f"Tune action head projector: {tune_projector}")
-        print(f"Tune action head DiT: {tune_diffusion_model}")
-
-        # get the current model path being downloaded
-        try:
-            # NOTE(YL) This downloads the model to the local cache and returns the local path to the model
-            # saved in ~/.cache/huggingface/hub/
-            local_model_path = snapshot_download(pretrained_model_name_or_path, repo_type="model")
-            # HFValidationError, RepositoryNotFoundError
-        except (HFValidationError, RepositoryNotFoundError):
-            print(
-                f"Model not found or avail in the huggingface hub. Loading from local path: {pretrained_model_name_or_path}"
-            )
-            local_model_path = pretrained_model_name_or_path
-
-        pretrained_model = super().from_pretrained(
-            local_model_path, local_model_path=local_model_path, **kwargs
-        )
-
-        pretrained_model.backbone.set_trainable_parameters(tune_visual=tune_visual, tune_llm=tune_llm)
-        pretrained_model.action_head.set_trainable_parameters(
-            tune_projector=tune_projector, tune_diffusion_model=tune_diffusion_model
-        )
-        return pretrained_model

src/lerobot/policies/groot/groot_n1_7.py

@@ -0,0 +1,951 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+from contextlib import suppress
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
+from torch import nn
+from torch.distributions import Beta
+
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+from .action_head.cross_attention_dit import AlternateVLDiT, DiT, SelfAttentionTransformer
+from .configuration_groot import N1_7_DEFAULT_IMAGE_CROP_SIZE, N1_7_DEFAULT_IMAGE_TARGET_SIZE
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import (
+        AutoConfig,
+        AutoModel,
+        PretrainedConfig,
+        PreTrainedModel,
+        Qwen3VLConfig,
+        Qwen3VLForConditionalGeneration,
+    )
+    from transformers.feature_extraction_utils import BatchFeature
+else:
+    AutoConfig = None
+    AutoModel = None
+    PretrainedConfig = object
+    PreTrainedModel = object
+    BatchFeature = None
+    Qwen3VLConfig = None
+    Qwen3VLForConditionalGeneration = None
+
+try:
+    import tree
+except ImportError:
+    tree = None
+
+logger = logging.getLogger(__name__)
+
+
+def _tie_unused_qwen_lm_head(model: nn.Module) -> None:
+    """Restore the TF4 weight tie so the unused LM head stays frozen and is omitted on save."""
+    lm_head = getattr(model, "lm_head", None)
+    get_input_embeddings = getattr(model, "get_input_embeddings", None)
+    if lm_head is None or not callable(get_input_embeddings):
+        return
+    input_embeddings = get_input_embeddings()
+    embedding_weight = getattr(input_embeddings, "weight", None)
+    if embedding_weight is None:
+        return
+    lm_head.weight = embedding_weight
+
+
+GR00T_N1_7_DEFAULTS: dict[str, Any] = {
+    "model_dtype": "bfloat16",
+    "dtype": "bfloat16",
+    "model_name": "nvidia/Cosmos-Reason2-2B",
+    "backbone_model_type": "qwen",
+    "model_revision": None,
+    "tune_top_llm_layers": 0,
+    "backbone_embedding_dim": 2048,
+    "tune_llm": False,
+    "tune_visual": False,
+    "select_layer": 16,
+    "reproject_vision": False,
+    "use_flash_attention": False,
+    "load_bf16": False,
+    "backbone_trainable_params_fp32": True,
+    "image_crop_size": N1_7_DEFAULT_IMAGE_CROP_SIZE,
+    "image_target_size": N1_7_DEFAULT_IMAGE_TARGET_SIZE,
+    "shortest_image_edge": None,
+    "crop_fraction": None,
+    "random_rotation_angle": None,
+    "color_jitter_params": None,
+    "use_albumentations_transforms": True,
+    "extra_augmentation_config": None,
+    "formalize_language": True,
+    "apply_sincos_state_encoding": False,
+    "use_percentiles": True,
+    "use_relative_action": False,
+    "max_state_dim": 132,
+    "max_action_dim": 132,
+    "action_horizon": 40,
+    "hidden_size": 1024,
+    "input_embedding_dim": 1536,
+    "state_history_length": 1,
+    "add_pos_embed": True,
+    "attn_dropout": 0.2,
+    "use_vlln": True,
+    "max_seq_len": 1024,
+    "use_alternate_vl_dit": True,
+    "attend_text_every_n_blocks": 2,
+    "diffusion_model_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 32,
+        "num_attention_heads": 32,
+        "attention_head_dim": 48,
+        "norm_type": "ada_norm",
+        "dropout": 0.2,
+        "final_dropout": True,
+        "output_dim": 1024,
+        "interleave_self_attention": True,
+    },
+    "vl_self_attention_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 4,
+        "num_attention_heads": 32,
+        "attention_head_dim": 64,
+        "dropout": 0.2,
+        "final_dropout": True,
+    },
+    "num_inference_timesteps": 4,
+    "noise_beta_alpha": 1.5,
+    "noise_beta_beta": 1.0,
+    "noise_s": 0.999,
+    "num_timestep_buckets": 1000,
+    "tune_projector": True,
+    "tune_diffusion_model": True,
+    "tune_vlln": True,
+    "state_dropout_prob": 0.2,
+    "exclude_state": False,
+    "use_mean_std": False,
+    "max_num_embodiments": 32,
+    "rtc_ramp_rate": 6.0,
+}
+
+
+class GR00TN17Config(PretrainedConfig):
+    """Configuration for NVIDIA GR00T N1.7.
+
+    N1.7 uses the Cosmos-Reason2-2B / Qwen3-VL backbone and a multi-embodiment
+    flow-matching action head. This mirrors the public N1.7 checkpoint config
+    while keeping it local to LeRobot and independent from the external
+    Isaac-GR00T ``gr00t`` Python package.
+    """
+
+    model_type = "Gr00tN1d7"
+
+    _defaults = GR00T_N1_7_DEFAULTS
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in GR00T_N1_7_DEFAULTS.items():
+            setattr(self, key, deepcopy(kwargs.pop(key, value)))
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class CategorySpecificLinear(nn.Module):
+    """Linear layer with category-specific weights for multi-embodiment support."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int):
+        super().__init__()
+        self.num_categories = num_categories
+        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
+        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        selected_w = self.W[cat_ids]
+        selected_b = self.b[cat_ids]
+        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
+
+
+class CategorySpecificMLP(nn.Module):
+    """Two-layer MLP with category-specific weights."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int, output_dim: int):
+        super().__init__()
+        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
+        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        hidden = F.relu(self.layer1(x, cat_ids))
+        return self.layer2(hidden, cat_ids)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    """Sinusoidal encoding of shape ``(B, T, D)`` for timestep tensors ``(B, T)``.
+
+    The frequency scalar is intentionally created on CPU and then broadcast with
+    the device-local arange result. That mirrors Isaac-GR00T's N1.7 timestep
+    embedding and avoids tiny dtype/device construction differences in parity
+    tests.
+    """
+
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        timesteps = timesteps.float()
+        half_dim = self.embedding_dim // 2
+        exponent = -torch.arange(half_dim, dtype=torch.float, device=timesteps.device) * (
+            torch.log(torch.tensor(10000.0)) / half_dim
+        )
+        freqs = timesteps.unsqueeze(-1) * exponent.exp()
+        return torch.cat([torch.sin(freqs), torch.cos(freqs)], dim=-1)
+
+
+def swish(x: torch.Tensor) -> torch.Tensor:
+    return x * torch.sigmoid(x)
+
+
+class MultiEmbodimentActionEncoder(nn.Module):
+    """Action encoder with category-specific projections and sinusoidal time encoding."""
+
+    def __init__(self, action_dim: int, hidden_size: int, num_embodiments: int):
+        super().__init__()
+        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)
+        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)
+        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
+
+    def forward(self, actions: torch.Tensor, timesteps: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        batch_size, horizon, _ = actions.shape
+        if timesteps.dim() != 1 or timesteps.shape[0] != batch_size:
+            raise ValueError("Expected `timesteps` to have shape (B,).")
+        timesteps = timesteps.unsqueeze(1).expand(-1, horizon)
+        action_emb = self.W1(actions, cat_ids)
+        time_emb = self.pos_encoding(timesteps).to(dtype=action_emb.dtype)
+        x = swish(self.W2(torch.cat([action_emb, time_emb], dim=-1), cat_ids))
+        return self.W3(x, cat_ids)
+
+
+class Qwen3Backbone(nn.Module):
+    """Cosmos-Reason2/Qwen3-VL backbone used by GR00T N1.7.
+
+    The public checkpoint stores the action head in the GR00T checkpoint but
+    uses a Hugging Face Qwen3-VL-compatible backbone interface. This wrapper
+    keeps the nested HF module layout compatible across transformer versions
+    and exposes the hidden states consumed by the action head.
+    """
+
+    def __init__(
+        self,
+        model_name: str = "nvidia/Cosmos-Reason2-2B",
+        tune_llm: bool = False,
+        tune_visual: bool = False,
+        select_layer: int = -1,
+        reproject_vision: bool = False,
+        use_flash_attention: bool = False,
+        load_bf16: bool = False,
+        tune_top_llm_layers: int = 0,
+        trainable_params_fp32: bool = False,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_pretrained_weights: bool = True,
+    ):
+        require_package("transformers", extra="groot")
+        if Qwen3VLForConditionalGeneration is None:
+            raise ImportError(
+                "Qwen3VLForConditionalGeneration is required for GR00T N1.7. "
+                "Install a transformers version with Qwen3-VL support."
+            )
+        super().__init__()
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+
+        extra_kwargs: dict[str, Any] = {}
+        if use_flash_attention:
+            try:
+                import flash_attn  # noqa: F401
+
+                extra_kwargs["attn_implementation"] = "flash_attention_2"
+            except ImportError:
+                logger.warning("flash_attn is not installed. Falling back to SDPA attention.")
+                extra_kwargs["attn_implementation"] = "sdpa"
+        if load_bf16:
+            extra_kwargs["torch_dtype"] = torch.bfloat16
+
+        if load_pretrained_weights:
+            self.model = Qwen3VLForConditionalGeneration.from_pretrained(
+                model_name,
+                **extra_kwargs,
+                **transformers_loading_kwargs,
+            ).eval()
+        else:
+            self.model = self._from_backbone_config(
+                model_name=model_name,
+                model_kwargs=extra_kwargs,
+                config_kwargs=transformers_loading_kwargs,
+            ).eval()
+
+        _tie_unused_qwen_lm_head(self.model)
+        while len(self.language_model.layers) > select_layer:
+            self.language_model.layers.pop(-1)
+
+        self.select_layer = select_layer
+        self.set_trainable_parameters(tune_llm, tune_visual, tune_top_llm_layers)
+        if load_bf16 and trainable_params_fp32:
+            for parameter in self.parameters():
+                if parameter.requires_grad:
+                    parameter.data = parameter.data.to(torch.float32)
+
+    def set_trainable_parameters(
+        self, tune_llm: bool, tune_visual: bool, tune_top_llm_layers: int = 0
+    ) -> None:
+        self.tune_llm = tune_llm
+        self.tune_visual = tune_visual
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_llm:
+            self.language_model.requires_grad_(False)
+        if not tune_visual:
+            self.visual.requires_grad_(False)
+        if tune_top_llm_layers > 0:
+            for layer in self.language_model.layers[-tune_top_llm_layers:]:
+                for parameter in layer.parameters():
+                    parameter.requires_grad = True
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if self.language_model and not self.tune_llm:
+                self.language_model.eval()
+            if self.visual and not self.tune_visual:
+                self.visual.eval()
+
+    @property
+    def language_model(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).language_model
+
+    @property
+    def visual(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).visual
+
+    def _from_backbone_config(
+        self,
+        *,
+        model_name: str,
+        model_kwargs: dict[str, Any],
+        config_kwargs: dict[str, Any],
+    ) -> nn.Module:
+        if _is_cosmos_reason2_backbone(model_name):
+            backbone_config = _cosmos_reason2_qwen3_vl_config()
+        else:
+            backbone_config = AutoConfig.from_pretrained(model_name, **config_kwargs)
+        return Qwen3VLForConditionalGeneration._from_config(backbone_config, **model_kwargs)
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+    def _ensure_mm_token_type_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        if "mm_token_type_ids" in model_input:
+            return
+        if "image_grid_thw" not in model_input and "video_grid_thw" not in model_input:
+            return
+
+        input_ids = model_input.get("input_ids")
+        if input_ids is None:
+            return
+
+        mm_token_type_ids = torch.zeros(input_ids.shape, dtype=torch.int32, device=input_ids.device)
+        image_token_id = getattr(self.model.config, "image_token_id", None)
+        video_token_id = getattr(self.model.config, "video_token_id", None)
+        if image_token_id is not None:
+            mm_token_type_ids[input_ids == image_token_id] = 1
+        if video_token_id is not None:
+            mm_token_type_ids[input_ids == video_token_id] = 2
+
+        model_input["mm_token_type_ids"] = mm_token_type_ids
+
+    def _ensure_legacy_qwen3_position_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        """Restore the Qwen3-VL text position ids used by older Transformers releases.
+
+        Transformers 5.x computes 3-row multimodal RoPE ids for Qwen3-VL and then
+        drops text position ids before calling text-layer flash attention. GR00T
+        N1.7 was aligned against the older Transformers path, where a fourth text
+        position row is forwarded alongside the temporal/height/width rows. Adding
+        the row here preserves the newer multimodal position computation while
+        keeping flash attention on the legacy code path.
+        """
+
+        if "position_ids" in model_input:
+            return
+
+        qwen3_model = getattr(self.model, "model", self.model)
+        compute_3d_position_ids = getattr(qwen3_model, "compute_3d_position_ids", None)
+        if compute_3d_position_ids is None:
+            return
+
+        position_ids = compute_3d_position_ids(
+            input_ids=model_input.get("input_ids"),
+            image_grid_thw=model_input.get("image_grid_thw"),
+            video_grid_thw=model_input.get("video_grid_thw"),
+            inputs_embeds=None,
+            attention_mask=model_input.get("attention_mask"),
+            past_key_values=None,
+            mm_token_type_ids=model_input.get("mm_token_type_ids"),
+        )
+        if position_ids.ndim == 3 and position_ids.shape[0] == 3:
+            position_ids = torch.cat([position_ids[:1], position_ids], dim=0)
+
+        model_input["position_ids"] = position_ids
+
+    def _last_decoder_layer_output(self, model_input: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Return the pre-final-norm decoder output consumed by the N1.7 action head.
+
+        Older Transformers releases exposed this tensor as ``hidden_states[-1]``.
+        Newer releases expose the post-final-norm tensor there instead. Capturing
+        the last decoder layer output directly keeps the N1.7 action head input
+        stable across Transformers versions.
+        """
+
+        captured: dict[str, torch.Tensor] = {}
+
+        def capture_output(_module: nn.Module, _inputs: tuple[Any, ...], output: Any) -> None:
+            if isinstance(output, torch.Tensor):
+                captured["features"] = output
+            elif isinstance(output, (tuple, list)) and output:
+                captured["features"] = output[0]
+            elif hasattr(output, "last_hidden_state"):
+                captured["features"] = output.last_hidden_state
+
+        hook = self.language_model.layers[-1].register_forward_hook(capture_output)
+        try:
+            outputs = self.model(**model_input, output_hidden_states=True)
+        finally:
+            hook.remove()
+
+        return captured.get("features", outputs.hidden_states[-1])
+
+    def forward(self, vl_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        keys_to_use = ["input_ids", "attention_mask", "pixel_values", "image_grid_thw"]
+        optional_keys = ["mm_token_type_ids", "pixel_values_videos", "video_grid_thw"]
+        model_input = {key: vl_input[key] for key in keys_to_use}
+        model_input.update({key: vl_input[key] for key in optional_keys if key in vl_input})
+        self._ensure_mm_token_type_ids(model_input)
+        self._ensure_legacy_qwen3_position_ids(model_input)
+        features = self._last_decoder_layer_output(model_input)
+        image_mask = model_input["input_ids"] == self.model.config.image_token_id
+        attention_mask = model_input["attention_mask"] == 1
+        return BatchFeature(
+            data={
+                "backbone_features": features,
+                "backbone_attention_mask": attention_mask,
+                "image_mask": image_mask,
+            }
+        )
+
+
+class GR00TN17ActionHead(nn.Module):
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config: GR00TN17Config):
+        require_package("diffusers", extra="groot")
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.input_embedding_dim = config.input_embedding_dim
+
+        if config.use_alternate_vl_dit:
+            self.model = AlternateVLDiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+                attend_text_every_n_blocks=config.attend_text_every_n_blocks,
+            )
+        else:
+            self.model = DiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+            )
+
+        self.action_dim = config.max_action_dim
+        self.action_horizon = config.action_horizon
+        self.num_inference_timesteps = config.num_inference_timesteps
+        self.state_encoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=config.max_state_dim * config.state_history_length,
+            hidden_dim=self.hidden_size,
+            output_dim=self.input_embedding_dim,
+        )
+        self.action_encoder = MultiEmbodimentActionEncoder(
+            action_dim=self.action_dim,
+            hidden_size=self.input_embedding_dim,
+            num_embodiments=config.max_num_embodiments,
+        )
+        self.action_decoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=self.hidden_size,
+            hidden_dim=self.hidden_size,
+            output_dim=self.action_dim,
+        )
+        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
+        vl_self_attention_cfg = getattr(config, "vl_self_attention_cfg", None)
+        if vl_self_attention_cfg and vl_self_attention_cfg.get("num_layers", 0) > 0:
+            self.vl_self_attention = SelfAttentionTransformer(**vl_self_attention_cfg)
+        else:
+            self.vl_self_attention = nn.Identity()
+        if config.add_pos_embed:
+            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
+            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
+        self.state_dropout_prob = config.state_dropout_prob
+        self._noise_beta_alpha = config.noise_beta_alpha
+        self._noise_beta_beta = config.noise_beta_beta
+        self._beta_dist = None
+        self.num_timestep_buckets = config.num_timestep_buckets
+        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model, config.tune_vlln)
+
+    def set_trainable_parameters(
+        self, tune_projector: bool, tune_diffusion_model: bool, tune_vlln: bool
+    ) -> None:
+        self.tune_projector = tune_projector
+        self.tune_diffusion_model = tune_diffusion_model
+        self.tune_vlln = tune_vlln
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_projector:
+            self.state_encoder.requires_grad_(False)
+            self.action_encoder.requires_grad_(False)
+            self.action_decoder.requires_grad_(False)
+            if self.config.add_pos_embed:
+                self.position_embedding.requires_grad_(False)
+        if not tune_diffusion_model:
+            self.model.requires_grad_(False)
+        if not tune_vlln:
+            self.vlln.requires_grad_(False)
+            self.vl_self_attention.requires_grad_(False)
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if not self.tune_projector:
+                self.state_encoder.eval()
+                self.action_encoder.eval()
+                self.action_decoder.eval()
+                if self.config.add_pos_embed:
+                    self.position_embedding.eval()
+            if not self.tune_diffusion_model:
+                self.model.eval()
+            if not self.tune_vlln:
+                self.vlln.eval()
+                self.vl_self_attention.eval()
+
+    def sample_time(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        if self._beta_dist is None:
+            beta_alpha = torch.tensor(self._noise_beta_alpha, device="cpu", dtype=torch.float32)
+            beta_beta = torch.tensor(self._noise_beta_beta, device="cpu", dtype=torch.float32)
+            self._beta_dist = Beta(beta_alpha, beta_beta, validate_args=False)
+        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
+        return (1 - sample) * self.config.noise_s
+
+    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
+        backbone_features = self.vlln(backbone_output["backbone_features"])
+        backbone_output["backbone_features"] = self.vl_self_attention(backbone_features)
+        return backbone_output
+
+    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        backbone_output = self.process_backbone_output(backbone_output)
+        vl_embeds = backbone_output.backbone_features
+        device = vl_embeds.device
+        embodiment_id = action_input.embodiment_id
+
+        if action_input.state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = action_input.state.view(action_input.state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, embodiment_id)
+
+        if self.training and self.state_dropout_prob > 0:
+            do_dropout = (
+                torch.rand(state_features.shape[0], device=state_features.device) < self.state_dropout_prob
+            )
+            state_features = state_features * (1 - do_dropout[:, None, None].to(dtype=state_features.dtype))
+
+        actions = action_input.action
+        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
+        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
+        t = t[:, None, None]
+        noisy_trajectory = (1 - t) * noise + t * actions
+        velocity = actions - noise
+        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
+        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
+
+        if self.config.add_pos_embed:
+            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+            action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+
+        sa_embs = torch.cat((state_features, action_features), dim=1)
+        if self.config.use_alternate_vl_dit:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+                image_mask=backbone_output.image_mask,
+                backbone_attention_mask=backbone_output.backbone_attention_mask,
+            )
+        else:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+            )
+
+        pred = self.action_decoder(model_output, embodiment_id)
+        pred_actions = pred[:, -actions.shape[1] :]
+        action_mask = action_input.action_mask
+        action_loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
+        loss = action_loss.sum() / (action_mask.sum() + 1e-6)
+        return BatchFeature(
+            data={
+                "loss": loss,
+                "action_loss": action_loss,
+                "action_mask": action_mask,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    def _encode_features(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        backbone_output = self.process_backbone_output(backbone_output)
+        state = action_input.state
+        if state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = state.view(state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, action_input.embodiment_id)
+        return BatchFeature(
+            data={"backbone_features": backbone_output.backbone_features, "state_features": state_features}
+        )
+
+    @torch.no_grad()
+    def get_action_with_features(
+        self,
+        backbone_features: torch.Tensor,
+        state_features: torch.Tensor,
+        embodiment_id: torch.Tensor,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        vl_embeds = backbone_features
+        batch_size = vl_embeds.shape[0]
+        device = vl_embeds.device
+        actions = torch.randn(
+            size=(batch_size, self.config.action_horizon, self.action_dim),
+            dtype=vl_embeds.dtype,
+            device=device,
+        )
+        dt = 1.0 / self.num_inference_timesteps
+        vel_strength = torch.ones_like(actions)
+
+        if "action" in action_input:
+            if options is None:
+                raise ValueError("RTC options are required when action is provided to get_action.")
+            action_horizon_before_padding = options["action_horizon"]
+            actions[:, : options["rtc_overlap_steps"], :] = action_input["action"][
+                :,
+                action_horizon_before_padding - options["rtc_overlap_steps"] : action_horizon_before_padding,
+                :,
+            ]
+            vel_strength[:, : options["rtc_frozen_steps"], :] = 0.0
+            intermediate_steps = options["rtc_overlap_steps"] - options["rtc_frozen_steps"]
+            t = torch.linspace(0.0, 1.0, intermediate_steps + 2, device=device)
+            ramp = 1 - torch.exp(-options["rtc_ramp_rate"] * t)
+            ramp = ramp / ramp[-1].clamp_min(1e-8)
+            vel_strength[:, options["rtc_frozen_steps"] : options["rtc_overlap_steps"], :] = ramp[1:-1][
+                None, :, None
+            ].to(device)
+
+        for t_step in range(self.num_inference_timesteps):
+            t_cont = t_step / float(self.num_inference_timesteps)
+            t_discretized = int(t_cont * self.num_timestep_buckets)
+            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
+            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
+            if self.config.add_pos_embed:
+                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+                action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+            sa_embs = torch.cat((state_features, action_features), dim=1)
+
+            if self.config.use_alternate_vl_dit:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                    image_mask=backbone_output.image_mask,
+                    backbone_attention_mask=backbone_output.backbone_attention_mask,
+                )
+            else:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                )
+            pred = self.action_decoder(model_output, embodiment_id)
+            actions = actions + dt * pred[:, -self.action_horizon :] * vel_strength
+
+        return BatchFeature(
+            data={
+                "action_pred": actions,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    @torch.no_grad()
+    def get_action(
+        self,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        features = self._encode_features(backbone_output, action_input)
+        return self.get_action_with_features(
+            backbone_features=features.backbone_features,
+            state_features=features.state_features,
+            embodiment_id=action_input.embodiment_id,
+            backbone_output=backbone_output,
+            action_input=action_input,
+            options=options,
+        )
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+
+def _is_cosmos_reason2_backbone(model_name: str) -> bool:
+    return str(model_name).rstrip("/") == "nvidia/Cosmos-Reason2-2B"
+
+
+def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
+    """Hard-coded copy of the nvidia/Cosmos-Reason2-2B config.json (a Qwen3-VL-2B-Instruct layout)."""
+
+    return Qwen3VLConfig(
+        image_token_id=151655,
+        video_token_id=151656,
+        vision_start_token_id=151652,
+        vision_end_token_id=151653,
+        tie_word_embeddings=True,
+        text_config={
+            "attention_bias": False,
+            "attention_dropout": 0.0,
+            "bos_token_id": 151643,
+            "dtype": "bfloat16",
+            "eos_token_id": 151645,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_size": 2048,
+            "initializer_range": 0.02,
+            "intermediate_size": 6144,
+            "max_position_embeddings": 262144,
+            "model_type": "qwen3_vl_text",
+            "num_attention_heads": 16,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 8,
+            "rms_norm_eps": 1e-6,
+            "rope_scaling": {
+                "mrope_interleaved": True,
+                "mrope_section": [24, 20, 20],
+                "rope_type": "default",
+            },
+            "rope_theta": 5000000,
+            "tie_word_embeddings": True,
+            "use_cache": True,
+            "vocab_size": 151936,
+        },
+        vision_config={
+            "deepstack_visual_indexes": [5, 11, 17],
+            "depth": 24,
+            "hidden_act": "gelu_pytorch_tanh",
+            "hidden_size": 1024,
+            "in_channels": 3,
+            "initializer_range": 0.02,
+            "intermediate_size": 4096,
+            "model_type": "qwen3_vl",
+            "num_heads": 16,
+            "num_position_embeddings": 2304,
+            "out_hidden_size": 2048,
+            "patch_size": 16,
+            "spatial_merge_size": 2,
+            "temporal_patch_size": 2,
+        },
+    )
+
+
+def get_backbone_cls(config: GR00TN17Config):
+    if "nvidia/Cosmos-Reason2" in config.model_name or "Qwen/Qwen3-VL" in config.model_name:
+        return Qwen3Backbone
+    if config.backbone_model_type == "qwen":
+        logger.warning(
+            "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
+            "backbone because backbone_model_type='qwen'.",
+            config.model_name,
+        )
+        return Qwen3Backbone
+    raise ValueError(f"Unsupported GR00T N1.7 backbone model: {config.model_name}")
+
+
+class GR00TN17(PreTrainedModel):
+    """GR00T N1.7 model with a Cosmos-Reason2/Qwen3-VL backbone."""
+
+    config_class = GR00TN17Config
+    supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: GR00TN17Config,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_backbone_weights: bool = True,
+    ):
+        _register_with_transformers()
+        super().__init__(config)
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+        self.config = config
+        backbone_cls = get_backbone_cls(config)
+        self.backbone = backbone_cls(
+            model_name=config.model_name,
+            tune_llm=config.tune_llm,
+            tune_visual=config.tune_visual,
+            select_layer=config.select_layer,
+            reproject_vision=config.reproject_vision,
+            use_flash_attention=config.use_flash_attention,
+            load_bf16=config.load_bf16,
+            tune_top_llm_layers=config.tune_top_llm_layers,
+            trainable_params_fp32=config.backbone_trainable_params_fp32,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_pretrained_weights=load_backbone_weights,
+        )
+        self.action_head = GR00TN17ActionHead(config)
+        self.post_init()
+
+    def prepare_input(self, inputs: dict[str, Any]) -> tuple[BatchFeature, BatchFeature]:
+        require_package("dm-tree", extra="groot", import_name="tree")
+        backbone_inputs = self.backbone.prepare_input(inputs)
+        action_inputs = self.action_head.prepare_input(inputs)
+
+        def to_device_with_dtype(x):
+            if not isinstance(x, torch.Tensor):
+                return x
+            if torch.is_floating_point(x):
+                return x.to(self.device, dtype=self.dtype)
+            return x.to(self.device)
+
+        return (
+            tree.map_structure(to_device_with_dtype, backbone_inputs),
+            tree.map_structure(to_device_with_dtype, action_inputs),
+        )
+
+    def forward(self, inputs: dict[str, Any]) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head(backbone_outputs, action_inputs)
+
+    def get_action(self, inputs: dict[str, Any], options: dict[str, Any] | None = None) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head.get_action(backbone_outputs, action_inputs, options)
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        tune_visual = kwargs.pop("tune_visual", True)
+        tune_llm = kwargs.pop("tune_llm", False)
+        tune_projector = kwargs.pop("tune_projector", True)
+        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
+        tune_vlln = kwargs.pop("tune_vlln", True)
+        transformers_loading_kwargs = kwargs.pop("transformers_loading_kwargs", None) or {
+            "trust_remote_code": True
+        }
+        load_backbone_weights = kwargs.pop("load_backbone_weights", False)
+        for key in ("cache_dir", "local_files_only", "token"):
+            if key in kwargs:
+                transformers_loading_kwargs.setdefault(key, kwargs[key])
+
+        try:
+            local_model_path = snapshot_download(
+                pretrained_model_name_or_path,
+                repo_type="model",
+                revision=kwargs.get("revision"),
+                cache_dir=kwargs.get("cache_dir"),
+                local_files_only=kwargs.get("local_files_only", False),
+                token=kwargs.get("token"),
+            )
+        except (HFValidationError, RepositoryNotFoundError):
+            local_model_path = pretrained_model_name_or_path
+
+        pretrained_model = super().from_pretrained(
+            local_model_path,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_backbone_weights=load_backbone_weights,
+            **kwargs,
+        )
+        pretrained_model.backbone.set_trainable_parameters(
+            tune_visual=tune_visual,
+            tune_llm=tune_llm,
+            tune_top_llm_layers=pretrained_model.config.tune_top_llm_layers,
+        )
+        pretrained_model.action_head.set_trainable_parameters(
+            tune_projector=tune_projector,
+            tune_diffusion_model=tune_diffusion_model,
+            tune_vlln=tune_vlln,
+        )
+        return pretrained_model
+
+
+def _register_with_transformers() -> None:
+    """Register GR00T N1.7 with transformers' Auto* factories.
+
+    Idempotent: ``register(..., exist_ok=True)`` makes repeat calls no-ops (with a fallback that
+    suppresses the already-registered error on transformers builds whose ``register()`` predates
+    ``exist_ok``), so no run-once guard is needed.
+    """
+    if AutoConfig is None or AutoModel is None:
+        return
+    try:
+        AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config)
+    try:
+        AutoModel.register(GR00TN17Config, GR00TN17, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoModel.register(GR00TN17Config, GR00TN17)

src/lerobot/policies/groot/modeling_groot.py

@@ -17,37 +17,47 @@
 """
 Groot Policy Wrapper for LeRobot Integration
 
-Minimal integration that delegates to Isaac-GR00T components where possible
-without porting their code. The intent is to:
-
-- Download and load the pretrained GR00T model via GR00TN15.from_pretrained
-- Optionally align action horizon similar to gr00t_finetune.py
-- Expose predict_action via GR00T model.get_action
-- Provide a training forward that can call the GR00T model forward if batch
-  structure matches.
-
-Notes:
-- Dataset loading and full training orchestration is handled by Isaac-GR00T
-  TrainRunner in their codebase. If you want to invoke that flow end-to-end
-  from LeRobot, see `GrootPolicy.finetune_with_groot_runner` below.
+Minimal integration that delegates to Isaac-GR00T N1.7 components where
+possible without porting their code. Dataset loading and training
+orchestration are handled by LeRobot's standard training stack.
 """
 
 import builtins
+import logging
 import os
 from collections import deque
 from pathlib import Path
-from typing import TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
 import torch
+from huggingface_hub import hf_hub_download
+from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
+from huggingface_hub.errors import HfHubHTTPError
 from torch import Tensor
 
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.utils.constants import ACTION, OBS_IMAGES
-from lerobot.utils.import_utils import require_package
+from lerobot.utils.import_utils import _transformers_available, require_package
 
 from ..pretrained import PreTrainedPolicy
-from .configuration_groot import GrootConfig
-from .groot_n1 import GR00TN15
+from ..utils import get_device_from_parameters
+from .configuration_groot import (
+    GROOT_N1_5,
+    GROOT_N1_5_REMOVAL_GUIDANCE,
+    GROOT_N1_7,
+    GrootConfig,
+    infer_groot_model_version,
+    infer_groot_n1_7_action_execution_horizon,
+    infer_groot_n1_7_action_horizon,
+)
+from .groot_n1_7 import GR00TN17, _tie_unused_qwen_lm_head
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers.trainer_pt_utils import get_parameter_names
+else:
+    get_parameter_names = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
 
 T = TypeVar("T", bound="GrootPolicy")
 
@@ -67,37 +77,77 @@ class GrootPolicy(PreTrainedPolicy):
 
         # Initialize GR00T model using ported components
         self._groot_model = self._create_groot_model()
+        self._action_queue_steps = self._resolve_action_queue_steps()
+        self._warned_native_relative_rtc_prefix_disabled = False
 
         self.reset()
 
     def _create_groot_model(self):
-        """Create and initialize the GR00T model using Isaac-GR00T API.
+        """Create and initialize the GR00T N1.7 model using the ported components."""
+        model_kwargs = {
+            "pretrained_model_name_or_path": self.config.base_model_path,
+            "tune_llm": self.config.tune_llm,
+            "tune_visual": self.config.tune_visual,
+            "tune_projector": self.config.tune_projector,
+            "tune_diffusion_model": self.config.tune_diffusion_model,
+            # Forwarded as a GR00TN17Config override; read back by set_trainable_parameters.
+            "tune_top_llm_layers": self.config.tune_top_llm_layers,
+            "use_flash_attention": self.config.use_flash_attention,
+        }
+        # Surface the inference-time knobs onto the model config only when the user set them; None
+        # leaves the value baked into the checkpoint untouched.
+        if self.config.num_inference_timesteps is not None:
+            model_kwargs["num_inference_timesteps"] = self.config.num_inference_timesteps
+        if self.config.rtc_ramp_rate is not None:
+            model_kwargs["rtc_ramp_rate"] = self.config.rtc_ramp_rate
 
-        This is only called when creating a NEW policy (not when loading from checkpoint).
-
-        Steps (delegating to Isaac-GR00T):
-        1) Download and load pretrained model via GR00TN15.from_pretrained
-        2) Align action horizon with data_config if provided
-        """
-        # Handle Flash Attention compatibility issues
-        self._handle_flash_attention_compatibility()
-
-        model = GR00TN15.from_pretrained(
-            pretrained_model_name_or_path=self.config.base_model_path,
-            tune_llm=self.config.tune_llm,
-            tune_visual=self.config.tune_visual,
-            tune_projector=self.config.tune_projector,
-            tune_diffusion_model=self.config.tune_diffusion_model,
+        model = GR00TN17.from_pretrained(
+            **model_kwargs,
+            tune_vlln=self.config.tune_vlln,
+            transformers_loading_kwargs={"trust_remote_code": True},
         )
-
-        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
-        model.config.compute_dtype = model.compute_dtype
-
+        backbone = getattr(model, "backbone", None)
+        qwen_model = getattr(backbone, "model", None)
+        if qwen_model is not None:
+            _tie_unused_qwen_lm_head(qwen_model)
+        if self.config.model_params_fp32:
+            self._cast_model_parameters_to_fp32(model)
         return model
 
+    @staticmethod
+    def _cast_model_parameters_to_fp32(model: torch.nn.Module) -> None:
+        for parameter in model.parameters():
+            if parameter.is_floating_point():
+                parameter.data = parameter.data.to(torch.float32)
+
+    @staticmethod
+    def _build_weight_decay_parameter_groups(model: torch.nn.Module) -> list[dict[str, object]]:
+        forbidden_name_patterns = [
+            r"bias",
+            r"layernorm",
+            r"rmsnorm",
+            r"(?:^|\.)norm(?:$|\.)",
+            r"_norm(?:$|\.)",
+        ]
+        decay_names = set(get_parameter_names(model, [torch.nn.LayerNorm], forbidden_name_patterns))
+        decay_params = [
+            parameter
+            for name, parameter in model.named_parameters()
+            if parameter.requires_grad and name in decay_names
+        ]
+        no_decay_params = [
+            parameter
+            for name, parameter in model.named_parameters()
+            if parameter.requires_grad and name not in decay_names
+        ]
+        return [
+            {"params": decay_params},
+            {"params": no_decay_params, "weight_decay": 0.0},
+        ]
+
     def reset(self):
         """Reset policy state when environment resets."""
-        self._action_queue = deque([], maxlen=self.config.n_action_steps)
+        self._action_queue = deque([], maxlen=self._action_queue_steps)
 
     @classmethod
     def from_pretrained(
@@ -118,7 +168,7 @@ class GrootPolicy(PreTrainedPolicy):
         """Load Groot policy from pretrained model.
 
         Handles two cases:
-        1. Base GR00T models (e.g., 'nvidia/GR00T-N1.5-3B') - loads the raw model
+        1. Base GR00T N1.7 models - loads the raw model
         2. Fine-tuned LeRobot checkpoints - loads config and weights from safetensors
 
         Args:
@@ -137,13 +187,11 @@ class GrootPolicy(PreTrainedPolicy):
         Returns:
             Initialized GrootPolicy instance with loaded model
         """
-        from huggingface_hub import hf_hub_download
-        from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
-        from huggingface_hub.errors import HfHubHTTPError
-
-        print(
-            "The Groot policy is a wrapper around Nvidia's GR00T N1.5 model.\n"
-            f"Loading pretrained model from: {pretrained_name_or_path}"
+        requested_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
+        logger.info(
+            "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
+            requested_version,
+            pretrained_name_or_path,
         )
 
         model_id = str(pretrained_name_or_path)
@@ -174,7 +222,7 @@ class GrootPolicy(PreTrainedPolicy):
 
         if is_finetuned_checkpoint:
             # This is a fine-tuned LeRobot checkpoint - use parent class loading
-            print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
+            logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
             return super().from_pretrained(
                 pretrained_name_or_path=pretrained_name_or_path,
                 config=config,
@@ -190,11 +238,13 @@ class GrootPolicy(PreTrainedPolicy):
             )
 
         # This is a base GR00T model - load it fresh
-        print("Detected base GR00T model, loading from HuggingFace...")
+        logger.info("Detected base GR00T model, loading from HuggingFace...")
 
         if config is None:
             # Create default config with the pretrained path
-            config = GrootConfig(base_model_path=str(pretrained_name_or_path))
+            config = GrootConfig(
+                base_model_path=str(pretrained_name_or_path),
+            )
 
             # Add minimal visual feature required for validation
             # validate_features() will automatically add state and action features
@@ -215,6 +265,15 @@ class GrootPolicy(PreTrainedPolicy):
             if hasattr(config, key):
                 setattr(config, key, value)
 
+        inferred_version = infer_groot_model_version(config.base_model_path)
+        if inferred_version is not None and inferred_version != GROOT_N1_7:
+            message = (
+                f"GR00T model_version '{GROOT_N1_7}' does not match base_model_path "
+                f"'{config.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
         # Create a fresh policy instance - this will automatically load the GR00T model
         # in __init__ via _create_groot_model()
         policy = cls(config)
@@ -222,24 +281,174 @@ class GrootPolicy(PreTrainedPolicy):
         policy.eval()
         return policy
 
-    def get_optim_params(self) -> dict:
-        return self.parameters()
+    def get_optim_params(self):  # type: ignore[override]
+        """Isaac-GR00T excludes biases and normalization parameters from weight decay."""
+        return self._build_weight_decay_parameter_groups(self)
+
+    def _resolve_action_queue_steps(self) -> int:
+        n_action_steps = int(self.config.n_action_steps)
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        execution_horizon = infer_groot_n1_7_action_execution_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        horizons = [n_action_steps]
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+        if execution_horizon is not None:
+            horizons.append(execution_horizon)
+        return min(horizons)
+
+    def _resolve_prediction_horizon(self, actions: Tensor) -> int:
+        """Return the policy-facing action horizon for a native GR00T prediction."""
+
+        horizons = [actions.shape[1]]
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+
+        for horizon in (self.config.chunk_size, self.config.n_action_steps):
+            horizon = int(horizon)
+            if horizon > 0:
+                horizons.append(horizon)
+
+        return max(1, min(horizons))
+
+    def _filter_groot_inputs(self, batch: dict[str, Tensor], *, include_action: bool) -> dict[str, Tensor]:
+        allowed_base = {"state", "state_mask", "action_mask", "embodiment_id"}
+        if include_action:
+            allowed_base.add("action")
+
+        allowed_base.update(
+            {
+                "input_ids",
+                "attention_mask",
+                "pixel_values",
+                "image_grid_thw",
+                "mm_token_type_ids",
+                "pixel_values_videos",
+                "video_grid_thw",
+            }
+        )
+
+        return {
+            k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")
+        }
+
+    def _prepare_n1_7_rtc_inputs(
+        self,
+        inputs: dict[str, Tensor],
+        *,
+        inference_delay: object,
+        prev_chunk_left_over: object,
+    ) -> tuple[dict[str, Tensor], dict[str, object] | None]:
+        if prev_chunk_left_over is None:
+            return inputs, None
+        if getattr(self.config, "use_relative_actions", False):
+            # Generic RTC only provides normalized leftovers from the previous chunk. For
+            # native relative-action N1.7 checkpoints those rows are tied to the old
+            # observation state and old per-horizon stats row, so using them as the next
+            # prefix can push the policy in the wrong direction. Run without native RTC
+            # overlap guidance until a GROOT-specific RTC path can pass re-anchored
+            # absolute leftovers through.
+            if not getattr(self, "_warned_native_relative_rtc_prefix_disabled", False):
+                logger.info("Disabling native GR00T RTC prefix for relative-action policy")
+                self._warned_native_relative_rtc_prefix_disabled = True
+            return inputs, None
+        if not isinstance(prev_chunk_left_over, torch.Tensor):
+            raise TypeError("prev_chunk_left_over must be a torch.Tensor for GR00T N1.7 RTC.")
+        if prev_chunk_left_over.numel() == 0:
+            return inputs, None
+
+        prev_actions = prev_chunk_left_over
+        if prev_actions.ndim == 2:
+            prev_actions = prev_actions.unsqueeze(0)
+        elif prev_actions.ndim != 3:
+            raise ValueError("prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC.")
+
+        state = inputs.get("state")
+        if state is None:
+            raise ValueError("GR00T N1.7 RTC requires `state` in the preprocessed batch.")
+        batch_size = state.shape[0]
+        if prev_actions.shape[0] == 1 and batch_size > 1:
+            prev_actions = prev_actions.expand(batch_size, -1, -1).clone()
+        elif prev_actions.shape[0] != batch_size:
+            raise ValueError("prev_chunk_left_over batch size must match the current GR00T N1.7 batch size.")
+
+        # The generic LeRobot RTC engine pads short leftovers with exact zero
+        # rows for fixed-shape policy calls. Native GR00T N1.7 RTC treats every
+        # provided prefix row as a real action constraint, so strip that padding
+        # before constructing the native overlap options.
+        valid_prefix_rows = prev_actions.detach().abs().sum(dim=(0, 2)) > 0
+        if valid_prefix_rows.any():
+            valid_prefix_steps = int(valid_prefix_rows.nonzero()[-1].item()) + 1
+            prev_actions = prev_actions[:, :valid_prefix_steps, :]
+        else:
+            return inputs, None
+
+        model_action_horizon = int(
+            getattr(self._groot_model.config, "action_horizon", self.config.chunk_size)
+        )
+        max_action_dim = int(getattr(self._groot_model.config, "max_action_dim", self.config.max_action_dim))
+        if prev_actions.shape[1] > model_action_horizon:
+            prev_actions = prev_actions[:, -model_action_horizon:, :]
+
+        action_horizon = int(prev_actions.shape[1])
+        if action_horizon <= 0:
+            return inputs, None
+
+        if prev_actions.shape[2] > max_action_dim:
+            prev_actions = prev_actions[:, :, :max_action_dim]
+        elif prev_actions.shape[2] < max_action_dim:
+            pad = torch.zeros(
+                prev_actions.shape[0],
+                prev_actions.shape[1],
+                max_action_dim - prev_actions.shape[2],
+                dtype=prev_actions.dtype,
+                device=prev_actions.device,
+            )
+            prev_actions = torch.cat([prev_actions, pad], dim=2)
+
+        prev_actions = prev_actions.to(device=state.device, dtype=state.dtype)
+
+        rtc_config = getattr(self.config, "rtc_config", None)
+        execution_horizon = int(getattr(rtc_config, "execution_horizon", action_horizon))
+        overlap_steps = max(0, min(action_horizon, execution_horizon))
+        if overlap_steps == 0:
+            return inputs, None
+
+        try:
+            frozen_steps = int(inference_delay or 0)
+        except (TypeError, ValueError):
+            frozen_steps = 0
+        frozen_steps = max(0, min(frozen_steps, overlap_steps))
+
+        options = {
+            "action_horizon": action_horizon,
+            "rtc_overlap_steps": overlap_steps,
+            "rtc_frozen_steps": frozen_steps,
+            "rtc_ramp_rate": float(getattr(self._groot_model.config, "rtc_ramp_rate", 6.0)),
+        }
+
+        inputs = dict(inputs)
+        inputs["action"] = prev_actions
+        return inputs, options
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """Training forward pass.
 
         Delegates to Isaac-GR00T model.forward when inputs are compatible.
         """
-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        allowed_base = {"state", "state_mask", "action", "action_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        groot_inputs = self._filter_groot_inputs(batch, include_action=True)
 
         # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)
 
         # Run GR00T forward under bf16 autocast when enabled to reduce activation memory
         # Rationale: Matches original GR00T finetuning (bf16 compute, fp32 params) and avoids fp32 upcasts.
@@ -248,38 +457,52 @@ class GrootPolicy(PreTrainedPolicy):
 
         # Isaac-GR00T returns a BatchFeature; loss key is typically 'loss'
         loss = outputs.get("loss")
+        if loss is None:
+            raise RuntimeError(
+                "GR00T model.forward did not return a 'loss'. Training batches must include "
+                "'action' and 'action_mask'; check the preprocessor output."
+            )
 
         loss_dict = {"loss": loss.item()}
 
         return loss, loss_dict
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: object) -> Tensor:
         """Predict a chunk of actions for inference by delegating to Isaac-GR00T.
 
         Returns a tensor of shape (B, n_action_steps, action_dim).
+
+        For N1.7, LeRobot's RTC leftovers are converted into the native GR00T
+        action-overlap options before calling the underlying model.
         """
         self.eval()
 
-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        # Preprocessing is handled by the processor pipeline, so we just filter the batch
-        # NOTE: During inference, we should NOT pass action/action_mask (that's what we're predicting)
-        allowed_base = {"state", "state_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        # Preprocessing is handled by the processor pipeline, so we just filter the batch.
+        # During inference, we do not pass action because it is predicted.
+        # N1.7 still carries a 2-D action horizon mask from its checkpoint processor.
+        groot_inputs = self._filter_groot_inputs(batch, include_action=False)
+        groot_inputs, groot_options = self._prepare_n1_7_rtc_inputs(
+            groot_inputs,
+            inference_delay=kwargs.get("inference_delay"),
+            prev_chunk_left_over=kwargs.get("prev_chunk_left_over"),
+        )
 
         # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)
 
         # Use bf16 autocast for inference to keep memory low and match backbone dtype
         with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
-            outputs = self._groot_model.get_action(groot_inputs)
+            if groot_options is not None:
+                outputs = self._groot_model.get_action(groot_inputs, options=groot_options)
+            else:
+                outputs = self._groot_model.get_action(groot_inputs)
 
         actions = outputs.get("action_pred")
 
+        prediction_horizon = self._resolve_prediction_horizon(actions)
+        actions = actions[:, :prediction_horizon]
+
         original_action_dim = self.config.output_features[ACTION].shape[0]
         actions = actions[:, :, :original_action_dim]
 
@@ -288,44 +511,17 @@ class GrootPolicy(PreTrainedPolicy):
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
         """Select single action from action queue."""
+        if getattr(self.config, "use_relative_actions", False):
+            raise NotImplementedError(
+                "GrootPolicy.select_action does not support relative-action policies because cached "
+                "relative chunk actions can be decoded against newer observation states. Use "
+                "predict_action_chunk and postprocess the full chunk before queuing actions, or use "
+                "the RTC/chunked rollout inference path."
+            )
+
         self.eval()
 
         if len(self._action_queue) == 0:
             actions = self.predict_action_chunk(batch)
-            self._action_queue.extend(actions.transpose(0, 1))
+            self._action_queue.extend(actions[:, : self._action_queue_steps].transpose(0, 1))
         return self._action_queue.popleft()
-
-    # -------------------------
-    # Internal helpers
-    # -------------------------
-    def _handle_flash_attention_compatibility(self) -> None:
-        """Handle Flash Attention compatibility issues by setting environment variables.
-
-        This addresses the common 'undefined symbol' error that occurs when Flash Attention
-        is compiled against a different PyTorch version than what's currently installed.
-        """
-
-        # Set environment variables to handle Flash Attention compatibility
-        # These help with symbol resolution issues
-        os.environ.setdefault("FLASH_ATTENTION_FORCE_BUILD", "0")
-        os.environ.setdefault("FLASH_ATTENTION_SKIP_CUDA_BUILD", "0")
-
-        # Try to import flash_attn and handle failures gracefully
-        try:
-            import flash_attn
-
-            print(f"[GROOT] Flash Attention version: {flash_attn.__version__}")
-        except ImportError as e:
-            print(f"[GROOT] Flash Attention not available: {e}")
-            print("[GROOT] Will use fallback attention mechanism")
-        except Exception as e:
-            if "undefined symbol" in str(e):
-                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
-                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
-                print("[GROOT] Consider reinstalling Flash Attention with compatible version:")
-                print("  pip uninstall flash-attn")
-                print("  pip install --no-build-isolation flash-attn==2.6.3")
-                print("[GROOT] Continuing with fallback attention mechanism")
-            else:
-                print(f"[GROOT] Flash Attention error: {e}")
-                print("[GROOT] Continuing with fallback attention mechanism")

src/lerobot/policies/groot/utils.py

@@ -1,47 +1,264 @@
+#!/usr/bin/env python
+
+# Copyright 2025 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Shared, side-effect-free utilities for the GR00T N1.7 policy.
+
+These helpers are consumed by both the config layer (checkpoint sidecar
+inspection) and the processor layer (stat flattening, action decoding, language
+and image packing). They are pure functions with no GR00T-specific state so they
+can be unit-tested in isolation and reused without importing the heavier
+config/processor modules.
+"""
+
+from __future__ import annotations
+
+import json
 from pathlib import Path
-from shutil import copytree
+from typing import Any
 
-from huggingface_hub import hf_hub_download
+import numpy as np
+import torch
 
 
-def ensure_eagle_cache_ready(vendor_dir: Path, cache_dir: Path, assets_repo: str) -> None:
-    """Populate the Eagle processor directory in cache and ensure tokenizer assets exist.
-
-    - Copies the vendored Eagle files into cache_dir (overwriting when needed).
-    - Downloads vocab.json and merges.txt into the same cache_dir if missing.
-    """
-    cache_dir = Path(cache_dir)
-    vendor_dir = Path(vendor_dir)
-
+def read_json(path: Path) -> dict[str, Any]:
+    """Read a JSON object from ``path``, returning ``{}`` on any read/parse error."""
     try:
-        # Populate/refresh cache with vendor files to ensure a complete processor directory
-        print(f"[GROOT] Copying vendor Eagle files to cache: {vendor_dir} -> {cache_dir}")
-        copytree(vendor_dir, cache_dir, dirs_exist_ok=True)
-    except Exception as exc:  # nosec: B110
-        print(f"[GROOT] Warning: Failed to copy vendor Eagle files to cache: {exc}")
+        with path.open() as f:
+            data = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return {}
+    return data if isinstance(data, dict) else {}
 
-    required_assets = [
-        "vocab.json",
-        "merges.txt",
-        "added_tokens.json",
-        "chat_template.json",
-        "special_tokens_map.json",
-        "config.json",
-        "generation_config.json",
-        "preprocessor_config.json",
-        "processor_config.json",
-        "tokenizer_config.json",
-    ]
 
-    print(f"[GROOT] Assets repo: {assets_repo} \n Cache dir: {cache_dir}")
+def as_int_pair(value: Any) -> list[int] | None:
+    if not isinstance(value, (list, tuple)) or len(value) != 2:
+        return None
+    try:
+        return [int(value[0]), int(value[1])]
+    except (TypeError, ValueError):
+        return None
 
-    for fname in required_assets:
-        dst = cache_dir / fname
-        if not dst.exists():
-            print(f"[GROOT] Fetching {fname}")
-            hf_hub_download(
-                repo_id=assets_repo,
-                filename=fname,
-                repo_type="model",
-                local_dir=str(cache_dir),
+
+def as_optional_int(value: Any) -> int | None:
+    if value is None:
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def as_optional_float(value: Any) -> float | None:
+    if value is None:
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def as_float_list(values: Any) -> list[float]:
+    if values is None:
+        return []
+    if isinstance(values, torch.Tensor):
+        return values.detach().cpu().reshape(-1).float().tolist()
+    if isinstance(values, np.ndarray):
+        return values.reshape(-1).astype(np.float32).tolist()
+    if isinstance(values, (list, tuple)):
+        flattened: list[float] = []
+        for value in values:
+            flattened.extend(as_float_list(value))
+        return flattened
+    return [float(values)]
+
+
+def config_value(value: Any) -> str:
+    if hasattr(value, "value"):
+        value = value.value
+    text = str(value).lower()
+    return {
+        "relative": "relative",
+        "absolute": "absolute",
+        "delta": "delta",
+        "eef": "eef",
+        "non_eef": "non_eef",
+        "default": "default",
+        "xyz_rot6d": "xyz+rot6d",
+        "xyz+rot6d": "xyz+rot6d",
+        "xyz_rotvec": "xyz+rotvec",
+        "xyz+rotvec": "xyz+rotvec",
+    }.get(text, text)
+
+
+def has_modality_stats(stats: dict[str, dict[str, Any]] | None) -> bool:
+    if not stats:
+        return False
+    return any(bool(modality_stats) for modality_stats in stats.values())
+
+
+def stat_dim_from_entry(entry: dict[str, Any]) -> int:
+    for stat_name in ("mean", "q01", "min", "max", "std"):
+        value = entry.get(stat_name)
+        if isinstance(value, torch.Tensor):
+            return int(value.shape[-1]) if value.ndim > 0 else 1
+        if isinstance(value, np.ndarray):
+            return int(value.shape[-1]) if value.ndim > 0 else 1
+        if isinstance(value, list) and len(value) > 0:
+            first = value[0]
+            if isinstance(first, (list, tuple)) and len(first) > 0:
+                return len(first)
+            return len(value)
+    return 0
+
+
+def flatten_n1_7_modality_stats(
+    *,
+    embodiment_stats: dict[str, Any],
+    embodiment_config: dict[str, Any],
+    modality: str,
+    use_percentiles: bool,
+    use_relative_action: bool,
+) -> dict[str, list[float]]:
+    """Flatten one N1.7 modality's grouped statistics in checkpoint order.
+
+    When checkpoints request percentile normalization, q01/q99 replace min/max
+    for regular groups. Relative action groups read from ``relative_action``
+    stats and keep min/max, matching Isaac-GR00T's processor override.
+    """
+
+    source_stats = embodiment_stats.get(modality, {})
+    modality_config = embodiment_config.get(modality, {})
+    if not isinstance(source_stats, dict) or not isinstance(modality_config, dict):
+        return {}
+    modality_keys = modality_config.get("modality_keys", [])
+    if not isinstance(modality_keys, list):
+        return {}
+
+    flattened: dict[str, list[float]] = {}
+    action_configs = modality_config.get("action_configs", []) if modality == "action" else []
+    if not isinstance(action_configs, list):
+        action_configs = []
+    relative_stats = embodiment_stats.get("relative_action", {})
+    if not isinstance(relative_stats, dict):
+        relative_stats = {}
+
+    for stat_name in ("min", "max", "mean", "std"):
+        values: list[float] = []
+        source_stat_name = stat_name
+        if use_percentiles and stat_name == "min":
+            source_stat_name = "q01"
+        elif use_percentiles and stat_name == "max":
+            source_stat_name = "q99"
+
+        for idx, modality_key in enumerate(modality_keys):
+            if not isinstance(modality_key, str):
+                continue
+            key_source_stats = source_stats
+            key_stat_name = source_stat_name
+            if modality == "action" and use_relative_action and idx < len(action_configs):
+                action_config = action_configs[idx]
+                if isinstance(action_config, dict) and config_value(action_config.get("rep")) == "relative":
+                    key_source_stats = relative_stats
+                    key_stat_name = stat_name
+            key_stats = key_source_stats.get(modality_key, {})
+            if not isinstance(key_stats, dict):
+                raise KeyError(f"Missing statistics for {modality}.{modality_key}")
+            raw_values = key_stats.get(key_stat_name)
+            if raw_values is None:
+                raise KeyError(f"Missing '{key_stat_name}' statistics for {modality}.{modality_key}")
+            values.extend(as_float_list(raw_values))
+        if values:
+            flattened[stat_name] = values
+
+    return flattened
+
+
+def rot6d_to_matrix(rot6d: np.ndarray) -> np.ndarray:
+    rows = rot6d.reshape(2, 3).astype(np.float64)
+    row1 = rows[0] / np.linalg.norm(rows[0])
+    row2 = rows[1] - np.dot(row1, rows[1]) * row1
+    row2 = row2 / np.linalg.norm(row2)
+    row3 = np.cross(row1, row2)
+    return np.vstack([row1, row2, row3])
+
+
+def xyz_rot6d_to_homogeneous(xyz_rot6d: np.ndarray) -> np.ndarray:
+    transform = np.eye(4, dtype=np.float64)
+    transform[:3, :3] = rot6d_to_matrix(xyz_rot6d[3:])
+    transform[:3, 3] = xyz_rot6d[:3]
+    return transform
+
+
+def homogeneous_to_xyz_rot6d(transform: np.ndarray) -> np.ndarray:
+    return np.concatenate([transform[:3, 3], transform[:2, :3].reshape(-1)], axis=0)
+
+
+def relative_eef_to_absolute(action: np.ndarray, reference_state: np.ndarray) -> np.ndarray:
+    """Convert relative EEF deltas in xyz+rot6d format to absolute EEF poses."""
+
+    out = np.empty_like(action, dtype=np.float64)
+    for batch_idx in range(action.shape[0]):
+        reference = xyz_rot6d_to_homogeneous(reference_state[batch_idx])
+        for timestep in range(action.shape[1]):
+            relative = xyz_rot6d_to_homogeneous(action[batch_idx, timestep])
+            out[batch_idx, timestep] = homogeneous_to_xyz_rot6d(reference @ relative)
+    return out.astype(np.float32)
+
+
+def infer_n1_7_batch_size_and_device(
+    obs: dict[str, Any], action: torch.Tensor | None
+) -> tuple[int, torch.device]:
+    for value in list(obs.values()) + [action]:
+        if isinstance(value, torch.Tensor):
+            return value.shape[0], value.device
+    video = obs.get("video")
+    if isinstance(video, np.ndarray):
+        return video.shape[0], torch.device("cpu")
+    return 1, torch.device("cpu")
+
+
+def prepare_n1_7_language_batch(
+    language: Any,
+    batch_size: int,
+    *,
+    formalize_language: bool,
+) -> list[str]:
+    default_language = "Perform the task."
+    if language is None or (isinstance(language, str) and language == ""):
+        languages = [default_language] * batch_size
+    elif isinstance(language, str):
+        languages = [language] * batch_size
+    elif isinstance(language, (list, tuple)):
+        languages = list(language)
+        if len(languages) == 0:
+            languages = [default_language] * batch_size
+        elif len(languages) == 1 and batch_size > 1:
+            languages = languages * batch_size
+        elif len(languages) != batch_size:
+            raise ValueError(
+                f"language batch has {len(languages)} entries, but GR00T N1.7 input batch has {batch_size}."
             )
+    else:
+        languages = [str(language)] * batch_size
+
+    formatted = []
+    for item in languages:
+        text = str(item) if item else default_language
+        if formalize_language:
+            text = text.lower()
+            text = "".join(ch for ch in text if ch.isalnum() or ch.isspace() or ch == "_")
+        formatted.append(text)
+    return formatted

src/lerobot/policies/molmoact2/configuration_molmoact2.py

@@ -73,19 +73,6 @@ class MolmoAct2Config(PreTrainedConfig):
     num_inference_steps: int | None = None
     mask_action_dim_padding: bool = True
     enable_inference_cuda_graph: bool = True
-
-    # Language conditioning (e.g. RECAP advantage). When set, RenderMessagesStep
-    # resolves language_persistent rows via the recipe YAML. Same mechanism as PI05.
-    recipe_path: str | None = None
-
-    # Inference-time advantage indicator (e.g. "Advantage: positive. ").
-    # Used during rollout when no language_persistent data is available.
-    # Placed after the user prompt, before action tokens.
-    advantage_prefix: str = ""
-
-    # Classifier-Free Guidance (CFG) scale for inference (RECAP Eq. 13).
-    # 1.0 = no guidance. >1.0 = dual-path: v = v_uncond + beta * (v_cond - v_uncond)
-    cfg_beta: float = 1.0
     # MolmoAct2-local eval option. When enabled, stochastic continuous action
     # generation uses a rollout-local generator derived from eval_seed.
     per_episode_seed: bool = False

src/lerobot/policies/molmoact2/modeling_molmoact2.py

@@ -497,56 +497,6 @@ def _weighted_per_example(
     return loss_sum * float(batch_size) / global_weight_sum
 
 
-def _cat_action_contexts(cond_ctx, uncond_ctx):
-    """Concatenate two ActionExpertContext objects along the batch dimension."""
-    from .molmoact2_hf_model.modeling_molmoact2 import ActionExpertContext
-
-    kv_contexts = []
-    for (k_c, v_c), (k_u, v_u) in zip(cond_ctx.kv_contexts, uncond_ctx.kv_contexts, strict=True):
-        kv_contexts.append((torch.cat([k_c, k_u], dim=0), torch.cat([v_c, v_u], dim=0)))
-
-    cross_mask = None
-    if cond_ctx.cross_mask is not None and uncond_ctx.cross_mask is not None:
-        cross_mask = torch.cat([cond_ctx.cross_mask, uncond_ctx.cross_mask], dim=0)
-
-    self_mask = None
-    if cond_ctx.self_mask is not None and uncond_ctx.self_mask is not None:
-        self_mask = torch.cat([cond_ctx.self_mask, uncond_ctx.self_mask], dim=0)
-    elif cond_ctx.self_mask is not None:
-        self_mask = cond_ctx.self_mask.repeat(2, *([1] * (cond_ctx.self_mask.ndim - 1)))
-
-    valid_action = None
-    if cond_ctx.valid_action is not None and uncond_ctx.valid_action is not None:
-        valid_action = torch.cat([cond_ctx.valid_action, uncond_ctx.valid_action], dim=0)
-
-    rope_cache = cond_ctx.rope_cache
-
-    return ActionExpertContext(
-        kv_contexts=kv_contexts,
-        cross_mask=cross_mask,
-        self_mask=self_mask,
-        valid_action=valid_action,
-        rope_cache=rope_cache,
-    )
-
-
-def _clone_modulation_with_conditioning(modulation, batched_conditioning):
-    """Create a modulation with doubled batch for batched CFG forward."""
-    from .molmoact2_hf_model.modeling_molmoact2 import ActionExpertStepModulation
-
-    batched_block_modulations = []
-    for block_mod in modulation.block_modulations:
-        batched_block_modulations.append(tuple(torch.cat([m, m], dim=0) for m in block_mod))
-
-    batched_final_modulation = tuple(torch.cat([m, m], dim=0) for m in modulation.final_modulation)
-
-    return ActionExpertStepModulation(
-        conditioning=batched_conditioning,
-        block_modulations=batched_block_modulations,
-        final_modulation=batched_final_modulation,
-    )
-
-
 class MolmoAct2Policy(PreTrainedPolicy):
     """MolmoAct2 policy wrapping the vendored HF model for LeRobot.
 
@@ -1672,183 +1622,6 @@ class MolmoAct2Policy(PreTrainedPolicy):
         metrics["loss"] = loss.detach().float().mean().item()
         return loss, metrics
 
-    def _cfg_enabled_for_batch(self, batch: dict[str, Tensor]) -> bool:
-        """Check if CFG should be used for this batch."""
-        return self.config.cfg_beta > 1.0 and batch.get("uncond_input_ids") is not None
-
-    def _uncond_model_inputs(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        """Extract unconditional model inputs from the batch (prepared by processor)."""
-        compute_dtype = _torch_dtype(self.config.model_dtype)
-        uncond_inputs: dict[str, Tensor] = {}
-        for key in _MODEL_INPUT_KEYS:
-            uncond_key = f"uncond_{key}"
-            value = batch.get(uncond_key)
-            if value is not None:
-                uncond_inputs[key] = value.to(dtype=compute_dtype) if value.is_floating_point() else value
-        return uncond_inputs
-
-    def _generate_actions_with_cfg(
-        self,
-        *,
-        cond_model_inputs: dict[str, Tensor],
-        uncond_model_inputs: dict[str, Tensor],
-        action_dim_is_pad: Tensor | None,
-        num_steps: int | None,
-        generator: torch.Generator | None,
-    ) -> Tensor:
-        """CFG inference: dual VLM forward + batched flow denoising.
-
-        Caching strategy:
-        1. VLM backbone runs once per branch (cond + uncond) — KV states cached.
-        2. Action expert context prepared once per branch from cached KV states.
-        3. Modulation cache (timestep embeddings) shared across branches.
-        4. Denoising loop: cond + uncond batched into a single action expert
-           forward per step (2x batch dim), then split and blended.
-        """
-        backbone = self._backbone()
-        action_expert = self._action_expert()
-
-        # === VLM prefill (cached — runs once per branch) ===
-
-        cond_outputs = backbone(
-            **cond_model_inputs,
-            use_cache=True,
-            output_attentions=False,
-            output_hidden_states=False,
-        )
-        cond_encoder_kv_states = backbone._extract_kv_states(cond_outputs.past_key_values)
-        cond_encoder_attention_mask = self._encoder_attention_mask_for_action_expert(
-            input_ids=cond_model_inputs.get("input_ids"),
-            attention_mask=cond_model_inputs.get("attention_mask"),
-        )
-        cond_depth_gate, cond_depth_mask = backbone._depth_gate_from_condition(
-            input_ids=cond_model_inputs.get("input_ids"),
-            encoder_attention_mask=cond_encoder_attention_mask,
-            layer_kv_states=cond_encoder_kv_states,
-        )
-        cond_encoder_kv_states = backbone._apply_depth_gate_to_layer_kv_states(
-            cond_encoder_kv_states, cond_depth_mask, cond_depth_gate
-        )
-
-        uncond_outputs = backbone(
-            **uncond_model_inputs,
-            use_cache=True,
-            output_attentions=False,
-            output_hidden_states=False,
-        )
-        uncond_encoder_kv_states = backbone._extract_kv_states(uncond_outputs.past_key_values)
-        uncond_encoder_attention_mask = self._encoder_attention_mask_for_action_expert(
-            input_ids=uncond_model_inputs.get("input_ids"),
-            attention_mask=uncond_model_inputs.get("attention_mask"),
-        )
-        uncond_depth_gate, uncond_depth_mask = backbone._depth_gate_from_condition(
-            input_ids=uncond_model_inputs.get("input_ids"),
-            encoder_attention_mask=uncond_encoder_attention_mask,
-            layer_kv_states=uncond_encoder_kv_states,
-        )
-        uncond_encoder_kv_states = backbone._apply_depth_gate_to_layer_kv_states(
-            uncond_encoder_kv_states, uncond_depth_mask, uncond_depth_gate
-        )
-
-        # === Setup flow denoising ===
-
-        steps = int(num_steps or backbone.config.flow_matching_num_steps)
-        if steps <= 0:
-            raise ValueError(f"num_steps must be >= 1, got {steps}.")
-        source_tensor = cond_encoder_kv_states[0][0]
-        batch_size = int(source_tensor.shape[0])
-        device = source_tensor.device
-        trajectory = torch.randn(
-            batch_size,
-            self._generation_action_horizon(),
-            int(backbone.config.max_action_dim),
-            device=device,
-            dtype=torch.float32,
-            generator=generator,
-        )
-        if self.config.mask_action_dim_padding:
-            trajectory = _mask_action_dim_tensor(trajectory, action_dim_is_pad)
-
-        # === Prepare action contexts (cached — reused across all denoising steps) ===
-
-        cond_action_context = action_expert.prepare_context(
-            encoder_kv_states=cond_encoder_kv_states,
-            encoder_attention_mask=cond_encoder_attention_mask,
-            state_embeddings=None,
-            batch_size=batch_size,
-            seq_len=trajectory.shape[1],
-            device=device,
-            dtype=trajectory.dtype,
-        )
-        uncond_action_context = action_expert.prepare_context(
-            encoder_kv_states=uncond_encoder_kv_states,
-            encoder_attention_mask=uncond_encoder_attention_mask,
-            state_embeddings=None,
-            batch_size=batch_size,
-            seq_len=trajectory.shape[1],
-            device=device,
-            dtype=trajectory.dtype,
-        )
-
-        # Modulation cache shared between branches (timestep is prompt-independent)
-        flow_timesteps = [
-            torch.full((batch_size,), idx / steps, device=device, dtype=trajectory.dtype)
-            for idx in range(steps)
-        ]
-        modulation_cache = action_expert.get_or_prepare_modulation_cache(
-            flow_timesteps,
-            cache_key=(steps, batch_size, device, trajectory.dtype),
-        )
-
-        # === Batched CFG denoising loop ===
-        # Instead of two sequential action expert forwards per step, we batch
-        # cond + uncond on the batch dimension for a single forward (2x batch).
-        # This maximizes GPU utilization (same pattern as PI05).
-
-        dt = 1.0 / steps
-        mask_enabled = self.config.mask_action_dim_padding
-        cfg_beta = self.config.cfg_beta
-        batched_action_dim_is_pad = (
-            torch.cat([action_dim_is_pad, action_dim_is_pad], dim=0)
-            if action_dim_is_pad is not None
-            else None
-        )
-
-        for idx in range(steps):
-            modulation = modulation_cache[idx]
-
-            # Duplicate trajectory and conditioning for batched forward
-            batched_trajectory = torch.cat([trajectory, trajectory], dim=0)
-            batched_conditioning = torch.cat([modulation.conditioning, modulation.conditioning], dim=0)
-
-            # Build batched context by concatenating cond + uncond contexts
-            batched_context = _cat_action_contexts(cond_action_context, uncond_action_context)
-
-            # Build batched modulation with doubled conditioning
-            batched_modulation = _clone_modulation_with_conditioning(modulation, batched_conditioning)
-
-            # Single batched forward through action expert
-            v_all = action_expert.forward_with_context(
-                batched_trajectory,
-                batched_conditioning,
-                context=batched_context,
-                modulation=batched_modulation,
-            )
-            if mask_enabled:
-                v_all = _mask_action_dim_tensor(v_all, batched_action_dim_is_pad)
-
-            # Split: first half = cond, second half = uncond
-            v_cond, v_uncond = v_all.chunk(2, dim=0)
-
-            # CFG interpolation: v = v_uncond + beta * (v_cond - v_uncond)
-            velocity = v_uncond + cfg_beta * (v_cond - v_uncond)
-
-            trajectory = trajectory + dt * velocity
-            if mask_enabled:
-                trajectory = _mask_action_dim_tensor(trajectory, action_dim_is_pad)
-
-        return trajectory
-
     @torch.no_grad()
     def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
         """Generate an action chunk via continuous flow matching or discrete AR decoding."""
@@ -1884,15 +1657,6 @@ class MolmoAct2Policy(PreTrainedPolicy):
                     model_inputs=model_inputs,
                     action_dim=action_dim,
                 )
-            elif self._cfg_enabled_for_batch(batch):
-                uncond_model_inputs = self._uncond_model_inputs(batch)
-                actions = self._generate_actions_with_cfg(
-                    cond_model_inputs=model_inputs,
-                    uncond_model_inputs=uncond_model_inputs,
-                    action_dim_is_pad=batch.get("action_dim_is_pad"),
-                    num_steps=num_steps,
-                    generator=generator,
-                )
             elif self._rtc_enabled():
                 actions = self._generate_actions_from_inputs_with_rtc(
                     model_inputs=model_inputs,

src/lerobot/policies/molmoact2/processor_molmoact2.py

@@ -359,7 +359,6 @@ def _build_robot_text(
     add_setup_tokens: bool,
     add_control_tokens: bool,
     num_images: int,
-    advantage: str = "",
 ) -> str:
     setup_text = _wrap_setup_text(setup_type, add_setup_tokens=add_setup_tokens)
     control_text = _wrap_control_text(control_mode, add_control_tokens=add_control_tokens)
@@ -376,10 +375,7 @@ def _build_robot_text(
         image_prefix = "<|image|>"
     else:
         image_prefix = "".join(f"Image {idx + 1}<|image|>" for idx in range(num_images))
-    # Per RECAP paper (Section V-B): advantage indicator goes after context,
-    # before actions, so only action log-likelihoods are affected.
-    advantage_clause = f"Advantage: {advantage}. " if advantage else ""
-    return f"{image_prefix}<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{advantage_clause}{ACTION_OUTPUT_TOKEN}"
+    return f"{image_prefix}<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{ACTION_OUTPUT_TOKEN}"
 
 
 def _as_text_list(value: Any, batch_size: int) -> list[str]:
@@ -699,39 +695,6 @@ class MolmoAct2ClampNormalizedProcessorStep(ProcessorStep):
         return features
 
 
-@ProcessorStepRegistry.register(name="molmoact2_normalize_task")
-@dataclass
-class MolmoAct2NormalizeTaskStep(ProcessorStep):
-    """Normalize the task text in complementary_data before recipe rendering.
-
-    Ensures ${task} in recipe templates gets the same normalized form that
-    MolmoAct2PackInputsProcessorStep would produce, so training prompts
-    match inference prompts.
-    """
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        complementary = transition.get(TransitionKey.COMPLEMENTARY_DATA)
-        if not isinstance(complementary, dict):
-            return transition
-        task = complementary.get("task")
-        if task is None:
-            return transition
-
-        transition = transition.copy()
-        complementary = dict(complementary)
-        if isinstance(task, str):
-            complementary["task"] = _normalize_question_text(task)
-        elif isinstance(task, list):
-            complementary["task"] = [_normalize_question_text(t) for t in task]
-        transition[TransitionKey.COMPLEMENTARY_DATA] = complementary
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-
 @ProcessorStepRegistry.register(name="molmoact2_pack_inputs")
 @dataclass
 class MolmoAct2PackInputsProcessorStep(ProcessorStep):
@@ -752,10 +715,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
     chunk_size: int = 30
     max_action_dim: int = 32
     env_action_dim: int | None = None
-    # RECAP: advantage indicator for inference (e.g. "Advantage: positive. ")
-    advantage_prefix: str = ""
-    # CFG scale for inference. >1.0 builds unconditional inputs for guidance.
-    cfg_beta: float = 1.0
 
     def __post_init__(self) -> None:
         require_package("transformers", extra="molmoact2")
@@ -798,7 +757,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
             "chunk_size": self.chunk_size,
             "max_action_dim": self.max_action_dim,
             "env_action_dim": self.env_action_dim,
-            "advantage_prefix": self.advantage_prefix,
         }
 
     def _resolve_max_sequence_length(
@@ -961,40 +919,8 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
         if task_source is None:
             task_source = complementary.get("language_instruction")
         tasks = _as_text_list(task_source, batch_size)
-
-        # Resolve the advantage indicator. Per RECAP paper (Section V-B), it goes
-        # after all context but before actions — handled by _build_robot_text.
-        # Source priority: recipe-rendered "advantage" key > config advantage_prefix.
-        advantages: list[str] = []
-        recipe_rendered = "base_task" in complementary
-        if recipe_rendered:
-            # Recipe rendered the task as "<task> Advantage: <value>".
-            # Extract the advantage value and restore the clean task.
-            clean_tasks: list[str] = []
-            for t in tasks:
-                if " Advantage: " in t:
-                    split_idx = t.rindex(" Advantage: ")
-                    clean_task = t[:split_idx]
-                    adv = t[split_idx + len(" Advantage: ") :]
-                    advantages.append(adv)
-                    clean_tasks.append(clean_task)
-                else:
-                    advantages.append("")
-                    clean_tasks.append(t)
-            tasks = clean_tasks
-        else:
-            if self.normalize_language:
-                tasks = [_normalize_question_text(task) for task in tasks]
-            if self.advantage_prefix:
-                # Extract just the value from prefix like "Advantage: positive. "
-                prefix = self.advantage_prefix.strip()
-                if prefix.startswith("Advantage:"):
-                    adv_val = prefix[len("Advantage:") :].strip().rstrip(".")
-                else:
-                    adv_val = prefix
-                advantages = [adv_val] * batch_size
-            else:
-                advantages = [""] * batch_size
+        if self.normalize_language:
+            tasks = [_normalize_question_text(task) for task in tasks]
         complementary["task"] = tasks
 
         action_padded = None
@@ -1027,7 +953,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
                 add_setup_tokens=self.add_setup_tokens,
                 add_control_tokens=self.add_control_tokens,
                 num_images=len(images),
-                advantage=advantages[batch_idx],
             )
             prompt_texts.append(prompt)
             if build_action_labels:
@@ -1064,33 +989,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
         if build_action_labels:
             inputs["labels"] = self._build_labels(inputs["input_ids"], inputs["attention_mask"])
 
-        # CFG: build unconditional inputs (no advantage) for inference-time guidance.
-        # Only produced when cfg_beta > 1.0 and we have advantage conditioning.
-        if self.cfg_beta > 1.0 and action is None and any(advantages):
-            uncond_prompt_texts: list[str] = []
-            for batch_idx in range(batch_size):
-                images = images_by_example[batch_idx]
-                discrete_state = _build_discrete_state_string(state_np[batch_idx], self.num_state_tokens)
-                uncond_prompt = _build_robot_text(
-                    task=tasks[batch_idx],
-                    discrete_state_string=discrete_state,
-                    setup_type=self.setup_type,
-                    control_mode=self.control_mode,
-                    add_setup_tokens=self.add_setup_tokens,
-                    add_control_tokens=self.add_control_tokens,
-                    num_images=len(images),
-                    advantage="",
-                )
-                uncond_prompt_texts.append(uncond_prompt)
-            uncond_inputs = self.processor(
-                text=uncond_prompt_texts, images=flat_images, return_tensors="pt", padding=True
-            )
-            complementary["uncond_input_ids"] = uncond_inputs["input_ids"]
-            complementary["uncond_attention_mask"] = uncond_inputs["attention_mask"]
-            for key in ("pixel_values", "image_token_pooling", "image_grids", "image_num_crops"):
-                if key in uncond_inputs:
-                    complementary[f"uncond_{key}"] = uncond_inputs[key]
-
         complementary.update(dict(inputs))
         complementary["action_dim_is_pad"] = action_dim_is_pad
         if action_horizon_is_pad is not None:
@@ -1266,49 +1164,28 @@ def make_molmoact2_pre_post_processors(
             stats=masked_dataset_stats,
         ),
         MolmoAct2ClampNormalizedProcessorStep(normalization_masks=normalization_masks),
+        MolmoAct2PackInputsProcessorStep(
+            checkpoint_path=config.checkpoint_path,
+            checkpoint_revision=config.checkpoint_revision,
+            checkpoint_force_download=config.checkpoint_force_download,
+            action_mode=config.action_mode,
+            discrete_action_tokenizer=config.discrete_action_tokenizer,
+            image_keys=image_keys,
+            allow_image_key_fallback=not bool(config.image_keys),
+            setup_type=setup_type,
+            control_mode=control_mode,
+            normalize_language=config.normalize_language,
+            add_setup_tokens=config.add_setup_tokens,
+            add_control_tokens=config.add_control_tokens,
+            num_state_tokens=config.num_state_tokens,
+            max_sequence_length=config.max_sequence_length,
+            chunk_size=chunk_size,
+            max_action_dim=config.expected_max_action_dim,
+            env_action_dim=env_action_dim,
+        ),
+        DeviceProcessorStep(device=config.device),
     ]
 
-    # Insert language rendering steps when a recipe is configured (e.g. RECAP advantage)
-    if config.recipe_path is not None:
-        from lerobot.configs.recipe import load_recipe
-        from lerobot.processor.render_messages_processor import RenderMessagesStep
-        from lerobot.processor.rendered_messages_to_task import RenderedMessagesToTaskStep
-
-        recipe = load_recipe(config.recipe_path)
-        # Normalize task text before recipe uses ${task}, ensuring consistency
-        # between training (recipe-rendered) and inference (advantage_prefix).
-        if config.normalize_language:
-            input_steps.append(MolmoAct2NormalizeTaskStep())
-        input_steps.append(RenderMessagesStep(recipe=recipe))
-        input_steps.append(RenderedMessagesToTaskStep())
-
-    input_steps.extend(
-        [
-            MolmoAct2PackInputsProcessorStep(
-                checkpoint_path=config.checkpoint_path,
-                checkpoint_revision=config.checkpoint_revision,
-                checkpoint_force_download=config.checkpoint_force_download,
-                action_mode=config.action_mode,
-                discrete_action_tokenizer=config.discrete_action_tokenizer,
-                image_keys=image_keys,
-                allow_image_key_fallback=not bool(config.image_keys),
-                setup_type=setup_type,
-                control_mode=control_mode,
-                normalize_language=config.normalize_language,
-                add_setup_tokens=config.add_setup_tokens,
-                add_control_tokens=config.add_control_tokens,
-                num_state_tokens=config.num_state_tokens,
-                max_sequence_length=config.max_sequence_length,
-                chunk_size=chunk_size,
-                max_action_dim=config.expected_max_action_dim,
-                env_action_dim=env_action_dim,
-                advantage_prefix=config.advantage_prefix,
-                cfg_beta=config.cfg_beta,
-            ),
-            DeviceProcessorStep(device=config.device),
-        ]
-    )
-
     output_steps: list[ProcessorStep] = [
         MolmoAct2ClampActionProcessorStep(),
         MolmoAct2MaskedUnnormalizerProcessorStep(

src/lerobot/policies/pi05/configuration_pi05.py

@@ -87,17 +87,6 @@ class PI05Config(PreTrainedConfig):
     freeze_vision_encoder: bool = False  # Freeze only the vision encoder
     train_expert_only: bool = False  # Freeze entire VLM, train only action expert and projections
 
-    # Language conditioning (e.g. RECAP advantage). When set, RenderMessagesStep
-    # is inserted into the preprocessor to resolve language_persistent rows via
-    # the recipe YAML before prompt construction.
-    recipe_path: str | None = None
-
-    # Classifier-Free Guidance (CFG) scale for inference (Eq. 13 in RECAP paper).
-    # 1.0 = no guidance (default). >1.0 enables dual-path denoising where:
-    #   v = v_uncond + cfg_beta * (v_cond - v_uncond)
-    # VLM runs twice (cond + uncond prompts), action expert runs 2x per step.
-    cfg_beta: float = 1.0
-
     # Optimizer settings: see openpi `AdamW`
     optimizer_lr: float = 2.5e-5  # see openpi `CosineDecaySchedule: peak_lr`
     optimizer_betas: tuple[float, float] = (0.9, 0.95)

src/lerobot/policies/pi05/modeling_pi05.py

@@ -52,8 +52,6 @@ from lerobot.utils.constants import (
     ACTION,
     OBS_LANGUAGE_ATTENTION_MASK,
     OBS_LANGUAGE_TOKENS,
-    OBS_LANGUAGE_UNCOND_ATTENTION_MASK,
-    OBS_LANGUAGE_UNCOND_TOKENS,
     OPENPI_ATTENTION_MASK_VALUE,
 )
 
@@ -150,20 +148,6 @@ def clone_past_key_values(past_key_values):
     )
 
 
-def cat_past_key_values(kv_a, kv_b):
-    """Concatenate two DynamicCaches along the batch dimension for batched CFG."""
-    return DynamicCache(
-        tuple(
-            (
-                torch.cat([ka, kb], dim=0),
-                torch.cat([va, vb], dim=0),
-                sw_a,
-            )
-            for (ka, va, sw_a), (kb, vb, _sw_b) in zip(kv_a, kv_b, strict=True)
-        )
-    )
-
-
 def pad_vector(vector, new_dim):
     """Pad the last dimension of a vector to new_dim with zeros.
 
@@ -813,17 +797,9 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         masks,
         noise=None,
         num_steps=None,
-        uncond_tokens=None,
-        uncond_masks=None,
         **kwargs: Unpack[ActionSelectKwargs],
     ) -> Tensor:
-        """Do a full inference forward and compute the action.
-
-        When cfg_beta > 1.0 and uncond_tokens/uncond_masks are provided, performs
-        Classifier-Free Guidance: VLM runs twice (conditioned + unconditional), action
-        expert runs twice per denoising step, and velocities are interpolated via
-        v = v_uncond + cfg_beta * (v_cond - v_uncond).
-        """
+        """Do a full inference forward and compute the action."""
         if num_steps is None:
             num_steps = self.config.num_inference_steps
 
@@ -839,9 +815,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             )  # Use config max_action_dim for internal processing
             noise = self.sample_noise(actions_shape, device)
 
-        cfg_enabled = self.config.cfg_beta > 1.0 and uncond_tokens is not None and uncond_masks is not None
-
-        # Prefill VLM for conditioned prompt
         prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(images, img_masks, tokens, masks)
         prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
@@ -857,23 +830,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             use_cache=True,
         )
 
-        # Prefill VLM for unconditional prompt (CFG)
-        if cfg_enabled:
-            uncond_prefix_embs, uncond_prefix_pad_masks, uncond_prefix_att_masks = self.embed_prefix(
-                images, img_masks, uncond_tokens, uncond_masks
-            )
-            uncond_prefix_att_2d_masks = make_att_2d_masks(uncond_prefix_pad_masks, uncond_prefix_att_masks)
-            uncond_prefix_position_ids = torch.cumsum(uncond_prefix_pad_masks, dim=1) - 1
-            uncond_prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(uncond_prefix_att_2d_masks)
-
-            _, uncond_past_key_values = self.paligemma_with_expert.forward(
-                attention_mask=uncond_prefix_att_2d_masks_4d,
-                position_ids=uncond_prefix_position_ids,
-                past_key_values=None,
-                inputs_embeds=[uncond_prefix_embs, None],
-                use_cache=True,
-            )
-
         dt = -1.0 / num_steps
 
         x_t = noise
@@ -882,15 +838,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)
 
             def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
-                if cfg_enabled:
-                    return self.denoise_step_cfg_batched(
-                        cond_prefix_pad_masks=prefix_pad_masks,
-                        cond_past_key_values=past_key_values,
-                        uncond_prefix_pad_masks=uncond_prefix_pad_masks,
-                        uncond_past_key_values=uncond_past_key_values,
-                        x_t=input_x_t,
-                        timestep=current_timestep,
-                    )
                 return self.denoise_step(
                     prefix_pad_masks=prefix_pad_masks,
                     past_key_values=past_key_values,
@@ -960,80 +907,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         suffix_out = suffix_out.to(dtype=torch.float32)
         return self.action_out_proj(suffix_out)
 
-    def denoise_step_cfg_batched(
-        self,
-        cond_prefix_pad_masks,
-        cond_past_key_values,
-        uncond_prefix_pad_masks,
-        uncond_past_key_values,
-        x_t,
-        timestep,
-    ):
-        """Batched CFG denoising: runs cond + uncond in a single forward pass.
-
-        Concatenates cond and uncond inputs along the batch dimension, runs one
-        action expert forward (2x batch), then splits and applies CFG interpolation.
-        This is ~1.5x faster than two sequential denoise_step calls due to better
-        GPU utilization (inspired by Qwen2.5-Omni DiT / diffusers batched CFG).
-        """
-        # Embed suffix once (same x_t and timestep for both branches)
-        suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, timestep)
-
-        bsize = cond_prefix_pad_masks.shape[0]
-        suffix_len = suffix_pad_masks.shape[1]
-        cond_prefix_len = cond_prefix_pad_masks.shape[1]
-        uncond_prefix_len = uncond_prefix_pad_masks.shape[1]
-
-        # Build attention masks for cond branch
-        cond_prefix_2d = cond_prefix_pad_masks[:, None, :].expand(bsize, suffix_len, cond_prefix_len)
-        cond_suffix_att_2d = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
-        cond_full_att = torch.cat([cond_prefix_2d, cond_suffix_att_2d], dim=2)
-        cond_prefix_offsets = torch.sum(cond_prefix_pad_masks, dim=-1)[:, None]
-        cond_position_ids = cond_prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
-
-        # Build attention masks for uncond branch
-        uncond_prefix_2d = uncond_prefix_pad_masks[:, None, :].expand(bsize, suffix_len, uncond_prefix_len)
-        uncond_suffix_att_2d = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
-        uncond_full_att = torch.cat([uncond_prefix_2d, uncond_suffix_att_2d], dim=2)
-        uncond_prefix_offsets = torch.sum(uncond_prefix_pad_masks, dim=-1)[:, None]
-        uncond_position_ids = uncond_prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
-
-        # Concatenate on batch dim: [cond_batch; uncond_batch]
-        batched_full_att = torch.cat([cond_full_att, uncond_full_att], dim=0)
-        batched_full_att_4d = self._prepare_attention_masks_4d(batched_full_att)
-        batched_position_ids = torch.cat([cond_position_ids, uncond_position_ids], dim=0)
-        batched_suffix_embs = torch.cat([suffix_embs, suffix_embs], dim=0)
-        batched_adarms_cond = torch.cat([adarms_cond, adarms_cond], dim=0)
-
-        # Concatenate KV caches on batch dim
-        batched_past_kv = cat_past_key_values(
-            clone_past_key_values(cond_past_key_values),
-            clone_past_key_values(uncond_past_key_values),
-        )
-
-        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
-
-        # Single forward pass for both branches
-        outputs_embeds, _ = self.paligemma_with_expert.forward(
-            attention_mask=batched_full_att_4d,
-            position_ids=batched_position_ids,
-            past_key_values=batched_past_kv,
-            inputs_embeds=[None, batched_suffix_embs],
-            use_cache=False,
-            adarms_cond=[None, batched_adarms_cond],
-        )
-
-        suffix_out = outputs_embeds[1]
-        suffix_out = suffix_out[:, -self.config.chunk_size :]
-        suffix_out = suffix_out.to(dtype=torch.float32)
-        v_all = self.action_out_proj(suffix_out)
-
-        # Split: first half = cond, second half = uncond
-        v_cond, v_uncond = v_all.chunk(2, dim=0)
-
-        # CFG interpolation: v = v_uncond + beta * (v_cond - v_uncond)
-        return v_uncond + self.config.cfg_beta * (v_cond - v_uncond)
-
 
 class PI05Policy(PreTrainedPolicy):
     """PI05 Policy for LeRobot."""
@@ -1370,20 +1243,8 @@ class PI05Policy(PreTrainedPolicy):
         images, img_masks = self._preprocess_images(batch)
         tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
 
-        # CFG: pass unconditional tokens if available
-        uncond_tokens = batch.get(f"{OBS_LANGUAGE_UNCOND_TOKENS}")
-        uncond_masks = batch.get(f"{OBS_LANGUAGE_UNCOND_ATTENTION_MASK}")
-
         # Sample actions using the model (pass through RTC kwargs, no separate state needed for PI05)
-        actions = self.model.sample_actions(
-            images,
-            img_masks,
-            tokens,
-            masks,
-            uncond_tokens=uncond_tokens,
-            uncond_masks=uncond_masks,
-            **kwargs,
-        )
+        actions = self.model.sample_actions(images, img_masks, tokens, masks, **kwargs)
 
         # Unpad actions to actual action dimension
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/policies/pi05/processor_pi05.py

@@ -40,8 +40,6 @@ from lerobot.processor import (
 )
 from lerobot.types import EnvTransition, TransitionKey
 from lerobot.utils.constants import (
-    OBS_LANGUAGE_UNCOND_ATTENTION_MASK,
-    OBS_LANGUAGE_UNCOND_TOKENS,
     OBS_STATE,
     POLICY_POSTPROCESSOR_DEFAULT_NAME,
     POLICY_PREPROCESSOR_DEFAULT_NAME,
@@ -59,7 +57,6 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
 
     max_state_dim: int = 32
     task_key: str = "task"
-    cfg_enabled: bool = False
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         transition = transition.copy()
@@ -87,25 +84,8 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
             full_prompts.append(full_prompt)
 
         transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts
-
-        # Build unconditional prompts for CFG (same state but original task without advantage)
-        if self.cfg_enabled:
-            base_tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get("base_task")
-            if base_tasks is None:
-                base_tasks = tasks
-
-            if isinstance(base_tasks, str):
-                base_tasks = [base_tasks] * len(tasks)
-
-            uncond_prompts = []
-            for i, base_task in enumerate(base_tasks):
-                cleaned_text = base_task.strip().replace("_", " ").replace("\n", " ")
-                state_str = " ".join(map(str, discretized_states[i]))
-                uncond_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: "
-                uncond_prompts.append(uncond_prompt)
-
-            transition[TransitionKey.COMPLEMENTARY_DATA]["uncond_task"] = uncond_prompts
-
+        # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!)
+        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         return transition
 
     def transform_features(
@@ -131,10 +111,9 @@ def make_pi05_pre_post_processors(
     1. Renaming features to match pretrained configurations.
     2. Normalizing input and output features based on dataset statistics.
     3. Adding a batch dimension.
-    4. (Optional) Rendering language annotations via recipe YAML.
-    5. (Optional) Flattening rendered messages into the task string.
-    6. Tokenizing the text prompt using the PaliGemma tokenizer.
-    7. Moving all data to the specified device.
+    4. Appending a newline character to the task description for tokenizer compatibility.
+    5. Tokenizing the text prompt using the PaliGemma tokenizer.
+    6. Moving all data to the specified device.
 
     The post-processing pipeline handles the model's output by:
     1. Moving data to the CPU.
@@ -143,6 +122,8 @@ def make_pi05_pre_post_processors(
     Args:
         config: The configuration object for the PI0 policy.
         dataset_stats: A dictionary of statistics for normalization.
+        preprocessor_kwargs: Additional arguments for the pre-processor pipeline.
+        postprocessor_kwargs: Additional arguments for the post-processor pipeline.
 
     Returns:
         A tuple containing the configured pre-processor and post-processor pipelines.
@@ -166,51 +147,16 @@ def make_pi05_pre_post_processors(
             norm_map=config.normalization_mapping,
             stats=dataset_stats,
         ),
+        Pi05PrepareStateTokenizerProcessorStep(max_state_dim=config.max_state_dim),
+        TokenizerProcessorStep(
+            tokenizer_name="google/paligemma-3b-pt-224",
+            max_length=config.tokenizer_max_length,
+            padding_side="right",
+            padding="max_length",
+        ),
+        DeviceProcessorStep(device=config.device),
     ]
 
-    # Insert language rendering steps when a recipe is configured (e.g. RECAP advantage)
-    if config.recipe_path is not None:
-        from lerobot.configs.recipe import load_recipe
-        from lerobot.processor.render_messages_processor import RenderMessagesStep
-        from lerobot.processor.rendered_messages_to_task import RenderedMessagesToTaskStep
-
-        recipe = load_recipe(config.recipe_path)
-        input_steps.append(RenderMessagesStep(recipe=recipe))
-        input_steps.append(RenderedMessagesToTaskStep())
-
-    cfg_enabled = config.cfg_beta > 1.0
-
-    input_steps.extend(
-        [
-            Pi05PrepareStateTokenizerProcessorStep(
-                max_state_dim=config.max_state_dim,
-                cfg_enabled=cfg_enabled,
-            ),
-            TokenizerProcessorStep(
-                tokenizer_name="google/paligemma-3b-pt-224",
-                max_length=config.tokenizer_max_length,
-                padding_side="right",
-                padding="max_length",
-            ),
-        ]
-    )
-
-    # Add unconditional prompt tokenizer for CFG inference
-    if cfg_enabled:
-        input_steps.append(
-            TokenizerProcessorStep(
-                tokenizer_name="google/paligemma-3b-pt-224",
-                max_length=config.tokenizer_max_length,
-                padding_side="right",
-                padding="max_length",
-                task_key="uncond_task",
-                output_tokens_key=OBS_LANGUAGE_UNCOND_TOKENS,
-                output_mask_key=OBS_LANGUAGE_UNCOND_ATTENTION_MASK,
-            )
-        )
-
-    input_steps.append(DeviceProcessorStep(device=config.device))
-
     output_steps: list[ProcessorStep] = [
         UnnormalizerProcessorStep(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -613,15 +613,14 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         device = tokens.device
         lm_head = self.paligemma_with_expert.paligemma.lm_head
 
-        # add bos token after tokens
-        bos_token = torch.full(
-            (bsize, 1), self._paligemma_tokenizer.bos_token_id, dtype=torch.long, device=device
-        )
-        tokens = torch.cat([tokens, bos_token], dim=1)
-        masks = torch.cat([masks, torch.ones((bsize, 1), dtype=torch.bool, device=device)], dim=1)
+        # NOTE (bug 2 fix): do NOT append a second <bos> here. The language tokens
+        # already begin with <bos> (standard PaliGemma prefix "[image] <bos> prompt \n").
+        # Appending another <bos> right before decoding pushes the checkpoint into a
+        # bos->bos attractor and yields degenerate generation. Generate directly after
+        # the prompt instead.
 
         # 1. Initial Embedding (matches training prefix)
-        # prefix_embs will include [Images, Language Prompt, BOS]
+        # prefix_embs will include [Images, Language Prompt]
         prefix_embs, prefix_pad_masks, prefix_att_masks, total_t_images, _ = self.embed_prefix_fast(
             images, img_masks, tokens, masks, fast_action_tokens=None, fast_action_masks=None
         )
@@ -709,14 +708,13 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         # --- 1. PREFILL PHASE ---
         # Process Images + Text Prompt + BOS token once to populate the KV cache.
 
-        # Add BOS token to the prompt
-        bos_token = torch.full(
-            (bsize, 1), self._paligemma_tokenizer.bos_token_id, dtype=torch.long, device=device
-        )
-        tokens_in = torch.cat([tokens, bos_token], dim=1)
-        masks_in = torch.cat([masks, torch.ones((bsize, 1), dtype=torch.bool, device=device)], dim=1)
+        # NOTE (bug 2 fix): do NOT append a second <bos> here. The language tokens
+        # already begin with <bos> (standard PaliGemma prefix "[image] <bos> prompt \n").
+        # A second <bos> right before decoding causes degenerate bos->bos generation.
+        tokens_in = tokens
+        masks_in = masks
 
-        # Embed prefix [Images, Language, BOS]
+        # Embed prefix [Images, Language]
         # fast_action_tokens=None means we are just embedding the condition (images+text)
         prefix_embs, prefix_pad_masks, prefix_att_masks, total_t_images, _ = self.embed_prefix_fast(
             images, img_masks, tokens_in, masks_in, fast_action_tokens=None, fast_action_masks=None

src/lerobot/processor/converters.py

@@ -164,10 +164,6 @@ _COMPLEMENTARY_KEYS = (
     "messages",
     "message_streams",
     "target_message_indices",
-    "mc_return",
-    "is_terminal",
-    "next.success",
-    "intervention",
 )
 
 

src/lerobot/processor/pipeline.py

@@ -1054,20 +1054,8 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             try:
                 step_class = ProcessorStepRegistry.get(step_entry["registry_name"])
                 return step_class, step_entry["registry_name"]
-            except KeyError:
-                registry_name = step_entry["registry_name"]
-                module_path = f"lerobot.processor.{registry_name}"
-                try:
-                    importlib.import_module(module_path)
-                    step_class = ProcessorStepRegistry.get(registry_name)
-                    return step_class, registry_name
-                except (ImportError, ModuleNotFoundError, KeyError):
-                    raise ImportError(
-                        f"Failed to load processor step from registry. "
-                        f"Processor step '{registry_name}' not found in registry. "
-                        f"Available steps: {list(ProcessorStepRegistry._registry.keys())}. "
-                        f"Make sure the step is registered using @ProcessorStepRegistry.register()"
-                    ) from None
+            except KeyError as e:
+                raise ImportError(f"Failed to load processor step from registry. {str(e)}") from e
         else:
             # Fallback to dynamic import using the full class path
             full_class_path = step_entry["class"]

src/lerobot/processor/render_messages_processor.py

@@ -40,14 +40,11 @@ class RenderMessagesStep(ProcessorStep):
     ``message_streams`` / ``target_message_indices`` keys.
     """
 
-    recipe: TrainingRecipe | None = None
+    recipe: TrainingRecipe
     dataset_ctx: Any | None = None
 
     def __call__(self, transition: EnvTransition) -> EnvTransition | None:
         """Render messages for a single transition; return ``None`` to drop it."""
-        if self.recipe is None:
-            return transition
-
         complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
         persistent = complementary_data.get(LANGUAGE_PERSISTENT) or []
         events = complementary_data.get(LANGUAGE_EVENTS) or []

src/lerobot/processor/rendered_messages_to_task.py

@@ -1,86 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Adapter step that flattens rendered chat messages back into a task string.
-
-Bridges RenderMessagesStep (which outputs structured messages) to policies
-that expect a plain task string in complementary_data["task"] (e.g. PI05).
-"""
-
-from __future__ import annotations
-
-from lerobot.configs import PipelineFeatureType, PolicyFeature
-
-from .pipeline import ComplementaryDataProcessorStep, ProcessorStepRegistry
-
-
-@ProcessorStepRegistry.register(name="rendered_messages_to_task")
-class RenderedMessagesToTaskStep(ComplementaryDataProcessorStep):
-    """Extract user-role message content from rendered messages into the task string.
-
-    After RenderMessagesStep renders a recipe into structured messages, this
-    step extracts content from all user-role messages, joins them, and writes
-    the result to complementary_data["task"]. This allows downstream steps
-    (like Pi05PrepareStateTokenizerProcessorStep) to consume the
-    advantage-conditioned prompt without modification.
-
-    No-ops when the "messages" key is absent (backward compatible with
-    pipelines that don't use language annotations).
-    """
-
-    def complementary_data(self, complementary_data: dict) -> dict:
-        messages = complementary_data.get("messages")
-        if messages is None:
-            return complementary_data
-
-        user_parts = []
-        for msg in messages:
-            if msg.get("role") == "user":
-                content = msg.get("content", "")
-                if isinstance(content, str) and content:
-                    user_parts.append(content)
-                elif isinstance(content, list):
-                    # HF multimodal blocks: extract text blocks
-                    for block in content:
-                        if isinstance(block, dict) and block.get("type") == "text":
-                            text = block.get("text", "")
-                            if text:
-                                user_parts.append(text)
-
-        new_complementary_data = dict(complementary_data)
-
-        if user_parts:
-            task = complementary_data.get("task")
-            # Preserve the original task for CFG unconditional prompt
-            new_complementary_data["base_task"] = task
-            # Wrap in list if the original task was a list (batched)
-            joined = "\n".join(user_parts)
-            if isinstance(task, list):
-                new_complementary_data["task"] = [joined] * len(task)
-            else:
-                new_complementary_data["task"] = joined
-
-        # Remove consumed rendering outputs
-        new_complementary_data.pop("messages", None)
-        new_complementary_data.pop("message_streams", None)
-        new_complementary_data.pop("target_message_indices", None)
-
-        return new_complementary_data
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features

src/lerobot/processor/tokenizer_processor.py

@@ -81,8 +81,6 @@ class TokenizerProcessorStep(ObservationProcessorStep):
     padding_side: str = "right"
     padding: str = "max_length"
     truncation: bool = True
-    output_tokens_key: str = OBS_LANGUAGE_TOKENS
-    output_mask_key: str = OBS_LANGUAGE_ATTENTION_MASK
 
     # Internal tokenizer instance (not part of the config)
     input_tokenizer: Any = field(default=None, init=False, repr=False)
@@ -203,8 +201,8 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         new_observation = dict(observation)
 
         # Add tokenized data to the observation
-        new_observation[self.output_tokens_key] = tokenized_prompt["input_ids"]
-        new_observation[self.output_mask_key] = tokenized_prompt["attention_mask"].to(dtype=torch.bool)
+        new_observation[OBS_LANGUAGE_TOKENS] = tokenized_prompt["input_ids"]
+        new_observation[OBS_LANGUAGE_ATTENTION_MASK] = tokenized_prompt["attention_mask"].to(dtype=torch.bool)
 
         # Tokenize subtask if available
         subtask = self.get_subtask(self.transition)
@@ -311,14 +309,14 @@ class TokenizerProcessorStep(ObservationProcessorStep):
             The updated dictionary of policy features.
         """
         # Add a feature for the token IDs if it doesn't already exist
-        if self.output_tokens_key not in features[PipelineFeatureType.OBSERVATION]:
-            features[PipelineFeatureType.OBSERVATION][self.output_tokens_key] = PolicyFeature(
+        if OBS_LANGUAGE_TOKENS not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_TOKENS] = PolicyFeature(
                 type=FeatureType.LANGUAGE, shape=(self.max_length,)
             )
 
         # Add a feature for the attention mask if it doesn't already exist
-        if self.output_mask_key not in features[PipelineFeatureType.OBSERVATION]:
-            features[PipelineFeatureType.OBSERVATION][self.output_mask_key] = PolicyFeature(
+        if OBS_LANGUAGE_ATTENTION_MASK not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_ATTENTION_MASK] = PolicyFeature(
                 type=FeatureType.LANGUAGE, shape=(self.max_length,)
             )
 
@@ -478,11 +476,12 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
             if tokens.dim() > 1:
                 tokens = tokens.flatten()
 
-            bos_id = self._paligemma_tokenizer.bos_token_id
-            # add bos
+            # NOTE (bug 2 fix): do NOT prepend a <bos> to the action target. The prompt
+            # already carries the leading <bos>; a second one before "Action:" mismatches
+            # the generation-time prefix (see sample_actions_fast*) and drives degenerate
+            # bos->bos decoding. Target is "Action: <fast tokens> |".
             tokens = torch.cat(
                 [
-                    torch.tensor([bos_id], device=action.device),
                     torch.tensor(
                         self._paligemma_tokenizer.encode("Action: ", add_special_tokens=False),
                         device=action.device,

src/lerobot/rewards/__init__.py

@@ -13,35 +13,23 @@
 # limitations under the License.
 
 from .classifier.configuration_classifier import RewardClassifierConfig as RewardClassifierConfig
-from .distributional_value_function.configuration_distributional_value_function import (
-    DistributionalVFConfig as DistributionalVFConfig,
-)
 from .factory import (
     get_reward_model_class as get_reward_model_class,
     make_reward_model as make_reward_model,
     make_reward_model_config as make_reward_model_config,
     make_reward_pre_post_processors as make_reward_pre_post_processors,
 )
-from .nanovlm_value_function.configuration_nanovlm_value_function import (
-    NanoVLMVFConfig as NanoVLMVFConfig,
-)
 from .pretrained import PreTrainedRewardModel as PreTrainedRewardModel
 from .robometer.configuration_robometer import RobometerConfig as RobometerConfig
 from .sarm.configuration_sarm import SARMConfig as SARMConfig
-from .temporal_siglip_value_function.configuration_temporal_siglip_value_function import (
-    TemporalSiglipVFConfig as TemporalSiglipVFConfig,
-)
 from .topreward.configuration_topreward import TOPRewardConfig as TOPRewardConfig
 
 __all__ = [
     # Configuration classes
-    "DistributionalVFConfig",
-    "NanoVLMVFConfig",
     "RewardClassifierConfig",
     "RobometerConfig",
     "SARMConfig",
     "TOPRewardConfig",
-    "TemporalSiglipVFConfig",
     # Base class
     "PreTrainedRewardModel",
     # Factory functions

src/lerobot/rewards/distributional_value_function/common.py

@@ -1,124 +0,0 @@
-"""Shared distributional targets, loss, and metrics for VF experiments."""
-
-from __future__ import annotations
-
-import math
-from typing import Any
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor
-
-
-class DistributionalValueMixin:
-    """Mixin for models that expose ``_get_value_readout(batch)``."""
-
-    config: Any
-    value_head: Any
-    hl_gauss_sigma: float
-
-    def hl_gauss_target(self, target_value: Tensor) -> Tensor:
-        target_value = target_value.reshape(-1).clamp(
-            self.config.value_support_min, self.config.value_support_max
-        )
-        target_value = target_value.to(self.value_head.bin_centers.dtype)
-        bin_width = (self.config.value_support_max - self.config.value_support_min) / (
-            self.config.num_value_bins - 1
-        )
-        support_edges = torch.linspace(
-            self.config.value_support_min - bin_width / 2,
-            self.config.value_support_max + bin_width / 2,
-            self.config.num_value_bins + 1,
-            device=target_value.device,
-            dtype=target_value.dtype,
-        )
-        cdf = 0.5 * (
-            1.0
-            + torch.erf((support_edges[None] - target_value[:, None]) / (self.hl_gauss_sigma * math.sqrt(2)))
-        )
-        normalization = (cdf[:, -1] - cdf[:, 0]).unsqueeze(-1).clamp_min(1e-10)
-        return (cdf[:, 1:] - cdf[:, :-1]) / normalization
-
-    def dirac_delta_target(self, target_value: Tensor) -> Tensor:
-        """Two-hot scalar projection (legacy configuration name: ``dirac_delta``)."""
-        target_value = target_value.reshape(-1).clamp(
-            self.config.value_support_min, self.config.value_support_max
-        )
-        target_value = target_value.to(self.value_head.bin_centers.dtype)
-        bin_width = self.value_head.bin_centers[1] - self.value_head.bin_centers[0]
-        position = (target_value - self.config.value_support_min) / bin_width
-        lower = position.floor().long().clamp(0, self.config.num_value_bins - 1)
-        upper = position.ceil().long().clamp(0, self.config.num_value_bins - 1)
-        weight_upper = position - lower.float()
-        weight_lower = upper.float() - position
-        same = lower == upper
-        weight_upper = torch.where(same, torch.zeros_like(weight_upper), weight_upper)
-        weight_lower = torch.where(same, torch.ones_like(weight_lower), weight_lower)
-        distribution = torch.zeros(
-            target_value.shape[0],
-            self.config.num_value_bins,
-            device=target_value.device,
-            dtype=target_value.dtype,
-        )
-        rows = torch.arange(target_value.shape[0], device=target_value.device)
-        distribution[rows, lower] += weight_lower
-        distribution[rows, upper] += weight_upper
-        return distribution
-
-    def compute_target_distribution(self, target_value: Tensor, is_terminal: Tensor) -> Tensor:
-        if self.config.target_method == "hl_gauss":
-            base = self.hl_gauss_target(target_value)
-        elif self.config.target_method == "dirac_delta":
-            base = self.dirac_delta_target(target_value)
-        else:
-            raise ValueError(f"Unknown target method: {self.config.target_method}")
-        if not self.config.use_one_hot_terminal:
-            return base
-        is_terminal = is_terminal.reshape(-1)
-        if is_terminal.numel() != base.shape[0]:
-            raise ValueError(f"Expected {base.shape[0]} terminal flags, got {is_terminal.numel()}")
-        nearest = torch.argmin(
-            torch.abs(
-                self.value_head.bin_centers[None]
-                - target_value.reshape(-1, 1).to(self.value_head.bin_centers.dtype)
-            ),
-            dim=-1,
-        )
-        terminal = F.one_hot(nearest, num_classes=self.config.num_value_bins).to(base.dtype)
-        return torch.where(is_terminal[:, None].bool(), terminal, base)
-
-    def _distributional_forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        readout = self._get_value_readout(batch)
-        logits = self.value_head(readout)
-        probabilities = logits.softmax(-1)
-        predicted_value = (probabilities * self.value_head.bin_centers.to(probabilities.dtype)).sum(-1)
-        targets = self.compute_target_distribution(batch["mc_return"], batch["is_terminal"])
-        loss = -(targets * logits.log_softmax(-1)).sum(-1).mean()
-        target_values = (
-            batch["mc_return"].reshape(-1).clamp(self.config.value_support_min, self.config.value_support_max)
-        )
-        return loss, {
-            "loss": loss.item(),
-            "predicted_value_mean": predicted_value.mean().item(),
-            "mc_return_mean": target_values.mean().item(),
-            "mae": (predicted_value - target_values).abs().mean().item(),
-            "acc_best": (logits.argmax(-1) == targets.argmax(-1)).float().mean().item(),
-            "acc_neighbor": _neighbor_accuracy(
-                logits.argmax(-1),
-                target_values,
-                self.value_head.bin_centers,
-            ),
-        }
-
-    def compute_reward(self, batch: dict[str, Tensor]) -> Tensor:
-        logits = self.value_head(self._get_value_readout(batch))
-        probabilities = logits.softmax(-1)
-        return (probabilities * self.value_head.bin_centers.to(probabilities.dtype)).sum(-1)
-
-
-def _neighbor_accuracy(predicted_bin: Tensor, target: Tensor, centers: Tensor) -> float:
-    width = centers[1] - centers[0]
-    position = (target - centers[0]) / width
-    lower = position.floor().long().clamp(0, len(centers) - 1)
-    upper = position.ceil().long().clamp(0, len(centers) - 1)
-    return ((predicted_bin == lower) | (predicted_bin == upper)).float().mean().item()

src/lerobot/rewards/distributional_value_function/configuration_distributional_value_function.py

@@ -1,121 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Configuration for RECAP's distributional value function.
-
-Paper: "π*0.6: a VLA That Learns From Experience" (Physical Intelligence, 2025)
-       https://pi.website/blog/pistar06
-Architecture source of truth: "π0.6 Model Card", Section 2 (Model Design)
-       https://website.pi-asset.com/pi06star/PI06_model_card.pdf
-
-Distributional value function V^{pi_ref}(o_t, l) (Section IV-A).
-
-Architecture (~670M params):
-    Vision:  SigLIP2-so400m — 27 layers, 1152-dim, 1024 patches/image at 448px
-    LM:      Gemma3-270M   — 18 layers, 640-dim
-    Proj:    2x2 pool → RMSNorm → Linear(1152, 640), 256 soft tokens/image
-    Readout: one-way learned value query → 2-layer MLP → 201 bins
-
-Inputs:  multi-camera images (3 x 256 soft tokens) + ``"Task: {task}."`` prompt
-Targets: MC returns in [-1, 0], cross-entropy on Dirac delta (default) or HL-Gauss
-Init:    SigLIP2 + Gemma3 from pretrained HF checkpoints; head normal_(std=0.02)
-"""
-
-from dataclasses import dataclass, field
-
-from lerobot.configs import FeatureType, NormalizationMode
-from lerobot.configs.rewards import RewardModelConfig
-from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
-
-
-@RewardModelConfig.register_subclass("distributional_value_function")
-@dataclass
-class DistributionalVFConfig(RewardModelConfig):
-    """Configuration for RECAP's distributional value function.
-
-    Predicts V^{pi_ref}(o_t, l) as a categorical distribution over B=201 bins in [-1, 0].
-    Trained with cross-entropy on Dirac delta (C51, default) or HL-Gauss soft targets,
-    with optional one-hot targets for terminal states.
-
-    Architecture: adapted from the native Gemma3 multimodal VLM design and
-    scaled to π0.6's ~670M value backbone:
-    448px SigLIP2-so400m images are pooled from 1024 patches to 256 soft
-    tokens, RMS-normalized, projected into Gemma3-270M, and followed by a
-    one-way learned value-query token. Image tokens attend bidirectionally;
-    text and the value query remain causal.
-    """
-
-    # Backbone pretrained paths
-    siglip_path: str = "google/siglip2-so400m-patch14-384"
-    gemma3_path: str = "google/gemma-3-270m"
-    # Optional standard Gemma3ForConditionalGeneration checkpoint produced by
-    # standalone VLM alignment. When set, it supplies vision, connector, and LM.
-    vlm_pretrained_path: str | None = None
-
-    # Distributional head
-    num_value_bins: int = 201
-    value_support_min: float = -1.0
-    value_support_max: float = 0.0
-    # Stop Regressing (Farebrother et al., 2024) default: spreads most
-    # probability mass across approximately six neighboring bins.
-    hl_gauss_sigma_ratio: float = 0.75
-
-    # Target distribution method: "dirac_delta" (paper-faithful C51) or "hl_gauss" (soft)
-    target_method: str = "dirac_delta"
-
-    # Whether to use one-hot targets for terminal states (exact return, no smoothing).
-    use_one_hot_terminal: bool = True
-
-    # Image
-    image_resolution: tuple[int, int] = (448, 448)
-    num_image_tokens: int = 256
-
-    # Tokenizer (uses Gemma3's tokenizer)
-    tokenizer_max_length: int = 200
-
-    # Training controls
-    value_dropout: float = 0.0
-    freeze_vision_encoder: bool = False
-    freeze_language_model: bool = False
-    stop_gradient_to_vlm: bool = False
-    vision_encoder_lr_multiplier: float = 0.5
-
-    # Normalization
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-        }
-    )
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(
-            lr=5e-5,
-            weight_decay=1e-10,
-            grad_clip_norm=1.0,
-        )
-
-    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
-        return CosineDecayWithWarmupSchedulerConfig(
-            num_warmup_steps=500,
-            num_decay_steps=40000,
-            peak_lr=5e-5,
-            decay_lr=5e-5,
-        )
-
-    def validate_features(self) -> None:
-        if not self.input_features:
-            return
-        has_image = any(ft.type == FeatureType.VISUAL for ft in self.input_features.values())
-        if not has_image:
-            raise ValueError("DistributionalVFConfig requires at least one VISUAL input feature.")

src/lerobot/rewards/distributional_value_function/modeling_distributional_value_function.py

@@ -1,602 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Modeling for RECAP's distributional value function.
-
-Paper: "π*0.6: a VLA That Learns From Experience" (Physical Intelligence, 2025)
-       https://pi.website/blog/pistar06
-Architecture source of truth: "π0.6 Model Card", Section 2 (Model Design)
-       https://website.pi-asset.com/pi06star/PI06_model_card.pdf
-
-Implements the distributional value function V^{pi_ref}(o_t, l) from Section IV-A.
-It adapts the native Gemma3 multimodal VLM design to π0.6's smaller ~670M scale:
-448px SigLIP images are pooled to 256 soft tokens, RMS-normalized, projected
-into Gemma3-270M, and processed with bidirectional image / causal text
-attention. A final learned value-query token supplies the 201-bin readout.
-"""
-
-from __future__ import annotations
-
-import copy
-import math
-from typing import TYPE_CHECKING, Any
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor, nn
-
-from lerobot.configs.types import FeatureType
-from lerobot.rewards.pretrained import PreTrainedRewardModel
-from lerobot.utils.constants import (
-    OBS_LANGUAGE_ATTENTION_MASK,
-    OBS_LANGUAGE_TOKENS,
-)
-from lerobot.utils.import_utils import _transformers_available, require_package
-
-from .configuration_distributional_value_function import DistributionalVFConfig
-from .processor_distributional_value_function import IMAGE_MASK_SUFFIX
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers import (
-        Gemma3Config,
-        Gemma3ForCausalLM,
-        Gemma3ForConditionalGeneration,
-        SiglipVisionModel,
-    )
-    from transformers.models.gemma3.modeling_gemma3 import (
-        Gemma3MultiModalProjector,
-        create_causal_mask_mapping,
-    )
-else:
-    Gemma3Config = None  # type: ignore[assignment]
-    Gemma3ForCausalLM = None  # type: ignore[assignment]
-    Gemma3ForConditionalGeneration = None  # type: ignore[assignment]
-    Gemma3MultiModalProjector = None  # type: ignore[assignment]
-    SiglipVisionModel = None  # type: ignore[assignment]
-    create_causal_mask_mapping = None  # type: ignore[assignment]
-
-
-class ValueHead(nn.Module):
-    """Categorical value projection: hidden state → bin logits.
-
-    The 2-layer MLP topology is adapted from Robometer's prediction head:
-    Linear → LayerNorm → GELU → Dropout → Linear. Unlike Robometer's progress
-    and success heads, this head predicts RECAP's 201-bin MC-return
-    distribution over [-1, 0] from the final value-query representation.
-
-    Also holds the ``bin_centers`` buffer used to compute E[V] = Σ p_i · c_i.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_bins: int,
-        v_min: float,
-        v_max: float,
-        dropout: float = 0.0,
-    ):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_bins = num_bins
-
-        self.mlp = nn.Sequential(
-            nn.Linear(hidden_size, hidden_size // 2),
-            nn.LayerNorm(hidden_size // 2),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Linear(hidden_size // 2, num_bins),
-        )
-
-        self.register_buffer("bin_centers", torch.linspace(v_min, v_max, num_bins), persistent=False)
-
-    def forward(self, hidden_states: Tensor) -> Tensor:
-        """Project hidden state to value logits. Returns [B, num_bins]."""
-        hidden_states = hidden_states.to(self.mlp[0].weight.dtype)
-        return self.mlp(hidden_states)
-
-
-class DistributionalVFRewardModel(PreTrainedRewardModel):
-    """Distributional value function model for RECAP.
-
-    Predicts V^{pi_ref}(o_t, l) as a categorical distribution over B bins (default 201).
-    Trained with cross-entropy on HL-Gauss or Dirac delta targets centered on
-    per-task normalized Monte Carlo returns.
-
-    Architecture: adapted from the native Gemma3 multimodal VLM using a
-    448px SigLIP2-so400m vision tower, Gemma3 multimodal projector, and
-    Gemma3-270M language backbone. Each camera is represented by 256 soft
-    image tokens. Image tokens are bidirectional, text is causal, and a final
-    one-way value query supplies the hidden state consumed by the value head.
-    """
-
-    name = "distributional_value_function"
-    config_class = DistributionalVFConfig
-
-    def __init__(self, config: DistributionalVFConfig, **kwargs) -> None:
-        require_package("transformers", extra="recap")
-        super().__init__(config)
-        self.config = config
-
-        if config.vlm_pretrained_path:
-            aligned_vlm = Gemma3ForConditionalGeneration.from_pretrained(config.vlm_pretrained_path)
-            self._vlm_config = aligned_vlm.config
-            self.vision_encoder = aligned_vlm.model.vision_tower
-            self.multi_modal_projector = aligned_vlm.model.multi_modal_projector
-            self.language_model = aligned_vlm.model.language_model
-        else:
-            self.vision_encoder = SiglipVisionModel.from_pretrained(config.siglip_path)
-            gemma3 = Gemma3ForCausalLM.from_pretrained(config.gemma3_path)
-            self.language_model = gemma3.model
-
-            # Adapt Gemma3's native multimodal connector to the 270M text
-            # backbone and π0.6's 448px input layout.
-            vision_config = copy.deepcopy(self.vision_encoder.config)
-            vision_config.image_size = config.image_resolution[0]
-            text_config = copy.deepcopy(gemma3.config)
-            self._vlm_config = Gemma3Config(
-                vision_config=vision_config,
-                text_config=text_config,
-                mm_tokens_per_image=config.num_image_tokens,
-            )
-            self.multi_modal_projector = Gemma3MultiModalProjector(self._vlm_config)
-            nn.init.normal_(
-                self.multi_modal_projector.mm_input_projection_weight,
-                mean=0.0,
-                std=self._vlm_config.initializer_range,
-            )
-
-        self._validate_vlm_config()
-        self.gemma3_hidden = self._vlm_config.text_config.hidden_size  # 640
-
-        # One-way suffix query, analogous to PI05's suffix/action tokens.
-        self.value_query = nn.Embedding(1, self.gemma3_hidden)
-        nn.init.normal_(self.value_query.weight, std=0.02)
-
-        # Value head: value-query hidden state → MLP → num_bins logits
-        self.value_head = ValueHead(
-            hidden_size=self.gemma3_hidden,
-            num_bins=config.num_value_bins,
-            v_min=config.value_support_min,
-            v_max=config.value_support_max,
-            dropout=config.value_dropout,
-        )
-
-        # HL-Gauss sigma for soft targets
-        bin_width = (config.value_support_max - config.value_support_min) / (config.num_value_bins - 1)
-        self.hl_gauss_sigma = float(config.hl_gauss_sigma_ratio * bin_width)
-
-        # Apply freezing
-        self._set_requires_grad()
-
-    @property
-    def gemma3(self) -> nn.Module:
-        """Backward-compatible access to the Gemma3 text backbone."""
-        return self.language_model
-
-    def _validate_vlm_config(self) -> None:
-        """Validate the π0.6 448px → 256-token multimodal layout."""
-        vision_config = self._vlm_config.vision_config
-        image_size = self.config.image_resolution[0]
-        if vision_config.image_size != image_size:
-            raise ValueError(
-                f"VLM vision image_size ({vision_config.image_size}) does not match "
-                f"DistributionalVFConfig.image_resolution ({image_size})"
-            )
-
-        patches_per_side = image_size // vision_config.patch_size
-        tokens_per_side = int(self.config.num_image_tokens**0.5)
-        if tokens_per_side**2 != self.config.num_image_tokens:
-            raise ValueError("num_image_tokens must be a perfect square")
-        if patches_per_side % tokens_per_side:
-            raise ValueError(
-                f"{patches_per_side} patches/side cannot be evenly pooled to {tokens_per_side} tokens/side"
-            )
-        if self._vlm_config.mm_tokens_per_image != self.config.num_image_tokens:
-            raise ValueError(
-                f"VLM emits {self._vlm_config.mm_tokens_per_image} image tokens, "
-                f"expected {self.config.num_image_tokens}"
-            )
-
-    def _set_requires_grad(self) -> None:
-        if self.config.freeze_vision_encoder:
-            for param in self.vision_encoder.parameters():
-                param.requires_grad = False
-            self.vision_encoder.eval()
-
-        if self.config.freeze_language_model:
-            for param in self.language_model.parameters():
-                param.requires_grad = False
-            self.language_model.eval()
-
-    def train(self, mode: bool = True):
-        super().train(mode)
-        if self.config.freeze_vision_encoder:
-            self.vision_encoder.eval()
-        if self.config.freeze_language_model:
-            self.language_model.eval()
-        return self
-
-    def get_optim_params(self) -> list[dict]:
-        """Optimizer param groups with per-component learning rates."""
-        vision_params = []
-        other_params = []
-
-        for name, param in self.named_parameters():
-            if not param.requires_grad:
-                continue
-            if name.startswith("vision_encoder"):
-                vision_params.append(param)
-            else:
-                other_params.append(param)
-
-        base_lr = self.config.get_optimizer_preset().lr
-        return [
-            {"params": other_params},
-            {"params": vision_params, "lr": base_lr * self.config.vision_encoder_lr_multiplier},
-        ]
-
-    def embed_image(self, image: Tensor) -> Tensor:
-        """Embed images with π0.6's Gemma3 visual connector.
-
-        Args:
-            image: [batch_size, channels, height, width] preprocessed image in [-1, 1].
-
-        Returns:
-            [B, 256, gemma3_hidden] pooled, normalized, projected features.
-        """
-        vision_dtype = next(self.vision_encoder.parameters()).dtype
-        image_features = self.vision_encoder(
-            pixel_values=image.to(dtype=vision_dtype),
-            interpolate_pos_encoding=True,
-        ).last_hidden_state
-        projected_features = self.multi_modal_projector(image_features)
-        if projected_features.shape[1] != self.config.num_image_tokens:
-            raise RuntimeError(
-                f"Expected {self.config.num_image_tokens} image tokens, got {projected_features.shape[1]}"
-            )
-        return projected_features
-
-    def embed_text(self, token_ids: Tensor) -> Tensor:
-        """Embed text using Gemma3's embedding table (includes sqrt(d) scaling).
-
-        Args:
-            token_ids: [B, seq_len] integer token IDs.
-
-        Returns:
-            [B, seq_len, gemma3_hidden] text embeddings.
-        """
-        return self.language_model.embed_tokens(token_ids)
-
-    def embed_prefix(
-        self,
-        images: list[Tensor],
-        img_masks: list[Tensor],
-        text_embeddings: Tensor,
-        text_padding_mask: Tensor,
-    ) -> tuple[Tensor, Tensor, Tensor]:
-        """Build [image soft tokens..., text] plus masks.
-
-        Returns:
-            embs: [B, total_prefix_len, hidden_dim]
-            pad_masks: [B, total_prefix_len] boolean
-            token_type_ids: [B, total_prefix_len], 1=image and 0=text
-        """
-        embs: list[Tensor] = []
-        pad_masks: list[Tensor] = []
-        token_types: list[Tensor] = []
-
-        for img, img_mask in zip(images, img_masks, strict=True):
-            img_emb = self.embed_image(img)
-            bsize, num_image_tokens = img_emb.shape[:2]
-            embs.append(img_emb)
-            pad_masks.append(img_mask[:, None].expand(bsize, num_image_tokens))
-            token_types.append(torch.ones(bsize, num_image_tokens, dtype=torch.long, device=img_emb.device))
-
-        embs.append(text_embeddings)
-        pad_masks.append(text_padding_mask)
-        token_types.append(torch.zeros_like(text_padding_mask, dtype=torch.long))
-
-        return torch.cat(embs, dim=1), torch.cat(pad_masks, dim=1), torch.cat(token_types, dim=1)
-
-    def hl_gauss_target(self, target_value: Tensor) -> Tensor:
-        """HL-Gauss soft target distribution.
-
-        Places a Gaussian N(target, sigma^2) over the bin support and computes
-        per-bin probabilities as CDF differences at bin edges, normalized to sum to 1.
-
-        Reference: Farebrother et al. 2024, "Stop Regressing: Training Value
-        Functions via Classification for Scalable Deep RL", Section 3.1.
-        arXiv:2403.03950
-
-        Args:
-            target_value: [batch_size] or [batch_size, 1] target values.
-
-        Returns:
-            [batch_size, num_value_bins] target probability distribution.
-        """
-        target_value = target_value.reshape(-1).clamp(
-            self.config.value_support_min, self.config.value_support_max
-        )
-        target_value = target_value.to(dtype=self.value_head.bin_centers.dtype)
-
-        # Bin edges: half a bin-width outside the first/last center
-        bin_width = (self.config.value_support_max - self.config.value_support_min) / (
-            self.config.num_value_bins - 1
-        )
-        support_edges = torch.linspace(
-            self.config.value_support_min - bin_width / 2,
-            self.config.value_support_max + bin_width / 2,
-            self.config.num_value_bins + 1,
-            device=target_value.device,
-            dtype=target_value.dtype,
-        )
-
-        # CDF of N(target, sigma^2) evaluated at each edge
-        cdf_at_edges = 0.5 * (
-            1.0
-            + torch.erf(
-                (support_edges.unsqueeze(0) - target_value.unsqueeze(-1))
-                / (self.hl_gauss_sigma * math.sqrt(2))
-            )
-        )  # [batch_size, num_bins + 1]
-
-        # Normalize: z = cdf(max_edge) - cdf(min_edge)
-        normalization_constant = (cdf_at_edges[:, -1] - cdf_at_edges[:, 0]).unsqueeze(-1).clamp(min=1e-10)
-
-        # Bin probabilities = differences of consecutive CDF values, normalized
-        bin_probabilities = (cdf_at_edges[:, 1:] - cdf_at_edges[:, :-1]) / normalization_constant
-
-        return bin_probabilities
-
-    def dirac_delta_target(self, target_value: Tensor) -> Tensor:
-        """Two-hot scalar projection between adjacent bins.
-
-        ``dirac_delta`` is retained as the public configuration name for
-        checkpoint compatibility. This is the scalar two-hot projection, not
-        the full distributional Bellman projection used by C51.
-
-        Args:
-            target_value: [batch_size] or [batch_size, 1] target values.
-
-        Returns:
-            [batch_size, num_value_bins] target probability distribution.
-        """
-        target_value = target_value.reshape(-1).clamp(
-            self.config.value_support_min, self.config.value_support_max
-        )
-        target_value = target_value.to(dtype=self.value_head.bin_centers.dtype)
-
-        bin_width = self.value_head.bin_centers[1] - self.value_head.bin_centers[0]
-        normalized_position = (target_value - self.config.value_support_min) / bin_width
-        lower_bin_idx = normalized_position.floor().long().clamp(0, self.config.num_value_bins - 1)
-        upper_bin_idx = normalized_position.ceil().long().clamp(0, self.config.num_value_bins - 1)
-
-        weight_upper = normalized_position - lower_bin_idx.float()
-        weight_lower = upper_bin_idx.float() - normalized_position
-
-        same_bin = lower_bin_idx == upper_bin_idx
-        weight_upper = torch.where(same_bin, torch.zeros_like(weight_upper), weight_upper)
-        weight_lower = torch.where(same_bin, torch.ones_like(weight_lower), weight_lower)
-
-        batch_size = target_value.shape[0]
-        target_distribution = torch.zeros(
-            batch_size,
-            self.config.num_value_bins,
-            device=target_value.device,
-            dtype=target_value.dtype,
-        )
-        batch_indices = torch.arange(batch_size, device=target_value.device)
-        target_distribution[batch_indices, lower_bin_idx] += weight_lower
-        target_distribution[batch_indices, upper_bin_idx] += weight_upper
-
-        return target_distribution
-
-    def one_hot_target(self, target_value: Tensor) -> Tensor:
-        """One-hot target at the nearest support bin for terminal states.
-
-        Args:
-            target_value: [batch_size] or [batch_size, 1] target values.
-
-        Returns:
-            [batch_size, num_value_bins] one-hot distribution at the nearest bin.
-        """
-        target_value = target_value.reshape(-1).clamp(
-            self.config.value_support_min, self.config.value_support_max
-        )
-        target_value = target_value.to(dtype=self.value_head.bin_centers.dtype)
-        nearest_bin_idx = torch.argmin(
-            torch.abs(self.value_head.bin_centers.unsqueeze(0) - target_value.unsqueeze(-1)), dim=-1
-        )
-        return F.one_hot(nearest_bin_idx, num_classes=self.config.num_value_bins).to(
-            dtype=self.value_head.bin_centers.dtype
-        )
-
-    def compute_target_distribution(
-        self,
-        target_value: Tensor,
-        is_terminal: Tensor,
-        method: str = "hl_gauss",
-        use_one_hot_terminal: bool = True,
-    ) -> Tensor:
-        """Compute target distribution using configured method.
-
-        Args:
-            target_value: [batch_size] scalar return targets
-            is_terminal: [batch_size] boolean terminal flags
-            method: "hl_gauss" or "dirac_delta"
-            use_one_hot_terminal: if True, terminal states get one-hot targets
-                (exact return, no smoothing). If False, all states use the same method.
-
-        Returns:
-            [batch_size, num_value_bins] target probability distribution
-        """
-        if method == "hl_gauss":
-            base_distribution = self.hl_gauss_target(target_value)
-        elif method == "dirac_delta":
-            base_distribution = self.dirac_delta_target(target_value)
-        else:
-            raise ValueError(f"Unknown target method: {method}. Use 'hl_gauss' or 'dirac_delta'.")
-
-        if not use_one_hot_terminal:
-            return base_distribution
-
-        is_terminal = is_terminal.reshape(-1)
-        if is_terminal.numel() != base_distribution.shape[0]:
-            raise ValueError(
-                f"Expected {base_distribution.shape[0]} terminal flags, got {is_terminal.numel()}"
-            )
-        terminal_distribution = self.one_hot_target(target_value)
-        return torch.where(is_terminal[:, None].bool(), terminal_distribution, base_distribution)
-
-    def _get_vlm_readout(self, batch: dict[str, Tensor]) -> Tensor:
-        """Run Gemma3 image-bidirectional/text-causal attention plus value query."""
-        images, img_masks, token_ids, text_pad_mask = self._get_model_inputs(batch)
-
-        text_embs = self.embed_text(token_ids)
-        prefix_embs, prefix_pad_masks, prefix_token_types = self.embed_prefix(
-            images, img_masks, text_embs, text_pad_mask
-        )
-
-        if self.config.stop_gradient_to_vlm:
-            prefix_embs = prefix_embs.detach()
-
-        batch_size, prefix_len = prefix_pad_masks.shape
-        device = prefix_embs.device
-        model_dtype = next(self.language_model.parameters()).dtype
-
-        query_ids = torch.zeros(batch_size, 1, dtype=torch.long, device=device)
-        query_emb = self.value_query(query_ids)
-        hidden_states = torch.cat([prefix_embs, query_emb], dim=1)
-
-        query_pad_mask = torch.ones(batch_size, 1, dtype=torch.bool, device=device)
-        pad_masks = torch.cat([prefix_pad_masks, query_pad_mask], dim=1)
-        token_type_ids = torch.cat(
-            [prefix_token_types, torch.zeros(batch_size, 1, dtype=torch.long, device=device)],
-            dim=1,
-        )
-
-        prefix_position_ids = torch.cumsum(prefix_pad_masks.long(), dim=1) - 1
-        query_position_ids = prefix_pad_masks.sum(dim=1, keepdim=True).long()
-        position_ids = torch.cat([prefix_position_ids, query_position_ids], dim=1).clamp_min(0)
-
-        if hidden_states.dtype != model_dtype:
-            hidden_states = hidden_states.to(model_dtype)
-
-        attention_masks = create_causal_mask_mapping(
-            self._vlm_config,
-            inputs_embeds=hidden_states,
-            attention_mask=pad_masks,
-            past_key_values=None,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            is_training=self.training,
-        )
-        outputs = self.language_model(
-            inputs_embeds=hidden_states,
-            attention_mask=attention_masks,
-            position_ids=position_ids,
-            use_cache=False,
-        )
-        return outputs.last_hidden_state[:, -1, :]
-
-    def _vlm_forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
-        """Run the VLM and value head.
-
-        Returns:
-            (value_logits [B, num_bins], predicted_value [B, 1])
-        """
-        readout = self._get_vlm_readout(batch)
-        value_logits = self.value_head(readout)
-        value_probs = F.softmax(value_logits, dim=-1)
-        predicted_value = (value_probs * self.value_head.bin_centers.to(dtype=value_probs.dtype)).sum(
-            dim=-1, keepdim=True
-        )
-        return value_logits, predicted_value
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        """Training forward pass — cross-entropy loss on MC return targets."""
-        mc_return = batch["mc_return"]
-        is_terminal = batch["is_terminal"]
-
-        value_logits, predicted_value = self._vlm_forward(batch)
-
-        # Compute target distribution from MC returns
-        target_dist = self.compute_target_distribution(
-            mc_return,
-            is_terminal,
-            method=self.config.target_method,
-            use_one_hot_terminal=self.config.use_one_hot_terminal,
-        )
-
-        # Cross-entropy loss between predicted and target distributions (Eq. 1 in pi*0.6 paper)
-        log_probs = F.log_softmax(value_logits, dim=-1)
-        loss = -(target_dist * log_probs).sum(dim=-1).mean()
-
-        # Diagnostic metrics
-        clamped_return = (
-            mc_return.float().view(-1).clamp(self.config.value_support_min, self.config.value_support_max)
-        )
-        bin_width = self.value_head.bin_centers[1] - self.value_head.bin_centers[0]
-        normalized_position = (clamped_return - self.config.value_support_min) / bin_width
-        lower_bin_idx = normalized_position.floor().long().clamp(0, self.config.num_value_bins - 1)
-        upper_bin_idx = normalized_position.ceil().long().clamp(0, self.config.num_value_bins - 1)
-
-        dist_to_lower = normalized_position - lower_bin_idx.float()
-        dist_to_upper = upper_bin_idx.float() - normalized_position
-        same_bin = lower_bin_idx == upper_bin_idx
-        dist_to_lower = torch.where(same_bin, torch.zeros_like(dist_to_lower), dist_to_lower)
-        dist_to_upper = torch.where(same_bin, torch.ones_like(dist_to_upper), dist_to_upper)
-
-        pred_bin = value_logits.argmax(dim=-1)
-        best_target_bin = torch.where(dist_to_upper >= dist_to_lower, lower_bin_idx, upper_bin_idx)
-
-        acc_best = (pred_bin == best_target_bin).float().mean().item()
-        acc_neighbor = ((pred_bin == lower_bin_idx) | (pred_bin == upper_bin_idx)).float().mean().item()
-
-        min_bin_dist = torch.min((pred_bin - lower_bin_idx).abs(), (pred_bin - upper_bin_idx).abs()).float()
-        mae = (min_bin_dist * bin_width).mean().item()
-
-        output_dict: dict[str, Any] = {
-            "loss": loss.item(),
-            "predicted_value_mean": predicted_value.mean().item(),
-            "mc_return_mean": mc_return.mean().item(),
-            "acc_best": acc_best,
-            "acc_neighbor": acc_neighbor,
-            "mae": mae,
-        }
-
-        return loss, output_dict
-
-    def _get_model_inputs(
-        self, batch: dict[str, Tensor]
-    ) -> tuple[list[Tensor], list[Tensor], Tensor, Tensor]:
-        """Extract images, masks, token_ids, text_pad_mask from a preprocessed batch."""
-        image_keys = [k for k, v in self.config.input_features.items() if v.type == FeatureType.VISUAL]
-        images = [batch[k] for k in image_keys]
-        img_masks = [batch[k + IMAGE_MASK_SUFFIX].bool() for k in image_keys]
-        token_ids = batch[OBS_LANGUAGE_TOKENS]
-        text_pad_mask = batch[OBS_LANGUAGE_ATTENTION_MASK].bool()
-        return images, img_masks, token_ids, text_pad_mask
-
-    def compute_reward(self, batch: dict[str, Tensor]) -> Tensor:
-        """Compute V(s) for a batch of observations. Used for advantage scoring.
-
-        Args:
-            batch: preprocessed batch with images, masks, and tokenized text.
-
-        Returns:
-            [batch_size] tensor of predicted values V(s).
-        """
-        _, predicted_value = self._vlm_forward(batch)
-        return predicted_value.squeeze(-1)

src/lerobot/rewards/distributional_value_function/processor_distributional_value_function.py

@@ -1,284 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Processor for RECAP's distributional value function.
-
-Paper: "π*0.6: a VLA That Learns From Experience" (Physical Intelligence, 2025)
-       https://pi.website/blog/pistar06
-
-Prepares inputs for V^{pi_ref}(o_t, l):
-1. Resize multi-camera images to 448x448 (with aspect-preserving padding)
-2. Normalize images from [0,1] → [-1,1] (SigLIP standard)
-3. Handle missing cameras (placeholder + mask)
-4. Format task prompt: ``"Task: {task}."``
-5. Tokenize with Gemma3 tokenizer
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor
-
-from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStep,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    TokenizerProcessorStep,
-    batch_to_transition,
-    policy_action_to_transition,
-    transition_to_batch,
-)
-from lerobot.types import EnvTransition, TransitionKey
-from lerobot.utils.constants import (
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-
-from .configuration_distributional_value_function import DistributionalVFConfig
-
-# Keys used by the image processor to store per-camera validity masks.
-IMAGE_MASK_SUFFIX = ".mask"
-
-
-def resize_with_pad_torch(
-    images: Tensor,
-    height: int,
-    width: int,
-    mode: str = "bilinear",
-) -> Tensor:
-    """Resize images preserving aspect ratio, padding with black.
-
-    Matches ``resize_with_pad_torch`` in PI0/PI05/PI0-FAST.
-
-    Args:
-        images: [*b, h, w, c] or [*b, c, h, w] tensor.
-        height: Target height.
-        width: Target width.
-        mode: Interpolation mode.
-
-    Returns:
-        Resized and padded tensor with same shape format as input.
-    """
-    if images.shape[-1] <= 4:
-        channels_last = True
-        if images.dim() == 3:
-            images = images.unsqueeze(0)
-        images = images.permute(0, 3, 1, 2)
-    else:
-        channels_last = False
-        if images.dim() == 3:
-            images = images.unsqueeze(0)
-
-    batch_size, channels, cur_height, cur_width = images.shape
-
-    ratio = max(cur_width / width, cur_height / height)
-    resized_height = int(cur_height / ratio)
-    resized_width = int(cur_width / ratio)
-
-    resized_images = F.interpolate(
-        images,
-        size=(resized_height, resized_width),
-        mode=mode,
-        align_corners=False if mode == "bilinear" else None,
-    )
-
-    if images.dtype == torch.uint8:
-        resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
-    elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(-1.0, 1.0)
-
-    pad_h0, remainder_h = divmod(height - resized_height, 2)
-    pad_h1 = pad_h0 + remainder_h
-    pad_w0, remainder_w = divmod(width - resized_width, 2)
-    pad_w1 = pad_w0 + remainder_w
-
-    constant_value = 0 if images.dtype == torch.uint8 else -1.0
-    padded_images = F.pad(
-        resized_images,
-        (pad_w0, pad_w1, pad_h0, pad_h1),
-        mode="constant",
-        value=constant_value,
-    )
-
-    if channels_last:
-        padded_images = padded_images.permute(0, 2, 3, 1)
-
-    return padded_images
-
-
-@ProcessorStepRegistry.register(name="distributional_vf_image_preprocessor")
-@dataclass
-class DistributionalVFImagePreprocessorStep(ProcessorStep):
-    """Resize and normalize multi-camera images for the VF.
-
-    Expects LeRobot's standard float image range [0, 1].
-    Produces [B, 3, H, W] tensors in [-1, 1] for each camera, plus boolean
-    masks indicating which cameras are present. Missing cameras get a black
-    placeholder image and mask=False.
-    """
-
-    image_resolution: tuple[int, int] = (448, 448)
-    image_keys: tuple[str, ...] = ()
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-        observation = dict(transition.get(TransitionKey.OBSERVATION, {}))
-
-        for key in self.image_keys:
-            if key in observation:
-                img = observation[key]
-                if img.dtype != torch.float32:
-                    img = img.to(torch.float32)
-
-                is_channels_first = img.shape[1] == 3
-                if is_channels_first:
-                    img = img.permute(0, 2, 3, 1)  # BCHW → BHWC
-
-                # Gemma3's SigLIP vision tower expects [-1, 1].
-                img = img * 2.0 - 1.0
-
-                if img.shape[1:3] != self.image_resolution:
-                    img = resize_with_pad_torch(img, *self.image_resolution)
-
-                observation[key] = img.permute(0, 3, 1, 2)  # BHWC → BCHW
-                observation[key + IMAGE_MASK_SUFFIX] = torch.ones(
-                    img.shape[0], dtype=torch.bool, device=img.device
-                )
-            else:
-                bsize = self._infer_batch_size(observation)
-                h, w = self.image_resolution
-                observation[key] = torch.full((bsize, 3, h, w), -1.0)
-                observation[key + IMAGE_MASK_SUFFIX] = torch.zeros(bsize, dtype=torch.bool)
-
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
-
-    def _infer_batch_size(self, observation: dict) -> int:
-        for v in observation.values():
-            if isinstance(v, Tensor) and v.ndim >= 2:
-                return v.shape[0]
-        return 1
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "image_resolution": self.image_resolution,
-            "image_keys": self.image_keys,
-        }
-
-
-@ProcessorStepRegistry.register(name="distributional_vf_prepare_task_prompt")
-@dataclass
-class DistributionalVFPrepareTaskPromptStep(ProcessorStep):
-    """Format the task string: ``"Task: {task}."``"""
-
-    task_key: str = "task"
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-
-        tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key)
-        if tasks is None:
-            raise ValueError("No task found in complementary data")
-
-        if isinstance(tasks, str):
-            tasks = [tasks]
-
-        full_prompts = []
-        for task in tasks:
-            cleaned_text = task.strip().replace("_", " ").replace("\n", " ")
-            full_prompts.append(f"Task: {cleaned_text}.")
-
-        new_complementary_data = dict(transition.get(TransitionKey.COMPLEMENTARY_DATA, {}))
-        new_complementary_data[self.task_key] = full_prompts
-        transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {"task_key": self.task_key}
-
-
-def make_distributional_vf_pre_post_processors(
-    config: DistributionalVFConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Create pre/post processors for the distributional value function.
-
-    Preprocessor steps:
-        1. Rename observations (no-op by default)
-        2. Add a batch dimension
-        3. Normalize features (identity for images)
-        4. Resize + normalize images → [B, 3, 448, 448] in [-1, 1]
-        5. Format task prompt: ``"Task: {task}."``
-        6. Tokenize with Gemma3 tokenizer
-        7. Move tensors to the configured device
-
-    Training targets (mc_return, is_terminal) are not processed here.
-    The postprocessor is a no-op (value function does not produce actions).
-    """
-    image_keys = tuple(k for k, v in config.input_features.items() if v.type == FeatureType.VISUAL)
-
-    preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-        steps=[
-            RenameObservationsProcessorStep(rename_map={}),
-            AddBatchDimensionProcessorStep(),
-            NormalizerProcessorStep(
-                features={**config.input_features, **config.output_features},
-                norm_map=config.normalization_mapping,
-                stats=dataset_stats,
-            ),
-            DistributionalVFImagePreprocessorStep(
-                image_resolution=config.image_resolution,
-                image_keys=image_keys,
-            ),
-            DistributionalVFPrepareTaskPromptStep(),
-            TokenizerProcessorStep(
-                tokenizer_name=config.vlm_pretrained_path or config.gemma3_path,
-                max_length=config.tokenizer_max_length,
-                padding_side="right",
-                padding="max_length",
-            ),
-            DeviceProcessorStep(device=config.device or "cpu"),
-        ],
-        name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        to_transition=batch_to_transition,
-        to_output=transition_to_batch,
-    )
-    postprocessor = PolicyProcessorPipeline(
-        name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-        to_transition=policy_action_to_transition,
-    )
-    return preprocessor, postprocessor

src/lerobot/rewards/factory.py

@@ -20,20 +20,13 @@ from typing import Any
 
 import torch
 
-from lerobot.configs import FeatureType
 from lerobot.configs.rewards import RewardModelConfig
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline
-from lerobot.utils.feature_utils import dataset_to_policy_features
 
 from .classifier.configuration_classifier import RewardClassifierConfig
-from .distributional_value_function.configuration_distributional_value_function import DistributionalVFConfig
-from .nanovlm_value_function.configuration_nanovlm_value_function import NanoVLMVFConfig
 from .pretrained import PreTrainedRewardModel
 from .robometer.configuration_robometer import RobometerConfig
 from .sarm.configuration_sarm import SARMConfig
-from .temporal_siglip_value_function.configuration_temporal_siglip_value_function import (
-    TemporalSiglipVFConfig,
-)
 from .topreward.configuration_topreward import TOPRewardConfig
 
 
@@ -70,24 +63,6 @@ def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
         from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
 
         return TOPRewardModel
-    elif name == "distributional_value_function":
-        from lerobot.rewards.distributional_value_function.modeling_distributional_value_function import (
-            DistributionalVFRewardModel,
-        )
-
-        return DistributionalVFRewardModel
-    elif name == "temporal_siglip_value_function":
-        from lerobot.rewards.temporal_siglip_value_function.modeling_temporal_siglip_value_function import (
-            TemporalSiglipVFRewardModel,
-        )
-
-        return TemporalSiglipVFRewardModel
-    elif name == "nanovlm_value_function":
-        from lerobot.rewards.nanovlm_value_function.modeling_nanovlm_value_function import (
-            NanoVLMVFRewardModel,
-        )
-
-        return NanoVLMVFRewardModel
     else:
         try:
             return _get_reward_model_cls_from_name(name=name)
@@ -121,12 +96,6 @@ def make_reward_model_config(reward_type: str, **kwargs) -> RewardModelConfig:
         return RobometerConfig(**kwargs)
     elif reward_type == "topreward":
         return TOPRewardConfig(**kwargs)
-    elif reward_type == "distributional_value_function":
-        return DistributionalVFConfig(**kwargs)
-    elif reward_type == "temporal_siglip_value_function":
-        return TemporalSiglipVFConfig(**kwargs)
-    elif reward_type == "nanovlm_value_function":
-        return NanoVLMVFConfig(**kwargs)
     else:
         try:
             config_cls = RewardModelConfig.get_choice_class(reward_type)
@@ -149,13 +118,6 @@ def make_reward_model(cfg: RewardModelConfig, **kwargs) -> PreTrainedRewardModel
     Returns:
         An instantiated and device-placed reward model.
     """
-    dataset_meta = kwargs.get("dataset_meta")
-    if dataset_meta is not None and not cfg.input_features:
-        features = dataset_to_policy_features(dataset_meta.features)
-        cfg.input_features = {
-            key: feature for key, feature in features.items() if feature.type is not FeatureType.ACTION
-        }
-
     reward_cls = get_reward_model_class(cfg.type)
 
     kwargs["config"] = cfg
@@ -230,34 +192,6 @@ def make_reward_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
-    elif isinstance(reward_cfg, DistributionalVFConfig):
-        from lerobot.rewards.distributional_value_function.processor_distributional_value_function import (
-            make_distributional_vf_pre_post_processors,
-        )
-
-        return make_distributional_vf_pre_post_processors(
-            config=reward_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-    elif isinstance(reward_cfg, TemporalSiglipVFConfig):
-        from lerobot.rewards.temporal_siglip_value_function.processor_temporal_siglip_value_function import (
-            make_temporal_siglip_vf_pre_post_processors,
-        )
-
-        return make_temporal_siglip_vf_pre_post_processors(
-            config=reward_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-    elif isinstance(reward_cfg, NanoVLMVFConfig):
-        from lerobot.rewards.nanovlm_value_function.processor_nanovlm_value_function import (
-            make_nanovlm_vf_pre_post_processors,
-        )
-
-        return make_nanovlm_vf_pre_post_processors(
-            config=reward_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-
     else:
         try:
             processors = _make_processors_from_reward_model_config(

src/lerobot/rewards/nanovlm_value_function/__init__.py

@@ -1,9 +0,0 @@
-from .configuration_nanovlm_value_function import NanoVLMVFConfig
-from .modeling_nanovlm_value_function import NanoVLMVFRewardModel
-from .processor_nanovlm_value_function import make_nanovlm_vf_pre_post_processors
-
-__all__ = [
-    "NanoVLMVFConfig",
-    "NanoVLMVFRewardModel",
-    "make_nanovlm_vf_pre_post_processors",
-]

src/lerobot/rewards/nanovlm_value_function/configuration_nanovlm_value_function.py

@@ -1,46 +0,0 @@
-"""Configuration for the pretrained nanoVLM-460M value-function experiment."""
-
-from dataclasses import dataclass, field
-
-from lerobot.configs import FeatureType, NormalizationMode
-from lerobot.configs.rewards import RewardModelConfig
-from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
-
-
-@RewardModelConfig.register_subclass("nanovlm_value_function")
-@dataclass
-class NanoVLMVFConfig(RewardModelConfig):
-    nanovlm_pretrained_path: str = "lusxvr/nanoVLM-460M-8k"
-    nanovlm_code_path: str = "third_party/nanoVLM"
-    # The checkpoint was aligned with an 8k context. Native image tiling can
-    # require thousands of placeholder tokens for several robot cameras.
-    tokenizer_max_length: int = 8192
-    num_value_bins: int = 201
-    value_support_min: float = -1.0
-    value_support_max: float = 0.0
-    hl_gauss_sigma_ratio: float = 0.75
-    target_method: str = "dirac_delta"
-    use_one_hot_terminal: bool = True
-    value_dropout: float = 0.0
-    freeze_vision_encoder: bool = True
-    freeze_multimodal_projector: bool = True
-    freeze_language_model: bool = True
-
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {"VISUAL": NormalizationMode.IDENTITY}
-    )
-
-    def validate_features(self) -> None:
-        if not any(feature.type == FeatureType.VISUAL for feature in self.input_features.values()):
-            raise ValueError("NanoVLMVFConfig requires visual input features")
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(lr=1e-4, weight_decay=1e-4, grad_clip_norm=1.0)
-
-    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
-        return CosineDecayWithWarmupSchedulerConfig(
-            num_warmup_steps=500,
-            num_decay_steps=40000,
-            peak_lr=1e-4,
-            decay_lr=1e-6,
-        )

src/lerobot/rewards/nanovlm_value_function/modeling_nanovlm_value_function.py

@@ -1,115 +0,0 @@
-"""Distributional value head on the pretrained nanoVLM-460M checkpoint."""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-from typing import Any
-
-import torch
-from torch import Tensor, nn
-
-from lerobot.rewards.distributional_value_function.common import DistributionalValueMixin
-from lerobot.rewards.distributional_value_function.modeling_distributional_value_function import ValueHead
-from lerobot.rewards.nanovlm_value_function.processor_nanovlm_value_function import (
-    NANOVLM_ATTENTION_MASK,
-    NANOVLM_IMAGES,
-    NANOVLM_INPUT_IDS,
-)
-from lerobot.rewards.pretrained import PreTrainedRewardModel
-
-from .configuration_nanovlm_value_function import NanoVLMVFConfig
-
-
-class NanoVLMVFRewardModel(DistributionalValueMixin, PreTrainedRewardModel):
-    """Use nanoVLM's aligned decoder readout for RECAP return classification."""
-
-    name = "nanovlm_value_function"
-    config_class = NanoVLMVFConfig
-
-    def __init__(self, config: NanoVLMVFConfig, **kwargs):
-        super().__init__(config)
-        self.config = config
-        config.validate_features()
-        code_path = Path(config.nanovlm_code_path)
-        if not code_path.is_absolute():
-            code_path = Path(__file__).resolve().parents[4] / code_path
-        if not code_path.exists():
-            raise FileNotFoundError(f"nanoVLM code not found at {code_path}")
-        if str(code_path) not in sys.path:
-            sys.path.insert(0, str(code_path))
-        from models.vision_language_model import VisionLanguageModel
-
-        self.nanovlm = VisionLanguageModel.from_pretrained(config.nanovlm_pretrained_path)
-        hidden_size = self.nanovlm.cfg.lm_hidden_dim
-        self.value_query = nn.Embedding(1, hidden_size)
-        nn.init.normal_(self.value_query.weight, std=0.02)
-        self.value_head = ValueHead(
-            hidden_size,
-            config.num_value_bins,
-            config.value_support_min,
-            config.value_support_max,
-            config.value_dropout,
-        )
-        bin_width = (config.value_support_max - config.value_support_min) / (config.num_value_bins - 1)
-        self.hl_gauss_sigma = config.hl_gauss_sigma_ratio * bin_width
-        self._set_requires_grad()
-
-    def _set_requires_grad(self):
-        if self.config.freeze_vision_encoder:
-            self.nanovlm.vision_encoder.requires_grad_(False).eval()
-        if self.config.freeze_multimodal_projector:
-            self.nanovlm.MP.requires_grad_(False).eval()
-        if self.config.freeze_language_model:
-            self.nanovlm.decoder.requires_grad_(False).eval()
-
-    def train(self, mode: bool = True):
-        super().train(mode)
-        if self.config.freeze_vision_encoder:
-            self.nanovlm.vision_encoder.eval()
-        if self.config.freeze_multimodal_projector:
-            self.nanovlm.MP.eval()
-        if self.config.freeze_language_model:
-            self.nanovlm.decoder.eval()
-        return self
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        return self._distributional_forward(batch)
-
-    def _get_value_readout(self, batch: dict[str, Tensor]) -> Tensor:
-        input_ids = batch[NANOVLM_INPUT_IDS]
-        attention_mask = batch[NANOVLM_ATTENTION_MASK].bool()
-        batch_size = input_ids.shape[0]
-        images = self.nanovlm._process_images(batch[NANOVLM_IMAGES], input_ids.device)
-        text_tokens = self.nanovlm.decoder.token_embedding(input_ids)
-        if images is not None:
-            image_tokens = self.nanovlm.MP(self.nanovlm.vision_encoder(images))
-            placeholder_count = (input_ids == self.nanovlm.tokenizer.image_token_id).sum().item()
-            image_token_count = image_tokens.shape[0] * image_tokens.shape[1]
-            if placeholder_count != image_token_count:
-                raise ValueError(
-                    "nanoVLM image placeholders do not match projected image tokens: "
-                    f"{placeholder_count} placeholders versus {image_token_count} tokens. "
-                    "The prompt may have been truncated; increase tokenizer_max_length."
-                )
-            text_tokens = self.nanovlm._replace_img_tokens_with_embd(
-                input_ids,
-                text_tokens,
-                image_tokens,
-            )
-        query = self.value_query(torch.zeros(batch_size, 1, dtype=torch.long, device=text_tokens.device)).to(
-            text_tokens.dtype
-        )
-        inputs = torch.cat([text_tokens, query], dim=1)
-        attention_mask = torch.cat(
-            [
-                attention_mask,
-                torch.ones(batch_size, 1, dtype=torch.bool, device=text_tokens.device),
-            ],
-            dim=1,
-        )
-        hidden, _ = self.nanovlm.decoder(inputs, attention_mask=attention_mask)
-        return hidden[:, -1]
-
-    def get_optim_params(self):
-        return [parameter for parameter in self.parameters() if parameter.requires_grad]

src/lerobot/rewards/nanovlm_value_function/processor_nanovlm_value_function.py

@@ -1,214 +0,0 @@
-"""Processor using nanoVLM's native image splitting and chat-token layout."""
-
-import json
-import sys
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any
-
-import torch
-from torchvision.transforms.functional import to_pil_image
-
-from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    ComplementaryDataProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    batch_to_transition,
-    policy_action_to_transition,
-    transition_to_batch,
-)
-from lerobot.types import TransitionKey
-from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
-
-from .configuration_nanovlm_value_function import NanoVLMVFConfig
-
-NANOVLM_IMAGES = "observation.nanovlm.images"
-NANOVLM_INPUT_IDS = "observation.nanovlm.input_ids"
-NANOVLM_ATTENTION_MASK = "observation.nanovlm.attention_mask"
-
-
-@ProcessorStepRegistry.register(name="nanovlm_native_processor")
-@dataclass
-class NanoVLMNativeProcessorStep(ComplementaryDataProcessorStep):
-    pretrained_path: str
-    code_path: str
-    image_keys: tuple[str, ...]
-    max_length: int
-    _tokenizer: Any = field(default=None, init=False, repr=False)
-    _image_processor: Any = field(default=None, init=False, repr=False)
-    _get_image_string: Any = field(default=None, init=False, repr=False)
-    _mp_image_token_length: int = field(default=64, init=False, repr=False)
-
-    def __post_init__(self):
-        code_path = Path(self.code_path)
-        if not code_path.is_absolute():
-            code_path = Path(__file__).resolve().parents[4] / code_path
-        if str(code_path) not in sys.path:
-            sys.path.insert(0, str(code_path))
-
-        from data.processors import get_image_processor, get_image_string, get_tokenizer
-
-        config_path = _resolve_checkpoint_file(self.pretrained_path, "config.json")
-        config = json.loads(Path(config_path).read_text())
-        if self.max_length > config["lm_max_length"]:
-            raise ValueError(
-                f"tokenizer_max_length={self.max_length} exceeds nanoVLM's "
-                f"lm_max_length={config['lm_max_length']}"
-            )
-        self._tokenizer = get_tokenizer(
-            config["lm_tokenizer"],
-            config["vlm_extra_tokens"],
-            config["lm_chat_template"],
-        )
-        self._image_processor = get_image_processor(
-            config["max_img_size"],
-            config["vit_img_size"],
-            config["resize_to_max_side_len"],
-        )
-        self._get_image_string = get_image_string
-        self._mp_image_token_length = config["mp_image_token_length"]
-
-    def complementary_data(self, complementary_data):
-        raw_tasks = complementary_data.get("task")
-        if raw_tasks is None:
-            raise ValueError("Task is required for nanoVLM value processing")
-        observation = self.transition[TransitionKey.OBSERVATION]
-        present_image_keys = [key for key in self.image_keys if key in observation]
-        if not present_image_keys:
-            raise ValueError("No configured nanoVLM image key is present in the observation")
-        batch_size = observation[present_image_keys[0]].shape[0]
-        tasks = [raw_tasks] * batch_size if isinstance(raw_tasks, str) else list(raw_tasks)
-        if len(tasks) != batch_size:
-            raise ValueError(f"Received {len(tasks)} tasks for an image batch of size {batch_size}")
-
-        processed_batch = []
-        input_rows = []
-        attention_rows = []
-        for batch_index in range(batch_size):
-            processed_images = []
-            split_counts = []
-            for key in self.image_keys:
-                if key not in observation:
-                    continue
-                image = observation[key][batch_index]
-                if image.ndim != 3:
-                    raise ValueError(f"nanoVLM expects CHW images, got {tuple(image.shape)} for {key}")
-                if image.shape[0] not in (1, 3, 4) and image.shape[-1] in (1, 3, 4):
-                    image = image.permute(2, 0, 1)
-                if image.dtype != torch.uint8:
-                    image = image.float()
-                    if image.min() < -1e-6 or image.max() > 1.0 + 1e-6:
-                        raise ValueError(
-                            f"nanoVLM expects uint8 [0,255] or float [0,1] images; "
-                            f"{key} has range [{image.min().item()}, {image.max().item()}]"
-                        )
-                    image = image.clamp(0, 1)
-                pil_image = to_pil_image(image.cpu()).convert("RGB")
-                processed, split_count = self._image_processor(pil_image)
-                processed_images.append(processed)
-                split_counts.append(split_count)
-
-            image_string = self._get_image_string(
-                self._tokenizer,
-                split_counts,
-                self._mp_image_token_length,
-            )
-            prompt = self._tokenizer.apply_chat_template(
-                [{"role": "user", "content": image_string + f"Task: {tasks[batch_index]}."}],
-                tokenize=False,
-                add_generation_prompt=True,
-            )
-            tokenized = self._tokenizer(
-                prompt,
-                truncation=False,
-                add_special_tokens=False,
-            )
-            if len(tokenized["input_ids"]) > self.max_length:
-                raise ValueError(
-                    f"nanoVLM prompt has {len(tokenized['input_ids'])} tokens, exceeding "
-                    f"tokenizer_max_length={self.max_length}. The native nanoVLM collator "
-                    "discards over-length examples instead of truncating image placeholders."
-                )
-            input_rows.append(tokenized["input_ids"])
-            attention_rows.append(tokenized.get("attention_mask", [1] * len(tokenized["input_ids"])))
-            processed_batch.append(processed_images)
-
-        max_length = max(map(len, input_rows))
-        for input_ids, attention_mask in zip(input_rows, attention_rows, strict=True):
-            padding = max_length - len(input_ids)
-            input_ids[:0] = [self._tokenizer.pad_token_id] * padding
-            attention_mask[:0] = [0] * padding
-
-        observation = dict(observation)
-        observation[NANOVLM_IMAGES] = processed_batch
-        observation[NANOVLM_INPUT_IDS] = torch.tensor(input_rows, dtype=torch.long)
-        observation[NANOVLM_ATTENTION_MASK] = torch.tensor(attention_rows, dtype=torch.bool)
-        self.transition[TransitionKey.OBSERVATION] = observation
-        return complementary_data
-
-    def transform_features(
-        self,
-        features: dict[PipelineFeatureType, dict[str, PolicyFeature]],
-    ):
-        return features
-
-    def get_config(self):
-        return {
-            "pretrained_path": self.pretrained_path,
-            "code_path": self.code_path,
-            "image_keys": self.image_keys,
-            "max_length": self.max_length,
-        }
-
-
-def _resolve_checkpoint_file(repo_id_or_path: str, filename: str) -> str:
-    local_path = Path(repo_id_or_path) / filename
-    if local_path.exists():
-        return str(local_path)
-    from huggingface_hub import hf_hub_download
-
-    return hf_hub_download(repo_id=repo_id_or_path, filename=filename)
-
-
-def make_nanovlm_vf_pre_post_processors(
-    config: NanoVLMVFConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    image_keys = tuple(
-        key for key, feature in config.input_features.items() if feature.type == FeatureType.VISUAL
-    )
-    preprocessor = PolicyProcessorPipeline(
-        steps=[
-            RenameObservationsProcessorStep(rename_map={}),
-            AddBatchDimensionProcessorStep(),
-            NormalizerProcessorStep(
-                features={**config.input_features, **config.output_features},
-                norm_map=config.normalization_mapping,
-                stats=dataset_stats,
-            ),
-            NanoVLMNativeProcessorStep(
-                pretrained_path=config.nanovlm_pretrained_path,
-                code_path=config.nanovlm_code_path,
-                image_keys=image_keys,
-                max_length=config.tokenizer_max_length,
-            ),
-            DeviceProcessorStep(device=config.device or "cpu"),
-        ],
-        name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        to_transition=batch_to_transition,
-        to_output=transition_to_batch,
-    )
-    postprocessor = PolicyProcessorPipeline(
-        name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-        to_transition=policy_action_to_transition,
-    )
-    return preprocessor, postprocessor

src/lerobot/rewards/temporal_siglip_value_function/__init__.py

@@ -1,9 +0,0 @@
-from .configuration_temporal_siglip_value_function import TemporalSiglipVFConfig
-from .modeling_temporal_siglip_value_function import TemporalSiglipVFRewardModel
-from .processor_temporal_siglip_value_function import make_temporal_siglip_vf_pre_post_processors
-
-__all__ = [
-    "TemporalSiglipVFConfig",
-    "TemporalSiglipVFRewardModel",
-    "make_temporal_siglip_vf_pre_post_processors",
-]

src/lerobot/rewards/temporal_siglip_value_function/configuration_temporal_siglip_value_function.py

@@ -1,57 +0,0 @@
-"""Configuration for the experimental temporal SigLIP2 value function."""
-
-from dataclasses import dataclass, field
-
-from lerobot.configs import FeatureType, NormalizationMode
-from lerobot.configs.rewards import RewardModelConfig
-from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
-
-
-@RewardModelConfig.register_subclass("temporal_siglip_value_function")
-@dataclass
-class TemporalSiglipVFConfig(RewardModelConfig):
-    siglip_path: str = "google/siglip2-so400m-patch14-384"
-    image_resolution: tuple[int, int] = (384, 384)
-    tokenizer_max_length: int = 64
-    history_steps: int = 6
-    frame_gap: int = 30
-    state_key: str = "observation.state"
-    state_dim: int = 32
-    hidden_size: int = 512
-    num_layers: int = 4
-    num_heads: int = 8
-    dropout: float = 0.1
-    num_value_bins: int = 201
-    value_support_min: float = -1.0
-    value_support_max: float = 0.0
-    hl_gauss_sigma_ratio: float = 0.75
-    target_method: str = "dirac_delta"
-    use_one_hot_terminal: bool = True
-
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-        }
-    )
-
-    @property
-    def observation_delta_indices(self) -> list[int]:
-        return [-self.frame_gap * index for index in range(self.history_steps - 1, -1, -1)]
-
-    def validate_features(self) -> None:
-        if not any(feature.type == FeatureType.VISUAL for feature in self.input_features.values()):
-            raise ValueError("TemporalSiglipVFConfig requires visual input features")
-        if self.state_key not in self.input_features:
-            raise ValueError(f"TemporalSiglipVFConfig requires {self.state_key!r}")
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(lr=1e-4, weight_decay=1e-4, grad_clip_norm=1.0)
-
-    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
-        return CosineDecayWithWarmupSchedulerConfig(
-            num_warmup_steps=500,
-            num_decay_steps=40000,
-            peak_lr=1e-4,
-            decay_lr=1e-6,
-        )

src/lerobot/rewards/temporal_siglip_value_function/modeling_temporal_siglip_value_function.py

@@ -1,176 +0,0 @@
-"""Past-only temporal SigLIP2 distributional value function."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import Tensor, nn
-
-from lerobot.configs.types import FeatureType
-from lerobot.rewards.distributional_value_function.common import DistributionalValueMixin
-from lerobot.rewards.distributional_value_function.modeling_distributional_value_function import ValueHead
-from lerobot.rewards.distributional_value_function.processor_distributional_value_function import (
-    IMAGE_MASK_SUFFIX,
-)
-from lerobot.rewards.pretrained import PreTrainedRewardModel
-from lerobot.utils.constants import OBS_LANGUAGE_ATTENTION_MASK, OBS_LANGUAGE_TOKENS
-from lerobot.utils.import_utils import _transformers_available, require_package
-
-from .configuration_temporal_siglip_value_function import TemporalSiglipVFConfig
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoModel
-else:
-    AutoModel = None  # type: ignore[assignment]
-
-
-class TemporalSiglipVFRewardModel(DistributionalValueMixin, PreTrainedRewardModel):
-    """Fuse three-camera history, task, and state before causal temporal attention."""
-
-    name = "temporal_siglip_value_function"
-    config_class = TemporalSiglipVFConfig
-
-    def __init__(self, config: TemporalSiglipVFConfig, **kwargs):
-        require_package("transformers", extra="recap")
-        super().__init__(config)
-        self.config = config
-        config.validate_features()
-        self.image_keys = [
-            key for key, feature in config.input_features.items() if feature.type == FeatureType.VISUAL
-        ]
-        self.siglip = AutoModel.from_pretrained(config.siglip_path)
-        self.siglip.requires_grad_(False).eval()
-
-        vision_dim = self.siglip.config.vision_config.hidden_size
-        text_dim = self.siglip.config.text_config.hidden_size
-        hidden_size = config.hidden_size
-        self.camera_proj = nn.Linear(vision_dim, hidden_size)
-        self.camera_embedding = nn.Embedding(len(self.image_keys), hidden_size)
-        self.task_proj = nn.Linear(text_dim, hidden_size)
-        self.state_proj = nn.Linear(config.state_dim, hidden_size)
-        self.frame_fusion = nn.Sequential(
-            nn.LayerNorm((len(self.image_keys) + 2) * hidden_size),
-            nn.Linear((len(self.image_keys) + 2) * hidden_size, hidden_size),
-            nn.GELU(),
-        )
-        layer = nn.TransformerEncoderLayer(
-            d_model=hidden_size,
-            nhead=config.num_heads,
-            dim_feedforward=4 * hidden_size,
-            dropout=config.dropout,
-            batch_first=True,
-            activation="gelu",
-        )
-        self.temporal_transformer = nn.TransformerEncoder(
-            layer,
-            num_layers=config.num_layers,
-            norm=nn.LayerNorm(hidden_size),
-        )
-        self.time_embedding = nn.Embedding(config.history_steps, hidden_size)
-        self.value_head = ValueHead(
-            hidden_size,
-            config.num_value_bins,
-            config.value_support_min,
-            config.value_support_max,
-            config.dropout,
-        )
-        bin_width = (config.value_support_max - config.value_support_min) / (config.num_value_bins - 1)
-        self.hl_gauss_sigma = config.hl_gauss_sigma_ratio * bin_width
-
-    def train(self, mode: bool = True):
-        super().train(mode)
-        self.siglip.eval()
-        return self
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        return self._distributional_forward(batch)
-
-    def _get_value_readout(self, batch: dict[str, Tensor]) -> Tensor:
-        images = [batch[key] for key in self.image_keys]
-        masks = [batch[key + IMAGE_MASK_SUFFIX].bool() for key in self.image_keys]
-        state = batch[self.config.state_key]
-        if state.ndim == 2:
-            state = state[:, None]
-        batch_size, history_steps = images[0].shape[:2]
-        if history_steps != self.config.history_steps:
-            raise ValueError(f"Expected {self.config.history_steps} frames, got {history_steps}")
-
-        camera_tokens = []
-        vision_dtype = next(self.siglip.vision_model.parameters()).dtype
-        for camera_index, (image, mask) in enumerate(zip(images, masks, strict=True)):
-            with torch.no_grad():
-                features = self.siglip.vision_model(
-                    pixel_values=image.flatten(0, 1).to(vision_dtype),
-                    interpolate_pos_encoding=True,
-                    return_dict=True,
-                ).pooler_output
-            features = self.camera_proj(features).unflatten(0, (batch_size, history_steps))
-            camera_ids = torch.full(
-                (batch_size, history_steps),
-                camera_index,
-                dtype=torch.long,
-                device=features.device,
-            )
-            camera_tokens.append(
-                (features + self.camera_embedding(camera_ids)) * mask[..., None].to(features.dtype)
-            )
-
-        with torch.no_grad():
-            task_features = self.siglip.text_model(
-                input_ids=batch[OBS_LANGUAGE_TOKENS],
-                attention_mask=batch[OBS_LANGUAGE_ATTENTION_MASK],
-                return_dict=True,
-            ).pooler_output
-        task_token = self.task_proj(task_features)[:, None].expand(-1, history_steps, -1)
-        state = self._fit_state_dim(state).to(task_token.dtype)
-        state_token = self.state_proj(state)
-        frame_tokens = self.frame_fusion(torch.cat([*camera_tokens, task_token, state_token], -1))
-        frame_tokens = (
-            frame_tokens + self.time_embedding(torch.arange(history_steps, device=frame_tokens.device))[None]
-        )
-
-        frame_valid = torch.stack(masks).any(0)
-        attention_mask = self._make_temporal_attention_mask(frame_valid)
-        hidden = self.temporal_transformer(
-            frame_tokens,
-            mask=attention_mask,
-        )
-        # The history window is ordered oldest→current and the current frame is
-        # always the final, non-padding element.
-        return hidden[:, -1]
-
-    def _make_temporal_attention_mask(self, frame_valid: Tensor) -> Tensor:
-        """Combine causal and padding masks without fully masked padded queries.
-
-        A left-padded causal query has no valid past keys. PyTorch's optimized
-        eval path returns NaNs for such rows, which then contaminate later valid
-        tokens. Padded queries attend only to themselves; valid queries retain
-        causal attention and cannot attend to padded keys.
-        """
-        batch_size, history_steps = frame_valid.shape
-        causal_mask = torch.triu(
-            torch.ones(history_steps, history_steps, dtype=torch.bool, device=frame_valid.device),
-            diagonal=1,
-        )
-        attention_mask = causal_mask[None].expand(batch_size, -1, -1) | (~frame_valid)[:, None, :]
-        padded_queries = (~frame_valid).nonzero(as_tuple=False)
-        attention_mask[
-            padded_queries[:, 0],
-            padded_queries[:, 1],
-            padded_queries[:, 1],
-        ] = False
-        return (
-            attention_mask[:, None]
-            .expand(-1, self.config.num_heads, -1, -1)
-            .reshape(batch_size * self.config.num_heads, history_steps, history_steps)
-        )
-
-    def _fit_state_dim(self, state: Tensor) -> Tensor:
-        if state.shape[-1] > self.config.state_dim:
-            return state[..., : self.config.state_dim]
-        return F.pad(state, (0, self.config.state_dim - state.shape[-1]))
-
-    def get_optim_params(self):
-        return [parameter for parameter in self.parameters() if parameter.requires_grad]

src/lerobot/rewards/temporal_siglip_value_function/processor_temporal_siglip_value_function.py

@@ -1,185 +0,0 @@
-"""Processor for past-only temporal SigLIP2 value inputs."""
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
-
-import torch
-from torch import Tensor
-
-from lerobot.configs import FeatureType
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStep,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    batch_to_transition,
-    policy_action_to_transition,
-    transition_to_batch,
-)
-from lerobot.rewards.distributional_value_function.processor_distributional_value_function import (
-    IMAGE_MASK_SUFFIX,
-    DistributionalVFPrepareTaskPromptStep,
-    resize_with_pad_torch,
-)
-from lerobot.types import EnvTransition, TransitionKey
-from lerobot.utils.constants import (
-    OBS_LANGUAGE_ATTENTION_MASK,
-    OBS_LANGUAGE_TOKENS,
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-from lerobot.utils.import_utils import _transformers_available, require_package
-
-from .configuration_temporal_siglip_value_function import TemporalSiglipVFConfig
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoTokenizer
-else:
-    AutoTokenizer = None
-
-
-@ProcessorStepRegistry.register(name="temporal_siglip_vf_image_processor")
-@dataclass
-class TemporalSiglipImageProcessorStep(ProcessorStep):
-    image_resolution: tuple[int, int]
-    image_keys: tuple[str, ...]
-    history_steps: int
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-        observation = dict(transition.get(TransitionKey.OBSERVATION, {}))
-        batch_size = self._batch_size(observation)
-        for key in self.image_keys:
-            if key not in observation:
-                height, width = self.image_resolution
-                observation[key] = torch.full((batch_size, self.history_steps, 3, height, width), -1.0)
-                observation[key + IMAGE_MASK_SUFFIX] = torch.zeros(
-                    batch_size, self.history_steps, dtype=torch.bool
-                )
-                continue
-
-            image = observation[key]
-            if image.ndim == 4:
-                image = image[:, None]
-            if image.ndim != 5 or image.shape[2] != 3:
-                raise ValueError(f"Expected {key} as [B,T,3,H,W], got {tuple(image.shape)}")
-            batch_size, history = image.shape[:2]
-            image = image.float() / 127.5 - 1.0 if image.dtype == torch.uint8 else image.float() * 2.0 - 1.0
-            image = image.flatten(0, 1).permute(0, 2, 3, 1)
-            if image.shape[1:3] != self.image_resolution:
-                image = resize_with_pad_torch(image, *self.image_resolution)
-            observation[key] = image.permute(0, 3, 1, 2).unflatten(0, (batch_size, history))
-            padding_key = f"{key}_is_pad"
-            if padding_key in observation:
-                observation[key + IMAGE_MASK_SUFFIX] = ~observation[padding_key].bool()
-            else:
-                observation[key + IMAGE_MASK_SUFFIX] = torch.ones(
-                    batch_size, history, dtype=torch.bool, device=image.device
-                )
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
-
-    @staticmethod
-    def _batch_size(observation: dict[str, Any]) -> int:
-        for value in observation.values():
-            if isinstance(value, Tensor) and value.ndim >= 2:
-                return value.shape[0]
-        return 1
-
-    def transform_features(self, features):
-        return features
-
-    def get_config(self):
-        return {
-            "image_resolution": self.image_resolution,
-            "image_keys": self.image_keys,
-            "history_steps": self.history_steps,
-        }
-
-
-@ProcessorStepRegistry.register(name="temporal_siglip_vf_tokenizer")
-@dataclass
-class TemporalSiglipTokenizerStep(ProcessorStep):
-    tokenizer_name: str
-    max_length: int
-    _tokenizer: Any = field(default=None, init=False, repr=False)
-
-    def __post_init__(self):
-        require_package("transformers", extra="recap")
-        self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name)
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-        task = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get("task")
-        if task is None:
-            raise ValueError("Task is required for TemporalSiglipTokenizerStep")
-        task = [task] if isinstance(task, str) else list(task)
-        tokenized = self._tokenizer(
-            task,
-            padding="max_length",
-            max_length=self.max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        input_ids = tokenized["input_ids"]
-        attention_mask = tokenized.get("attention_mask")
-        if attention_mask is None:
-            attention_mask = input_ids.ne(self._tokenizer.pad_token_id)
-
-        observation = dict(transition.get(TransitionKey.OBSERVATION, {}))
-        observation[OBS_LANGUAGE_TOKENS] = input_ids
-        observation[OBS_LANGUAGE_ATTENTION_MASK] = attention_mask.bool()
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
-
-    def transform_features(self, features):
-        return features
-
-    def get_config(self):
-        return {"tokenizer_name": self.tokenizer_name, "max_length": self.max_length}
-
-
-def make_temporal_siglip_vf_pre_post_processors(
-    config: TemporalSiglipVFConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    image_keys = tuple(
-        key for key, feature in config.input_features.items() if feature.type == FeatureType.VISUAL
-    )
-    preprocessor = PolicyProcessorPipeline(
-        steps=[
-            RenameObservationsProcessorStep(rename_map={}),
-            AddBatchDimensionProcessorStep(),
-            NormalizerProcessorStep(
-                features={**config.input_features, **config.output_features},
-                norm_map=config.normalization_mapping,
-                stats=dataset_stats,
-            ),
-            TemporalSiglipImageProcessorStep(
-                image_resolution=config.image_resolution,
-                image_keys=image_keys,
-                history_steps=config.history_steps,
-            ),
-            DistributionalVFPrepareTaskPromptStep(),
-            TemporalSiglipTokenizerStep(
-                tokenizer_name=config.siglip_path,
-                max_length=config.tokenizer_max_length,
-            ),
-            DeviceProcessorStep(device=config.device or "cpu"),
-        ],
-        name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        to_transition=batch_to_transition,
-        to_output=transition_to_batch,
-    )
-    postprocessor = PolicyProcessorPipeline(
-        name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-        to_transition=policy_action_to_transition,
-    )
-    return preprocessor, postprocessor

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 from dataclasses import dataclass, field
 from typing import Any
 
@@ -33,6 +34,8 @@ from lerobot.processor import (
 )
 from lerobot.utils.rotation import Rotation
 
+logger = logging.getLogger(__name__)
+
 
 @ProcessorStepRegistry.register("ee_reference_and_delta")
 @dataclass
@@ -194,11 +197,17 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
     Attributes:
         end_effector_bounds: A dictionary with "min" and "max" keys for position clipping.
         max_ee_step_m: The maximum allowed change in position (in meters) between steps.
+        raise_on_jump: When ``True`` (default) an over-limit per-frame step raises
+            ``ValueError`` (aborting the control loop). When ``False`` the step is
+            rate-limited to ``max_ee_step_m`` and a warning is logged instead — the
+            safer choice for live teleoperation, where a transient tracking glitch
+            should not crash the loop and leave the robot uncontrolled.
         _last_pos: Internal state storing the last commanded position.
     """
 
     end_effector_bounds: dict
     max_ee_step_m: float = 0.05
+    raise_on_jump: bool = True
     _last_pos: np.ndarray | None = field(default=None, init=False, repr=False)
 
     def action(self, action: RobotAction) -> RobotAction:
@@ -226,8 +235,19 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
             dpos = pos - self._last_pos
             n = float(np.linalg.norm(dpos))
             if n > self.max_ee_step_m and n > 0:
+                # Clamp the step to the per-frame limit (rate-limit). The clamped
+                # value is computed either way; raise_on_jump only decides whether
+                # an over-limit step aborts the loop or is rate-limited + warned.
                 pos = self._last_pos + dpos * (self.max_ee_step_m / n)
-                raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
+                if self.raise_on_jump:
+                    raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
+                logger.warning(
+                    "EE jump %.3fm > %.3fm; rate-limited to the per-frame step "
+                    "(likely a transient tracking glitch; if it recurs every frame "
+                    "the commanded target is systematically out of workspace).",
+                    n,
+                    self.max_ee_step_m,
+                )
 
         self._last_pos = pos
 
@@ -264,12 +284,18 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
         q_curr: Internal state storing the last joint positions, used as an initial guess for the IK solver.
         initial_guess_current_joints: If True, use the robot's current joint state as the IK guess.
             If False, use the solution from the previous step.
+        orientation_weight: Weight for the orientation constraint passed to
+            ``RobotKinematics.inverse_kinematics``. Defaults to ``0.01`` (matching the solver
+            default, so existing callers are unchanged). Set to ``0.0`` for position-only IK on
+            under-actuated arms; a small nonzero weight gives soft-orientation IK on the 5-DOF
+            SO-101, where the wrist tracks orientation only partially (position dominates).
     """
 
     kinematics: RobotKinematics
     motor_names: list[str]
     q_curr: np.ndarray | None = field(default=None, init=False, repr=False)
     initial_guess_current_joints: bool = True
+    orientation_weight: float = 0.01
 
     def action(self, action: RobotAction) -> RobotAction:
         x = action.pop("ee.x")
@@ -308,7 +334,9 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
         t_des[:3, 3] = [x, y, z]
 
         # Compute inverse kinematics
-        q_target = self.kinematics.inverse_kinematics(self.q_curr, t_des)
+        q_target = self.kinematics.inverse_kinematics(
+            self.q_curr, t_des, orientation_weight=self.orientation_weight
+        )
         self.q_curr = q_target
 
         # TODO: This is sentitive to order of motor_names = q_target mapping

src/lerobot/robots/so_follower/so_follower.py

@@ -182,7 +182,7 @@ class SOFollower(Robot):
     def get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
-        obs_dict = self.bus.sync_read("Present_Position", num_retry=3)
+        obs_dict = self.bus.sync_read("Present_Position")
         obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read state: {dt_ms:.1f}ms")
@@ -223,12 +223,12 @@ class SOFollower(Robot):
         # Cap goal position when too far away from present position.
         # /!\ Slower fps expected due to reading from the follower.
         if self.config.max_relative_target is not None:
-            present_pos = self.bus.sync_read("Present_Position", num_retry=3)
+            present_pos = self.bus.sync_read("Present_Position")
             goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
             goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
 
         # Send goal position to the arm
-        self.bus.sync_write("Goal_Position", goal_pos, num_retry=3)
+        self.bus.sync_write("Goal_Position", goal_pos)
         return {f"{motor}.pos": val for motor, val in goal_pos.items()}
 
     @check_if_not_connected

src/lerobot/rollout/configs.py

@@ -106,8 +106,6 @@ class DAggerKeyboardConfig:
     pause_resume: str = "space"
     correction: str = "tab"
     upload: str = "enter"
-    success: str = "s"
-    failure: str = "f"
 
 
 @dataclass
@@ -121,8 +119,6 @@ class DAggerPedalConfig:
     pause_resume: str = "KEY_A"
     correction: str = "KEY_B"
     upload: str = "KEY_C"
-    success: str = "KEY_D"
-    failure: str = "KEY_E"
 
 
 @RolloutStrategyConfig.register_subclass("episodic")
@@ -169,10 +165,6 @@ class DAggerStrategyConfig(RolloutStrategyConfig):
     2. **correction** — toggle human correction recording.
     3. **upload** — push dataset to hub on demand (corrections-only mode).
 
-    Episode success labeling:
-    4. **success** — mark current episode as successful.
-    5. **failure** — mark current episode as failed.
-
     When ``record_autonomous=False`` (default) only human-correction windows
     are recorded — each correction becomes its own episode.  Set to ``True``
     to record both autonomous and correction frames with size-based episode

src/lerobot/rollout/context.py

@@ -350,11 +350,6 @@ def build_rollout_context(
                     "shape": (1,),
                     "names": None,
                 }
-                dataset_features["next.success"] = {
-                    "dtype": "bool",
-                    "shape": (1,),
-                    "names": None,
-                }
 
             repo_name = cfg.dataset.repo_id.split("/", 1)[-1]
             if not repo_name.startswith("rollout_"):

src/lerobot/rollout/strategies/dagger.py

@@ -112,14 +112,6 @@ class DAggerEvents:
         # Session-level flags
         self.stop_recording = Event()
         self.upload_requested = Event()
-        # Set when operator presses success/failure key to end the current episode.
-        self.save_episode_requested = Event()
-
-        # Episode success labeling
-        self._episode_success: bool | None = None
-
-        # Episode success labeling
-        self._episode_success: bool | None = None
 
     # -- Thread-safe phase access ------------------------------------------
 
@@ -163,43 +155,7 @@ class DAggerEvents:
         with self._lock:
             self._phase = DAggerPhase.AUTONOMOUS
             self._pending_transition = None
-            self._episode_success = None
         self.upload_requested.clear()
-        self.save_episode_requested.clear()
-
-    def mark_success(self) -> None:
-        """Mark the current episode as successful (called from input threads)."""
-        with self._lock:
-            self._episode_success = True
-
-    def mark_failure(self) -> None:
-        """Mark the current episode as failed (called from input threads)."""
-        with self._lock:
-            self._episode_success = False
-
-    def consume_episode_success(self) -> bool | None:
-        """Consume and reset the episode success label. Returns None if unlabeled."""
-        with self._lock:
-            result = self._episode_success
-            self._episode_success = None
-            return result
-
-    def mark_success(self) -> None:
-        """Mark the current episode as successful (called from input threads)."""
-        with self._lock:
-            self._episode_success = True
-
-    def mark_failure(self) -> None:
-        """Mark the current episode as failed (called from input threads)."""
-        with self._lock:
-            self._episode_success = False
-
-    def consume_episode_success(self) -> bool | None:
-        """Consume and reset the episode success label. Returns None if unlabeled."""
-        with self._lock:
-            result = self._episode_success
-            self._episode_success = None
-            return result
 
 
 # ---------------------------------------------------------------------------
@@ -230,20 +186,12 @@ def _init_dagger_keyboard(events: DAggerEvents, cfg: DAggerKeyboardConfig):
             events.request_transition(key_to_event[name])
         if name == cfg.upload:
             events.upload_requested.set()
-        if name == cfg.success:
-            events.mark_success()
-            events.save_episode_requested.set()
-            logger.info("Episode marked as SUCCESS — saving")
-        if name == cfg.failure:
-            events.mark_failure()
-            events.save_episode_requested.set()
-            logger.info("Episode marked as FAILURE — saving")
 
     return create_key_listener(
         dispatch,
         controls_help=(
             f"pause_resume='{cfg.pause_resume}', correction='{cfg.correction}', "
-            f"upload='{cfg.upload}', success='{cfg.success}', failure='{cfg.failure}', ESC=stop"
+            f"upload='{cfg.upload}', ESC=stop"
         ),
     )
 
@@ -263,12 +211,6 @@ def _init_dagger_pedal(events: DAggerEvents, cfg: DAggerPedalConfig):
             events.request_transition(code_to_event[code])
         if code == cfg.upload:
             events.upload_requested.set()
-        if code == cfg.success:
-            events.mark_success()
-            logger.info("Episode marked as SUCCESS (pedal)")
-        if code == cfg.failure:
-            events.mark_failure()
-            logger.info("Episode marked as FAILURE (pedal)")
 
     logger.info("Initializing DAgger foot pedal listener (device=%s)", cfg.device_path)
     return start_pedal_listener(on_press, device_path=cfg.device_path)
@@ -371,31 +313,6 @@ class DAggerStrategy(RolloutStrategy):
         )
         logger.info("DAgger strategy teardown complete")
 
-    # ------------------------------------------------------------------
-    # Episode success labeling
-    # ------------------------------------------------------------------
-
-    def _stamp_episode_success(self, dataset) -> None:
-        """Set next.success on the terminal frame based on operator label.
-
-        Called just before save_episode(). If the operator pressed the success
-        key during this episode, the last frame's next.success is set to True.
-        Otherwise all frames remain False (unlabeled = assumed failure).
-        """
-        buf = dataset.writer.episode_buffer
-        if buf is None:
-            return
-
-        success_buf = buf.get("next.success")
-        if not success_buf:
-            return
-
-        label = self._events.consume_episode_success()
-
-        if label:
-            success_buf[-1] = np.array([True], dtype=bool)
-            logger.info("Terminal frame stamped next.success=True")
-
     # ------------------------------------------------------------------
     # Continuous recording mode (record_autonomous=True)
     # ------------------------------------------------------------------
@@ -433,12 +350,7 @@ class DAggerStrategy(RolloutStrategy):
         episode_start = time.perf_counter()
         episodes_since_push = 0
         episode_duration_s = self._episode_duration_s
-        num_episodes = self.config.num_episodes
-        logger.info(
-            "DAgger continuous recording started (episode_duration=%.0fs, target=%s eps)",
-            episode_duration_s,
-            num_episodes if num_episodes is not None else "∞",
-        )
+        logger.info("DAgger continuous recording started (episode_duration=%.0fs)", episode_duration_s)
 
         with VideoEncodingManager(dataset):
             try:
@@ -487,7 +399,6 @@ class DAggerStrategy(RolloutStrategy):
                                 **action_frame,
                                 "task": task_str,
                                 "intervention": np.array([True], dtype=bool),
-                                "next.success": np.array([False], dtype=bool),
                             }
                             dataset.add_frame(frame)
                         record_tick += 1
@@ -516,32 +427,23 @@ class DAggerStrategy(RolloutStrategy):
                                     **action_frame,
                                     "task": task_str,
                                     "intervention": np.array([False], dtype=bool),
-                                    "next.success": np.array([False], dtype=bool),
                                 }
                                 dataset.add_frame(frame)
                             record_tick += 1
 
-                    # Episode rotation: either the operator pressed success/failure,
-                    # or the video file-size target was reached.
-                    # Defer the save while a correction is ongoing so the episode
-                    # boundary lands on a clean autonomous frame. The event stays
-                    # set until we actually save, so it won't be lost.
-                    manual_save = events.save_episode_requested.is_set()
-
+                    # Episode rotation derived from the video file-size target.
+                    # Saving is deferred while a correction is ongoing so the
+                    # episode boundary lands on a clean autonomous frame.
                     elapsed = time.perf_counter() - episode_start
-                    if (manual_save or elapsed >= episode_duration_s) and phase != DAggerPhase.CORRECTING:
-                        if manual_save:
-                            events.save_episode_requested.clear()
+                    if elapsed >= episode_duration_s and phase != DAggerPhase.CORRECTING:
                         with self._episode_lock:
-                            self._stamp_episode_success(dataset)
                             dataset.save_episode()
                         episodes_since_push += 1
                         self._needs_push.set()
-                        save_reason = "manual save" if manual_save else f"elapsed {elapsed:.1f}s"
                         logger.info(
-                            "Episode saved (%s, total: %d)",
-                            save_reason,
+                            "Episode saved (total: %d, elapsed: %.1fs)",
                             dataset.num_episodes,
+                            elapsed,
                         )
                         log_say(f"Episode {dataset.num_episodes} saved", play_sounds)
 
@@ -549,25 +451,6 @@ class DAggerStrategy(RolloutStrategy):
                             self._background_push(dataset, cfg)
                             episodes_since_push = 0
 
-                        if num_episodes is not None and dataset.num_episodes >= num_episodes:
-                            logger.info("Target episode count reached (%d), stopping session", num_episodes)
-                            log_say(f"All {num_episodes} episodes collected", play_sounds)
-                            events.stop_recording.set()
-                            break
-
-                        # Pause after manual save: stop the policy, return robot to
-                        # initial position, and wait for the operator to reset the
-                        # environment and press SPACE.
-                        if manual_save:
-                            engine.pause()
-                            events.phase = DAggerPhase.PAUSED
-                            self._return_to_initial_position(ctx.hardware)
-                            last_action = None
-                            logger.info(
-                                "Episode saved — paused for environment reset. Press SPACE to start next episode."
-                            )
-                            log_say("Reset the environment, then press space", play_sounds)
-
                         episode_start = time.perf_counter()
 
                     dt = time.perf_counter() - loop_start
@@ -582,13 +465,10 @@ class DAggerStrategy(RolloutStrategy):
                 logger.info("DAgger continuous control loop ended — pausing engine")
                 engine.pause()
                 with contextlib.suppress(Exception):
-                    buf = dataset.writer.episode_buffer
-                    if buf and any(len(v) > 0 for v in buf.values() if isinstance(v, list)):
-                        with self._episode_lock:
-                            self._stamp_episode_success(dataset)
-                            dataset.save_episode()
-                        self._needs_push.set()
-                        logger.info("Final in-progress episode saved")
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
 
     # ------------------------------------------------------------------
     # Corrections-only mode (record_autonomous=False)
@@ -660,7 +540,6 @@ class DAggerStrategy(RolloutStrategy):
                         # Correction ended -> save episode (blocking if not streaming)
                         if old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
                             with self._episode_lock:
-                                self._stamp_episode_success(dataset)
                                 dataset.save_episode()
                             recorded += 1
                             self._needs_push.set()
@@ -702,7 +581,6 @@ class DAggerStrategy(RolloutStrategy):
                                     **action_frame,
                                     "task": task_str,
                                     "intervention": np.array([True], dtype=bool),
-                                    "next.success": np.array([False], dtype=bool),
                                 }
                             )
                         record_tick += 1
@@ -736,13 +614,10 @@ class DAggerStrategy(RolloutStrategy):
                 logger.info("DAgger corrections-only loop ended — pausing engine")
                 engine.pause()
                 with contextlib.suppress(Exception):
-                    buf = dataset.writer.episode_buffer
-                    if buf and any(len(v) > 0 for v in buf.values() if isinstance(v, list)):
-                        with self._episode_lock:
-                            self._stamp_episode_success(dataset)
-                            dataset.save_episode()
-                        self._needs_push.set()
-                        logger.info("Final in-progress episode saved")
+                    with self._episode_lock:
+                        dataset.save_episode()
+                    self._needs_push.set()
+                    logger.info("Final in-progress episode saved")
 
     # ------------------------------------------------------------------
     # State-machine transition side-effects