feat(kiwi): excluding LeKiwi from depth support (for now)

numeric values before calling `is_integer()`

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

.dockerignore

@@ -22,10 +22,6 @@ outputs
 rl
 media
 
-# Local virtualenvs (the image provides its own)
-.venv
-venv
-
 
 # Logging
 logs

.github/workflows/benchmark_tests.yml

@@ -167,9 +167,9 @@ jobs:
 
       # ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
       # Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
-      # immediately runs eval inside the training loop (env_eval_freq=1, 1 episode).
+      # immediately runs eval inside the training loop (eval_freq=1, 1 episode).
       # Tests the full train→eval-within-training pipeline end-to-end.
-      - name: Run Libero train+eval smoke (1 step, env_eval_freq=1)
+      - name: Run Libero train+eval smoke (1 step, eval_freq=1)
         if: env.HF_USER_TOKEN != ''
         run: |
           docker run --name libero-train-smoke --gpus all \
@@ -196,7 +196,7 @@ jobs:
                 --output_dir=/tmp/train-smoke \
                 --steps=1 \
                 --batch_size=1 \
-                --env_eval_freq=1 \
+                --eval_freq=1 \
                 --eval.n_episodes=1 \
                 --eval.batch_size=1 \
                 --eval.use_async_envs=false \

AGENT_GUIDE.md

@@ -138,7 +138,7 @@ lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.
   --dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
 ```
 
-**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`, list them with `hf jobs hardware`) to run on Hugging Face Jobs instead. See §6/§7 for policy and duration.
+**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
 
 ```bash
 lerobot-train \

Makefile

@@ -58,7 +58,7 @@ test-act-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \
@@ -96,7 +96,7 @@ test-diffusion-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -126,7 +126,7 @@ test-tdmpc-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -161,7 +161,7 @@ test-smolvla-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \

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, 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.
+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.
 
 <p align="center">
   <img alt="Gr00t Architecture" src="./media/readme/VLA_architecture.jpg" width="640px">
@@ -97,17 +97,17 @@ Training a policy is as simple as running a script configuration:
 
 ```bash
 lerobot-train \
-  --policy.type=act \
+  --policy=act \
   --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 N1.7](./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)                                                                                                                                                                                                                                                                         |
+| 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)                                                                                                                                                                                                                                         |
 
 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

@@ -69,14 +69,8 @@
     title: VLA-JEPA
   - local: eo1
     title: EO-1
-  - local: lingbot_va
-    title: LingBot-VA
-  - local: fastwam
-    title: FastWAM
-  - local: evo1
-    title: EVO1
   - local: groot
-    title: NVIDIA GR00T
+    title: NVIDIA GR00T N1.5
   - local: xvla
     title: X-VLA
   - local: multi_task_dit

docs/source/bring_your_own_policies.mdx

@@ -295,12 +295,11 @@ The file names are load-bearing: the factory does lazy imports by name, and the
 
 ### Wiring
 
-Four places need to know about your policy. All by name.
+Three 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.
 
@@ -372,8 +371,6 @@ 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/cheat-sheet.mdx

@@ -89,36 +89,6 @@ Control the data recording flow using keyboard shortcuts:
 - Press **Left Arrow (`←`)**: Delete current episode and retry.
 - Press **Escape (`ESC`)**: Stop, encode videos, and upload.
 
-### Recording depth
-
-Intel RealSense cameras (`type: intelrealsense`) record a depth stream when you set `use_depth: true`. Depth is quantized to 12-bit codes and stored as its own video.
-
-```bash
-lerobot-record \
-    ... \
-    --robot.cameras="{ head: {type: intelrealsense, serial_number_or_name: \"0123456789\", width: 640, height: 480, fps: 30, use_depth: true} }" \
-    --dataset.repo_id=${HF_USER}/so101_depth_test \
-    --dataset.single_task="put the red brick in a bowl" \
-    --dataset.depth_encoder.depth_min=0.01 \
-    --dataset.depth_encoder.depth_max=10.0 \
-    --dataset.depth_encoder.shift=0.0 \
-    --dataset.depth_encoder.use_log=true
-```
-
-### Video encoding parameters
-
-RGB and depth streams are encoded independently via the `--dataset.rgb_encoder.*` and `--dataset.depth_encoder.*` keys.
-
-```bash
-lerobot-record \
-    ... \
-    --dataset.rgb_encoder.vcodec=h264 \
-    --dataset.rgb_encoder.pix_fmt=yuv420p \
-    --dataset.rgb_encoder.crf=23 \
-    --dataset.depth_encoder.vcodec=hevc \
-    --dataset.depth_encoder.extra_options='{"x265-params": "lossless=1"}'
-```
-
 ### Training
 
 Depending on your hardware training the policy might take a few hours. That's how you train simple `ACT` policy:
@@ -150,14 +120,6 @@ lerobot-train \
     --steps=20000
 ```
 
-No local GPU? Add `--job.target=<flavor>` (e.g. `a10g-small`) to either command and `lerobot-train` runs it on [Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) instead — it uploads a local-only dataset for you and pushes the trained model. List flavors with `hf jobs hardware`.
-
-To resume, point `--config_path` at a checkpoint and add `--resume=true`. It accepts a local path or a Hub repo id (the latest checkpoint is fetched), and works locally or on a job by adding `--job.target=<flavor>`:
-
-```bash
-lerobot-train --config_path=${HF_USER}/policy_test --resume=true --job.target=a10g-small
-```
-
 ### Inference
 
 Inference means running the trained policy/model on a robot. For that we use `lerobot-rollout`. You will need to provide a path to your policy. It can be a local path or a path to Hugging Face for example "lerobot/folding_latest". Your cameras configuration needs to match what was used when collecting the dataset. Duration is in seconds if unspecified, it will run forever.

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 N1.7](./groot)
+- [GR00T N1.5](./groot)
 
 Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:
 

docs/source/evo1.mdx

@@ -1,191 +0,0 @@
-# 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/fastwam.mdx

@@ -1,167 +0,0 @@
-# FastWAM
-
-FastWAM is a World Action Model policy for robot control. The LeRobot integration exposes FastWAM through the standard policy API so it can be configured with `policy.type=fastwam`, trained with `lerobot-train`, and loaded through the LeRobot pretrained policy interface.
-
-## Model Overview
-
-FastWAM keeps video modeling during training, but uses direct action prediction at inference time instead of iteratively generating future observations. This LeRobot policy wraps the FastWAM action model, adapts LeRobot batches to FastWAM training samples, and provides the standard processor pipeline for normalization and action postprocessing.
-
-The implementation initializes the visual world-model components from `Wan-AI/Wan2.2-TI2V-5B` by default and predicts action chunks with shape `[batch, action_horizon, action_dim]`.
-
-### What the LeRobot Integration Covers
-
-- Standard `policy.type=fastwam` configuration through LeRobot
-- Image, state, action, and language-task batch adaptation
-- Action chunk inference through `select_action` and `predict_action_chunk`
-- Checkpoint save/load through the LeRobot policy APIs
-- Configurable LIBERO gripper action postprocessing
-
-## Installation Requirements
-
-Install LeRobot from source, then install FastWAM dependencies:
-
-```bash
-pip install -e ".[fastwam]"
-```
-
-This installs the FastWAM policy extra from `pyproject.toml`: `transformers`,
-`diffusers`, `ftfy`, and `regex`, plus LeRobot's base dependencies.
-
-For LIBERO evaluation, install the benchmark dependencies too:
-
-```bash
-pip install -e ".[fastwam,libero]"
-```
-
-This installs both extras. In addition to the FastWAM dependencies above, the
-`libero` extra installs LeRobot dataset dependencies, `hf-libero` on Linux, and
-`scipy`.
-
-FastWAM uses the Wan2.2 TI2V backbone. The default model id is:
-
-```python
-policy.model_id=Wan-AI/Wan2.2-TI2V-5B
-```
-
-## Data Requirements
-
-FastWAM expects a LeRobot dataset with:
-
-- one or more visual observations whose widths concatenate to `policy.image_size[1]`
-- `observation.state` when `policy.proprio_dim` is not `None`
-- `action`
-- a language task instruction through the dataset task field, or precomputed `context` and `context_mask` tensors
-
-The default visual setup is one image feature named `observation.images.image` with shape `(3, 224, 448)`. If the dataset uses two cameras, configure `policy.input_features` so their heights match `224` and their widths sum to `448`.
-
-## Usage
-
-Create a new FastWAM policy with:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=your-org/your-dataset \
-  --policy.type=fastwam \
-  --policy.action_dim=7 \
-  --policy.proprio_dim=8 \
-  --policy.action_horizon=32 \
-  --policy.n_action_steps=10 \
-  --policy.image_size='[224,448]' \
-  --output_dir=./outputs/fastwam_training \
-  --job_name=fastwam_training \
-  --steps=300000 \
-  --batch_size=8 \
-  --policy.device=cuda
-```
-
-Evaluate an existing LeRobot-format checkpoint on LIBERO-10 with:
-
-```bash
-lerobot-eval \
-  --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
-  --policy.device=cuda \
-  --policy.torch_dtype=float32 \
-  --policy.n_action_steps=10 \
-  --env.type=libero \
-  --env.task=libero_10 \
-  --env.observation_height=224 \
-  --env.observation_width=224 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=50 \
-  --seed=0 \
-  --env.episode_length=600
-```
-
-For `libero_goal`, `libero_spatial`, and `libero_object`, use
-`--env.episode_length=300`.
-
-For real-robot rollout, use the same checkpoint path:
-
-```bash
-lerobot-rollout \
-  --robot.type=so101_follower \
-  --robot.port=/dev/ttyACM0 \
-  --policy.path=your-org/fastwam-real-robot
-```
-
-## Configuration Notes
-
-### Image Features
-
-`policy.image_size` is the size of the concatenated FastWAM image tensor as `(height, width)`. Each configured image feature must have shape `(3, height, camera_width)`, and all camera widths must sum to the configured width.
-
-### Action Chunking
-
-`policy.action_horizon` controls the number of future actions supervised during training and predicted during inference. `policy.n_action_steps` controls how many actions are consumed before the policy predicts a fresh chunk. `policy.n_action_steps` must be less than or equal to `policy.action_horizon`.
-
-### Wan Components
-
-FastWAM loads the Wan VAE, video DiT, text encoder, and tokenizer from the configured Wan model directory or Hugging Face Hub model id. LeRobot-format FastWAM checkpoints saved by `save_pretrained` also copy the local Wan component files needed by `from_pretrained`.
-
-### Attention Backend
-
-FastWAM's DiT uses PyTorch's `scaled_dot_product_attention` (SDPA) for all attention. It does **not** use FlashAttention: its Mixture-of-Transformers (MoT) routing needs arbitrary boolean `[query, key]` attention masks, which the FlashAttention varlen API cannot express. Installing the `flash-attn` package therefore has no effect on the FastWAM path. (Note that SDPA itself may still select PyTorch's own flash / memory-efficient / math kernel internally — this is unrelated to the `flash-attn` package.)
-
-### LIBERO Action Toggle
-
-FastWAM LIBERO checkpoints use `policy.toggle_action_dimensions=[-1]` by
-default to match the gripper action convention used by the original FastWAM
-evaluation pipeline:
-
-```bash
---policy.toggle_action_dimensions='[-1]'
-```
-
-## Results
-
-Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
-
-| Suite          | Success rate | n_episodes |
-| -------------- | -----------: | ---------: |
-| libero_spatial |        97.6% |        500 |
-| libero_object  |        99.0% |        500 |
-| libero_goal    |        95.0% |        500 |
-| libero_10      |        94.0% |        500 |
-| **average**    |    **96.4%** |       2000 |
-
-Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300` (1x H20 140 GB).
-
-## References
-
-- [Fast-WAM paper](https://arxiv.org/abs/2603.16666)
-- [Fast-WAM project page](https://yuantianyuan01.github.io/FastWAM/)
-- [Fast-WAM code](https://github.com/yuantianyuan01/FastWAM)
-- [Released upstream checkpoints](https://huggingface.co/yuanty/fastwam)
-- [Wan2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)
-
-## Citation
-
-```bibtex
-@article{yuan2026fastwam,
-  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
-  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
-  journal = {arXiv preprint arXiv:2603.16666},
-  year = {2026},
-  url = {https://arxiv.org/abs/2603.16666}
-}
-```

docs/source/groot.mdx

@@ -1,19 +1,16 @@
-# GR00T Policy
+# GR00T N1.5 Policy
 
-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.
+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.
 
-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)).
+This document outlines the specifics of its integration and usage within the LeRobot framework.
 
 ## Model Overview
 
-GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.
+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.
 
-Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+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.
 
-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.
+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.
 
 <img
   src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
@@ -31,24 +28,33 @@ This approach allows the model to be highly adaptable through post-training for
 
 ## Installation Requirements
 
-GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra:
+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:
 
 ```bash
-pip install "lerobot[groot]"
+# 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')"
 ```
 
-For a source checkout:
+3. Install LeRobot by running:
 
 ```bash
-pip install -e ".[groot]"
+pip install lerobot[groot]
 ```
 
 ## Usage
 
-To use GR00T N1.7:
+To use GR00T in your LeRobot configuration, specify the policy type as:
 
-```bash
---policy.type=groot
+```python
+policy.type=groot
 ```
 
 ## Training
@@ -57,171 +63,72 @@ To use GR00T N1.7:
 
 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
-# 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 \
-  --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 \
+# Using a multi-GPU setup
+accelerate launch \
+  --multi_gpu \
+  --num_processes=$NUM_GPUS \
+  $(which lerobot-train) \
   --output_dir=$OUTPUT_DIR \
-  --job_name=$DATASET \
+  --save_checkpoint=true \
+  --batch_size=$BATCH_SIZE \
+  --steps=$NUM_STEPS \
+  --save_freq=$SAVE_FREQ \
+  --log_freq=$LOG_FREQ \
+  --policy.push_to_hub=true \
+  --policy.type=groot \
+  --policy.repo_id=$REPO_ID \
+  --policy.tune_diffusion_model=false \
+  --dataset.repo_id=$DATASET_ID \
   --wandb.enable=true \
-  --wandb.disable_artifact=true
-
+  --wandb.disable_artifact=true \
+  --job_name=$JOB_NAME
 ```
 
 ## Performance Results
 
-### LIBERO Benchmark Results
+### Libero Benchmark Results
 
 > [!NOTE]
-> Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`.
+> Follow our instructions for Libero usage: [Libero](./libero)
 
-GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
+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.
 
-### Train on LIBERO
+| 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%           |
 
-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=$JOB_NAME
-```
-
-This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/LIBERO/README.md).
-
-### GR00T N1.7 LIBERO Results
-
-Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):
-
-| Suite            | Success rate | Checkpoint                                                                                                    |
-| ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- |
-| LIBERO Spatial   |          91% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) |
-| LIBERO Object    |          81% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640)   |
-| LIBERO Goal      |          97% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640)       |
-| LIBERO 10 (Long) |          84% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640)           |
-| **Average**      |   **88.25%** |                                                                                                               |
-
-```bash
-export MODEL_ID=your_trained_model_on_huggingface
-
-lerobot-eval \
-  --policy.type=groot \
-  --policy.base_model_path=$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.
+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.
 
 ### 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
-# 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 \
+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},
+  }' \
   --display_data=true \
-  --inference.type=rtc \
-  --inference.rtc.enabled=True \ # set to False if it causes inference instability
-  --inference.rtc.execution_horizon=8 \
-  --inference.queue_threshold=0
+  --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
 ```
 
-> [!NOTE]
-> Value of `inference.queue_threshold` should not exceed 5 to ensure stable inference.
-
 ## 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/).
+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**.

docs/source/hardware_guide.mdx

@@ -82,18 +82,17 @@ VRAM is the first filter. Within a tier, pick by budget and availability — the
 
 ### Hugging Face Jobs
 
-[Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) lets you run training on managed HF infrastructure, billed by the second, without owning a GPU. `lerobot-train` submits and streams the job for you — just add `--job.target=<flavor>` to a normal training command:
+[Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) lets you run training on managed HF infrastructure, billed by the second. The repo publishes a ready-to-use image: **`huggingface/lerobot-gpu:latest`**, rebuilt **every night at 02:00 UTC from `main`** ([`docker_publish.yml`](https://github.com/huggingface/lerobot/blob/main/.github/workflows/docker_publish.yml)) — so it tracks the current state of the repo, not a tagged release.
 
 ```bash
-lerobot-train \
-  --policy.type=act --dataset.repo_id=<USER>/<DATASET> \
-  --policy.repo_id=<USER>/act_<task> \
-  --job.target=a10g-large
+hf jobs run --flavor a10g-large huggingface/lerobot-gpu:latest \
+  bash -c "nvidia-smi && lerobot-train \
+    --policy.type=act --dataset.repo_id=<USER>/<DATASET> \
+    --policy.repo_id=<USER>/act_<task> --batch_size=8 --steps=50000"
 ```
 
 Notes:
 
-- Run `hf auth login` once before submitting, the job runs under your token.
-- `--job.target` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). List the current catalogue with pricing via `hf jobs hardware`, or see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).
-- The job defaults to a `2d` (48h) timeout. Override it with `--job.timeout=4h` (or any other valid duration string) to shorten or extend the timeout. The job automatically stops when the command completes.
-- For the full walkthrough — dataset upload, checkpoint streaming, resuming a run on a job — see the [imitation-learning training guide](./il_robots#train-using-hugging-face-jobs).
+- The leading `nvidia-smi` is a quick sanity check that CUDA is visible inside the container — useful to fail fast if the flavor or driver mismatched.
+- The default Job timeout is 30 minutes; pass `--timeout 4h` (or longer) for real training.
+- `--flavor` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). For the current full catalogue + pricing see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).

docs/source/hilserl.mdx

@@ -719,7 +719,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
   "num_workers": 4,
   "steps": 5000,
   "log_freq": 10,
-  "env_eval_freq": 1000,
+  "eval_freq": 1000,
   "save_freq": 1000,
   "save_checkpoint": true,
   "seed": 2,

docs/source/hope_jr.mdx

@@ -117,14 +117,6 @@ middle_dip           |   1484 |   1500 |   1547
 
 Once calibration is complete, the system will save the calibration to `/Users/your_username/.cache/huggingface/lerobot/calibration/teleoperators/homunculus_glove/red.json`
 
-#### Visualizing Teleoperator Glove
-
-After calibration, you can visualize the glove movements in real-time. Open the visualizer by navigating to the visualizer directory and opening the HTML file in your browser:
-
-```bash
-open examples/hopejr/visualizer/index.html
-```
-
 ### 1.3 Calibrate Robot Arm
 
 ```bash

docs/source/il_robots.mdx

@@ -126,7 +126,7 @@ import time
 from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
 from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.utils.visualization_utils import init_visualization, log_visualization_data, shutdown_visualization
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun
 
 robot_config = SO101FollowerConfig(
     port="/dev/tty.usbmodem5AB90687491",
@@ -142,7 +142,7 @@ teleop_config = SO101LeaderConfig(
     id="my_leader_arm",
 )
 
-init_visualization("rerun", session_name="teleoperation")  # pass "foxglove" to stream to Foxglove instead
+init_rerun(session_name="teleoperation")
 
 robot = SO101Follower(robot_config)
 teleop_device = SO101Leader(teleop_config)
@@ -158,7 +158,7 @@ while True:
     observation = robot.get_observation()
     action = teleop_device.get_action()
     robot.send_action(action)
-    log_visualization_data("rerun", observation=observation, action=action)
+    log_rerun_data(observation=observation, action=action)
 
     elapsed_time = time.perf_counter() - start_time
     sleep_time = TIME_PER_FRAME - elapsed_time
@@ -223,7 +223,7 @@ from lerobot.teleoperators.so_leader.config_so_leader import SO101LeaderConfig
 from lerobot.teleoperators.so_leader.so_leader import SO101Leader
 from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
-from lerobot.utils.visualization_utils import init_visualization
+from lerobot.utils.visualization_utils import init_rerun
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.processor import make_default_processors
 
@@ -270,7 +270,7 @@ def main():
 
     # Initialize the keyboard listener and rerun visualization
     _, events = init_keyboard_listener()
-    init_visualization("rerun", session_name="recording")
+    init_rerun(session_name="recording")
 
     # Connect the robot and teleoperator
     robot.connect()
@@ -390,17 +390,9 @@ Set the flow of data recording using command-line arguments:
 
 Control the data recording flow using keyboard shortcuts:
 
-- Press **Right Arrow (`→`)** or **`n`**: Early stop the current episode or reset time and move to the next.
-- Press **Left Arrow (`←`)** or **`r`**: Cancel the current episode and re-record it.
-- Press **Escape (`ESC`)** or **`q`**: Immediately stop the session, encode videos, and upload the dataset.
-
-<Tip>
-
-These control-flow shortcuts work on **X11, Wayland, and headless/SSH** sessions. When a global keyboard backend isn't available (Wayland, a headless machine, or macOS without Accessibility permission), `lerobot-record` automatically reads the same keys from the terminal — launch it from an interactive terminal and keep it focused. You can also use the letter equivalents **`n`** (next, same as `→`), **`r`** (re-record, same as `←`) and **`q`** (quit, same as `ESC`). No `$DISPLAY` setup is required.
-
-This applies to the recording control flow only. Keyboard **teleoperation** (driving the robot with the keyboard) still needs a global key backend, so it works only on an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — not on Wayland or headless sessions.
-
-</Tip>
+- Press **Right Arrow (`→`)**: Early stop the current episode or reset time and move to the next.
+- Press **Left Arrow (`←`)**: Cancel the current episode and re-record it.
+- Press **Escape (`ESC`)**: Immediately stop the session, encode videos, and upload the dataset.
 
 #### Tips for gathering data
 
@@ -414,7 +406,7 @@ If you want to dive deeper into this important topic, you can check out the [blo
 
 #### Troubleshooting:
 
-- On Linux, the recording control-flow keys (arrow keys, Escape) work on X11, Wayland, and headless/SSH sessions as long as `lerobot-record` runs in an interactive terminal — no `$DISPLAY` setup is needed. If the keys have no effect, make sure you are in an interactive (TTY) terminal, not a piped/non-TTY session, and that it is focused; the letter equivalents `n` / `r` / `q` also work. Keyboard _teleoperation_ (as opposed to the recording control flow) still requires a global key backend — an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — and is unavailable on Wayland or headless machines. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
+- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
 
 ## Visualize a dataset
 
@@ -514,12 +506,6 @@ lerobot-train \
   --resume=true
 ```
 
-`--config_path` also accepts a **Hub repo id**: if a run pushed its checkpoints to the Hub (with `--save_checkpoint_to_hub=true`), you can resume straight from the repo — its latest checkpoint is downloaded and training continues, restoring the optimizer, scheduler, step counter and data order:
-
-```bash
-lerobot-train --config_path=${HF_USER}/my_policy --resume=true
-```
-
 If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
 
 Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
@@ -532,48 +518,78 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 
 Hugging Face jobs let's you easily select hardware and run the training in the cloud. So if you don't have a powerful GPU or you need more VRAM or just want to train a model much faster use HF Jobs! It's pay as you go and you simply pay for each second of use, you can see the pricing and additional information [here](https://huggingface.co/docs/hub/jobs).
 
-`lerobot-train` runs locally by default. To run on a HuggingFace GPU, pass `--job.target` with a hardware flavor name:
+To run the training use this command:
 
+<hfoptions id="train_with_hf_jobs">
+<hfoption id="Command">
 ```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/so101_test \
-  --policy.type=act \
-  --policy.repo_id=${HF_USER}/my_policy \
-  --job.target=a10g-small
+hf jobs run \
+  --flavor a10g-small \
+  --timeout 4h \
+  --secrets HF_TOKEN \
+  huggingface/lerobot-gpu:latest \
+  -- \
+  python -m lerobot.scripts.lerobot_train \
+    --dataset.repo_id=username/dataset \
+    --policy.type=act \
+    --steps=5000 \
+    --batch_size=16 \
+    --policy.device=cuda \
+    --policy.repo_id=username/your_policy \
+    --log_freq=100
 ```
+</hfoption>
+<hfoption id="API example">
 
-List available flavors and pricing with `hf jobs hardware`. The run streams its logs to your terminal; press Ctrl-C to detach (the job keeps running in the cloud). Re-attach or cancel with:
+<!-- prettier-ignore-start -->
+```python
+from huggingface_hub import run_job, get_token
 
-```bash
-hf jobs logs <job-id>
-hf jobs cancel <job-id>
+run_name = "act_so101_hf_jobs"
+dataset_id = "username/dataset"
+user_hub_id = "username"
+
+command_args = [
+    "python", "-m", "lerobot.scripts.lerobot_train",
+    "--dataset.repo_id", dataset_id,
+    "--policy.type", "act",
+    "--steps", "5000",
+    "--batch_size", "16",
+    "--num_workers", "4",
+    "--policy.device", "cuda",
+    "--log_freq", "100",
+    "--save_freq", "1000",
+    "--save_checkpoint", "true",
+    "--wandb.enable", "false",
+    "--policy.repo_id", f"{user_hub_id}/{run_name}"
+]
+
+print(f"Submitting job '{run_name}' to Hugging Face Infrastructure...")
+
+job_info = run_job(
+    image="huggingface/lerobot-gpu:latest",
+    command=command_args,
+    flavor="a10g-small",
+    timeout="4h",
+    secrets={"HF_TOKEN": get_token()}
+)
+
+print("\n🚀 Job successfully launched!")
+print(f"🔹 Job ID: {job_info.id}")
+print(f"🔗 Live UI Dashboard & Logs: {job_info.url}")
 ```
+<!-- prettier-ignore-end -->
 
-If your dataset exists only locally (not yet on the Hub), it is automatically pushed to a **private** Hub repo so the job can download it by `repo_id` (nothing is made public). The trained model is pushed to the model repo at the end of the run. To also push every intermediate checkpoint to the Hub as it is saved (so you can monitor progress mid-run), add `--save_checkpoint_to_hub=true` — this requires a runtime image that includes this feature.
+</hfoption>
+</hfoptions>
 
-Every job (and any dataset pushed by the run) is tagged `lerobot` so it's easy to find on the Hub. Add your own with `--job.tags '["my-tag"]'`.
+You can modify the `--flavor` to use different hardware, for example: `t4-small`, `a100-large`, `h200`. Use `hf jobs hardware` to see the full list with pricing.
+Depending on the model you want to train and the hardware you selected you can also modify the `--batch_size` and `--number_of_workers`.
+For longer training sessions increase the timeout.
 
-By default the job is capped at `2d` (48h) of wall-clock. Override it with an HF Jobs duration string, e.g. `--job.timeout=4h` to fail faster or `--job.timeout=7d` for a longer run.
+Once the training is started you can go to [Jobs](https://huggingface.co/settings/jobs) and see if your jobs is running as well as all the outputs. Sometimes it takes a few minutes to schedule your job so be patient.
 
-> **Note:** the model repo is created up front (it holds the staged training config the job runs from). If a run fails before the model is pushed, that repo is left on the Hub so you can inspect it — it is not deleted automatically, so repeated failures can leave empty repos behind. Remove one with `hf repo delete <repo-id>`.
-
-**Prerequisites:** run `hf auth login` before submitting. For Weights & Biases integration, run `wandb login` or set `WANDB_API_KEY` on your machine — the key is forwarded to the job automatically.
-
-**Resuming on a job.** Adding `--job.target` to a resume command runs the resume in the cloud — the same command works locally or remotely. The checkpoint repo is the source of truth, and new checkpoints continue the lineage in the same repo:
-
-```bash
-# resume a Hub run on a job (its checkpoints are already on the Hub)
-lerobot-train --config_path=${HF_USER}/my_policy --resume=true --job.target=a10g-small
-
-# resume a LOCAL run on a job — the checkpoint is uploaded to a private Hub repo first,
-# then the job resumes from it (a local-only dataset is uploaded the same way)
-lerobot-train \
-  --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
-  --resume=true \
-  --job.target=a10g-small
-```
-
-Job settings come from the current command, so override `--job.target`, `--job.timeout`, etc. as needed; for the resumed run to itself be resumable later, keep `--save_checkpoint_to_hub=true`.
+After training the model will be pushed to hub and you can use it as any other model with LeRobot.
 
 #### Upload policy checkpoints
 
@@ -596,8 +612,6 @@ hf upload ${HF_USER}/act_so101_test${CKPT} \
 
 Use `lerobot-rollout` to deploy a trained policy on your robot. You can choose different strategies depending on your needs:
 
-The examples below load the model from `--policy.path`. To pin a specific pushed version — useful once `--save_checkpoint_to_hub=true` has committed several checkpoints — add `--policy.pretrained_revision` with a commit hash, branch, or tag. Each pushed checkpoint is tagged with its step (e.g. `--policy.pretrained_revision=010000`), so you can recover a checkpoint by step without looking up its commit sha.
-
 <hfoptions id="eval">
 <hfoption id="Base mode (no recording)">
 ```bash

docs/source/lekiwi.mdx

@@ -319,7 +319,7 @@ If you want to dive deeper into this important topic, you can check out the [blo
 
 #### Troubleshooting:
 
-- On Linux, the recording control-flow keys (arrow keys, Escape) work on X11, Wayland, and headless/SSH sessions as long as you run the recording from an interactive terminal (keep it focused) — no `$DISPLAY` setup is needed; the letter equivalents `n` / `r` / `q` also work. Note that **keyboard teleoperation of the LeKiwi base** is different: it relies on a global key backend and therefore works only on an X11 session, a Windows desktop, or macOS with Accessibility/Input Monitoring granted — not on Wayland or headless machines. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
+- On Linux, if the left and right arrow keys and escape key don't have any effect during data recording, make sure you've set the `$DISPLAY` environment variable. See [pynput limitations](https://pynput.readthedocs.io/en/latest/limitations.html#linux).
 
 ## Replay an episode
 

docs/source/libero.mdx

@@ -143,7 +143,7 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Reproducing published results

docs/source/libero_plus.mdx

@@ -173,7 +173,7 @@ lerobot-train \
     --batch_size=4 \
     --eval.batch_size=1 \
     --eval.n_episodes=1 \
-    --env_eval_freq=1000
+    --eval_freq=1000
 ```
 
 ## Relationship to LIBERO

docs/source/lingbot_va.mdx

@@ -1,187 +0,0 @@
-# LingBot-VA
-
-LingBot-VA is an **autoregressive video-action world-model policy** built on the **Wan2.2**
-video-diffusion stack. It interleaves, in one autoregressive sequence, the prediction of
-future **video latents** and **robot actions** ("VA" = Video-Action). The LeRobot
-integration wires LingBot-VA into the standard training, evaluation and processor
-interfaces.
-
-## Model Overview
-
-LingBot-VA is a **dual-stream "mixture-of-transformers"**: a video/latent stream
-(`patch_embedding_mlp → blocks → proj_out`) and an action stream
-(`action_embedder → blocks → action_proj_out`) share the same 30 transformer blocks and
-text conditioning.
-
-| Component                | Class                   | Role                                                        |
-| ------------------------ | ----------------------- | ----------------------------------------------------------- |
-| DiT backbone (trainable) | `WanTransformer3DModel` | ~5B-param dual-stream transformer.                          |
-| VAE (frozen)             | `AutoencoderKLWan`      | Wan2.2 VAE, `z_dim=48`. Lazy-pulled from the source repo.   |
-| Text encoder (frozen)    | `UMT5EncoderModel`      | UMT5-XXL, `d_model=4096`. Lazy-pulled from the source repo. |
-
-At inference the policy runs an autoregressive loop per chunk: it denoises the video-latent
-stream (CFG, ~20 steps) and the action stream (~50 steps) with two independent
-flow-matching schedulers, maintaining a KV cache across chunks. Real observed keyframes are
-fed back into the KV cache as the chunk is executed (closed-loop world modeling).
-
-### What the LeRobot Integration Covers
-
-- Standard `policy.type=lingbot_va` configuration through LeRobot.
-- Ready-to-use LeRobot-format checkpoints on the Hub (converted from the released upstream ones).
-- Autoregressive dual-stream inference behind the standard `select_action` interface
-  (single-environment eval, `--eval.batch_size=1`).
-- Opt-in saving of the policy's **predicted (imagined) videos** during eval / training.
-- Evaluation with `lerobot-eval` on LIBERO and RoboTwin.
-- Training / fine-tuning via the dual-stream flow-matching loss (`policy.forward`), see below.
-
-## Installation
-
-1. Install LeRobot by following the [Installation Guide](./installation).
-2. Install the LingBot-VA extra:
-
-```bash
-pip install -e ".[lingbot_va]"
-```
-
-## Checkpoints
-
-The released upstream checkpoints have been converted to LeRobot format and pushed to the Hub:
-
-| Variant                | LeRobot checkpoint               |
-| ---------------------- | -------------------------------- |
-| LIBERO-Long post-train | `lerobot/lingbot_va_libero_long` |
-| RoboTwin post-train    | `lerobot/lingbot_va_robotwin`    |
-| Pretrained base        | `lerobot/lingbot_va_base`        |
-
-Only the trainable ~5B transformer is stored in the LeRobot
-`model.safetensors`. The frozen VAE + UMT5 + tokenizer (~20 GB) are pulled from
-`config.wan_pretrained_path` at load time (defaults to the source `robbyant/*` repo). The
-UMT5-XXL text encoder runs on CPU by default (`config.text_encoder_device`) so the 5B
-transformer + VAE fit on a single 24–32 GB GPU.
-
-## Evaluation (LIBERO)
-
-```bash
-lerobot-eval \
-    --policy.path=lerobot/lingbot_va_libero_long \
-    --policy.device=cuda \
-    --env.type=libero --env.task=libero_10 \
-    --env.observation_height=128 --env.observation_width=128 \
-    --eval.n_episodes=50 --eval.batch_size=1 \
-    --output_dir=outputs/eval/lingbot_va_libero
-```
-
-LingBot-VA's streaming inference (KV cache + observed-keyframe feedback) is implemented for
-single-environment eval; use `--eval.batch_size=1`.
-
-## Evaluation (RoboTwin)
-
-RoboTwin 2.0 needs the SAPIEN + CuRobo simulator stack. You can use the benchmark Docker image
-(`docker/Dockerfile.benchmark.robotwin`, which also needs `warp-lang==1.3.1` and CuRobo built
-with the GPU's compute capability in `TORCH_CUDA_ARCH_LIST`). RoboTwin uses **end-effector-pose
-control**, so run with `--env.action_mode=ee`: the policy predicts per-arm `xyz+quaternion+gripper`
-deltas (`robotwin_tshape` latent layout) that are composed onto the episode's initial eef pose and
-executed via CuRobo IK.
-
-```bash
-lerobot-eval \
-    --policy.path=lerobot/lingbot_va_robotwin \
-    --policy.device=cuda \
-    --env.type=robotwin --env.task=beat_block_hammer --env.action_mode=ee \
-    --eval.n_episodes=10 --eval.batch_size=1 \
-    --output_dir=outputs/eval/lingbot_va_robotwin
-```
-
-### Saving predicted (imagined) videos
-
-Set `--policy.save_predicted_video=true` to additionally VAE-decode the predicted video
-latents and write `pred_episode_*.mp4` next to the env-rendered `eval_episode_*.mp4` videos.
-The same flag works for the periodic eval during `lerobot-train`.
-
-## Training / fine-tuning
-
-`LingBotVAPolicy.forward(batch)` implements the dual-stream **flow-matching** loss
-(`latent_loss + action_loss`, timestep-weighted, action-masked) from the paper: it VAE-encodes
-the camera clips into video latents, UMT5-encodes the task, noises both streams, runs the
-transformer's block-causal training pass and returns `(loss, metrics)`. Optimizer preset is AdamW
-with a linear-warmup-then-constant schedule (matching upstream).
-
-Requirements:
-
-- The block-causal masks use PyTorch **flex-attention**, so build the policy with
-  `--policy.attn_mode=flex` for training (the default `torch` SDPA is inference-only).
-- The full 5B DiT does not fit a single 24–32 GB GPU under AdamW; fine-tune with **LoRA**
-  (`--policy.use_peft=true`) and/or optimizer offload. `get_optim_params` returns only the
-  trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen.
-
-```bash
-lerobot-train \
-  --policy.path=lerobot/lingbot_va_libero_long --policy.attn_mode=flex \
-  --policy.use_peft=true \
-  --dataset.repo_id=<your LeRobot-format dataset> \
-  --batch_size=1 --steps=... --output_dir=outputs/train/lingbot_va
-```
-
-The dataset must provide camera clips (a temporal window per camera, VAE-encoded to
-`frame_chunk_size` latent frames) and `frame_chunk_size * action_per_frame` action steps per item.
-
-## Data format (action channels & camera order)
-
-LingBot-VA is an **end-effector (Cartesian) pose** policy, it predicts EEF poses + gripper, not
-joint positions. Actions live in a fixed multi-embodiment **30-dim** layout; map your robot's
-action dimensions into these channels and pad the rest with `0` (`used_action_channel_ids` selects
-the channels a given checkpoint actually uses):
-
-| channels | meaning                                               |
-| -------- | ----------------------------------------------------- |
-| 0–6      | Left-arm end-effector pose                            |
-| 7–13     | Right-arm end-effector pose                           |
-| 14–20    | Left-arm joints (unused by the released checkpoints)  |
-| 21–27    | Right-arm joints (unused by the released checkpoints) |
-| 28       | Left gripper                                          |
-| 29       | Right gripper                                         |
-
-- **LIBERO** uses channels `0–6`: a 6-DoF EEF delta (xyz + rotation) + gripper (single arm).
-- **RoboTwin** uses channels `[0–6, 28, 7–13, 29]`: left EEF (xyz + quaternion) + left gripper +
-  right EEF + right gripper (16 dims). The env converts these poses to joint trajectories via
-  CuRobo IK — joints are never predicted.
-
-Joint-space datasets (or a different EEF convention) must be remapped into this schema before
-fine-tuning these checkpoints.
-
-**Camera order is fixed and order-sensitive**, per-camera latents are concatenated spatially in
-`obs_cam_keys` order, so the physical camera→slot mapping must match training:
-
-| benchmark | `obs_cam_keys` (in order)                                                                             | `camera_layout`                                                     |
-| --------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
-| LIBERO    | `observation.images.image` (agentview / 3rd-person), `observation.images.image2` (eye-in-hand wrist)  | `width_concat` (latents concatenated on width)                      |
-| RoboTwin  | `observation.images.head_camera`, `observation.images.left_camera`, `observation.images.right_camera` | `robotwin_tshape` (full-res head below, two half-res wrists on top) |
-
-The first camera is the exterior/head view and the rest are wrist views.
-
-## Inference Hyperparameters (LIBERO)
-
-| Key                                    | Value                                                                             |
-| -------------------------------------- | --------------------------------------------------------------------------------- |
-| height × width                         | 128 × 128                                                                         |
-| cameras                                | `observation.images.image` (agentview), `observation.images.image2` (eye-in-hand) |
-| action channels used                   | 0–6 (7-DoF arm + gripper)                                                         |
-| action_per_frame / frame_chunk_size    | 4 / 4                                                                             |
-| attn_window                            | 30                                                                                |
-| video / action denoising steps         | 20 / 50                                                                           |
-| guidance_scale / action_guidance_scale | 5 / 1                                                                             |
-| snr_shift / action_snr_shift           | 5.0 / 0.05                                                                        |
-
-These are the defaults of `LingBotVAConfig`; override any of them via `--policy.<name>=...`.
-
-## Notes
-
-- **Attention backend:** inference uses the `torch` SDPA backend (always available). The
-  `flashattn` and `flex` backends are optional; `flex` is only needed for training.
-- **Model size:** the DiT is ~5B params and the frozen VAE+UMT5 add ~20 GB; inference needs
-  roughly 18–24 GB of VRAM.
-
-## License
-
-LingBot-VA is released under Apache-2.0. See the
-[upstream repository](https://github.com/Robbyant/lingbot-va).

docs/source/metaworld.mdx

@@ -120,11 +120,11 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Practical tips
 
 - Use the one-hot task conditioning for multi-task training (MT10/MT50 conventions) so policies have explicit task context.
 - Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.
-- Adjust `batch_size`, `steps`, and `env_eval_freq` to match your compute budget.
+- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.

docs/source/molmoact2.mdx

@@ -17,7 +17,7 @@ the paper, see [allenai/molmoact2](https://github.com/allenai/molmoact2).
 Install LeRobot with the MolmoAct2 optional dependencies:
 
 ```bash
-uv sync --locked --extra molmoact2
+pip install -e ".[molmoact2]"
 ```
 
 To run the models in this repository, you need an NVIDIA GPU. The measurements
@@ -46,8 +46,8 @@ The repo has been tested with Ubuntu 22.04.
 
 To use MolmoAct2 in a LeRobot training config, set:
 
-```bash
---policy.type=molmoact2
+```python
+policy.type=molmoact2
 ```
 
 ## Training
@@ -103,7 +103,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```
@@ -142,7 +142,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```
@@ -386,68 +386,6 @@ These results demonstrate MolmoAct2's strong performance across diverse robotic
 manipulation tasks. To reproduce them, follow the instructions in the LIBERO
 evaluation section.
 
-## Hardware Deployment (lerobot-rollout)
-
-LeRobot-format checkpoints are available on the Hub for direct use with
-`lerobot-rollout`. Each checkpoint uses specific camera names that must
-match your robot's camera configuration.
-
-### Camera naming convention
-
-Each checkpoint expects specific `observation.images.*` keys.
-If your robot cameras have different names, use `--rename_map` to map them:
-
-| Checkpoint                    | Camera keys            | Description              |
-| ----------------------------- | ---------------------- | ------------------------ |
-| MolmoAct2-LIBERO-LeRobot      | `image`, `wrist_image` | LIBERO sim cameras       |
-| MolmoAct2-BimanualYAM-LeRobot | `top`, `left`, `right` | YAM 3-camera setup       |
-| MolmoAct2-DROID-LeRobot       | `cam0`, `cam1`         | External + wrist         |
-| MolmoAct2-SO100_101-LeRobot   | `cam0`, `cam1`         | Primary + secondary view |
-
-Example with an SO-100 robot using top and side cameras:
-
-```bash
-lerobot-rollout \
-  --policy.path=lerobot/MolmoAct2-SO100_101-LeRobot \
-  --rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.side": "observation.images.cam1"}' \
-  --robot.type=so100_follower \
-  --robot.port=/dev/ttyACM0 \
-  --robot.cameras='{
-      top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30},
-      side: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}
-  }' \
-  --task="pick up the red cube" --duration=30
-```
-
-To use a wrist camera instead, just change the rename mapping:
-
-```bash
---rename_map='{"observation.images.top": "observation.images.cam0", "observation.images.wrist": "observation.images.cam1"}'
-```
-
-### Joint frame transform (SO-100/101 zero-shot)
-
-<Tip warning={true}>
-The MolmoAct2-SO100_101 checkpoint was trained on data that uses a different
-joint calibration convention than LeRobot >= 0.5.0. Without a frame
-correction, the arm may move in the wrong direction.
-
-This affects both **zero-shot deployment** and **fine-tuning** from the
-original checkpoint. The pretrained weights expect the old convention, so
-all joint data (observations and actions) must be transformed to match.
-
-The converted LeRobot checkpoint (`lerobot/MolmoAct2-SO100_101-LeRobot`)
-already includes this correction in its processor pipeline. If you convert
-or fine-tune the checkpoint yourself, set the following in the policy config (`configuration_molmoact2.py`):
-
-- `joint_signs`: `[1, -1, 1, 1, 1, 1]` (flips shoulder_lift direction)
-- `joint_offsets`: `[0, 90, 90, 0, 0, 0]` (shifts shoulder_lift and elbow_flex by 90°)
-
-See the [backward compatibility guide](./backwardcomp) for details on the
-calibration change.
-
-</Tip>
-
 ## Differences From the Original Implementation
 
 This LeRobot port is intended to match MolmoAct2 behavior while using LeRobot's

docs/source/multi_gpu_training.mdx

@@ -95,7 +95,7 @@ If you want to scale your hyperparameters when using multiple GPUs, you should d
 accelerate launch --num_processes=2 $(which lerobot-train) \
   --optimizer.lr=2e-4 \
   --dataset.repo_id=lerobot/pusht \
-  --policy.type=act
+  --policy=act
 ```
 
 **Training Steps Scaling:**
@@ -110,7 +110,7 @@ accelerate launch --num_processes=2 $(which lerobot-train) \
   --batch_size=8 \
   --steps=50000 \
   --dataset.repo_id=lerobot/pusht \
-  --policy.type=act
+  --policy=act
 ```
 
 ## Training Large Models with FSDP

docs/source/multi_task_dit.mdx

@@ -314,7 +314,7 @@ lerobot-train \
   --steps=30000 \
   --save_freq=1000 \
   --log_freq=100 \
-  --env_eval_freq=1000 \
+  --eval_freq=1000 \
   --policy.type=multi_task_dit \
   --policy.device=cuda \
   --policy.horizon=32 \

docs/source/pi0fast.mdx

@@ -96,7 +96,7 @@ lerobot-train \
     --policy.type=pi0_fast \
     --output_dir=./outputs/pi0fast_training \
     --job_name=pi0fast_training \
-    --policy.pretrained_path=lerobot/pi0fast-base \
+    --policy.pretrained_path=lerobot/pi0_fast_base \
     --policy.dtype=bfloat16 \
     --policy.gradient_checkpointing=true \
     --policy.chunk_size=10 \
@@ -187,7 +187,7 @@ lerobot-train \
   --dataset.repo_id=lerobot/libero \
   --output_dir=outputs/libero_pi0fast \
   --job_name=libero_pi0fast \
-  --policy.path=lerobot/pi0fast-base \
+  --policy.path=lerobot/pi0fast_base \
   --policy.dtype=bfloat16 \
   --steps=100000 \
   --save_freq=20000 \

docs/source/policy_evo1_README.md

@@ -1,18 +0,0 @@
-# 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_fastwam_README.md

@@ -1,56 +0,0 @@
-## Research Paper
-
-Paper: https://arxiv.org/abs/2603.16666
-
-## Repository
-
-Code: https://github.com/yuantianyuan01/FastWAM
-
-Project page: https://yuantianyuan01.github.io/FastWAM/
-
-## Citation
-
-```bibtex
-@article{yuan2026fastwam,
-  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
-  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
-  journal = {arXiv preprint arXiv:2603.16666},
-  year = {2026},
-  url = {https://arxiv.org/abs/2603.16666}
-}
-```
-
-## Additional Resources
-
-Base video model: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B
-
-Released upstream checkpoints: https://huggingface.co/yuanty/fastwam
-
-## Results
-
-Evaluated on LIBERO with [`ZibinDong/fastwam_libero_uncond_2cam224`](https://huggingface.co/ZibinDong/fastwam_libero_uncond_2cam224):
-
-| Suite          | Success rate | n_episodes |
-| -------------- | -----------: | ---------: |
-| libero_spatial |        97.6% |        500 |
-| libero_object  |        99.0% |        500 |
-| libero_goal    |        95.0% |        500 |
-| libero_10      |        94.0% |        500 |
-| **average**    |    **96.4%** |       2000 |
-
-Reproduce: `lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300`.
-
-For LIBERO-10, use `--env.task=libero_10 --env.episode_length=600`:
-
-```bash
-lerobot-eval \
-    --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
-    --policy.device=cuda \
-    --policy.torch_dtype=float32 \
-    --policy.n_action_steps=10 \
-    --env.type=libero \
-    --env.task=libero_10 --env.observation_height=256 --env.observation_width=256 \
-    --eval.batch_size=1 \
-    --eval.n_episodes=50 \
-    --seed=0 --env.episode_length=600
-```

docs/source/policy_groot_README.md

@@ -1,13 +1,6 @@
 ## Research Paper
 
-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.
+Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
 
 ## Repository
 
@@ -31,108 +24,4 @@ Code: https://github.com/NVIDIA/Isaac-GR00T
 
 Blog: https://developer.nvidia.com/isaac/gr00t
 
-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>
+Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B

docs/source/robocasa.mdx

@@ -166,7 +166,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_robocasa_CloseFridge \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=5 \
   --save_freq=10000

docs/source/so101.mdx

@@ -122,7 +122,7 @@ The video below shows the sequence of steps for setting the motor ids.
 
 #### Follower
 
-Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your follower arm a name with the `id` parameter.
+Connect the usb cable from your computer and the power supply to the follower arm's controller board. Then, run the following command or run the API example with the port you got from the previous step. You'll also need to give your leader arm a name with the `id` parameter.
 
 <hfoptions id="setup_motors">
 <hfoption id="Command">

docs/source/using_dataset_tools.mdx

@@ -181,8 +181,8 @@ lerobot-edit-dataset \
 lerobot-edit-dataset \
     --repo_id lerobot/pusht_depth \
     --operation.type reencode_videos \
-    --operation.rgb_encoder.vcodec h264 \
-    --operation.depth_encoder.crf 50
+    --operation.rgb_encoder.vcodec libx264 \
+    --operation.depth_encoder.vcodec ffv1
 ```
 
 **Parameters:**
@@ -265,8 +265,6 @@ lerobot-dataset-viz \
 
 Once executed, the tool opens `rerun.io` and displays the camera streams, robot states, and actions for the selected episode.
 
-To use [Foxglove](https://foxglove.dev) instead of Rerun, install the extra add `--display-mode foxglove`. This starts a WebSocket server (connect the Foxglove app to `ws://127.0.0.1:8765`) that serves the episode as a seekable timeline you can play/pause and scrub.
-
 For advanced usage—including visualizing datasets stored on a remote server—run:
 
 ```bash

docs/source/video_encoding_parameters.mdx

@@ -100,15 +100,14 @@ lerobot-record \
     --dataset.depth_encoder.use_log=true
 ```
 
-| Parameter       | Type    | Default                         | Description                                                                                                                            |
-| --------------- | ------- | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |
-| `vcodec`        | `str`   | `"hevc"`                        | HEVC Main 12 (a 12-bit-capable codec, MP4-compatible).                                                                                 |
-| `extra_options` | `dict`  | `{"x265-params": "lossless=1"}` | **Depth defaults to lossless** (exact round-trip); `crf` is ignored. Pass `extra_options={}` and set `crf` for a smaller lossy stream. |
-| `pix_fmt`       | `str`   | `"gray12le"`                    | Single-channel 12-bit pixel format used to carry the quantized codes.                                                                  |
-| `depth_min`     | `float` | `0.01`                          | Depth in metres mapped to quantum `0`. Values below are clipped on decode.                                                             |
-| `depth_max`     | `float` | `10.0`                          | Depth in metres mapped to quantum `4095`. Values above are clipped on decode.                                                          |
-| `shift`         | `float` | `3.5`                           | Pre-log offset (metres) used in logarithmic quantization for numerical stability near zero. Must satisfy `depth_min + shift > 0`.      |
-| `use_log`       | `bool`  | `True`                          | If `true`, quantize in log-space (recommended for typical depth sensors). Set to `false` for uniform/linear quantization.              |
+| Parameter   | Type    | Default      | Description                                                                                                                       |
+| ----------- | ------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------- |
+| `vcodec`    | `str`   | `"hevc"`     | Defaults to HEVC Main 12 (a 12-bit-capable codec). `ffv1` is a lossless alternative.                                              |
+| `pix_fmt`   | `str`   | `"gray12le"` | Single-channel 12-bit pixel format used to carry the quantized codes.                                                             |
+| `depth_min` | `float` | `0.01`       | Depth in metres mapped to quantum `0`. Values below are clipped on decode.                                                        |
+| `depth_max` | `float` | `10.0`       | Depth in metres mapped to quantum `4095`. Values above are clipped on decode.                                                     |
+| `shift`     | `float` | `3.5`        | Pre-log offset (metres) used in logarithmic quantization for numerical stability near zero. Must satisfy `depth_min + shift > 0`. |
+| `use_log`   | `bool`  | `True`       | If `true`, quantize in log-space (recommended for typical depth sensors). Set to `false` for uniform/linear quantization.         |
 
 > [!TIP]
 > `depth_min`, `depth_max`, and `shift` are always interpreted in **metres**, regardless of the input depth's unit. Inputs are auto-detected: integer arrays (e.g. `uint16` millimetres straight from a RealSense) are treated as millimetres, floating arrays as metres.

docs/source/vlabench.mdx

@@ -165,7 +165,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_vlabench_primitive \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
   --save_freq=10000

examples/hopejr/visualizer/index.html

@@ -1,182 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>3D Hand Joint Visualizer</title>
-    <style>
-        body {
-            font-family: Arial, sans-serif;
-            margin: 0;
-            padding: 0;
-            overflow: hidden;
-            display: flex;
-            flex-direction: column;
-            height: 100vh;
-        }
-
-        .controls {
-            padding: 15px;
-            background-color: #f5f5f5;
-            z-index: 100;
-        }
-
-        .status {
-            padding: 10px;
-            border-radius: 5px;
-            margin: 10px 0;
-        }
-
-        .connected {
-            background-color: #d4edda;
-            color: #155724;
-        }
-
-        .disconnected {
-            background-color: #f8d7da;
-            color: #721c24;
-        }
-
-        button {
-            padding: 8px 16px;
-            background-color: #4CAF50;
-            color: white;
-            border: none;
-            border-radius: 4px;
-            cursor: pointer;
-            margin-right: 10px;
-        }
-
-        button:hover {
-            background-color: #45a049;
-        }
-
-        button:disabled {
-            background-color: #cccccc;
-            cursor: not-allowed;
-        }
-
-        .container {
-            display: flex;
-            flex: 1;
-            overflow: hidden;
-        }
-
-        #canvas-container {
-            flex: 3;
-            position: relative;
-        }
-
-        #sidebar {
-            flex: 1;
-            padding: 15px;
-            background-color: #f8f9fa;
-            overflow-y: auto;
-            max-width: 300px;
-            border-left: 1px solid #ddd;
-        }
-
-        .joint-info {
-            margin-bottom: 10px;
-            padding: 8px;
-            border: 1px solid #ddd;
-            border-radius: 4px;
-        }
-
-        .joint-name {
-            font-weight: bold;
-        }
-
-        .joint-value {
-            font-family: monospace;
-        }
-
-        .bar-container {
-            width: 100%;
-            background-color: #e0e0e0;
-            height: 10px;
-            border-radius: 5px;
-            overflow: hidden;
-            margin-top: 5px;
-        }
-
-        .bar {
-            height: 100%;
-            background-color: #4CAF50;
-            width: 0%;
-            transition: width 0.2s ease-in-out;
-        }
-
-        .log-container {
-            margin-top: 20px;
-            border: 1px solid #ddd;
-            border-radius: 5px;
-            padding: 10px;
-            height: 150px;
-            overflow-y: auto;
-            font-family: monospace;
-            background-color: #f8f9fa;
-        }
-
-        .view-controls {
-            position: absolute;
-            bottom: 10px;
-            left: 10px;
-            z-index: 10;
-        }
-
-        .view-button {
-            background-color: rgba(0, 0, 0, 0.5);
-            color: white;
-            border: none;
-            padding: 5px 10px;
-            margin-right: 5px;
-            border-radius: 3px;
-            cursor: pointer;
-        }
-    </style>
-</head>
-<body>
-    <div class="controls">
-        <button id="connectButton">Connect to Device</button>
-        <button id="disconnectButton" disabled>Disconnect</button>
-        <select id="baudRate">
-            <option value="9600">9600</option>
-            <option value="19200">19200</option>
-            <option value="38400">38400</option>
-            <option value="57600">57600</option>
-            <option value="115200" selected>115200</option>
-        </select>
-        <span id="statusIndicator" class="status disconnected">Status: Disconnected</span>
-    </div>
-
-    <div class="container">
-        <div id="canvas-container">
-            <!-- 3D canvas will be inserted here -->
-            <div class="view-controls">
-                <button class="view-button" id="frontView">Front</button>
-                <button class="view-button" id="sideView">Side</button>
-                <button class="view-button" id="topView">Top</button>
-                <button class="view-button" id="resetView">Reset</button>
-            </div>
-        </div>
-
-        <div id="sidebar">
-            <h3>Joint Values</h3>
-            <div id="jointsContainer">
-                <!-- Joint info will be added here -->
-            </div>
-
-            <div class="log-container" id="logContainer">
-                <!-- Log messages will be added here -->
-            </div>
-        </div>
-    </div>
-
-    <!-- Import Three.js -->
-    <script src="https://cdn.jsdelivr.net/npm/three@0.132.2/build/three.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/three@0.132.2/examples/js/controls/OrbitControls.js"></script>
-
-    <script src="script.js"></script>
-</body>
-</html>

examples/hopejr/visualizer/script.js

@@ -1,669 +0,0 @@
-// === Hand Visualizer with Pre-Connect Sliders + Per-Joint Angle Limits ===
-// Assumes your HTML already has elements with the following IDs:
-// connectButton, disconnectButton, baudRate, statusIndicator, jointsContainer, logContainer,
-// canvas-container, frontView, sideView, topView, resetView
-// Requires Three.js + OrbitControls loaded on the page.
-
-// -------------------- Config --------------------
-const MAX_JOINTS = 16;
-const RAW_MIN = 0, RAW_MAX = 4096;
-const RAW_CENTER = (RAW_MIN + RAW_MAX) / 2;
-const DEG = Math.PI / 180;
-const UI_DEG_MIN = -90, UI_DEG_MAX = 90; // UI sliders for angle limits
-
-// -------------------- State --------------------
-let port;
-let reader;
-let keepReading = false;
-let isConnected = false;
-const decoder = new TextDecoder();
-let inputBuffer = '';
-
-let jointValues = new Array(MAX_JOINTS).fill(RAW_CENTER);
-
-// Auto-calibration: track observed min/max per joint
-let observedMin = new Array(MAX_JOINTS).fill(Infinity);
-let observedMax = new Array(MAX_JOINTS).fill(-Infinity);
-let calibrationEnabled = true;
-
-// Three.js
-let scene, camera, renderer, controls;
-let hand = { palm: null, fingers: [] };
-
-// DOM
-const connectButton     = document.getElementById('connectButton');
-const disconnectButton  = document.getElementById('disconnectButton');
-const baudRateSelect    = document.getElementById('baudRate');
-const statusIndicator   = document.getElementById('statusIndicator');
-const jointsContainer   = document.getElementById('jointsContainer');
-const logContainer      = document.getElementById('logContainer');
-const canvasContainer   = document.getElementById('canvas-container');
-const frontViewBtn      = document.getElementById('frontView');
-const sideViewBtn       = document.getElementById('sideView');
-const topViewBtn        = document.getElementById('topView');
-const resetViewBtn      = document.getElementById('resetView');
-
-// Helpers
-const clamp = (x, a, b) => Math.max(a, Math.min(b, x));
-const invLerp = (a, b, x) => clamp((x - a) / (b - a), 0, 1);
-
-// -------------------- Joint Map with per-joint angle limits --------------------
-const fingerJointMap = [
-  // Thumb (4)
-  { finger:0, joint:0, type:'CMC_ABDUCTION',  min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:0, joint:1, type:'CMC_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:0, joint:2, type:'MCP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:true }, // +45° only
-  { finger:0, joint:3, type:'IP_FLEXION',     min:RAW_MIN, max:RAW_MAX, inverted:true }, // +45° only
-
-  // Index (3)
-  { finger:1, joint:0, type:'MCP_ABDUCTION',  min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:1, joint:1, type:'MCP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:false },
-  { finger:1, joint:2, type:'PIP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:true }, // +45° only
-
-  // Middle (3)
-  { finger:2, joint:0, type:'MCP_ABDUCTION',  min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:2, joint:1, type:'MCP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:2, joint:2, type:'PIP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:true }, // +45° only
-
-  // Ring (3)
-  { finger:3, joint:0, type:'MCP_ABDUCTION',  min:RAW_MIN, max:RAW_MAX, inverted:true },
-  { finger:3, joint:1, type:'MCP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:false },
-  { finger:3, joint:2, type:'PIP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:false }, // +45° only
-
-  // Pinky (3)
-  { finger:4, joint:0, type:'MCP_ABDUCTION',  min:RAW_MIN, max:RAW_MAX, inverted:false },
-  { finger:4, joint:1, type:'MCP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:false },
-  { finger:4, joint:2, type:'PIP_FLEXION',    min:RAW_MIN, max:RAW_MAX, inverted:false }  // +45° only
-];
-
-// Assign angle limits (radians) per joint (default ±45°, exceptions: +45° only)
-for (const j of fingerJointMap) {
-  const isThumb = j.finger === 0;
-  const isPIP   = j.type === 'PIP_FLEXION';
-  let minA = -45 * DEG, maxA = +45 * DEG;
-  if ((isThumb && (j.type === 'MCP_FLEXION' || j.type === 'IP_FLEXION')) || (!isThumb && isPIP)) {
-    minA = 0;
-    maxA = +45 * DEG;
-  }
-  j.angleMin = minA;
-  j.angleMax = maxA;
-}
-
-// -------------------- UI: Joint Panel --------------------
-const uiRefs = []; // per joint: { valueLabel, bar, barWrap, slider, invertChk, minDeg, maxDeg }
-
-function initializeJointElements() {
-  jointsContainer.innerHTML = '';
-  uiRefs.length = 0;
-
-  for (let i = 0; i < MAX_JOINTS; i++) {
-    const wrap = document.createElement('div');
-    wrap.className = 'joint-info';
-
-    const fingerIndex = i < 4 ? 0 : Math.floor((i - 4) / 3) + 1;
-    const jointInfo = fingerJointMap[i];
-    const jointType = jointInfo?.type || 'Unknown';
-    const fingerName = ['Thumb', 'Index', 'Middle', 'Ring', 'Pinky'][fingerIndex];
-
-    // Header
-    const nameEl = document.createElement('div');
-    nameEl.className = 'joint-name';
-    nameEl.textContent = `${fingerName} – ${jointType}`;
-
-    // Value + bar
-    const valueEl = document.createElement('div');
-    valueEl.className = 'joint-value';
-    valueEl.textContent = `Value: ${jointValues[i]}`;
-
-    const barWrap = document.createElement('div');
-    barWrap.className = 'bar-container';
-    const barEl = document.createElement('div');
-    barEl.className = 'bar';
-    barWrap.appendChild(barEl);
-
-    // Slider for pre-connect manual control
-    const slider = document.createElement('input');
-    slider.type = 'range';
-    slider.min = String(RAW_MIN);
-    slider.max = String(RAW_MAX);
-    slider.value = String(jointValues[i]);
-    slider.step = '1';
-    slider.className = 'joint-slider';
-
-    slider.addEventListener('input', () => {
-      if (isConnected) return; // ignore while connected
-      let v = parseInt(slider.value, 10);
-      if (jointInfo?.inverted) v = (jointInfo.min + jointInfo.max) - v;
-      jointValues[i] = clamp(jointInfo ? v : 0, RAW_MIN, RAW_MAX);
-      updateJointDisplay(i, jointValues[i]);
-      updateHandModel();
-    });
-
-    // Invert checkbox
-    const invertLbl = document.createElement('label');
-    invertLbl.className = 'invert-toggle';
-    const invertChk = document.createElement('input');
-    invertChk.type = 'checkbox';
-    invertChk.checked = !!jointInfo?.inverted;
-    invertChk.addEventListener('change', () => {
-      if (jointInfo) jointInfo.inverted = invertChk.checked;
-      addLogMessage(`${fingerName} ${jointType} inversion ${invertChk.checked ? 'enabled' : 'disabled'}`);
-    });
-    invertLbl.appendChild(invertChk);
-    invertLbl.appendChild(document.createTextNode('Invert Values'));
-
-    // Angle limits (deg) controls
-    const limitsRow = document.createElement('div');
-    limitsRow.className = 'limits-row';
-
-    const minDeg = document.createElement('input');
-    minDeg.type = 'number';
-    minDeg.min = String(UI_DEG_MIN);
-    minDeg.max = String(UI_DEG_MAX);
-    minDeg.step = '1';
-    minDeg.value = String(Math.round((jointInfo.angleMin || 0) / DEG));
-    minDeg.className = 'limit-num';
-
-    const maxDeg = document.createElement('input');
-    maxDeg.type = 'number';
-    maxDeg.min = String(UI_DEG_MIN);
-    maxDeg.max = String(UI_DEG_MAX);
-    maxDeg.step = '1';
-    maxDeg.value = String(Math.round((jointInfo.angleMax || 0) / DEG));
-    maxDeg.className = 'limit-num';
-
-    const minLbl = document.createElement('span'); minLbl.textContent = 'min°';
-    const maxLbl = document.createElement('span'); maxLbl.textContent = 'max°';
-    minLbl.className = 'limit-label'; maxLbl.className = 'limit-label';
-
-    function syncLimits() {
-      let mn = parseFloat(minDeg.value);
-      let mx = parseFloat(maxDeg.value);
-      if (isNaN(mn)) mn = -45;
-      if (isNaN(mx)) mx = +45;
-      if (mn > mx) [mn, mx] = [mx, mn];
-      jointInfo.angleMin = clamp(mn, UI_DEG_MIN, UI_DEG_MAX) * DEG;
-      jointInfo.angleMax = clamp(mx, UI_DEG_MIN, UI_DEG_MAX) * DEG;
-      minDeg.value = String(Math.round(jointInfo.angleMin / DEG));
-      maxDeg.value = String(Math.round(jointInfo.angleMax / DEG));
-      updateHandModel();
-    }
-    minDeg.addEventListener('change', syncLimits);
-    maxDeg.addEventListener('change', syncLimits);
-
-    limitsRow.appendChild(minLbl);
-    limitsRow.appendChild(minDeg);
-    limitsRow.appendChild(maxLbl);
-    limitsRow.appendChild(maxDeg);
-
-         // Calibration controls
-     const calibRow = document.createElement('div');
-     calibRow.className = 'calib-row';
-
-     const resetCalibBtn = document.createElement('button');
-     resetCalibBtn.textContent = 'Reset Calib';
-     resetCalibBtn.className = 'calib-btn';
-     resetCalibBtn.addEventListener('click', () => {
-       observedMin[i] = Infinity;
-       observedMax[i] = -Infinity;
-       addLogMessage(`Reset calibration for ${fingerName} ${jointType}`);
-     });
-
-     const calibStatus = document.createElement('span');
-     calibStatus.className = 'calib-status';
-     calibStatus.textContent = `Range: --`;
-
-     calibRow.appendChild(resetCalibBtn);
-     calibRow.appendChild(calibStatus);
-
-     // Compose
-     wrap.appendChild(nameEl);
-     wrap.appendChild(valueEl);
-     wrap.appendChild(barWrap);
-     wrap.appendChild(slider);
-     wrap.appendChild(invertLbl);
-     wrap.appendChild(limitsRow);
-     wrap.appendChild(calibRow);
-
-     jointsContainer.appendChild(wrap);
-
-     uiRefs[i] = { valueLabel: valueEl, bar: barEl, barWrap, slider, invertChk, minDeg, maxDeg, nameEl, calibStatus };
-  }
-
-  setConnectedUI(false); // initial state: sliders active
-}
-
- // Toggle UI between pre-connect SLIDERS vs post-connect BARS
- function setConnectedUI(connected) {
-   isConnected = connected;
-   for (let i = 0; i < uiRefs.length; i++) {
-     const ui = uiRefs[i];
-     if (!ui) continue;
-     // Show bars when connected; sliders disabled/hidden
-     ui.barWrap.style.display = connected ? '' : 'none';
-     ui.slider.disabled = connected;
-     ui.slider.style.display = connected ? 'none' : '';
-   }
-
-   // Reset calibration when connecting
-   if (connected) {
-     observedMin.fill(Infinity);
-     observedMax.fill(-Infinity);
-     addLogMessage('Calibration reset - move joints through full range for best results');
-   }
- }
-
-// Update joint display (value text + bar color/width + slider position if needed)
-function updateJointDisplay(jointIndex, value) {
-  const ui = uiRefs[jointIndex];
-  const info = fingerJointMap[jointIndex];
-  if (!ui || !info) return;
-
-  ui.valueLabel.textContent = `Value: ${value}`;
-
-  // bar
-  const min = info.min, max = info.max;
-  const pct = clamp((value - min) / (max - min), 0, 1) * 100;
-  ui.bar.style.width = `${pct}%`;
-  const hue = Math.floor(pct * 1.2); // 0..120
-  ui.bar.style.backgroundColor = `hsl(${hue}, 80%, 50%)`;
-
-  // slider (only meaningful when not connected; keep in sync anyway)
-  const rawForSlider = info.inverted ? (info.min + info.max) - value : value;
-  if (!isConnected) ui.slider.value = String(clamp(Math.round(rawForSlider), RAW_MIN, RAW_MAX));
-}
-
-// -------------------- Serial I/O --------------------
-async function readSerialData() {
-  while (port?.readable && keepReading) {
-    reader = port.readable.getReader();
-    try {
-      while (true) {
-        const { value, done } = await reader.read();
-        if (done) break;
-        if (value) processData(decoder.decode(value));
-      }
-    } catch (err) {
-      console.error('Error reading:', err);
-      addLogMessage(`Error: ${err.message}`);
-      break;
-    } finally {
-      reader.releaseLock();
-    }
-  }
-}
-
- function processData(chunk) {
-   inputBuffer += chunk;
-   let idx;
-   while ((idx = inputBuffer.indexOf('\n')) !== -1) {
-     const line = inputBuffer.slice(0, idx).trim();
-     inputBuffer = inputBuffer.slice(idx + 1);
-
-     const vals = line.split(/\s+/).map(v => parseInt(v, 10));
-     if (vals.length === MAX_JOINTS && vals.every(v => Number.isFinite(v))) {
-       for (let i = 0; i < MAX_JOINTS; i++) {
-         const info = fingerJointMap[i];
-         if (!info) continue;
-
-         let rawValue = vals[i];
-
-         // Update calibration tracking
-         if (calibrationEnabled) {
-           observedMin[i] = Math.min(observedMin[i], rawValue);
-           observedMax[i] = Math.max(observedMax[i], rawValue);
-
-           // Update calibration display
-           const ui = uiRefs[i];
-           if (ui && ui.calibStatus) {
-             if (observedMin[i] !== Infinity && observedMax[i] !== -Infinity) {
-               ui.calibStatus.textContent = `Range: ${observedMin[i]}-${observedMax[i]}`;
-             }
-           }
-
-           // Remap observed range to target range
-           if (observedMin[i] !== Infinity && observedMax[i] !== -Infinity && observedMax[i] > observedMin[i]) {
-             const observedRange = observedMax[i] - observedMin[i];
-             const targetRange = info.max - info.min;
-             const normalizedValue = (rawValue - observedMin[i]) / observedRange;
-             rawValue = info.min + (normalizedValue * targetRange);
-           }
-         }
-
-         let v = clamp(rawValue, info.min, info.max);
-         if (info.inverted) v = (info.min + info.max) - v;
-         jointValues[i] = v;
-         updateJointDisplay(i, v);
-       }
-       updateHandModel();
-     } else {
-       addLogMessage(`Received: ${line}`);
-     }
-   }
- }
-
-async function connectToDevice() {
-  try {
-    port = await navigator.serial.requestPort();
-    const baudRate = parseInt(baudRateSelect.value, 10) || 115200;
-    await port.open({ baudRate });
-
-    keepReading = true;
-    setConnectedUI(true);
-
-    statusIndicator.textContent = 'Status: Connected';
-    statusIndicator.className = 'status connected';
-    connectButton.disabled = true;
-    disconnectButton.disabled = false;
-    baudRateSelect.disabled = true;
-
-    addLogMessage(`Connected at ${baudRate} baud`);
-    readSerialData();
-  } catch (e) {
-    console.error('Connect error:', e);
-    addLogMessage(`Connection error: ${e.message}`);
-  }
-}
-
-async function disconnectFromDevice() {
-  try {
-    keepReading = false;
-    if (reader) {
-      try { reader.cancel(); } catch {}
-    }
-    if (port) {
-      await port.close();
-      port = null;
-    }
-  } catch (e) {
-    console.error('Disconnect error:', e);
-    addLogMessage(`Disconnection error: ${e.message}`);
-  } finally {
-    setConnectedUI(false);
-    statusIndicator.textContent = 'Status: Disconnected';
-    statusIndicator.className = 'status disconnected';
-    connectButton.disabled = false;
-    disconnectButton.disabled = true;
-    baudRateSelect.disabled = false;
-    addLogMessage('Disconnected');
-  }
-}
-
-// -------------------- Three.js Scene --------------------
-function initThreeJS() {
-  scene = new THREE.Scene();
-  scene.background = new THREE.Color(0xf0f0f0);
-
-  camera = new THREE.PerspectiveCamera(
-    75,
-    canvasContainer.clientWidth / canvasContainer.clientHeight,
-    0.1, 1000
-  );
-  camera.position.set(0, 15, 15);
-  camera.lookAt(0, 0, 0);
-
-  renderer = new THREE.WebGLRenderer({ antialias: true });
-  renderer.setSize(canvasContainer.clientWidth, canvasContainer.clientHeight);
-  renderer.setPixelRatio(window.devicePixelRatio);
-  canvasContainer.appendChild(renderer.domElement);
-
-  controls = new THREE.OrbitControls(camera, renderer.domElement);
-  controls.enableDamping = true;
-  controls.dampingFactor = 0.25;
-
-  const ambientLight = new THREE.AmbientLight(0x404040);
-  scene.add(ambientLight);
-  const dir1 = new THREE.DirectionalLight(0xffffff, 0.5);
-  dir1.position.set(1, 1, 1);
-  scene.add(dir1);
-  const dir2 = new THREE.DirectionalLight(0xffffff, 0.3);
-  dir2.position.set(-1, 1, -1);
-  scene.add(dir2);
-
-  const gridHelper = new THREE.GridHelper(20, 20);
-  scene.add(gridHelper);
-
-  createHandModel();
-  window.addEventListener('resize', onWindowResize);
-  animate();
-}
-
-function createHandModel() {
-  const palmMaterial   = new THREE.MeshPhongMaterial({ color: 0xf5c396 });
-  const fingerMaterial = new THREE.MeshPhongMaterial({ color: 0xf5c396 });
-  const jointMaterial  = new THREE.MeshPhongMaterial({ color: 0xe3a977 });
-
-     const palmGeometry = new THREE.BoxGeometry(7, 1, 8);
-   hand.palm = new THREE.Mesh(palmGeometry, palmMaterial);
-   hand.palm.position.set(0, 0, 0);
-   hand.palm.rotation.x = Math.PI / 2; // hand vertical, palm facing forward
-   scene.add(hand.palm);
-
-  const fingerWidth = 1, fingerHeight = 0.8;
-  const fingerSegmentLengths = [3, 2, 1.5];
-  const thumbSegmentLengths  = [2, 2, 1.5];
-
-  const fingerBasePositions = [
-    [ 3,   0,  -2],  // Thumb
-    [ 1.5,-0.5,-4],  // Index
-    [ 0,  -0.5,-4],  // Middle
-    [-1.5,-0.5,-4],  // Ring
-    [-3,  -0.5,-4],  // Pinky
-  ];
-  const fingerBaseRot = [
-    { x:0, y:-Math.PI/3,  z: Math.PI/3 },  // Thumb
-    { x:0, y:-Math.PI/48, z: 0 },
-    { x:0, y: Math.PI/48, z: 0 },
-    { x:0, y: Math.PI/32, z: 0 },
-    { x:0, y: Math.PI/24, z: 0 }
-  ];
-
-  for (let fIdx = 0; fIdx < 5; fIdx++) {
-    const finger = { name:['Thumb','Index','Middle','Ring','Pinky'][fIdx], segments:[], joints:[] };
-    const isThumb = fIdx === 0;
-    const segLens = isThumb ? thumbSegmentLengths : fingerSegmentLengths;
-
-    finger.group = new THREE.Group();
-    finger.group.position.set(...fingerBasePositions[fIdx]);
-    finger.group.rotation.x = fingerBaseRot[fIdx].x;
-    finger.group.rotation.y = fingerBaseRot[fIdx].y;
-    finger.group.rotation.z = fingerBaseRot[fIdx].z;
-    finger.group.userData.baseRot = {
-      x:finger.group.rotation.x,
-      y:finger.group.rotation.y,
-      z:finger.group.rotation.z
-    };
-    hand.palm.add(finger.group);
-
-    let parent = finger.group;
-    for (let s = 0; s < segLens.length; s++) {
-      const segGroup = new THREE.Group();
-
-      const jGeom = new THREE.SphereGeometry(fingerWidth * 0.6, 8, 8);
-      const joint = new THREE.Mesh(jGeom, jointMaterial);
-      segGroup.add(joint);
-
-      const segGeom = new THREE.BoxGeometry(fingerWidth, fingerHeight, segLens[s]);
-      const seg = new THREE.Mesh(segGeom, fingerMaterial);
-      seg.position.z = -segLens[s] / 2;
-      segGroup.add(seg);
-
-      parent.add(segGroup);
-
-      finger.segments.push(segGroup);
-      finger.joints.push(joint);
-
-      if (s < segLens.length - 1) {
-        const connector = new THREE.Group();
-        connector.position.z = -segLens[s];
-        segGroup.add(connector);
-        parent = connector;
-      }
-    }
-
-    hand.fingers.push(finger);
-  }
-
-  addFingerLabels();
-  addHandLabel();
-}
-
-function addFingerLabels() {
-  const names = ['Thumb','Index','Middle','Ring','Pinky'];
-  for (let i = 0; i < hand.fingers.length; i++) {
-    const finger = hand.fingers[i];
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-    canvas.width = 128; canvas.height = 32;
-    ctx.fillStyle = '#ffffff'; ctx.fillRect(0,0,canvas.width,canvas.height);
-    ctx.font = 'bold 16px Arial';
-    ctx.fillStyle = '#000000';
-    ctx.textAlign = 'center'; ctx.textBaseline = 'middle';
-    ctx.fillText(names[i], canvas.width/2, canvas.height/2);
-
-    const texture = new THREE.CanvasTexture(canvas);
-    const geom = new THREE.PlaneGeometry(2, 0.5);
-    const mat = new THREE.MeshBasicMaterial({ map:texture, transparent:true, side:THREE.DoubleSide });
-    const label = new THREE.Mesh(geom, mat);
-    label.position.set(0, -1.5, -2);
-    label.rotation.x = Math.PI / 2;
-    finger.group.add(label);
-  }
-}
-
-function addHandLabel() {
-  const canvas = document.createElement('canvas');
-  const ctx = canvas.getContext('2d');
-  canvas.width = 256; canvas.height = 64;
-  ctx.fillStyle = '#ffffff'; ctx.fillRect(0,0,canvas.width,canvas.height);
-  ctx.font = 'bold 24px Arial';
-  ctx.fillStyle = '#000000';
-  ctx.textAlign = 'center'; ctx.textBaseline = 'middle';
-     ctx.fillText('RIGHT HAND (VERTICAL)', canvas.width/2, canvas.height/2);
-
-  const texture = new THREE.CanvasTexture(canvas);
-  const geom = new THREE.PlaneGeometry(7, 1.75);
-  const mat = new THREE.MeshBasicMaterial({ map:texture, transparent:true, side:THREE.DoubleSide });
-  const label = new THREE.Mesh(geom, mat);
-  label.position.set(0, -2, 0);
-  label.rotation.x = Math.PI / 2;
-  scene.add(label);
-}
-
-function updateHandModel() {
-  for (let i = 0; i < MAX_JOINTS; i++) {
-    const info = fingerJointMap[i];
-    if (!info) continue;
-    const { finger, joint, type, min, max, angleMin, angleMax } = info;
-    const raw = jointValues[i];
-    const f = hand.fingers[finger];
-    if (!f) continue;
-
-    const center = (min + max) / 2;
-    let angle = 0;
-
-         if (type.includes('ABDUCTION')) {
-       // symmetric around neutral
-       const k = clamp((raw - center) / ((max - min) / 2), -1, 1);
-       angle = angleMin + (k + 1) * 0.5 * (angleMax - angleMin);
-
-       const base = f.group.userData.baseRot || {x:0,y:0,z:0};
-       if (finger === 0 && joint === 0) {
-         // Thumb: abduction about Z (toward/away from palm)
-         f.group.rotation.z = base.z + angle;
-       } else {
-         // Other fingers: side-to-side about Y
-         f.group.rotation.y = base.y + angle;
-       }
-     } else if (type.includes('FLEXION')) {
-       const isThumb = finger === 0;
-       const isMCP = type === 'MCP_FLEXION';
-       const isPIP = type === 'PIP_FLEXION';
-       const positiveOnly = (isThumb && (type === 'MCP_FLEXION' || type === 'IP_FLEXION')) || (!isThumb && isPIP);
-
-       if (positiveOnly) {
-         const t = raw <= center ? 0 : invLerp(center, max, raw); // 0..1
-         angle = angleMin + t * (angleMax - angleMin);           // 0..+limit
-       } else {
-         const k = clamp((raw - center) / ((max - min) / 2), -1, 1);
-         angle = angleMin + (k + 1) * 0.5 * (angleMax - angleMin);
-       }
-
-       if (isMCP) {
-         // MCP flexion applies to the finger base group (same as abduction)
-         const base = f.group.userData.baseRot || {x:0,y:0,z:0};
-         f.group.rotation.x = base.x + angle;
-       } else if (f.segments[joint]) {
-         // PIP/DIP/IP flexion applies to individual segments
-         f.segments[joint].rotation.x = angle;
-       }
-     }
-  }
-}
-
-// -------------------- Render Loop --------------------
-function onWindowResize() {
-  camera.aspect = canvasContainer.clientWidth / canvasContainer.clientHeight;
-  camera.updateProjectionMatrix();
-  renderer.setSize(canvasContainer.clientWidth, canvasContainer.clientHeight);
-}
-
-function animate() {
-  requestAnimationFrame(animate);
-  controls.update();
-  renderer.render(scene, camera);
-}
-
-// -------------------- Misc UI --------------------
-function addLogMessage(msg) {
-  const el = document.createElement('div');
-  el.textContent = msg;
-  logContainer.appendChild(el);
-  logContainer.scrollTop = logContainer.scrollHeight;
-  while (logContainer.children.length > 100) {
-    logContainer.removeChild(logContainer.firstChild);
-  }
-}
-
-// Camera view controls
-frontViewBtn?.addEventListener('click', () => { camera.position.set(0, 0, 20); camera.lookAt(0,0,0); controls.update(); });
-sideViewBtn?.addEventListener('click',  () => { camera.position.set(20, 0, 0); camera.lookAt(0,0,0); controls.update(); });
-topViewBtn?.addEventListener('click',   () => { camera.position.set(0, 20, 0); camera.lookAt(0,0,0); controls.update(); });
-resetViewBtn?.addEventListener('click', () => { camera.position.set(10,10,10); camera.lookAt(0,0,0); controls.update(); });
-
-// Serial connect buttons
-connectButton?.addEventListener('click', connectToDevice);
-disconnectButton?.addEventListener('click', disconnectFromDevice);
-
-// Web Serial support check
-if (!navigator.serial) {
-  statusIndicator.textContent = 'Status: Web Serial API not supported in this browser';
-  connectButton.disabled = true;
-  addLogMessage('ERROR: Web Serial API is not supported in this browser. Try Chrome or Edge.');
-}
-
-// -------------------- Boot --------------------
-initThreeJS();
-initializeJointElements();
-
- // -------------------- Styles (inline) --------------------
- const styleElement = document.createElement('style');
- styleElement.textContent = `
- .joint-info { border-bottom: 1px solid #eee; padding: 8px 0; }
- .joint-name { font-weight: 600; margin-bottom: 4px; }
- .joint-value { font-size: 12px; color: #333; margin-bottom: 4px; }
- .bar-container { width: 100%; height: 8px; background: #ddd; border-radius: 4px; overflow: hidden; }
- .bar { height: 100%; width: 0%; background: #4caf50; }
- .joint-slider { width: 100%; margin: 6px 0; }
- .invert-toggle { display: inline-flex; align-items: center; gap: 6px; margin-top: 4px; font-size: 12px; color: #555; }
- .limits-row { display: flex; align-items: center; gap: 6px; margin-top: 6px; flex-wrap: wrap; }
- .limit-label { font-size: 11px; color: #666; }
- .limit-num { width: 60px; }
- .calib-row { display: flex; align-items: center; gap: 8px; margin-top: 4px; }
- .calib-btn { padding: 2px 6px; font-size: 11px; background: #f44336; color: white; border: none; border-radius: 3px; cursor: pointer; }
- .calib-btn:hover { background: #d32f2f; }
- .calib-status { font-size: 11px; color: #666; }
- .status.connected { color: #0a0; }
- .status.disconnected { color: #a00; }
- `;
- document.head.appendChild(styleElement);

examples/lekiwi/evaluate.py

@@ -17,7 +17,7 @@
 import logging
 import time
 
-from lerobot.common.control_utils import predict_action
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.datasets import LeRobotDataset
 from lerobot.policies import make_pre_post_processors
 from lerobot.policies.act import ACTPolicy
@@ -26,7 +26,6 @@ from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

examples/lekiwi/record.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.datasets import LeRobotDataset
 from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
@@ -22,7 +23,6 @@ from lerobot.teleoperators.keyboard import KeyboardTeleop, KeyboardTeleopConfig
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import hw_to_dataset_features
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 

examples/phone_to_so100/evaluate.py

@@ -18,7 +18,7 @@ import logging
 import time
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import predict_action
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
@@ -41,7 +41,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

examples/phone_to_so100/record.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
@@ -38,7 +39,6 @@ from lerobot.teleoperators.phone.config_phone import PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 

examples/so100_to_so100_EE/evaluate.py

@@ -18,7 +18,7 @@ import logging
 import time
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.common.control_utils import predict_action
+from lerobot.common.control_utils import init_keyboard_listener, predict_action
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
@@ -41,7 +41,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data

examples/so100_to_so100_EE/record.py

@@ -16,6 +16,7 @@
 
 
 from lerobot.cameras.opencv import OpenCVCameraConfig
+from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.datasets import LeRobotDataset, aggregate_pipeline_dataset_features, create_initial_features
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
@@ -35,7 +36,6 @@ from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
 from lerobot.types import RobotAction, RobotObservation
 from lerobot.utils.feature_utils import combine_feature_dicts
-from lerobot.utils.keyboard_input import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 

pyproject.toml

@@ -124,8 +124,7 @@ hardware = [
     "lerobot[deepdiff-dep]",
 ]
 viz = [
-    "rerun-sdk>=0.24.0,<0.34.0",
-    "foxglove-sdk>=0.25.1,<0.26.0",
+    "rerun-sdk>=0.24.0,<0.27.0",
 ]
 # ── User-facing composite extras (map to CLI scripts) ─────
 # lerobot-record, lerobot-replay, lerobot-calibrate, lerobot-teleoperate, etc.
@@ -141,14 +140,7 @@ av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 # NOTE: 0.9.16 links against liburdfdom_sensor.so.4, which is unavailable on Ubuntu 24.04
 # (noble ships urdfdom 3.x). Cap below 0.9.16 until system urdfdom 4.x is broadly available.
-#
-# NOTE: placo pulls in pin (Pinocchio), whose binary wheels dlopen specific cmeel sonames
-# (liburdfdom_sensor.so.4.0, libtinyxml2.so.10) but declare only `>=` floors on their cmeel
-# packages. The 2026-05-21 major bumps (cmeel-urdfdom 6.0.0 -> .so.6, cmeel-tinyxml2 11.0.0
-# -> .so.11) ship newer sonames, so left unpinned the resolver grabs them and `import placo`
-# fails at load with "liburdfdom_sensor.so.4.0: cannot open shared object file" (see #3755).
-# There is no cmeel-urdfdom 5.x; <5 selects the 4.x ABI the placo/pin wheels are built against.
-placo-dep = ["placo>=0.9.6,<0.9.16", "cmeel-urdfdom>=4,<5", "cmeel-tinyxml2<11"]
+placo-dep = ["placo>=0.9.6,<0.9.16"]
 transformers-dep = ["transformers>=5.4.0,<5.6.0"]
 grpcio-dep = ["grpcio>=1.73.1,<2.0.0", "protobuf>=6.31.1,<8.0.0"]
 accelerate-dep = ["accelerate>=1.14.0,<2.0.0"]
@@ -164,7 +156,6 @@ 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]"]
@@ -220,24 +211,19 @@ 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",
-    "lerobot[timm-dep]",
+    "timm>=1.0.0,<1.1.0",
     "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]"]
 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]"]
 
 # Features
 async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
@@ -315,13 +301,10 @@ all = [
     "lerobot[pi]",
     "lerobot[molmoact2]",
     "lerobot[smolvla]",
-    "lerobot[fastwam]",
-    "lerobot[groot]",
+    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
-    "lerobot[evo1]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",
-    "lerobot[lingbot_va]",
     "lerobot[async]",
     "lerobot[dev]",
     "lerobot[test]",
@@ -454,8 +437,7 @@ default.extend-ignore-identifiers-re = [
     "is_compileable",
     "ROBOTIS",
     "OT_VALUE",
-    "VanderBilt",
-    "seperated_timestep",
+    "VanderBilt"
 ]
 
 # TODO: Uncomment when ready to use

requirements-macos.txt

@@ -0,0 +1,729 @@
+#
+# 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

@@ -0,0 +1,882 @@
+#
+# 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

@@ -0,0 +1,9 @@
+# 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]

src/lerobot/common/control_utils.py

@@ -17,9 +17,12 @@ from __future__ import annotations
 ########################################################################################
 # Utilities
 ########################################################################################
+import logging
 import time
+import traceback
 from contextlib import nullcontext
 from copy import copy
+from functools import cache
 from typing import TYPE_CHECKING, Any
 
 import numpy as np
@@ -40,6 +43,34 @@ from lerobot.robots import Robot
 from lerobot.types import PolicyAction
 
 
+@cache
+def is_headless():
+    """
+    Detects if the Python script is running in a headless environment (e.g., without a display).
+
+    This function attempts to import `pynput`, a library that requires a graphical environment.
+    If the import fails, it assumes the environment is headless. The result is cached to avoid
+    re-running the check.
+
+    Returns:
+        True if the environment is determined to be headless, False otherwise.
+    """
+    try:
+        import pynput  # noqa
+
+        return False
+    except Exception:
+        print(
+            "Error trying to import pynput. Switching to headless mode. "
+            "As a result, the video stream from the cameras won't be shown, "
+            "and you won't be able to change the control flow with keyboards. "
+            "For more info, see traceback below.\n"
+        )
+        traceback.print_exc()
+        print()
+        return True
+
+
 def predict_action(
     observation: dict[str, np.ndarray],
     policy: PreTrainedPolicy,
@@ -91,6 +122,59 @@ def predict_action(
     return action
 
 
+def init_keyboard_listener():
+    """
+    Initializes a non-blocking keyboard listener for real-time user interaction.
+
+    This function sets up a listener for specific keys (right arrow, left arrow, escape) to control
+    the program flow during execution, such as stopping recording or exiting loops. It gracefully
+    handles headless environments where keyboard listening is not possible.
+
+    Returns:
+        A tuple containing:
+        - The `pynput.keyboard.Listener` instance, or `None` if in a headless environment.
+        - A dictionary of event flags (e.g., `exit_early`) that are set by key presses.
+    """
+    # Allow to exit early while recording an episode or resetting the environment,
+    # by tapping the right arrow key '->'. This might require a sudo permission
+    # to allow your terminal to monitor keyboard events.
+    events = {}
+    events["exit_early"] = False
+    events["rerecord_episode"] = False
+    events["stop_recording"] = False
+
+    if is_headless():
+        logging.warning(
+            "Headless environment detected. On-screen cameras display and keyboard inputs will not be available."
+        )
+        listener = None
+        return listener, events
+
+    # Only import pynput if not in a headless environment
+    from pynput import keyboard
+
+    def on_press(key):
+        try:
+            if key == keyboard.Key.right:
+                print("Right arrow key pressed. Exiting loop...")
+                events["exit_early"] = True
+            elif key == keyboard.Key.left:
+                print("Left arrow key pressed. Exiting loop and rerecord the last episode...")
+                events["rerecord_episode"] = True
+                events["exit_early"] = True
+            elif key == keyboard.Key.esc:
+                print("Escape key pressed. Stopping data recording...")
+                events["stop_recording"] = True
+                events["exit_early"] = True
+        except Exception as e:
+            print(f"Error handling key press: {e}")
+
+    listener = keyboard.Listener(on_press=on_press)
+    listener.start()
+
+    return listener, events
+
+
 def sanity_check_dataset_name(repo_id, policy_cfg):
     """
     Validates the dataset repository name against the presence of a policy configuration.

src/lerobot/common/train_utils.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 from pathlib import Path
 
-from huggingface_hub import HfApi, snapshot_download
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
@@ -36,7 +35,6 @@ from lerobot.utils.constants import (
     TRAINING_STATE_DIR,
     TRAINING_STEP,
 )
-from lerobot.utils.hub import find_latest_hub_checkpoint
 from lerobot.utils.io_utils import load_json, write_json
 from lerobot.utils.random_utils import load_rng_state, save_rng_state
 
@@ -285,61 +283,3 @@ def load_fsdp_optimizer_state(model, optimizer, checkpoint_dir: Path) -> None:
     with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
         sharded_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=full_osd)
     optimizer.load_state_dict(sharded_osd)
-
-
-def push_checkpoint_to_hub(
-    checkpoint_dir: Path,
-    repo_id: str,
-    *,
-    private: bool | None = None,
-) -> None:
-    """Upload a saved checkpoint directory to the Hub under checkpoints/<name>/.
-
-    Called once per save step when save_checkpoint_to_hub is enabled, so a
-    timed-out or crashed run still leaves recoverable checkpoints on the Hub.
-    The model repo is created idempotently, and the commit is tagged with the
-    checkpoint step so a checkpoint can be recovered with
-    --policy.pretrained_revision=<step> instead of a commit sha.
-    """
-    api = HfApi()
-    api.create_repo(repo_id=repo_id, repo_type="model", private=private, exist_ok=True)
-    commit = api.upload_folder(
-        folder_path=str(checkpoint_dir),
-        repo_id=repo_id,
-        repo_type="model",
-        path_in_repo=f"checkpoints/{checkpoint_dir.name}",
-        commit_message=f"checkpoint {checkpoint_dir.name}",
-    )
-    api.create_tag(
-        repo_id=repo_id,
-        tag=checkpoint_dir.name,
-        revision=commit.oid,
-        repo_type="model",
-        exist_ok=True,
-    )
-
-
-def resolve_resume_checkpoint(repo_id: str, output_dir: Path) -> Path:
-    """Download the latest checkpoint of a Hub training repo into a local run dir.
-
-    The symmetric counterpart to `push_checkpoint_to_hub`: given a model repo holding
-    `checkpoints/<step>/{pretrained_model,training_state}` subtrees, download the highest-numbered step
-    into `output_dir/checkpoints/<step>/`, recreate the local `last` symlink, and return that local
-    checkpoint dir. Used to resume training from the Hub on a machine (or HF Jobs pod) that does not
-    have the original local run dir.
-    """
-    latest = find_latest_hub_checkpoint(repo_id)
-    if latest is None:
-        raise FileNotFoundError(
-            f"No checkpoint found in '{repo_id}' under '{CHECKPOINTS_DIR}/'. "
-            "Was the run trained with --save_checkpoint_to_hub?"
-        )
-    snapshot_download(
-        repo_id=repo_id,
-        repo_type="model",
-        allow_patterns=f"{latest}/*",
-        local_dir=str(output_dir),
-    )
-    checkpoint_dir = output_dir / latest
-    update_last_checkpoint(checkpoint_dir)
-    return checkpoint_dir

src/lerobot/configs/__init__.py

@@ -22,7 +22,7 @@ Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """
 
 from .dataset import DatasetRecordConfig
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .recipe import MessageTurn, TrainingRecipe, load_recipe
 from .types import (
@@ -34,8 +34,6 @@ from .types import (
 )
 from .video import (
     DEFAULT_DEPTH_UNIT,
-    DEPTH_METER_UNIT,
-    DEPTH_MILLIMETER_UNIT,
     VALID_VIDEO_CODECS,
     VIDEO_ENCODER_INFO_KEYS,
     DepthEncoderConfig,
@@ -43,7 +41,6 @@ from .video import (
     VideoEncoderConfig,
     depth_encoder_defaults,
     encoder_config_from_video_info,
-    infer_depth_unit,
     rgb_encoder_defaults,
 )
 
@@ -58,7 +55,6 @@ __all__ = [
     "DatasetRecordConfig",
     "DatasetConfig",
     "EvalConfig",
-    "JobConfig",
     "MessageTurn",
     "PeftConfig",
     "PreTrainedConfig",
@@ -73,11 +69,8 @@ __all__ = [
     "depth_encoder_defaults",
     # Factories
     "encoder_config_from_video_info",
-    "infer_depth_unit",
     # Constants
     "DEFAULT_DEPTH_UNIT",
-    "DEPTH_METER_UNIT",
-    "DEPTH_MILLIMETER_UNIT",
     "VALID_VIDEO_CODECS",
     "VIDEO_ENCODER_INFO_KEYS",
 ]

src/lerobot/configs/default.py

@@ -44,16 +44,12 @@ class DatasetConfig:
     # Has no effect on datasets without depth cameras.
     depth_output_unit: str = DEFAULT_DEPTH_UNIT
     streaming: bool = False
-    # Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
-    eval_split: float = 0.0
 
     def __post_init__(self) -> None:
         if self.depth_output_unit not in (DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
             raise ValueError(
                 f"depth_output_unit must be '{DEPTH_METER_UNIT}' or '{DEPTH_MILLIMETER_UNIT}', got {self.depth_output_unit!r}"
             )
-        if not (0.0 <= self.eval_split < 1.0):
-            raise ValueError(f"eval_split must be in [0.0, 1.0), got {self.eval_split}")
         if self.episodes is not None:
             if any(ep < 0 for ep in self.episodes):
                 raise ValueError(
@@ -86,17 +82,8 @@ class EvalConfig:
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     # Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
     use_async_envs: bool = True
-    # Whether to record eval rollouts as a LeRobot dataset on disk.
-    recording: bool = False
-    # If set, push recorded eval datasets to the Hub under this repo id (one repo per task,
-    # suffixed by task and env index). Requires recording=true.
-    recording_repo_id: str | None = None
-    # Whether the pushed recording repositories should be private.
-    recording_private: bool = False
 
     def __post_init__(self) -> None:
-        if self.recording_repo_id is not None and not self.recording:
-            raise ValueError("eval.recording_repo_id requires eval.recording=true.")
         if self.batch_size == 0:
             self.batch_size = self._auto_batch_size()
         if self.batch_size > self.n_episodes:
@@ -145,35 +132,3 @@ class PeftConfig:
     # If None, the PEFT library defaults to alpha=8, which may dampen high-rank adapters.
     # Common values are r (alpha == rank) or 2*r.
     lora_alpha: int | None = None
-
-
-@dataclass
-class JobConfig:
-    # Where training runs. None (omitted) or "local" runs on this machine.
-    # Any other value is an HF Jobs flavor and submits the run to HF Jobs.
-    # List available flavors + pricing with `hf jobs hardware` command.
-    target: str | None = None
-    # Runtime image for the remote job (ignored for local runs).
-    image: str = "huggingface/lerobot-gpu:latest"
-    # Max wall-clock for the remote job as an HF Jobs duration string (e.g. "2h").
-    # Defaults to "2d": We pass an explicit, generous cap instead. Set a smaller
-    # value to fail fast, or a larger one for long runs.
-    timeout: str | None = "2d"
-    # Submit and exit instead of streaming the job logs in the foreground.
-    detach: bool = False
-    # Extra tags attached to the HF job and to any dataset this run pushes to the
-    # Hub. A "lerobot" tag is always added; e.g. --job.tags '["lelab"]' adds more.
-    tags: list[str] = field(default_factory=list)
-
-    # Two entry points to the same predicate: the staticmethod tests a raw target string
-    # straight from argv (before any JobConfig exists, to decide dispatch early), while the
-    # property is the ergonomic accessor for code that already holds a config instance.
-    @staticmethod
-    def is_remote_target(target: str | None) -> bool:
-        """True when `target` names an HF Jobs flavor rather than a local run."""
-        return target not in (None, "local")
-
-    @property
-    def is_remote(self) -> bool:
-        """True when training should run on HF Jobs rather than this machine."""
-        return self.is_remote_target(self.target)

src/lerobot/configs/train.py

@@ -26,12 +26,11 @@ from huggingface_hub.errors import HfHubHTTPError
 
 from lerobot import envs
 from lerobot.optim import LRSchedulerConfig, OptimizerConfig
-from lerobot.utils.constants import PRETRAINED_MODEL_DIR
-from lerobot.utils.hub import HubMixin, find_latest_hub_checkpoint
+from lerobot.utils.hub import HubMixin
 from lerobot.utils.sample_weighting import SampleWeightingConfig
 
 from . import parser
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .rewards import RewardModelConfig
 
@@ -84,11 +83,10 @@ class TrainPipelineConfig(HubMixin):
     # with the same value for `dir` its contents will be overwritten unless you set `resume` to true.
     output_dir: Path | None = None
     job_name: str | None = None
-    # Set `resume` to true to resume a previous run. Pass `--config_path` pointing at either a local
-    # checkpoint's train_config.json or a Hub repo id holding `checkpoints/<step>/` subtrees (the
-    # latest checkpoint is downloaded and resumed from). Note that when resuming, the default behavior
-    # is to use the configuration from the checkpoint, regardless of what's provided with the training
-    # command at the time of resumption (CLI `--*` flags still override).
+    # Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure
+    # `dir` is the directory of an existing run with at least one checkpoint in it.
+    # Note that when resuming a run, the default behavior is to use the configuration from the checkpoint,
+    # regardless of what's provided with the training command at the time of resumption.
     resume: bool = False
     # `seed` is used for training (eg: model initialization, dataset shuffling)
     # AND for the evaluation environments.
@@ -102,13 +100,8 @@ class TrainPipelineConfig(HubMixin):
     prefetch_factor: int = 4
     persistent_workers: bool = True
     steps: int = 100_000
-    # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
-    env_eval_freq: int = 20_000
+    eval_freq: int = 20_000
     log_freq: int = 200
-    # Compute eval loss on held-out episodes every N steps (0 = disabled). Requires eval_split > 0.
-    eval_steps: int = 0
-    # Cap on total eval samples, split uniformly across tasks (0 = use all held-out data).
-    max_eval_samples: int = 0
     tolerance_s: float = 1e-4
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.
@@ -120,13 +113,6 @@ class TrainPipelineConfig(HubMixin):
     wandb: WandBConfig = field(default_factory=WandBConfig)
     peft: PeftConfig | None = None
 
-    # Where to run training (local default, or an HF Jobs flavor). See JobConfig.
-    job: JobConfig = field(default_factory=JobConfig)
-    # Push each saved checkpoint to the Hub (policy.repo_id) as it is written, not
-    # just the final model (useful to monitor progress mid-run). Optional; the
-    # final model is pushed regardless. Works the same locally and remotely.
-    save_checkpoint_to_hub: bool = False
-
     # Sample weighting configuration (e.g., for RA-BC training)
     sample_weighting: SampleWeightingConfig | None = None
 
@@ -146,17 +132,10 @@ class TrainPipelineConfig(HubMixin):
             return self.reward_model  # type: ignore[return-value]
         return self.policy  # type: ignore[return-value]
 
-    def _resolve_pretrained_from_cli(self) -> None:
-        """Resolve the pretrained source passed on the CLI into a loaded config.
-
-        The pretrained paths (`--policy.path`, `--reward_model.path`) and
-        `--config_path` are only recoverable by re-reading the CLI args: draccus
-        has already consumed them by the time `validate()` runs, so they are not
-        reflected on `self`. Exactly one source applies, in priority order:
-        reward-model path, policy path, then resume.
-        """
-        reward_model_path = parser.get_path_arg("reward_model")
+    def validate(self) -> None:
+        # HACK: We parse again the cli args here to get the pretrained paths if there was some.
         policy_path = parser.get_path_arg("policy")
+        reward_model_path = parser.get_path_arg("reward_model")
 
         if reward_model_path:
             cli_overrides = parser.get_cli_overrides("reward_model")
@@ -165,54 +144,31 @@ class TrainPipelineConfig(HubMixin):
             )
             self.reward_model.pretrained_path = str(Path(reward_model_path))
         elif policy_path:
-            overrides = parser.get_yaml_overrides("policy") + (parser.get_cli_overrides("policy") or [])
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            self.policy = PreTrainedConfig.from_pretrained(
+                policy_path, cli_overrides=yaml_overrides + cli_overrides
+            )
             self.policy.pretrained_path = Path(policy_path)
         elif self.resume:
-            self._resolve_resume_checkpoint()
+            config_path = parser.parse_arg("config_path")
+            if not config_path:
+                raise ValueError(
+                    f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
+                )
 
-    def _resolve_resume_checkpoint(self) -> None:
-        """Point the trainable config at the checkpoint named by `--config_path`.
+            if not Path(config_path).resolve().exists():
+                raise NotADirectoryError(
+                    f"{config_path=} is expected to be a local path. "
+                    "Resuming from the hub is not supported for now."
+                )
 
-        `config_path` is either a local path (to a checkpoint's train_config.json or its
-        pretrained_model/ dir) or a Hub repo id. For a Hub repo, the latest checkpoint is downloaded
-        into a fresh local run dir and resumed from there. The download is skipped when dispatching to
-        an HF Job (`job.is_remote`): the pod performs it when it runs the resume locally, and
-        `submit_to_hf` resolves the source repo for the remote command.
-        """
-        config_path = parser.parse_arg("config_path")
-        if not config_path:
-            raise ValueError(
-                f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
-            )
-
-        if Path(config_path).resolve().exists():
             policy_dir = Path(config_path).parent
+            if self.policy is not None:
+                self.policy.pretrained_path = policy_dir
+            if self.reward_model is not None:
+                self.reward_model.pretrained_path = str(policy_dir)
             self.checkpoint_path = policy_dir.parent
-        elif self.job.is_remote:
-            return
-        else:
-            from lerobot.common.train_utils import resolve_resume_checkpoint
-
-            # `self.output_dir` was loaded from the checkpoint's config and points at the original
-            # run's (now-absent) local dir. Resume into a fresh local dir instead, unless the user
-            # passed --output_dir explicitly.
-            cli_output_dir = parser.parse_arg("output_dir")
-            if cli_output_dir:
-                self.output_dir = Path(cli_output_dir)
-            else:
-                now = dt.datetime.now()
-                self.output_dir = Path("outputs/train") / f"{now:%Y-%m-%d}/{now:%H-%M-%S}_resume"
-            self.checkpoint_path = resolve_resume_checkpoint(config_path, self.output_dir)
-            policy_dir = self.checkpoint_path / PRETRAINED_MODEL_DIR
-
-        if self.policy is not None:
-            self.policy.pretrained_path = policy_dir
-        if self.reward_model is not None:
-            self.reward_model.pretrained_path = str(policy_dir)
-
-    def validate(self) -> None:
-        self._resolve_pretrained_from_cli()
 
         if self.policy is None and self.reward_model is None:
             raise ValueError(
@@ -252,22 +208,9 @@ class TrainPipelineConfig(HubMixin):
             self.optimizer = active_cfg.get_optimizer_preset()
             self.scheduler = active_cfg.get_scheduler_preset()
 
-        if self.eval_steps > 0 and self.dataset.eval_split == 0.0:
-            raise ValueError("eval_steps > 0 requires dataset.eval_split > 0.0 to hold out eval data.")
-
-        # Remote runs auto-generate the repo_id in submit_to_hf (the policy may only be
-        # resolved here, from --policy.path), so don't demand it up front for them.
-        if (
-            hasattr(active_cfg, "push_to_hub")
-            and active_cfg.push_to_hub
-            and not active_cfg.repo_id
-            and not self.job.is_remote
-        ):
+        if hasattr(active_cfg, "push_to_hub") and active_cfg.push_to_hub and not active_cfg.repo_id:
             raise ValueError("'repo_id' argument missing. Please specify it to push the model to the hub.")
 
-        if self.save_checkpoint_to_hub and not (self.policy is not None and self.policy.repo_id):
-            raise ValueError("save_checkpoint_to_hub requires --policy.repo_id.")
-
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
         """Keys for draccus pretrained-path loading."""
@@ -304,30 +247,22 @@ class TrainPipelineConfig(HubMixin):
         elif Path(model_id).is_file():
             config_file = model_id
         else:
-            dl_kwargs = {
-                "repo_id": model_id,
-                "revision": revision,
-                "cache_dir": cache_dir,
-                "force_download": force_download,
-                "proxies": proxies,
-                "resume_download": resume_download,
-                "token": token,
-                "local_files_only": local_files_only,
-            }
             try:
-                config_file = hf_hub_download(filename=TRAIN_CONFIG_NAME, **dl_kwargs)
-            except HfHubHTTPError as e:
-                # No root train_config.json: this is a repo of periodic checkpoints from an
-                # interrupted run. Fall back to the latest checkpoint's config so the run can be
-                # resumed straight from the repo with `--config_path=<repo>`.
-                latest = find_latest_hub_checkpoint(model_id, token=token, revision=revision)
-                if latest is None:
-                    raise FileNotFoundError(
-                        f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
-                    ) from e
                 config_file = hf_hub_download(
-                    filename=f"{latest}/{PRETRAINED_MODEL_DIR}/{TRAIN_CONFIG_NAME}", **dl_kwargs
+                    repo_id=model_id,
+                    filename=TRAIN_CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    token=token,
+                    local_files_only=local_files_only,
                 )
+            except HfHubHTTPError as e:
+                raise FileNotFoundError(
+                    f"{TRAIN_CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
+                ) from e
 
         cli_args = kwargs.pop("cli_args", [])
         # Legacy RA-BC migration only applies to framework-saved checkpoints (always JSON).

src/lerobot/configs/video.py

@@ -22,8 +22,6 @@ import logging
 from dataclasses import dataclass, field
 from typing import Any, ClassVar, Self
 
-import numpy as np
-
 from lerobot.utils.import_utils import require_package
 
 logger = logging.getLogger(__name__)
@@ -39,7 +37,7 @@ HW_VIDEO_CODECS = [
     "h264_qsv",  # Intel Quick Sync
 ]
 VALID_VIDEO_CODECS: frozenset[str] = frozenset(
-    {"h264", "hevc", "libsvtav1", "libaom-av1", "auto", *HW_VIDEO_CODECS}
+    {"h264", "hevc", "libsvtav1", "ffv1", "auto", *HW_VIDEO_CODECS}
 )
 # Aliases for legacy video codec names.
 VIDEO_CODECS_ALIASES: dict[str, str] = {"av1": "libsvtav1"}
@@ -69,15 +67,6 @@ DEPTH_METER_UNIT: str = "m"
 DEPTH_MILLIMETER_UNIT: str = "mm"
 DEFAULT_DEPTH_UNIT: str = DEPTH_MILLIMETER_UNIT
 
-
-def infer_depth_unit(dtype: np.dtype | type) -> str:
-    """Infer the physical unit of raw depth frames from their dtype.
-
-    Floating-point frames are assumed to be in metres, integer frames in millimetres.
-    """
-    return DEPTH_METER_UNIT if np.issubdtype(np.dtype(dtype), np.floating) else DEPTH_MILLIMETER_UNIT
-
-
 # Depth-specific tuning fields persisted under ``features[*]["info"]`` as ``video.<name>``.
 DEPTH_ENCODER_INFO_FIELD_NAMES: frozenset[str] = frozenset({"depth_min", "depth_max", "shift", "use_log"})
 
@@ -226,24 +215,18 @@ class VideoEncoderConfig:
             if encoder_threads is not None:
                 svtav1_parts.append(f"lp={encoder_threads}")
             if svtav1_parts:
-                set_if("svtav1-params", ":".join(svtav1_parts))
+                opts["svtav1-params"] = ":".join(svtav1_parts)
         elif self.vcodec in ("h264", "hevc"):
             set_if("crf", self.crf)
             set_if("preset", self.preset)
             if self.fast_decode:
-                set_if("tune", "fastdecode")
+                opts["tune"] = "fastdecode"
             set_if("threads", encoder_threads)
-        elif self.vcodec == "libaom-av1":
-            set_if("crf", self.crf)
-            set_if("preset", self.preset)
-            if encoder_threads is not None:
-                set_if("threads", encoder_threads)
-                set_if("row-mt", 1)
         elif self.vcodec in ("h264_videotoolbox", "hevc_videotoolbox"):
             if self.crf is not None:
-                set_if("q:v", max(1, min(100, 100 - self.crf * 2)))
+                opts["q:v"] = max(1, min(100, 100 - self.crf * 2))
         elif self.vcodec in ("h264_nvenc", "hevc_nvenc"):
-            set_if("rc", 0)
+            opts["rc"] = 0
             set_if("qp", self.crf)
             set_if("preset", self.preset)
         elif self.vcodec == "h264_vaapi":
@@ -251,6 +234,10 @@ class VideoEncoderConfig:
         elif self.vcodec == "h264_qsv":
             set_if("global_quality", self.crf)
             set_if("preset", self.preset)
+        elif self.vcodec == "ffv1":
+            # Lossless intra-frame codec. ``crf``/``preset``/``fast_decode``
+            # are not meaningful.
+            set_if("threads", encoder_threads)
         else:
             set_if("crf", self.crf)
             set_if("preset", self.preset)
@@ -289,9 +276,8 @@ class DepthEncoderConfig(VideoEncoderConfig):
     ``"gray12le"``.
     """
 
-    vcodec: str = "hevc"  # Video codec name. Defaults to HEVC Main 12 (a 12-bit-capable codec).
-    pix_fmt: str = "gray12le"  # Pixel format. Defaults to 12-bit grayscale.
-    extra_options: dict[str, Any] = field(default_factory=lambda: {"x265-params": "lossless=1"})
+    vcodec: str = "hevc"
+    pix_fmt: str = "gray12le"
 
     depth_min: float = DEFAULT_DEPTH_MIN  # Minimum depth in meters, mapped to the lowest quantum.
     depth_max: float = DEFAULT_DEPTH_MAX  # Maximum depth in meters, mapped to the highest quantum.

src/lerobot/datasets/__init__.py

@@ -35,7 +35,7 @@ from .dataset_tools import (
     remove_feature,
     split_dataset,
 )
-from .factory import make_dataset, make_train_eval_datasets, resolve_delta_timestamps
+from .factory import make_dataset, resolve_delta_timestamps
 from .image_writer import safe_stop_image_writer
 from .io_utils import load_episodes, write_stats
 from .language import (
@@ -89,7 +89,6 @@ __all__ = [
     "get_feature_stats",
     "load_episodes",
     "make_dataset",
-    "make_train_eval_datasets",
     "merge_datasets",
     "modify_features",
     "modify_tasks",

src/lerobot/datasets/compute_stats.py

@@ -242,12 +242,12 @@ def sample_images(image_paths: list[str]) -> np.ndarray:
     images = None
     for i, idx in enumerate(sampled_indices):
         path = image_paths[idx]
-        # we load RGB images as uint8 to reduce memory usage; depth keeps its native dtype
+        # we load as uint8 to reduce memory usage
         img = load_image_as_numpy(path, dtype=np.uint8, channel_first=True)
         img = auto_downsample_height_width(img)
 
         if images is None:
-            images = np.empty((len(sampled_indices), *img.shape), dtype=img.dtype)
+            images = np.empty((len(sampled_indices), *img.shape), dtype=np.uint8)
 
         images[i] = img
 
@@ -509,7 +509,7 @@ def compute_episode_stats(
         For 'image'/'video' features, stats are computed per channel and kept with a
         leading channel axis (e.g. shape (3, 1, 1) for RGB). RGB stats are divided by
         255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) skip
-        this rescaling and remain in their stored units (stored in ``depth_unit``).
+        this rescaling and remain in their stored units.
     """
     if quantile_list is None:
         quantile_list = DEFAULT_QUANTILES

src/lerobot/datasets/dataset_metadata.py

@@ -26,13 +26,12 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 from huggingface_hub import snapshot_download
 
-from lerobot.configs import DEPTH_METER_UNIT, VideoEncoderConfig
+from lerobot.configs import VideoEncoderConfig
 from lerobot.utils.constants import DEFAULT_FEATURES, HF_LEROBOT_HOME, HF_LEROBOT_HUB_CACHE
 from lerobot.utils.feature_utils import _validate_feature_names
 from lerobot.utils.utils import flatten_dict
 
 from .compute_stats import aggregate_stats
-from .depth_utils import MM_PER_METRE
 from .feature_utils import create_empty_dataset_info
 from .io_utils import (
     get_file_size_in_mb,
@@ -359,35 +358,6 @@ class LeRobotDatasetMetadata:
 
         return [key for key, ft in self.features.items() if _is_depth(ft)]
 
-    def rescale_depth_stats(self, output_unit: str) -> None:
-        """Rescale depth feature stats in place from their recorded unit to ``output_unit``.
-
-        Depth stats are stored in the unit the frames were recorded in
-        (``features[key]["info"]["depth_unit"]``), while frames are returned in
-        ``output_unit`` on read. This converts the unit-bearing stat entries so
-        stats match the frames consumers see.
-        """
-        missing_unit_keys = [
-            key for key in self.depth_keys if (self.features[key].get("info") or {}).get("depth_unit") is None
-        ]
-        if missing_unit_keys:
-            logging.warning(
-                f"Depth feature(s) {missing_unit_keys} have no recorded 'depth_unit' in their info. "
-                f"Depth maps and stats for these keys will be returned AS IS, with no unit conversion "
-                f"to the requested output unit {output_unit!r}. Re-record the dataset or set 'depth_unit' "
-                f"in the feature info (meta/info.json) to enable conversion."
-            )
-        if self.stats is None:
-            return
-        for key in self.depth_keys:
-            stored_unit = (self.features[key].get("info") or {}).get("depth_unit")
-            if stored_unit is None or stored_unit == output_unit or key not in self.stats:
-                continue
-            factor = MM_PER_METRE if stored_unit == DEPTH_METER_UNIT else 1.0 / MM_PER_METRE
-            self.stats[key] = {
-                stat: value if stat == "count" else value * factor for stat, value in self.stats[key].items()
-            }
-
     @property
     def camera_keys(self) -> list[str]:
         """Keys to access visual modalities (regardless of their storage method)."""

src/lerobot/datasets/dataset_reader.py

@@ -22,14 +22,10 @@ from pathlib import Path
 import datasets
 import torch
 
-from lerobot.configs import (
-    DEFAULT_DEPTH_UNIT,
-    DEPTH_METER_UNIT,
-    DepthEncoderConfig,
-)
+from lerobot.configs import DEFAULT_DEPTH_UNIT, DepthEncoderConfig
 
 from .dataset_metadata import LeRobotDatasetMetadata
-from .depth_utils import MM_PER_METRE, dequantize_depth
+from .depth_utils import dequantize_depth
 from .feature_utils import (
     check_delta_timestamps,
     get_delta_indices,
@@ -106,13 +102,6 @@ class DatasetReader:
             for vid_key in self._meta.depth_keys
         }
 
-        # Get the input unit of each depth feature stored as raw images.
-        self._image_depth_units: dict[str, str | None] = {
-            key: (self._meta.features[key].get("info") or {}).get("depth_unit")
-            for key in self._meta.depth_keys
-            if key in self._meta.image_keys
-        }
-
     def set_image_transforms(self, image_transforms: Callable | None) -> None:
         """Replace the transform applied to visual observations."""
         if image_transforms is not None and not callable(image_transforms):
@@ -335,18 +324,10 @@ class DatasetReader:
             item = {**video_frames, **item}
 
         if self._image_transforms is not None:
-            for cam in self._meta.camera_keys:
-                if cam in self._meta.depth_keys:
-                    continue
+            image_keys = self._meta.camera_keys
+            for cam in image_keys:
                 item[cam] = self._image_transforms(item[cam])
 
-        # Convert depth features to the output unit.
-        for key, stored_unit in self._image_depth_units.items():
-            if key in item and stored_unit is not None and stored_unit != self._depth_output_unit:
-                item[key] = (
-                    item[key] * MM_PER_METRE if stored_unit == DEPTH_METER_UNIT else item[key] / MM_PER_METRE
-                )
-
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self._meta.tasks.iloc[task_idx].name

src/lerobot/datasets/dataset_writer.py

@@ -36,7 +36,6 @@ from lerobot.configs import (
     RGBEncoderConfig,
     VideoEncoderConfig,
     depth_encoder_defaults,
-    infer_depth_unit,
     rgb_encoder_defaults,
 )
 
@@ -210,15 +209,6 @@ class DatasetWriter:
         self.episode_buffer["timestamp"].append(timestamp)
         self.episode_buffer["task"].append(frame.pop("task"))
 
-        # Record each depth feature's input unit once, inferred from the first frame's dtype.
-        if frame_index == 0:
-            for depth_key in self._meta.depth_keys:
-                if depth_key not in frame:
-                    continue
-                info = self._meta.features[depth_key].setdefault("info", {})
-                if info.get("depth_unit") is None:
-                    info["depth_unit"] = infer_depth_unit(np.asarray(frame[depth_key]).dtype)
-
         # Start streaming encoder on first frame of episode
         if frame_index == 0 and self._streaming_encoder is not None:
             self._streaming_encoder.start_episode(

src/lerobot/datasets/depth_utils.py

@@ -34,13 +34,12 @@ from lerobot.configs.video import (
     DEPTH_METER_UNIT,
     DEPTH_MILLIMETER_UNIT,
     DEPTH_QMAX,
-    infer_depth_unit,
 )
 
 from .image_writer import squeeze_single_channel
 from .pyav_utils import write_u16_plane
 
-MM_PER_METRE = 1000.0
+_MM_PER_METRE = 1000.0
 _UINT16_MAX = 65535
 
 
@@ -58,7 +57,11 @@ def _depth_input_to_float32_and_unit(
     input_unit: Literal["auto", DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT],
 ) -> tuple[NDArray[np.float32], Literal[DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT]]:
     """Convert depth to float32 in the chosen unit, and return the resolved unit."""
-    resolved_unit = infer_depth_unit(depth.dtype) if input_unit == "auto" else input_unit
+    resolved_unit = (
+        (DEPTH_METER_UNIT if np.issubdtype(depth.dtype, np.floating) else DEPTH_MILLIMETER_UNIT)
+        if input_unit == "auto"
+        else input_unit
+    )
     return depth.astype(np.float32, order="K"), resolved_unit
 
 
@@ -76,7 +79,7 @@ def quantize_depth(
 
     Depth maps are packed into 12-bit integer frames so they fit in standard
     high-bit-depth pixel formats (e.g. ``yuv420p12le`` / ``gray12le``)
-    and can be encoded by widely supported video codecs (e.g. HEVC Main 12).
+    and can be encoded by widely supported video codecs (HEVC Main 12, ffv1).
     Logarithmic quantization is the default because it allocates more quanta
     to near-range depth, which matches the (1/depth) error profile of typical
     depth sensors. Math is ported from BEHAVIOR-1K's ``obs_utils.py``.
@@ -123,12 +126,12 @@ def quantize_depth(
 
     # Convert depth_min, depth_max, and shift to the resolved input unit.
     depth_min_u = (
-        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * MM_PER_METRE)
+        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * _MM_PER_METRE)
     )
     depth_max_u = (
-        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * MM_PER_METRE)
+        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * _MM_PER_METRE)
     )
-    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * _MM_PER_METRE)
 
     # Normalization and quantization is performed in the resolved input unit.
     if use_log:
@@ -233,7 +236,7 @@ def dequantize_depth(
 
         # mm path: round + clamp in float32, skipping the uint16 round-trip
         # when returning a tensor (torch.uint16 is poorly supported).
-        buf.mul_(MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
+        buf.mul_(_MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
         if output_tensor:
             return buf
         return buf.cpu().numpy().astype(np.uint16, copy=False)
@@ -256,7 +259,7 @@ def dequantize_depth(
     if output_unit == DEPTH_METER_UNIT:
         return torch.from_numpy(buf) if output_tensor else buf
 
-    np.multiply(buf, MM_PER_METRE, out=buf)
+    np.multiply(buf, _MM_PER_METRE, out=buf)
     np.rint(buf, out=buf)
     np.clip(buf, 0.0, _UINT16_MAX, out=buf)
     if output_tensor:

src/lerobot/datasets/factory.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-import math
 from pprint import pformat
 
 import torch
@@ -134,81 +133,3 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 
     return dataset
-
-
-def make_train_eval_datasets(
-    cfg: TrainPipelineConfig,
-) -> tuple[LeRobotDataset | MultiLeRobotDataset, LeRobotDataset | None]:
-    """Create train and optional eval datasets by splitting episodes based on eval_split.
-
-    The last ceil(n_episodes * eval_split) episodes per task are held out for evaluation.
-    If eval_split == 0.0, returns (full_dataset, None).
-    """
-    full_dataset = make_dataset(cfg)
-
-    if cfg.dataset.eval_split == 0.0:
-        return full_dataset, None
-
-    base_episodes = (
-        full_dataset.episodes if full_dataset.episodes is not None else list(range(full_dataset.num_episodes))
-    )
-
-    episode_tasks = full_dataset.meta.episodes["tasks"]
-    task_to_episodes: dict[str, list[int]] = {}
-    for ep_idx in base_episodes:
-        task_key = episode_tasks[ep_idx][0] if episode_tasks[ep_idx] else ""
-        task_to_episodes.setdefault(task_key, []).append(ep_idx)
-
-    train_episodes, eval_episodes = [], []
-    for eps in task_to_episodes.values():
-        n_eval = math.ceil(len(eps) * cfg.dataset.eval_split)
-        train_episodes.extend(eps[: len(eps) - n_eval])
-        eval_episodes.extend(eps[len(eps) - n_eval :])
-
-    if not train_episodes:
-        raise ValueError(
-            f"eval_split={cfg.dataset.eval_split} leaves 0 training episodes from {len(base_episodes)} total."
-        )
-
-    logging.info(
-        f"Train/eval split: {len(train_episodes)} train, {len(eval_episodes)} eval "
-        f"(eval_split={cfg.dataset.eval_split}, {len(task_to_episodes)} tasks)"
-    )
-
-    delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, full_dataset.meta)
-
-    train_image_transforms = (
-        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
-    )
-
-    train_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=train_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=train_image_transforms,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    eval_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=eval_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=None,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    if cfg.dataset.use_imagenet_stats:
-        for ds in (train_dataset, eval_dataset):
-            for key in ds.meta.camera_keys:
-                for stats_type, stats in IMAGENET_STATS.items():
-                    ds.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
-
-    return train_dataset, eval_dataset

src/lerobot/datasets/io_utils.py

@@ -226,50 +226,28 @@ def load_image_as_numpy(
     Args:
         fpath (str | Path): Path to the image file.
         dtype (np.dtype): The desired data type of the output array. If floating,
-            pixels are scaled to [0, 1]. Only used for RGB images.
+            pixels are scaled to [0, 1].
         channel_first (bool): If True, converts the image to (C, H, W) format.
             Otherwise, it remains in (H, W, C) format.
 
     Returns:
         np.ndarray: The image as a numpy array.
     """
-    is_depth = fpath.endswith(".tiff") or fpath.endswith(".tif")
-    if is_depth:
-        # Preserve the native depth dtype (uint16 -> "I;16", float32 -> "F").
-        img = PILImage.open(fpath)
-        img_array = np.array(img)
-    else:
-        img = PILImage.open(fpath).convert("RGB")
-        img_array = np.array(img, dtype=dtype)
-        if np.issubdtype(dtype, np.floating):
-            img_array /= 255.0
+    img = PILImage.open(fpath).convert("RGB")
+    img_array = np.array(img, dtype=dtype)
     if channel_first:  # (H, W, C) -> (C, H, W)
-        img_array = img_array[np.newaxis, ...] if img_array.ndim == 2 else np.transpose(img_array, (2, 0, 1))
+        img_array = np.transpose(img_array, (2, 0, 1))
+    if np.issubdtype(dtype, np.floating):
+        img_array /= 255.0
     return img_array
 
 
-# PIL modes for 16-bit unsigned depth maps.
-UINT16_PIL_MODES = {"I;16", "I;16B", "I;16L"}
-
-
-def pil_to_chw_tensor(img: PILImage.Image) -> torch.Tensor:
-    """Convert a PIL image to a channel-first tensor.
-
-    ``uint16`` depth maps become ``float32 (1, H, W)`` in native units (``ToTensor``
-    would overflow them to ``int16``); all other modes use the standard ``ToTensor`` path.
-    """
-    if img.mode in UINT16_PIL_MODES:
-        return torch.from_numpy(np.array(img, dtype=np.float32))[None, ...]
-    return transforms.ToTensor()(img)
-
-
 def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
     """Convert a batch from a Hugging Face dataset to torch tensors.
 
     This transform function converts items from Hugging Face dataset format (pyarrow)
-    to torch tensors. RGB images are converted from PIL objects (H, W, C, uint8)
-    to a torch image representation (C, H, W, float32) in the range [0, 1]. Depth
-    maps are returned as float32 (1, H, W) in their native units. Other
+    to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
+    to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
     types are converted to torch.tensor.
 
     Args:
@@ -284,7 +262,8 @@ def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[to
             continue
         first_item = items_dict[key][0]
         if isinstance(first_item, PILImage.Image):
-            items_dict[key] = [pil_to_chw_tensor(img) for img in items_dict[key]]
+            to_tensor = transforms.ToTensor()
+            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
         elif first_item is None or isinstance(first_item, dict):
             pass
         else:
@@ -350,11 +329,7 @@ def item_to_torch(item: dict) -> dict:
     """
     skip_keys = {"task", *LANGUAGE_COLUMNS}
     for key, val in item.items():
-        if key in skip_keys:
-            continue
-        if isinstance(val, PILImage.Image):
-            item[key] = pil_to_chw_tensor(val)
-        elif isinstance(val, (np.ndarray | list)):
+        if isinstance(val, (np.ndarray | list)) and key not in skip_keys:
             # Convert numpy arrays and lists to torch tensors
             item[key] = torch.tensor(val)
     return item

src/lerobot/datasets/lerobot_dataset.py

@@ -224,7 +224,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         )
         self.root = self.meta.root
         self.revision = self.meta.revision
-        self.meta.rescale_depth_stats(self._depth_output_unit)
 
         if episodes is not None and any(
             episode >= self.meta.total_episodes or episode < 0 for episode in episodes
@@ -351,11 +350,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         """Frames per second used during data collection."""
         return self.meta.fps
 
-    @property
-    def depth_output_unit(self) -> str:
-        """Physical unit (``"m"`` or ``"mm"``) depth maps and statistics are returned in on read."""
-        return self._depth_output_unit
-
     @property
     def num_frames(self) -> int:
         """Number of frames in selected episodes."""
@@ -387,18 +381,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
             self.reader.load_and_activate()
         return self.reader.hf_dataset
 
-    @property
-    def absolute_to_relative_idx(self) -> dict[int, int] | None:
-        """Mapping from absolute frame indices to HF dataset row positions.
-
-        Non-None only for episode-filtered datasets where absolute indices
-        (from metadata) differ from row positions in the loaded HF dataset.
-        """
-        reader = self._ensure_reader()
-        if reader.hf_dataset is None:
-            reader.load_and_activate()
-        return reader._absolute_to_relative_idx
-
     # ── Writer-delegated methods ──────────────────────────────────────
 
     def add_frame(self, frame: dict) -> None:

src/lerobot/datasets/sampler.py

@@ -53,7 +53,6 @@ class EpisodeAwareSampler:
         drop_n_last_frames: int = 0,
         shuffle: bool = False,
         seed: int = 0,
-        absolute_to_relative_idx: dict[int, int] | None = None,
     ):
         """
         Args:
@@ -108,7 +107,6 @@ class EpisodeAwareSampler:
         self.seed = seed
         self._epoch = 0
         self._start_index = 0
-        self._absolute_to_relative = absolute_to_relative_idx
 
     @property
     def indices(self) -> list[int]:
@@ -134,10 +132,7 @@ class EpisodeAwareSampler:
     def _frame_index(self, position: int) -> int:
         episode = int(np.searchsorted(self._cum_lengths, position, side="right"))
         position_in_episode = position - (int(self._cum_lengths[episode - 1]) if episode > 0 else 0)
-        absolute_idx = int(self._starts[episode]) + position_in_episode
-        if self._absolute_to_relative is not None:
-            return self._absolute_to_relative[absolute_idx]
-        return absolute_idx
+        return int(self._starts[episode]) + position_in_episode
 
     def __iter__(self) -> Iterator[int]:
         # Advance epoch state eagerly, not on first consumption of the generator.

src/lerobot/datasets/streaming_dataset.py

@@ -22,11 +22,11 @@ import numpy as np
 import torch
 from datasets import load_dataset
 
-from lerobot.configs import DEFAULT_DEPTH_UNIT, DEPTH_METER_UNIT, DepthEncoderConfig
+from lerobot.configs import DEFAULT_DEPTH_UNIT, DepthEncoderConfig
 from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE
 
 from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
-from .depth_utils import MM_PER_METRE, dequantize_depth
+from .depth_utils import dequantize_depth
 from .feature_utils import get_delta_indices
 from .io_utils import item_to_torch
 from .utils import (
@@ -310,7 +310,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         )
         self.root = self.meta.root
         self.revision = self.meta.revision
-        self.meta.rescale_depth_stats(self._depth_output_unit)
         # Check version
         check_version_compatibility(self.repo_id, self.meta._version, CODEBASE_VERSION)
 
@@ -319,13 +318,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             for vid_key in self.meta.depth_keys
         }
 
-        # Input unit of each depth feature stored as raw images (dequantized separately from videos).
-        self._image_depth_units: dict[str, str | None] = {
-            key: (self.meta.features[key].get("info") or {}).get("depth_unit")
-            for key in self.meta.depth_keys
-            if key in self.meta.image_keys
-        }
-
         self.delta_timestamps = None
         self.delta_indices = None
 
@@ -356,11 +348,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
     def fps(self):
         return self.meta.fps
 
-    @property
-    def depth_output_unit(self) -> str:
-        """Physical unit (``"m"`` or ``"mm"``) depth maps are returned in on read."""
-        return self._depth_output_unit
-
     @staticmethod
     def _iter_random_indices(
         rng: np.random.Generator, buffer_size: int, random_batch_size=100
@@ -543,15 +530,6 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         for update in updates:
             result.update(update)
 
-        # Convert raw-image depth features to the output unit (video depth is already converted).
-        for key, stored_unit in self._image_depth_units.items():
-            if key in result and stored_unit is not None and stored_unit != self._depth_output_unit:
-                result[key] = (
-                    result[key] * MM_PER_METRE
-                    if stored_unit == DEPTH_METER_UNIT
-                    else result[key] / MM_PER_METRE
-                )
-
         result["task"] = self.meta.tasks.iloc[item["task_index"]].name
 
         yield result

src/lerobot/envs/configs.py

@@ -757,7 +757,7 @@ class RoboTwinEnvConfig(EnvConfig):
 
     task: str = "beat_block_hammer"  # single task or comma-separated list
     fps: int = 25
-    episode_length: int = 1200
+    episode_length: int = 300
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     # Available cameras from RoboTwin's aloha-agilex embodiment: head_camera
@@ -768,9 +768,6 @@ class RoboTwinEnvConfig(EnvConfig):
     # must equal what SAPIEN actually renders.
     observation_height: int = 240
     observation_width: int = 320
-    # "joint": 14-d joint-space control. "ee": 16-d end-effector-pose deltas executed via CuRobo IK
-    # (for world-model policies like LingBot-VA that predict per-arm xyz+quaternion+gripper poses).
-    action_mode: str = "joint"
     features: dict[str, PolicyFeature] = field(
         default_factory=lambda: {
             ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)),
@@ -787,8 +784,6 @@ class RoboTwinEnvConfig(EnvConfig):
     )
 
     def __post_init__(self):
-        if self.action_mode == "ee":
-            self.features[ACTION] = PolicyFeature(type=FeatureType.ACTION, shape=(16,))
         cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
         for cam in cam_list:
             self.features[f"pixels/{cam}"] = PolicyFeature(
@@ -831,7 +826,6 @@ class RoboTwinEnvConfig(EnvConfig):
             observation_height=self.observation_height,
             observation_width=self.observation_width,
             episode_length=self.episode_length,
-            action_mode=self.action_mode,
         )
 
 

src/lerobot/envs/robotwin.py

@@ -17,7 +17,6 @@ from __future__ import annotations
 
 import importlib
 import logging
-import os
 from collections import defaultdict
 from collections.abc import Callable, Sequence
 from functools import partial
@@ -29,17 +28,9 @@ import torch
 from gymnasium import spaces
 
 from lerobot.types import RobotObservation
-from lerobot.utils.import_utils import _scipy_available
 
 from .utils import _LazyAsyncVectorEnv
 
-# scipy is only used for end-effector-pose composition (``--env.action_mode=ee``); guard it so this
-# module (and its base-env unit tests, which mock the RoboTwin runtime) imports without scipy installed.
-if _scipy_available:
-    from scipy.spatial.transform import Rotation
-else:
-    Rotation = None
-
 logger = logging.getLogger(__name__)
 
 # Camera names as used by RoboTwin 2.0. The wrapper appends "_rgb" when looking
@@ -50,124 +41,10 @@ ROBOTWIN_CAMERA_NAMES: tuple[str, ...] = (
     "right_camera",
 )
 
-ACTION_DIM = 14  # 7 DOF × 2 arms (joint-space control mode)
-# End-effector-pose control mode: per arm [x, y, z, qx, qy, qz, qw, gripper] = 8, dual-arm = 16.
-# Used by world-model policies (e.g. LingBot-VA) that predict eef-pose deltas executed via CuRobo IK.
-EEF_ACTION_DIM = 16
+ACTION_DIM = 14  # 7 DOF × 2 arms
 ACTION_LOW = -1.0
 ACTION_HIGH = 1.0
-DEFAULT_EPISODE_LENGTH = 1200
-OFFICIAL_INSTRUCTION_ENV = "LEROBOT_ROBOTWIN_OFFICIAL_INSTRUCTION"
-OFFICIAL_INSTRUCTION_TYPE_ENV = "LEROBOT_ROBOTWIN_INSTRUCTION_TYPE"
-OFFICIAL_INSTRUCTION_MAX_ENV = "LEROBOT_ROBOTWIN_INSTRUCTION_MAX"
-
-
-def _compose_eef_pose(new_pose: np.ndarray, init_pose: np.ndarray) -> np.ndarray:
-    """Compose a single-arm predicted delta pose onto the initial pose.
-
-    ``new_pose`` / ``init_pose`` are 8-vectors ``[x, y, z, qx, qy, qz, qw, gripper]``. Translation
-    is added, rotation is composed (``init_R * new_R``), and the gripper is taken from the
-    prediction. Mirrors ``add_eef_pose`` in the upstream LingBot-VA RoboTwin client.
-    """
-    new_r = Rotation.from_quat(new_pose[3:7])
-    init_r = Rotation.from_quat(init_pose[3:7])
-    out_rot = (init_r * new_r).as_quat()
-    out_trans = new_pose[:3] + init_pose[:3]
-    return np.concatenate([out_trans, out_rot, new_pose[7:8]])
-
-
-def _add_init_eef_pose(delta_pose: np.ndarray, init_pose: np.ndarray) -> np.ndarray:
-    """Compose a dual-arm (16-d) predicted delta pose onto the initial eef pose, normalizing quats."""
-    left = _compose_eef_pose(delta_pose[:8], init_pose[:8])
-    right = _compose_eef_pose(delta_pose[8:], init_pose[8:])
-    out = np.concatenate([left, right])
-    # Normalize the two quaternions (indices 3:7 and 11:15) as the upstream client does.
-    out[3:7] = out[3:7] / (np.linalg.norm(out[3:7]) + 1e-8)
-    out[11:15] = out[11:15] / (np.linalg.norm(out[11:15]) + 1e-8)
-    return out
-
-
-def _env_flag(name: str, default: bool = False) -> bool:
-    raw = os.environ.get(name)
-    if raw is None:
-        return default
-    return raw.strip().lower() in {"1", "true", "yes", "on"}
-
-
-def _arm_for_block(block: Any) -> str:
-    return "left" if float(block.get_pose().p[0]) < 0 else "right"
-
-
-def _robotwin_blocks_episode_info(task_name: str, env: Any) -> dict[str, str] | None:
-    """Infer the episode-info dict used by RoboTwin's official instruction generator for block ranking."""
-    if task_name == "blocks_ranking_rgb":
-        return {
-            "{A}": "red block",
-            "{B}": "green block",
-            "{C}": "blue block",
-            "{a}": _arm_for_block(env.block1),
-            "{b}": _arm_for_block(env.block2),
-            "{c}": _arm_for_block(env.block3),
-        }
-    if task_name == "blocks_ranking_size":
-        return {
-            "{A}": "large block",
-            "{B}": "medium block",
-            "{C}": "small block",
-            "{a}": _arm_for_block(env.block1),
-            "{b}": _arm_for_block(env.block2),
-            "{c}": _arm_for_block(env.block3),
-        }
-    return None
-
-
-def _generate_robotwin_official_instruction(task_name: str, env: Any) -> str:
-    """Generate language with RoboTwin's official task templates, matching its eval client."""
-    fallback = task_name.replace("_", " ")
-    episode_info = _robotwin_blocks_episode_info(task_name, env)
-    if episode_info is None:
-        logger.warning(
-            "Official RoboTwin instruction is not implemented for task=%s; using %r.", task_name, fallback
-        )
-        return fallback
-
-    try:
-        # Part of the robotwin simulator repo, this is being pulled by the docker image running robotwin
-        # see https://github.com/RoboTwin-Platform/RoboTwin/tree/main/description
-        # Used to generate the official instructions
-        from description.utils.generate_episode_instructions import generate_episode_descriptions
-    except Exception:
-        logger.warning(
-            "Failed to import RoboTwin official instruction generator; using %r.", fallback, exc_info=True
-        )
-        return fallback
-
-    instruction_type = os.environ.get(OFFICIAL_INSTRUCTION_TYPE_ENV, "seen")
-    try:
-        max_descriptions = int(os.environ.get(OFFICIAL_INSTRUCTION_MAX_ENV, "1000000"))
-    except ValueError:
-        max_descriptions = 1000000
-
-    results = generate_episode_descriptions(task_name, [episode_info], max_descriptions=max_descriptions)
-    if not results:
-        logger.warning(
-            "RoboTwin generated no official instructions for task=%s; using %r.", task_name, fallback
-        )
-        return fallback
-
-    options = results[0].get(instruction_type) or results[0].get("seen") or results[0].get("unseen")
-    if not options:
-        logger.warning(
-            "RoboTwin generated no %s official instructions for task=%s; using %r.",
-            instruction_type,
-            task_name,
-            fallback,
-        )
-        return fallback
-
-    return str(np.random.choice(options))
-
-
+DEFAULT_EPISODE_LENGTH = 300
 # D435 dims from task_config/_camera_config.yml (what demo_clean.yml selects).
 DEFAULT_CAMERA_H = 240
 DEFAULT_CAMERA_W = 320
@@ -357,7 +234,6 @@ class RoboTwinEnv(gym.Env):
         observation_width: int | None = None,
         episode_length: int = DEFAULT_EPISODE_LENGTH,
         render_mode: str = "rgb_array",
-        action_mode: str = "joint",
     ):
         super().__init__()
         self.task_name = task_name
@@ -365,13 +241,6 @@ class RoboTwinEnv(gym.Env):
         self.task_description = task_name.replace("_", " ")
         self.episode_index = episode_index
         self._reset_stride = n_envs
-        # "joint": 14-d joint-space actions via take_action(action). "ee": 16-d end-effector-pose
-        # deltas (added onto the episode's initial eef pose) executed via take_action(.., "ee") + IK.
-        if action_mode not in ("joint", "ee"):
-            raise ValueError(f"action_mode must be 'joint' or 'ee'; got {action_mode!r}")
-        self.action_mode = action_mode
-        self._action_dim = EEF_ACTION_DIM if action_mode == "ee" else ACTION_DIM
-        self._init_eef_pose: np.ndarray | None = None
         self.camera_names = list(camera_names)
         # Default to D435 dims (the camera type baked into task_config/demo_clean.yml).
         # The YAML-driven lookup is deferred to reset() so construction doesn't
@@ -402,7 +271,7 @@ class RoboTwinEnv(gym.Env):
             }
         )
         self.action_space = spaces.Box(
-            low=ACTION_LOW, high=ACTION_HIGH, shape=(self._action_dim,), dtype=np.float32
+            low=ACTION_LOW, high=ACTION_HIGH, shape=(ACTION_DIM,), dtype=np.float32
         )
 
     def _ensure_env(self) -> None:
@@ -448,18 +317,6 @@ class RoboTwinEnv(gym.Env):
 
         return {"pixels": images, "agent_pos": joint_state}
 
-    def _read_eef_pose(self) -> np.ndarray:
-        """Read the current 16-d dual-arm eef pose [left(xyz+quat)+grip, right(xyz+quat)+grip]."""
-        assert self._env is not None, "_read_eef_pose called before _ensure_env()"
-        ep = self._env.get_obs()["endpose"]
-        pose = (
-            list(ep["left_endpose"])
-            + [ep["left_gripper"]]
-            + list(ep["right_endpose"])
-            + [ep["right_gripper"]]
-        )
-        return np.asarray(pose, dtype=np.float64)
-
     def reset(self, seed: int | None = None, **kwargs) -> tuple[RobotObservation, dict]:
         self._ensure_env()
         super().reset(seed=seed)
@@ -473,32 +330,16 @@ class RoboTwinEnv(gym.Env):
         self.episode_index += self._reset_stride
         self._step_count = 0
 
-        use_official_instruction = self.task_name in {"blocks_ranking_rgb", "blocks_ranking_size"}
-        if _env_flag(OFFICIAL_INSTRUCTION_ENV, default=use_official_instruction):
-            self.task_description = _generate_robotwin_official_instruction(self.task_name, self._env)
-            if hasattr(self._env, "set_instruction"):
-                self._env.set_instruction(instruction=self.task_description)
-            logger.info("RoboTwin official instruction | task=%s | %s", self.task_name, self.task_description)
-        else:
-            self.task_description = self.task_name.replace("_", " ")
-
-        # In eef mode the policy predicts pose deltas relative to the initial eef pose.
-        if self.action_mode == "ee":
-            self._init_eef_pose = self._read_eef_pose()
-
         obs = self._get_obs()
         return obs, {"is_success": False, "task": self.task_name}
 
     def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
         assert self._env is not None, "step() called before reset()"
-        if action.ndim != 1 or action.shape[0] != self._action_dim:
-            raise ValueError(f"Expected 1-D action of shape ({self._action_dim},), got {action.shape}")
+        if action.ndim != 1 or action.shape[0] != ACTION_DIM:
+            raise ValueError(f"Expected 1-D action of shape ({ACTION_DIM},), got {action.shape}")
 
         with torch.enable_grad():
-            if self.action_mode == "ee":
-                ee_action = _add_init_eef_pose(np.asarray(action, dtype=np.float64), self._init_eef_pose)
-                self._env.take_action(ee_action, action_type="ee")
-            elif hasattr(self._env, "take_action"):
+            if hasattr(self._env, "take_action"):
                 self._env.take_action(action)
             else:
                 self._env.step(action)
@@ -557,7 +398,6 @@ def _make_env_fns(
     observation_height: int,
     observation_width: int,
     episode_length: int,
-    action_mode: str = "joint",
 ) -> list[Callable[[], RoboTwinEnv]]:
     """Return n_envs factory callables for a single task."""
 
@@ -570,7 +410,6 @@ def _make_env_fns(
             observation_height=observation_height,
             observation_width=observation_width,
             episode_length=episode_length,
-            action_mode=action_mode,
         )
 
     return [partial(_make_one, i) for i in range(n_envs)]
@@ -584,7 +423,6 @@ def create_robotwin_envs(
     observation_height: int = DEFAULT_CAMERA_H,
     observation_width: int = DEFAULT_CAMERA_W,
     episode_length: int = DEFAULT_EPISODE_LENGTH,
-    action_mode: str = "joint",
 ) -> dict[str, dict[int, Any]]:
     """Create vectorized RoboTwin 2.0 environments.
 
@@ -635,7 +473,6 @@ def create_robotwin_envs(
             observation_height=observation_height,
             observation_width=observation_width,
             episode_length=episode_length,
-            action_mode=action_mode,
         )
         if is_async:
             lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)

src/lerobot/jobs/__init__.py

@@ -1,23 +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.
-
-from lerobot.utils.import_utils import require_package
-
-# LeRobotDataset (imported at module top in dataset.py) pulls in heavy dataset deps;
-# guard the optional dependency here so importing this package fails loudly if it's missing.
-require_package("datasets", extra="dataset")
-
-from .hf import submit_to_hf
-
-__all__ = ["submit_to_hf"]

src/lerobot/jobs/dataset.py

@@ -1,53 +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.
-"""Make a training dataset reachable from an HF Job pod.
-
-The pod can't see the host's ~/.cache/huggingface/lerobot, so the dataset has to
-live on the Hub: the pod downloads it by repo_id at train time (the forwarded
-HF_TOKEN covers private datasets). A dataset already on the Hub is used as-is; a
-local-only dataset is pushed to a PRIVATE repo first (never public).
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from lerobot.datasets import LeRobotDataset
-from lerobot.utils.constants import HF_LEROBOT_HOME
-
-if TYPE_CHECKING:
-    from huggingface_hub import HfApi
-
-
-def ensure_dataset_available(repo_id: str, *, api: HfApi, tags: list[str] | None = None) -> None:
-    """Ensure repo_id resolves on the Hub, pushing a local-only dataset privately first.
-
-    `tags` are attached to the dataset only when we push it (an already-on-Hub
-    dataset is left untouched). Raises RuntimeError if the dataset is neither on
-    the Hub nor in the local cache.
-    """
-    if api.repo_exists(repo_id, repo_type="dataset"):
-        return
-
-    local_present = (HF_LEROBOT_HOME / repo_id / "meta" / "info.json").is_file()
-    if not local_present:
-        raise RuntimeError(
-            f"Dataset '{repo_id}' is not in the local cache ({HF_LEROBOT_HOME}) and could not be "
-            f"reached on the Hub — it may not exist, or be private and inaccessible with your "
-            f"token. Record or download it first, or run `hf auth login`."
-        )
-
-    print(f"[dataset] '{repo_id}' is local-only; pushing to a PRIVATE Hub repo...")
-    LeRobotDataset(repo_id).push_to_hub(private=True, tags=tags)
-    print(f"[dataset] '{repo_id}' uploaded (private). The job will download it by repo_id.")

src/lerobot/jobs/hf.py

@@ -1,425 +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.
-"""Run a lerobot training on HF Jobs (HuggingFace GPUs).
-
-Ported and simplified from lelab's runners/hf_cloud.py: no UI log queue, no
-registry — just submit and stream to stdout.
-"""
-
-from __future__ import annotations
-
-import copy
-import datetime as dt
-import json
-import netrc
-import os
-import re
-import signal
-import sys
-import tempfile
-import threading
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import httpx
-from huggingface_hub import (
-    HfApi,
-    create_repo,
-    fetch_job_logs,
-    get_token,
-    inspect_job,
-    run_job,
-    upload_file,
-)
-
-from lerobot.common.train_utils import push_checkpoint_to_hub
-from lerobot.configs import parser
-
-from .dataset import ensure_dataset_available
-
-if TYPE_CHECKING:
-    from lerobot.configs.train import TrainPipelineConfig
-
-_SLUG_RE = re.compile(r"[^a-zA-Z0-9._-]+")
-
-_TERMINAL_STAGES = {"COMPLETED", "CANCELED", "ERROR", "DELETED"}
-
-# huggingface_hub 1.x runs on httpx: transient HTTP/transport failures surface as
-# httpx.HTTPError and socket-level errors as OSError. Catching only these keeps real
-# bugs (TypeError, AttributeError, ...) from being silently retried or counted as
-# job failures.
-_TRANSIENT_NET_ERRORS = (OSError, httpx.HTTPError)
-
-# Always attached to remote jobs and pushed datasets so LeRobot-originated work
-# is identifiable on the Hub; callers (e.g. LeLab) add their own via --job.tags.
-LEROBOT_TAG = "lerobot"
-
-
-def resolve_job_tags(extra: list[str] | None) -> list[str]:
-    """Return the tag list for a run: the lerobot tag plus any extras, deduped, order-stable."""
-    tags = [LEROBOT_TAG, *(extra or [])]
-    seen: set[str] = set()
-    return [t for t in tags if not (t in seen or seen.add(t))]
-
-
-def resolve_wandb_api_key() -> str | None:
-    """Host's wandb key for forwarding to the job: $WANDB_API_KEY, else ~/.netrc."""
-    key = os.environ.get("WANDB_API_KEY")
-    if key:
-        return key
-    try:
-        rc = netrc.netrc()
-    except (FileNotFoundError, netrc.NetrcParseError, OSError):
-        return None
-    auth = rc.authenticators("api.wandb.ai")
-    if auth is None:
-        return None
-    _login, _account, password = auth
-    return password or None
-
-
-def build_repo_id(username: str, job_name: str, now: dt.datetime) -> str:
-    """Generate the model repo id for a remote run: <user>/<job_name>_<timestamp>."""
-    slug = _SLUG_RE.sub("-", job_name).strip("-") or "train"
-    stamp = now.strftime("%Y-%m-%d_%H-%M-%S")
-    return f"{username}/{slug}_{stamp}"
-
-
-def build_remote_config_file(cfg, repo_id: str, dest: Path, tags: list[str] | None = None) -> Path:
-    """Write a train_config.json for the pod, with remote overrides applied.
-
-    The pod runs `lerobot-train --config_path=<dest>` and downloads the dataset
-    by repo_id into its own cache. Client-only fields are stripped so the config
-    is accepted by the trainer image: `job` (pure client orchestration) is always
-    removed, and `save_checkpoint_to_hub` is removed unless explicitly enabled —
-    older lerobot images reject unknown keys, so the default keeps the config
-    compatible with the released `lerobot-gpu` image. `tags` are merged into
-    policy.tags so the trained model the pod pushes carries them too.
-    """
-    remote = copy.deepcopy(cfg)
-    remote.policy.push_to_hub = True
-    remote.policy.repo_id = repo_id
-    # Don't pin the client's resolved device (e.g. "mps"); let the pod auto-detect its GPU.
-    remote.policy.device = None
-    # Drop any host-local dataset root; the pod resolves the dataset by repo_id.
-    remote.dataset.root = None
-    if tags:
-        existing = list(remote.policy.tags or [])
-        remote.policy.tags = existing + [t for t in tags if t not in existing]
-
-    # Encode to the canonical, pod-parseable dict, then drop the keys the released
-    # trainer image doesn't know about.
-    data = remote.to_dict()
-    data.pop("job", None)
-    if not remote.save_checkpoint_to_hub:
-        data.pop("save_checkpoint_to_hub", None)
-
-    dest.parent.mkdir(parents=True, exist_ok=True)
-    dest.write_text(json.dumps(data, indent=4))
-    return dest
-
-
-def _stage_config_on_hub(cfg, repo_id: str, token: str, tags: list[str] | None = None) -> str:
-    """Upload train_config.json to the model repo and return the repo_id for --config_path."""
-    create_repo(repo_id, repo_type="model", private=True, exist_ok=True, token=token)
-    with tempfile.TemporaryDirectory() as tmp:
-        config_path = build_remote_config_file(cfg, repo_id, Path(tmp) / "train_config.json", tags=tags)
-        upload_file(
-            path_or_fileobj=config_path,
-            path_in_repo="train_config.json",
-            repo_id=repo_id,
-            repo_type="model",
-            token=token,
-        )
-    return repo_id
-
-
-def _tail_logs(
-    job_id: str,
-    done: threading.Event,
-    success_marker: str | None = None,
-    success_event: threading.Event | None = None,
-) -> None:
-    """Stream job logs to stdout, reconnecting on dropped streams until done is set.
-
-    Each reconnect re-fetches the full buffered log, so we track how many lines
-    were already printed and skip them — otherwise a fast-failing job's traceback
-    gets reprinted on every reconnect.
-
-    When `success_marker` appears in a line, set `success_event` and `done` so the
-    caller can finish as soon as the trained model lands on the Hub, rather than
-    waiting out the platform's post-run finalization (which can add ~30s).
-    """
-    printed = 0
-    while not done.is_set():
-        try:
-            seen = 0
-            for line in fetch_job_logs(job_id=job_id, follow=True):
-                seen += 1
-                if seen <= printed:
-                    continue  # already shown on a previous connection
-                printed = seen
-                # fetch_job_logs yields SSE data without trailing newlines, so add one
-                # per entry — otherwise all log lines concatenate onto a single line.
-                print(line.rstrip("\n"), flush=True)
-                if success_marker and success_event is not None and success_marker in line:
-                    success_event.set()
-                    done.set()
-                    return
-                if done.is_set():
-                    return
-            # Stream closed cleanly. Wait a moment so the status poller can mark
-            # the job terminal before we reconnect (avoids re-tailing the buffer).
-            if done.wait(3):
-                return
-        except _TRANSIENT_NET_ERRORS:
-            if done.wait(2):
-                return
-
-
-def _poll_until_done(
-    job_id: str,
-    done: threading.Event,
-    poll_interval: float = 5.0,
-    status_holder: dict | None = None,
-    max_failures: int = 6,
-) -> str | None:
-    """Poll inspect_job until a terminal stage or until `done` is set.
-
-    Returns the terminal stage string, or None if `done` was set first (detach)
-    or after `max_failures` consecutive inspect_job errors. When a terminal stage
-    is reached and `status_holder` is given, records `status_holder["message"]`
-    (the platform's status message, e.g. "Job timeout").
-    """
-    failures = 0
-    while not done.is_set():
-        try:
-            info = inspect_job(job_id=job_id)
-            failures = 0
-            # `stage` is an enum in some huggingface_hub versions and a plain str in others.
-            stage = getattr(info.status.stage, "value", info.status.stage)
-            if stage in _TERMINAL_STAGES:
-                if status_holder is not None:
-                    status_holder["message"] = getattr(info.status, "message", None)
-                done.set()
-                return stage
-        except _TRANSIENT_NET_ERRORS:
-            failures += 1
-            if failures >= max_failures:
-                done.set()
-                return None
-        done.wait(poll_interval)
-    return None
-
-
-def _pod_forwarded_args(
-    argv: list[str], drop_names: tuple[str, ...] = (), drop_prefixes: tuple[str, ...] = ()
-) -> list[str]:
-    """User CLI overrides to replay on the pod, minus flags the submitter sets itself.
-
-    Handles both `--name=value` and `--name value` forms. Forwarding the user's overrides (e.g.
-    `--steps`, `--save_checkpoint_to_hub`) makes a remote resume behave like the same local command.
-    """
-    out: list[str] = []
-    skip_next = False
-    for i, tok in enumerate(argv):
-        if skip_next:
-            skip_next = False
-            continue
-        name = tok.split("=", 1)[0]
-        if name in drop_names or any(name.startswith(p) for p in drop_prefixes):
-            if "=" not in tok and i + 1 < len(argv) and not argv[i + 1].startswith("--"):
-                skip_next = True  # also drop the space-separated value
-            continue
-        out.append(tok)
-    return out
-
-
-def _build_resume_job(cfg: TrainPipelineConfig, username: str) -> tuple[str, list[str]]:
-    """Resolve the model repo and pod command to resume a run on a job.
-
-    A Hub `config_path` is resumed from directly: its checkpoint config already targets that repo,
-    so new checkpoints continue the lineage there. A local `config_path` has its checkpoint uploaded
-    to a new PRIVATE repo first, and the resumed run is forced to push back to it. The pod command
-    always carries `--job.target=local` so the checkpoint's saved `job.target` can't make the pod
-    re-dispatch itself.
-    """
-    config_path = parser.parse_arg("config_path")
-    forwarded = _pod_forwarded_args(
-        sys.argv[1:],
-        drop_names=("--config_path", "--policy.repo_id", "--policy.push_to_hub", "--dataset.root"),
-        drop_prefixes=("--job.",),
-    )
-
-    if Path(config_path).exists():
-        # Local checkpoint: stage it on the Hub so the pod can resume from it, and push back there.
-        # Resolve so a `last` symlink uploads under its real step name (digit), which the pod's
-        # latest-checkpoint lookup keys on.
-        checkpoint_dir = Path(cfg.checkpoint_path).resolve()
-        source_repo = build_repo_id(username, cfg.job_name or "train", dt.datetime.now(dt.UTC))
-        push_checkpoint_to_hub(checkpoint_dir, source_repo, private=True)
-        extra = [f"--policy.repo_id={source_repo}", "--policy.push_to_hub=true"]
-    else:
-        source_repo = config_path
-        extra = []
-
-    command = [
-        "lerobot-train",
-        *forwarded,
-        f"--config_path={source_repo}",
-        "--job.target=local",
-        *extra,
-    ]
-    return source_repo, command
-
-
-def submit_to_hf(cfg: TrainPipelineConfig) -> None:
-    """Submit a training job to HF Jobs infrastructure.
-
-    Validates cfg, resolves credentials, ensures the dataset is on the Hub, then either stages a
-    sanitized config (fresh run) or resumes from a checkpoint repo, submits the job, and tails logs
-    until completion or detaches immediately. Ctrl-C detaches without cancelling the remote job.
-    """
-    token = get_token()
-    if not token:
-        raise RuntimeError("Not logged in to Hugging Face. Run `hf auth login` first.")
-
-    api = HfApi(token=token)
-    user_info = api.whoami(token=token)
-    username = user_info["name"]
-
-    now = dt.datetime.now(dt.UTC)
-    fresh_repo_id: str | None = None
-    if not cfg.resume:
-        # Resolve the model repo and mark it for push BEFORE validate(): validate() requires repo_id
-        # to be set whenever push_to_hub is True. (A resume reuses the checkpoint's repo instead.)
-        if cfg.policy is not None:
-            base_name = cfg.job_name or cfg.policy.type
-            fresh_repo_id = cfg.policy.repo_id or build_repo_id(username, base_name, now)
-            cfg.policy.repo_id = fresh_repo_id
-            cfg.policy.push_to_hub = True
-        else:
-            # Path-based policy is resolved inside validate(); fall back to a generic slug.
-            fresh_repo_id = build_repo_id(username, cfg.job_name or "train", now)
-
-    cfg.validate()
-
-    if cfg.is_reward_model_training:
-        raise ValueError(
-            "Remote training via --job.target only supports policy training, not reward models. "
-            "Run reward-model training locally."
-        )
-
-    secrets: dict[str, str] = {"HF_TOKEN": token}
-    if cfg.wandb.enable:
-        wandb_key = resolve_wandb_api_key()
-        if wandb_key is None:
-            raise ValueError(
-                "wandb is enabled but no WANDB_API_KEY found. "
-                "Set it via `export WANDB_API_KEY=...` or add it to ~/.netrc."
-            )
-        secrets["WANDB_API_KEY"] = wandb_key
-
-    tags = resolve_job_tags(cfg.job.tags)
-    # The dataset must be reachable from the pod for both fresh and resumed runs; a local-only
-    # dataset is pushed PRIVATE here. Hoisted before the resume/fresh branch since it applies to both.
-    ensure_dataset_available(cfg.dataset.repo_id, api=api, tags=tags)
-
-    if cfg.resume:
-        repo_id, command = _build_resume_job(cfg, username)
-    else:
-        config_repo_id = _stage_config_on_hub(cfg, fresh_repo_id, token, tags=tags)
-        repo_id = fresh_repo_id
-        command = ["lerobot-train", f"--config_path={config_repo_id}"]
-
-    print(f"Submitting job to HF Jobs (flavor={cfg.job.target}, image={cfg.job.image}) ...")
-    job_info = run_job(
-        image=cfg.job.image,
-        command=command,
-        flavor=cfg.job.target,
-        secrets=secrets,
-        timeout=cfg.job.timeout,
-        # HF Jobs labels are key/value; expose each tag as a queryable label.
-        labels=dict.fromkeys(tags, "true"),
-    )
-    job_id = job_info.id
-    job_url = getattr(job_info, "url", None)
-    print(f"Job submitted: {job_id}")
-    if job_url:
-        print(f"  Job page:   {job_url}")
-    print(f"  Model repo: https://huggingface.co/{repo_id}")
-    print(f"  Monitor:    hf jobs logs {job_id}")
-    print(f"  Cancel:     hf jobs cancel {job_id}")
-
-    if cfg.job.detach:
-        return
-
-    done = threading.Event()
-    detached = threading.Event()
-    pushed_ok = threading.Event()
-    stage_holder: dict[str, str | None] = {}
-
-    def _poll() -> None:
-        stage_holder["stage"] = _poll_until_done(job_id, done, status_holder=stage_holder)
-
-    poll_thread = threading.Thread(target=_poll, daemon=True)
-    poll_thread.start()
-    # Finish as soon as the model is pushed, rather than waiting out the platform's
-    # post-run finalization before the job stage flips to COMPLETED. This matches the
-    # exact log line emitted by PreTrainedPolicy.push_model_to_hub — the two must stay
-    # in sync. If it ever stops matching we just fall back to stage-based completion
-    # (~30s slower), so the contract is an optimization, not a correctness requirement.
-    success_marker = f"Model pushed to https://huggingface.co/{repo_id}"
-    log_thread = threading.Thread(
-        target=_tail_logs, args=(job_id, done, success_marker, pushed_ok), daemon=True
-    )
-    log_thread.start()
-
-    def _detach(sig, frame):
-        detached.set()
-        done.set()
-        print("\nDetached. Job is still running.")
-        print(f"  Monitor: hf jobs logs {job_id}")
-        print(f"  Cancel:  hf jobs cancel {job_id}")
-
-    # signal.signal only works on the main thread; when called from a worker thread
-    # (e.g. an orchestration framework) skip the Ctrl-C-detaches-instead-of-cancels
-    # handler rather than crashing with ValueError.
-    install_sigint = threading.current_thread() is threading.main_thread()
-    original_sigint = signal.getsignal(signal.SIGINT) if install_sigint else None
-    if install_sigint:
-        signal.signal(signal.SIGINT, _detach)
-    try:
-        # Timeout-based join so SIGINT is delivered to the main thread promptly.
-        while poll_thread.is_alive():
-            poll_thread.join(timeout=0.5)
-        log_thread.join(timeout=5)
-    finally:
-        if install_sigint:
-            signal.signal(signal.SIGINT, original_sigint)
-
-    if detached.is_set():
-        return
-
-    if pushed_ok.is_set():
-        print(f"\nTraining complete — model pushed to https://huggingface.co/{repo_id}")
-        return
-
-    stage = stage_holder.get("stage")
-    if stage != "COMPLETED":
-        message = stage_holder.get("message")
-        detail = f" ({message})" if message else ""
-        raise RuntimeError(
-            f"Job {job_id} ended with stage={stage}{detail}. Check logs: hf jobs logs {job_id}"
-        )

src/lerobot/optim/schedulers.py

@@ -83,50 +83,6 @@ class VQBeTSchedulerConfig(LRSchedulerConfig):
         return LambdaLR(optimizer, lr_lambda, -1)
 
 
-@LRSchedulerConfig.register_subclass("constant_with_warmup")
-@dataclass
-class ConstantWithWarmupSchedulerConfig(LRSchedulerConfig):
-    """Linear warmup followed by a constant learning rate.
-
-    Mirrors the ``warmup_constant_lambda`` used by LingBot-VA (upstream ``wan_va/train.py``):
-    the LR ramps linearly from 0 to the peak over ``num_warmup_steps`` steps, then stays flat.
-    """
-
-    num_warmup_steps: int = 1000
-
-    def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
-        warmup_steps = self.num_warmup_steps or 0
-
-        def lr_lambda(current_step):
-            if current_step < warmup_steps:
-                return float(current_step) / float(max(1, warmup_steps))
-            return 1.0
-
-        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

@@ -17,12 +17,9 @@ 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
 from .groot.configuration_groot import GrootConfig as GrootConfig
-from .lingbot_va.configuration_lingbot_va import LingBotVAConfig as LingBotVAConfig
 from .molmoact2.configuration_molmoact2 import MolmoAct2Config as MolmoAct2Config
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig as MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
@@ -45,11 +42,8 @@ __all__ = [
     "ACTConfig",
     "DiffusionConfig",
     "EO1Config",
-    "FastWAMConfig",
     "GaussianActorConfig",
-    "Evo1Config",
     "GrootConfig",
-    "LingBotVAConfig",
     "MolmoAct2Config",
     "MultiTaskDiTConfig",
     "PI0Config",

src/lerobot/policies/evo1/README.md

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

src/lerobot/policies/evo1/__init__.py

@@ -1,19 +0,0 @@
-# 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 .configuration_evo1 import Evo1Config
-from .modeling_evo1 import Evo1Policy
-from .processor_evo1 import make_evo1_pre_post_processors
-
-__all__ = ["Evo1Config", "Evo1Policy", "make_evo1_pre_post_processors"]

src/lerobot/policies/evo1/configuration_evo1.py

@@ -1,252 +0,0 @@
-# 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

@@ -1,210 +0,0 @@
-# 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

@@ -1,483 +0,0 @@
-# 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

@@ -1,369 +0,0 @@
-# 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

@@ -1,532 +0,0 @@
-# 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

@@ -1,400 +0,0 @@
-# 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

@@ -47,11 +47,8 @@ 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
-from .lingbot_va.configuration_lingbot_va import LingBotVAConfig
 from .molmoact2.configuration_molmoact2 import MolmoAct2Config
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config
@@ -94,7 +91,7 @@ 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", "eo1", "evo1".
+            "molmoact2".
     Returns:
         The policy class corresponding to the given name.
 
@@ -165,18 +162,6 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from .vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
 
         return VLAJEPAPolicy
-    elif name == "lingbot_va":
-        from .lingbot_va.modeling_lingbot_va import LingBotVAPolicy
-
-        return LingBotVAPolicy
-    elif name == "fastwam":
-        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)
@@ -194,7 +179,7 @@ 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", "eo1", "evo1".
+                     "smolvla", "wall_x", "molmoact2".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -233,12 +218,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return MolmoAct2Config(**kwargs)
     elif policy_type == "vla_jepa":
         return VLAJEPAConfig(**kwargs)
-    elif policy_type == "lingbot_va":
-        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)
@@ -302,23 +281,26 @@ 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):
-            from .groot.processor_groot import make_groot_pre_post_processors_from_pretrained
+            # 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,
+            }
 
-            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"
-                ),
-            )
+            # 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
 
         preprocessor = PolicyProcessorPipeline.from_pretrained(
             pretrained_model_name_or_path=pretrained_path,
@@ -341,14 +323,6 @@ 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
@@ -432,7 +406,6 @@ 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):
@@ -460,13 +433,6 @@ 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
@@ -485,22 +451,6 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
-    elif isinstance(policy_cfg, LingBotVAConfig):
-        from .lingbot_va.processor_lingbot_va import make_lingbot_va_pre_post_processors
-
-        processors = make_lingbot_va_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-
-    elif isinstance(policy_cfg, FastWAMConfig):
-        from .fastwam.processor_fastwam import make_fastwam_pre_post_processors
-
-        processors = make_fastwam_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-
     else:
         try:
             processors = _make_processors_from_policy_config(
@@ -590,7 +540,6 @@ 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/fastwam/README.md

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

src/lerobot/policies/fastwam/__init__.py

@@ -1,23 +0,0 @@
-# Copyright 2024 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 .configuration_fastwam import FastWAMConfig
-from .modeling_fastwam import FastWAMPolicy
-from .processor_fastwam import make_fastwam_pre_post_processors
-
-__all__ = [
-    "FastWAMConfig",
-    "FastWAMPolicy",
-    "make_fastwam_pre_post_processors",
-]

src/lerobot/policies/fastwam/configuration_fastwam.py

@@ -1,399 +0,0 @@
-# Copyright 2024 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 dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any
-
-from lerobot.configs import (
-    FeatureType,
-    NormalizationMode,
-    PolicyFeature,
-    PreTrainedConfig,
-)
-from lerobot.optim import AdamWConfig
-from lerobot.utils.constants import ACTION, OBS_STATE
-
-WAN22_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B"
-WAN22_DIFFUSERS_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-FASTWAM_BASE_MODEL_ID = "lerobot/fastwam_base"
-WAN_T5_TOKENIZER_ID = "google/umt5-xxl"
-
-
-_FASTWAM_VIDEO_BASE_COMPAT_KEYS = (
-    "patch_size",
-    "in_dim",
-    "hidden_dim",
-    "ffn_dim",
-    "freq_dim",
-    "text_dim",
-    "out_dim",
-    "num_heads",
-    "attn_head_dim",
-    "num_layers",
-)
-
-_FASTWAM_ACTION_BASE_COMPAT_KEYS = (
-    "hidden_dim",
-    "ffn_dim",
-    "num_heads",
-    "attn_head_dim",
-    "num_layers",
-    "text_dim",
-    "freq_dim",
-)
-
-
-def default_video_dit_config(action_dim: int) -> dict[str, Any]:
-    return {
-        "patch_size": [1, 2, 2],
-        "in_dim": 48,
-        "hidden_dim": 3072,
-        "ffn_dim": 14336,
-        "freq_dim": 256,
-        "text_dim": 4096,
-        "out_dim": 48,
-        "num_heads": 24,
-        "attn_head_dim": 128,
-        "num_layers": 30,
-        "eps": 1.0e-6,
-        "seperated_timestep": True,
-        "use_gradient_checkpointing": False,
-        "video_attention_mask_mode": "first_frame_causal",
-        "action_conditioned": False,
-        "action_dim": action_dim,
-        "action_group_causal_mask_mode": "group_diagonal",
-        "fp32_attention": True,
-    }
-
-
-def default_action_dit_config(action_dim: int) -> dict[str, Any]:
-    return {
-        "action_dim": action_dim,
-        "hidden_dim": 1024,
-        "ffn_dim": 4096,
-        "num_heads": 24,
-        "attn_head_dim": 128,
-        "num_layers": 30,
-        "text_dim": 4096,
-        "freq_dim": 256,
-        "eps": 1.0e-6,
-        "use_gradient_checkpointing": False,
-        "fp32_attention": True,
-    }
-
-
-def _coerce_enum(enum_cls: type, value: Any) -> Any:
-    if isinstance(value, enum_cls):
-        return value
-    try:
-        return enum_cls(value)
-    except (TypeError, ValueError) as exc:
-        member = getattr(enum_cls, str(value), None)
-        if member is None:
-            raise ValueError(f"Cannot coerce {value!r} into {enum_cls.__name__}.") from exc
-        return member
-
-
-def _coerce_policy_features(features: dict[str, Any] | None) -> dict[str, PolicyFeature] | None:
-    if features is None:
-        return None
-    coerced = {}
-    for name, feature in features.items():
-        if isinstance(feature, PolicyFeature):
-            coerced[name] = feature
-            continue
-        coerced[name] = PolicyFeature(
-            type=_coerce_enum(FeatureType, feature["type"]),
-            shape=tuple(feature["shape"]),
-        )
-    return coerced
-
-
-def _is_local_model_id(value: str) -> bool:
-    path = Path(value).expanduser()
-    return path.is_absolute() or value.startswith(("./", "../", "~")) or path.exists()
-
-
-def _validate_wan_model_id(value: str, field_name: str) -> str:
-    if value == WAN22_MODEL_ID or _is_local_model_id(value):
-        return value
-    raise ValueError(f"`{field_name}` must be `{WAN22_MODEL_ID}` or an explicit local path, got `{value}`.")
-
-
-def is_fastwam_base_compatible_config(config: FastWAMConfig) -> bool:
-    """Return whether `fastwam_base` partial weights can initialize this config."""
-
-    default_video_config = default_video_dit_config(config.action_dim)
-    default_action_config = default_action_dit_config(config.action_dim)
-    return all(
-        config.video_dit_config.get(key) == default_video_config.get(key)
-        for key in _FASTWAM_VIDEO_BASE_COMPAT_KEYS
-    ) and all(
-        config.action_dit_config.get(key) == default_action_config.get(key)
-        for key in _FASTWAM_ACTION_BASE_COMPAT_KEYS
-    )
-
-
-@PreTrainedConfig.register_subclass("fastwam")
-@dataclass
-class FastWAMConfig(PreTrainedConfig):
-    """Configuration for the FastWAM LeRobot policy.
-
-    Args:
-        action_dim (int): Number of scalar action channels per timestep.
-        proprio_dim (int | None): Number of proprioception channels used as an
-            extra text-context token. `None` disables proprio conditioning.
-        action_horizon (int): Number of actions predicted by one policy call.
-        num_video_frames (int): Raw video sampling window (in dataset frames). The
-            model actually operates on `model_video_frames` frames after subsampling
-            by `action_video_freq_ratio`.
-        action_video_freq_ratio (int): Actions are sampled at this multiple of the
-            video frame rate. Video frames are taken every `action_video_freq_ratio`-th
-            raw frame, so the model sees `(num_video_frames - 1) // ratio + 1` frames
-            spanning the same time window as `action_horizon` actions (ratio actions
-            per video frame).
-        image_size (tuple[int, int]): Concatenated image size as `(height, width)`.
-        context_len (int): Maximum text embedding token length.
-        video_dit_config (dict[str, Any] | None): Wan video expert config.
-        action_dit_config (dict[str, Any] | None): Action expert config.
-        use_gradient_checkpointing (bool): Enable activation checkpointing in both DiT
-            experts (trades compute for memory; propagated into the DiT configs).
-        freeze_video_expert (bool): Freeze the ~5B Wan video expert
-            (`model.video_expert`) so only the action expert + proprio encoder train.
-            Cuts the AdamW optimizer footprint substantially; the video expert keeps its
-            pretrained weights. (If enabled, also set `loss.lambda_video=0` to skip the
-            now-gradient-free video loss compute.)
-    """
-
-    n_obs_steps: int = 1
-    action_dim: int = 7
-    proprio_dim: int | None = 8
-    action_horizon: int = 32
-    n_action_steps: int = 32
-    num_video_frames: int = 33
-    action_video_freq_ratio: int = 4
-    image_size: tuple[int, int] = (224, 448)
-    context_len: int = 128
-    model_id: str = WAN22_MODEL_ID
-    tokenizer_model_id: str = WAN_T5_TOKENIZER_ID
-    text_encoder_model_id: str = WAN22_DIFFUSERS_MODEL_ID
-    base_model_id: str | None = FASTWAM_BASE_MODEL_ID
-    tokenizer_max_len: int = 128
-    load_text_encoder: bool = True
-    mot_checkpoint_mixed_attn: bool = False
-    torch_dtype: str = "bfloat16"
-    prompt_template: str = (
-        "A video recorded from a robot's point of view executing the following instruction: {task}"
-    )
-    num_inference_steps: int = 10
-    inference_seed: int | None = 42
-    rand_device: str = "cpu"
-    text_cfg_scale: float = 1.0
-    negative_prompt: str = ""
-    sigma_shift: float | None = None
-    tiled: bool = False
-    fp32_attention: bool = True
-    use_gradient_checkpointing: bool = False
-    freeze_video_expert: bool = False
-    toggle_action_dimensions: list[int] = field(default_factory=list)
-    video_scheduler: dict[str, float | int] = field(
-        default_factory=lambda: {"train_shift": 5.0, "infer_shift": 5.0, "num_train_timesteps": 1000}
-    )
-    action_scheduler: dict[str, float | int] = field(
-        default_factory=lambda: {"train_shift": 5.0, "infer_shift": 5.0, "num_train_timesteps": 1000}
-    )
-    loss: dict[str, float] = field(default_factory=lambda: {"lambda_video": 1.0, "lambda_action": 1.0})
-    video_dit_config: dict[str, Any] | None = None
-    action_dit_config: dict[str, Any] | None = None
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
-        }
-    )
-    input_features: dict[str, PolicyFeature] | None = None
-    output_features: dict[str, PolicyFeature] | None = None
-    optimizer_lr: float = 1.0e-4
-    optimizer_weight_decay: float = 1.0e-2
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.image_size = tuple(self.image_size)
-        self.model_id = _validate_wan_model_id(self.model_id, "model_id")
-        self.input_features = _coerce_policy_features(self.input_features)
-        self.output_features = _coerce_policy_features(self.output_features)
-        self.toggle_action_dimensions = [int(dim) for dim in self.toggle_action_dimensions]
-        self.video_dit_config = self.video_dit_config or default_video_dit_config(self.action_dim)
-        self.action_dit_config = self.action_dit_config or default_action_dit_config(self.action_dim)
-        self.video_dit_config["fp32_attention"] = bool(self.fp32_attention)
-        self.action_dit_config["fp32_attention"] = bool(self.fp32_attention)
-        self.video_dit_config["use_gradient_checkpointing"] = bool(self.use_gradient_checkpointing)
-        self.action_dit_config["use_gradient_checkpointing"] = bool(self.use_gradient_checkpointing)
-        if self.input_features is None:
-            height, width = self.image_size
-            self.input_features = {
-                "observation.images.image": PolicyFeature(
-                    type=FeatureType.VISUAL,
-                    shape=(3, height, width),
-                )
-            }
-            if self.proprio_dim is not None:
-                self.input_features[OBS_STATE] = PolicyFeature(
-                    type=FeatureType.STATE,
-                    shape=(self.proprio_dim,),
-                )
-        if self.output_features is None:
-            self.output_features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(self.action_dim,))}
-        self.validate_features()
-        if self.pretrained_path or self.use_peft or not self.base_model_id:
-            return
-        if not is_fastwam_base_compatible_config(self):
-            return
-        self.pretrained_path = Path(self.base_model_id)
-        self._auto_pretrained_path = True
-
-    def _save_pretrained(self, save_directory: Path) -> None:
-        if not getattr(self, "_auto_pretrained_path", False):
-            super()._save_pretrained(save_directory)
-            return
-
-        pretrained_path = self.pretrained_path
-        self.pretrained_path = None
-        try:
-            super()._save_pretrained(save_directory)
-        finally:
-            self.pretrained_path = pretrained_path
-
-    def get_optimizer_preset(self) -> AdamWConfig:
-        return AdamWConfig(lr=self.optimizer_lr, weight_decay=self.optimizer_weight_decay)
-
-    def get_scheduler_preset(self) -> None:
-        return None
-
-    def set_dataset_feature_metadata(self, dataset_features: dict[str, Any]) -> None:
-        """Rebuild visual input features from the dataset's real camera keys.
-
-        FastWAM's `__post_init__` installs a synthetic single-image default
-        (`observation.images.image` at full `image_size` width). For datasets
-        with one or more separately-named cameras (e.g. `observation.images.top`,
-        `observation.images.wrist`), this hook — invoked by `make_policy` once the
-        dataset metadata is known — replaces that default with the actual camera
-        keys, each declared at the policy's native per-camera resolution
-        (`image_size[0]` x `image_size[1] // num_cameras`). The accompanying
-        resize step in `make_fastwam_pre_post_processors` resizes raw frames to
-        match, so heterogeneous source resolutions (e.g. 480x640) are supported.
-        """
-        image_keys = sorted(
-            key
-            for key, feature in dataset_features.items()
-            if key.startswith("observation.images.") and feature.get("dtype") in ("video", "image")
-        )
-        if not image_keys:
-            return
-        height, total_width = self.image_size
-        per_cam_width = total_width // len(image_keys)
-        new_inputs: dict[str, PolicyFeature] = {
-            key: PolicyFeature(type=FeatureType.VISUAL, shape=(3, height, per_cam_width))
-            for key in image_keys
-        }
-        if self.proprio_dim is not None and OBS_STATE in dataset_features:
-            new_inputs[OBS_STATE] = PolicyFeature(type=FeatureType.STATE, shape=(self.proprio_dim,))
-        self.input_features = new_inputs
-        self.validate_features()
-
-    def validate_features(self) -> None:
-        if self.action_dim <= 0:
-            raise ValueError(f"`action_dim` must be positive, got {self.action_dim}.")
-        if self.action_horizon <= 0:
-            raise ValueError(f"`action_horizon` must be positive, got {self.action_horizon}.")
-        if self.n_action_steps > self.action_horizon:
-            raise ValueError("`n_action_steps` cannot exceed `action_horizon`.")
-        if self.action_video_freq_ratio <= 0:
-            raise ValueError(
-                f"`action_video_freq_ratio` must be positive, got {self.action_video_freq_ratio}."
-            )
-        # Video frames are subsampled by action_video_freq_ratio; the resulting model frame
-        # count must satisfy T % 4 == 1 for the VAE temporal tokenization (mirrors the
-        # original FastWAM dataset asserts).
-        if (self.num_video_frames - 1) % self.action_video_freq_ratio != 0:
-            raise ValueError(
-                f"`num_video_frames - 1` ({self.num_video_frames - 1}) must be divisible by "
-                f"`action_video_freq_ratio` ({self.action_video_freq_ratio})."
-            )
-        if ((self.num_video_frames - 1) // self.action_video_freq_ratio) % 4 != 0:
-            raise ValueError(
-                f"Subsampled video transitions ({(self.num_video_frames - 1) // self.action_video_freq_ratio}) "
-                "must be divisible by 4 for VAE tokenization (i.e. model_video_frames % 4 == 1)."
-            )
-        if self.action_horizon % ((self.num_video_frames - 1) // self.action_video_freq_ratio) != 0:
-            raise ValueError(
-                f"`action_horizon` ({self.action_horizon}) must be divisible by the number of "
-                f"video transitions ({(self.num_video_frames - 1) // self.action_video_freq_ratio})."
-            )
-        if not self.image_features:
-            raise ValueError("FastWAM requires at least one image feature.")
-        if self.action_feature is None:
-            raise ValueError("FastWAM requires `action` in output_features.")
-        action_shape = tuple(self.action_feature.shape)
-        if action_shape != (self.action_dim,):
-            raise ValueError(
-                f"FastWAM action feature shape must be ({self.action_dim},), got {action_shape}."
-            )
-        if self.proprio_dim is not None:
-            state_feature = self.robot_state_feature
-            if state_feature is None:
-                raise ValueError("FastWAM requires `observation.state` when `proprio_dim` is set.")
-            state_shape = tuple(state_feature.shape)
-            if state_shape != (self.proprio_dim,):
-                raise ValueError(
-                    f"FastWAM state feature shape must be ({self.proprio_dim},), got {state_shape}."
-                )
-        height, width = self.image_size
-        image_width_sum = 0
-        for name, feature in self.image_features.items():
-            shape = tuple(feature.shape)
-            if len(shape) != 3 or shape[0] != 3:
-                raise ValueError(f"FastWAM image feature `{name}` must have shape (3, H, W), got {shape}.")
-            if shape[1] != height:
-                raise ValueError(f"FastWAM image feature `{name}` height must be {height}, got {shape[1]}.")
-            image_width_sum += shape[2]
-        if image_width_sum != width:
-            raise ValueError(f"FastWAM image feature widths must sum to {width}, got {image_width_sum}.")
-
-    @property
-    def model_video_frames(self) -> int:
-        """Number of video frames the model actually operates on, after subsampling the
-        raw `num_video_frames` window by `action_video_freq_ratio` (e.g. 33 -> 9)."""
-        return (self.num_video_frames - 1) // self.action_video_freq_ratio + 1
-
-    @property
-    def observation_delta_indices(self) -> list[int]:
-        # Load the video frames the model is supervised on: the future window subsampled by
-        # action_video_freq_ratio (e.g. [0, 4, 8, ..., 32] -> 9 frames). Each video frame is
-        # thus `action_video_freq_ratio` actions apart, while actions load at the full rate
-        # (`action_delta_indices` = range(action_horizon)). Returning None would load only the
-        # current frame, making the video target a static repeat (degenerate supervision).
-        return list(range(0, self.num_video_frames, self.action_video_freq_ratio))
-
-    @property
-    def action_delta_indices(self) -> list[int]:
-        return list(range(self.action_horizon))
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None

src/lerobot/policies/fastwam/modeling_fastwam.py

@@ -1,440 +0,0 @@
-# Copyright 2024 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 import deque
-from typing import Any
-
-import torch
-from torch import Tensor
-
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.utils.constants import OBS_STATE
-from lerobot.utils.import_utils import require_package
-
-from .configuration_fastwam import FastWAMConfig
-from .wan import (
-    ActionDiT,
-    FastWAM,
-    MoT,
-    WanVideoDiT,
-    build_wan_tokenizer,
-    load_pretrained_wan_text_encoder,
-    load_pretrained_wan_vae,
-)
-
-
-class FastWAMPolicy(PreTrainedPolicy):
-    """LeRobot policy wrapper for FastWAM.
-
-    Attention backend: FastWAM's DiT uses ``torch.nn.functional.scaled_dot_product_attention``
-    (SDPA) for all attention. It does not use FlashAttention, because MoT routing requires
-    arbitrary boolean ``[query, key]`` masks that the FlashAttention varlen API cannot express;
-    installing ``flash-attn`` has no effect on the FastWAM path. (SDPA may still dispatch to
-    PyTorch's own flash/mem-efficient/math kernel internally, unrelated to the ``flash-attn`` package.)
-
-    Args:
-        config (FastWAMConfig): FastWAM policy configuration.
-        dataset_stats (dict[str, dict[str, Tensor]] | None): Optional LeRobot
-            dataset statistics passed by the training/evaluation stack.
-    """
-
-    config_class = FastWAMConfig
-    name = "fastwam"
-
-    def __init__(
-        self,
-        config: FastWAMConfig,
-        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
-        **kwargs: Any,
-    ):
-        # FastWAM's Wan2.2 backbone needs transformers (UMT5 text encoder/tokenizer) and
-        # diffusers (Wan VAE), both behind the `fastwam` extra. Fail fast with an actionable
-        # message in base installs rather than deep in Wan component construction.
-        require_package("transformers", extra="fastwam")
-        require_package("diffusers", extra="fastwam")
-        # `make_policy`/`from_pretrained` forward extra kwargs (e.g. `dataset_meta`); the
-        # dataset feature metadata is already applied to `config` by make_policy upstream,
-        # so we accept and ignore them, matching the other LeRobot policies.
-        super().__init__(config, dataset_stats)
-        config.validate_features()
-        self.config = config
-        self.dataset_stats = dataset_stats
-        self.model = self._build_core_model(config)
-        if config.freeze_video_expert and getattr(self.model, "video_expert", None) is not None:
-            # Freeze the ~5B Wan video expert; get_optim_params filters on requires_grad,
-            # so its params drop out of the optimizer (and DDP skips them).
-            self.model.video_expert.requires_grad_(False)
-            # The transformer blocks are re-parented onto the MoTLayers (single FSDP owner), so
-            # `video_expert.requires_grad_` no longer reaches them — freeze them via the layers.
-            mot = getattr(self.model, "mot", None)
-            if mot is not None and getattr(mot, "layers", None) is not None:
-                for layer in mot.layers:
-                    if "video" in layer.blocks:
-                        layer.blocks["video"].requires_grad_(False)
-        self.reset()
-
-    @classmethod
-    def _load_as_safetensor(cls, model, model_file: str, map_location: str, strict: bool):
-        """Shape-aware load that supports cross-embodiment fine-tuning.
-
-        `safetensors.load_model(strict=False)` ignores missing/unexpected keys but
-        still raises on a shape mismatch for a shared key. When fine-tuning from a
-        checkpoint trained on a different embodiment (e.g. the LIBERO 7-DoF / 8-dim
-        checkpoint adapted to a 6-DoF / 6-dim arm), the action encoder/head and
-        proprio encoder legitimately differ in shape. With `strict=False` we drop
-        only those shape-mismatched tensors — leaving them at their freshly
-        initialized values — and load every compatible tensor. With `strict=True`
-        the standard exact-match loader is used.
-        """
-        from safetensors import safe_open
-
-        model_state_dict = model.state_dict()
-        mismatched = []
-        with safe_open(model_file, framework="pt") as f:
-            checkpoint_keys = list(f.keys())
-            for key in checkpoint_keys:
-                if key in model_state_dict and tuple(model_state_dict[key].shape) != tuple(
-                    f.get_slice(key).get_shape()
-                ):
-                    mismatched.append(key)
-
-        if not mismatched:
-            return super()._load_as_safetensor(model, model_file, map_location, strict)
-        if strict:
-            raise RuntimeError(
-                f"FastWAM: {len(mismatched)} checkpoint tensors have a shape mismatch under "
-                f"strict=True: {mismatched}"
-            )
-
-        from safetensors.torch import load_file
-
-        logging.warning(
-            "FastWAM cross-embodiment load: reinitializing %d shape-mismatched tensor(s), keeping "
-            "every compatible weight: %s",
-            len(mismatched),
-            mismatched,
-        )
-        state_dict = load_file(model_file, device="cpu")
-        for key in mismatched:
-            state_dict.pop(key, None)
-        model.load_state_dict(state_dict, strict=False)
-        if map_location and map_location != "cpu":
-            model.to(map_location)
-        return model
-
-    def get_optim_params(self) -> list[Tensor]:
-        # Return the trainable tensors directly (a single param group). The optimizer
-        # builder wraps these in a param group; returning a bare {"params": [...]} dict
-        # instead would make `list(...)` yield the key string "params".
-        params = (
-            list(self.model.dit.parameters()) if hasattr(self.model, "dit") else list(self.model.parameters())
-        )
-        proprio_encoder = getattr(self.model, "proprio_encoder", None)
-        if proprio_encoder is not None:
-            params.extend(list(proprio_encoder.parameters()))
-        return [p for p in params if p.requires_grad]
-
-    def reset(self) -> None:
-        self._action_queue: deque[Tensor] = deque([], maxlen=self.config.n_action_steps)
-
-    def _batch_to_training_sample(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        """Adapt a standard LeRobot batch to the FastWAM-native sample that
-        `FastWAM.build_inputs` consumes (`video`, `action`, `context`/`context_mask`,
-        per-frame `proprio`).
-
-        The LeRobot training loop passes raw `observation.images.*`, a single-step
-        `observation.state` `[B, D]`, `action`, and a language `task` string. We do
-        only the translation `build_inputs` can't: stack the camera frames into a
-        video, encode the prompt with the (frozen) text encoder (mirroring inference,
-        so language-conditioned datasets need no precomputed context), and give proprio
-        the per-frame axis `build_inputs` indexes. All shape/presence validation is
-        left to `build_inputs`, the single authority on the contract.
-        """
-        sample = dict(batch)
-        if "video" not in sample:
-            sample["video"] = _stack_video_from_images(batch, self.config)
-        if "context" not in sample or "context_mask" not in sample:
-            prompt = _prompt_from_batch(batch=batch, config=self.config)
-            if prompt is None:
-                raise KeyError(
-                    "FastWAM training requires a `task`/`prompt` to encode text context, "
-                    "or precomputed `context`/`context_mask` in the batch."
-                )
-            sample["context"], sample["context_mask"] = self.model.encode_prompt(prompt)
-        if self.config.proprio_dim is not None and "proprio" not in sample:
-            state = sample.get(OBS_STATE)
-            if state is not None:
-                # LeRobot gives a single-step state [B, D]; build_inputs expects
-                # per-frame [B, T, D] and uses frame 0, so add a T=1 axis.
-                sample["proprio"] = state.unsqueeze(1) if state.ndim == 2 else state
-        return sample
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Any]]:
-        """Compute FastWAM training loss for a LeRobot batch.
-
-        Args:
-            batch (dict[str, Tensor]): Batch containing FastWAM-ready keys
-                (`video`, `action`, `context`, `context_mask`) or LeRobot keys
-                that can be adapted (`observation.images.*`, `observation.state`,
-                `action`, `action_is_pad`).
-
-        Returns:
-            tuple[Tensor, dict[str, Any]]: The scalar loss to backprop, and a dict of
-            logging metrics (e.g. `loss_video`, `loss_action`) — the `(loss, output_dict)`
-            contract the LeRobot training loop expects.
-        """
-
-        sample = self._batch_to_training_sample(batch)
-        loss, metrics = self.model.training_loss(sample)
-        return loss, dict(metrics or {})
-
-    @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], **_: Any) -> Tensor:
-        """Predict a chunk of actions from the current FastWAM observation.
-
-        Args:
-            batch (dict[str, Tensor]): Inference batch with `input_image` or
-                image observation keys, plus `context/context_mask` or `prompt`.
-
-        Returns:
-            Tensor: Action chunk with shape `[B, action_horizon, action_dim]`.
-        """
-
-        self.eval()
-        infer_kwargs = _batch_to_infer_kwargs(batch=batch, config=self.config)
-        batch_size = _infer_kwargs_batch_size(infer_kwargs)
-        if batch_size == 1:
-            action = _action_from_model_output(self.model.infer_action(**infer_kwargs))
-        else:
-            action = torch.cat(
-                [
-                    _action_from_model_output(
-                        self.model.infer_action(
-                            **_slice_infer_kwargs(infer_kwargs, index=i, batch_size=batch_size)
-                        )
-                    )
-                    for i in range(batch_size)
-                ],
-                dim=0,
-            )
-        return action.to(device=batch_device(batch), dtype=torch.float32)
-
-    @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], **kwargs: Any) -> Tensor:
-        self.eval()
-        if len(self._action_queue) == 0:
-            actions = self.predict_action_chunk(batch, **kwargs)[:, : self.config.n_action_steps]
-            self._action_queue.extend(actions.transpose(0, 1))
-        return self._action_queue.popleft()
-
-    def _build_core_model(self, config: FastWAMConfig) -> FastWAM:
-        """Build the FastWAM core for training / inference.
-
-        Only the trainable parts (the MoT DiT and the proprio encoder) are
-        materialized empty here and then filled from the policy's
-        `model.safetensors` by the base `from_pretrained`. The *frozen* Wan2.2 VAE
-        and UMT5 text encoder are loaded with their real weights from the
-        `Wan-AI/Wan2.2-TI2V-5B-Diffusers` repo (cached in the HF cache, shared
-        across checkpoints) and are intentionally excluded from `model.safetensors`
-        — see `FastWAM.__init__`. The tokenizer comes from `google/umt5-xxl`.
-        """
-        dtype = _dtype_from_name(config.torch_dtype)
-        device = config.device
-        video_expert = WanVideoDiT(**config.video_dit_config).to(device=device, dtype=dtype)
-        action_expert = ActionDiT(**config.action_dit_config).to(device=device, dtype=dtype)
-        mot = MoT(
-            mixtures={"video": video_expert, "action": action_expert},
-            mot_checkpoint_mixed_attn=config.mot_checkpoint_mixed_attn,
-        )
-        text_encoder = (
-            load_pretrained_wan_text_encoder(
-                model_id=config.text_encoder_model_id, torch_dtype=dtype, device=device
-            )
-            if config.load_text_encoder
-            else None
-        )
-        return FastWAM(
-            video_expert=video_expert,
-            action_expert=action_expert,
-            mot=mot,
-            vae=load_pretrained_wan_vae(torch_dtype=dtype, device=device),
-            text_encoder=text_encoder,
-            tokenizer=build_wan_tokenizer(
-                model_id=config.tokenizer_model_id, tokenizer_max_len=config.tokenizer_max_len
-            ),
-            text_dim=int(config.video_dit_config["text_dim"]),
-            proprio_dim=config.proprio_dim,
-            device=device,
-            torch_dtype=dtype,
-            video_train_shift=float(config.video_scheduler["train_shift"]),
-            video_infer_shift=float(config.video_scheduler["infer_shift"]),
-            video_num_train_timesteps=int(config.video_scheduler["num_train_timesteps"]),
-            action_train_shift=float(config.action_scheduler["train_shift"]),
-            action_infer_shift=float(config.action_scheduler["infer_shift"]),
-            action_num_train_timesteps=int(config.action_scheduler["num_train_timesteps"]),
-            loss_lambda_video=float(config.loss["lambda_video"]),
-            loss_lambda_action=float(config.loss["lambda_action"]),
-        )
-
-
-def _scalar(value: Any) -> Any:
-    """Unwrap a 0-/1-element tensor (e.g. from DataLoader collation) to a Python scalar."""
-    return value.item() if isinstance(value, Tensor) else value
-
-
-def _batch_to_infer_kwargs(batch: dict[str, Tensor], config: FastWAMConfig) -> dict[str, Any]:
-    return {
-        "prompt": _prompt_from_batch(batch=batch, config=config),
-        "input_image": _input_image_from_batch(batch, config),
-        "action_horizon": config.action_horizon,
-        "proprio": batch.get("proprio", batch.get(OBS_STATE)),
-        "context": batch.get("context"),
-        "context_mask": batch.get("context_mask"),
-        "negative_prompt": batch.get("negative_prompt", config.negative_prompt),
-        "text_cfg_scale": float(_scalar(batch.get("text_cfg_scale", config.text_cfg_scale))),
-        "num_inference_steps": int(_scalar(batch.get("num_inference_steps", config.num_inference_steps))),
-        "sigma_shift": batch.get("sigma_shift", config.sigma_shift),
-        "seed": batch.get("seed", config.inference_seed),
-        "rand_device": batch.get("rand_device", config.rand_device),
-        "tiled": bool(batch.get("tiled", config.tiled)),
-    }
-
-
-def _prompt_from_batch(batch: dict[str, Tensor], config: FastWAMConfig) -> Any:
-    prompt = batch.get("prompt")
-    if prompt is not None:
-        return prompt
-
-    task = batch.get("task")
-    if task is None:
-        return None
-    if isinstance(task, str):
-        return config.prompt_template.format(task=task)
-    if isinstance(task, (list, tuple)):
-        return [config.prompt_template.format(task=str(item)) for item in task]
-    return config.prompt_template.format(task=str(task))
-
-
-def _action_from_model_output(output: Any) -> Tensor:
-    action = output["action"] if isinstance(output, dict) else output
-    if action.ndim == 2:
-        action = action.unsqueeze(0)
-    return action
-
-
-def _infer_kwargs_batch_size(infer_kwargs: dict[str, Any]) -> int:
-    image = infer_kwargs["input_image"]
-    if not isinstance(image, Tensor):
-        raise TypeError(f"`input_image` must be a tensor, got {type(image).__name__}.")
-    if image.ndim == 3:
-        return 1
-    if image.ndim == 4:
-        return int(image.shape[0])
-    raise ValueError(f"`input_image` must be [B,C,H,W] or [C,H,W], got {tuple(image.shape)}.")
-
-
-def _slice_infer_kwargs(infer_kwargs: dict[str, Any], *, index: int, batch_size: int) -> dict[str, Any]:
-    return {
-        key: _slice_infer_value(value, index=index, batch_size=batch_size)
-        for key, value in infer_kwargs.items()
-    }
-
-
-def _slice_infer_value(value: Any, *, index: int, batch_size: int) -> Any:
-    if isinstance(value, Tensor) and value.ndim > 0 and value.shape[0] == batch_size:
-        return value[index : index + 1]
-    if isinstance(value, (list, tuple)) and len(value) == batch_size:
-        return value[index]
-    return value
-
-
-def _dtype_from_name(name: str) -> torch.dtype:
-    dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16}
-    if name not in dtype_map:
-        raise ValueError(f"Unsupported torch dtype `{name}`.")
-    return dtype_map[name]
-
-
-def batch_device(batch: dict[str, Any]) -> torch.device:
-    for value in batch.values():
-        if isinstance(value, Tensor):
-            return value.device
-    return torch.device("cpu")
-
-
-def _resize_frames(frames: Tensor, size: tuple[int, int]) -> Tensor:
-    """Resize a frame tensor to `size` (H, W), tolerating a leading temporal/batch stack.
-
-    `interpolate` only accepts a single leading batch dim (`[N, C, H, W]`), but FastWAM camera
-    tensors arrive as `[B, C, H, W]` (live eval) or `[B, T, C, H, W]` (temporal stack), so flatten
-    any leading dims into the batch, resize, then restore. A no-op when already at `size`.
-    """
-    if tuple(frames.shape[-2:]) == size:
-        return frames
-    lead = frames.shape[:-3]
-    flat = frames.reshape(-1, *frames.shape[-3:])
-    flat = torch.nn.functional.interpolate(
-        flat, size=size, mode="bilinear", align_corners=False, antialias=True
-    )
-    return flat.reshape(*lead, *flat.shape[-3:])
-
-
-def _stack_video_from_images(batch: dict[str, Tensor], config: FastWAMConfig) -> Tensor:
-    # Exclude the `*_is_pad` companion tensors that delta-timestamp loading adds alongside
-    # each camera (shape [B, T]); they share the `observation.images.` prefix but are not frames.
-    image_keys = sorted(k for k in batch if k.startswith("observation.images.") and not k.endswith("_is_pad"))
-    if not image_keys:
-        raise KeyError("FastWAM batch must contain `video` or `observation.images.*` keys.")
-    per_cam = (int(config.image_size[0]), int(config.image_size[1]) // len(image_keys))
-    images = [_resize_frames(batch[key], per_cam) for key in image_keys]
-    # Cameras concatenate along width (last dim) in both the single-frame and temporal case.
-    image = torch.cat(images, dim=-1) if len(images) > 1 else images[0]
-    if image.ndim == 4:
-        # [B, C, H, W]: a single frame (e.g. the live eval observation) -> repeat across time.
-        image = image.unsqueeze(2).repeat(1, 1, config.model_video_frames, 1, 1)
-    elif image.ndim == 5:
-        # [B, T, C, H, W]: temporal stack from delta-timestamp loading -> [B, C, T, H, W].
-        image = image.permute(0, 2, 1, 3, 4)
-    else:
-        raise ValueError(f"Expected image batch [B,C,H,W] or temporal [B,T,C,H,W], got {tuple(image.shape)}.")
-    return image
-
-
-def _input_image_from_batch(batch: dict[str, Tensor], config: FastWAMConfig) -> Tensor:
-    if "input_image" in batch:
-        return _prepare_infer_image(batch["input_image"], config)
-    video = batch.get("video")
-    if video is None:
-        video = _stack_video_from_images(batch, config)
-    if video.ndim == 5:
-        return _prepare_infer_image(video[:, :, 0], config)
-    if video.ndim == 4:
-        return _prepare_infer_image(video, config)
-    raise ValueError(f"Cannot build input image from tensor with shape {tuple(video.shape)}.")
-
-
-def _prepare_infer_image(image: Tensor, config: FastWAMConfig) -> Tensor:
-    if image.ndim == 3:
-        image = image.unsqueeze(0)
-    if image.ndim != 4:
-        raise ValueError(f"Expected image tensor [B,C,H,W] or [C,H,W], got {tuple(image.shape)}.")
-
-    # Resize to the full configured resolution (no-op when the video path already produced it, but
-    # also covers a directly-supplied `input_image`). The model owns its input resolution — see
-    # `_stack_video_from_images` — so we resize rather than assert on a mismatch.
-    target_h, target_w = int(config.image_size[0]), int(config.image_size[1])
-    return _resize_frames(image, (target_h, target_w))

src/lerobot/policies/fastwam/processor_fastwam.py

@@ -1,142 +0,0 @@
-# Copyright 2024 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 dataclasses import dataclass
-from typing import Any
-
-import torch
-
-from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.processor import (
-    ActionProcessorStep,
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStepRegistry,
-    RenameObservationsProcessorStep,
-    UnnormalizerProcessorStep,
-    policy_action_to_transition,
-    transition_to_policy_action,
-)
-from lerobot.utils.constants import (
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-
-from .configuration_fastwam import FastWAMConfig
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="fastwam_action_toggle_processor")
-class FastWAMActionToggleProcessorStep(ActionProcessorStep):
-    """Apply FastWAM LIBERO toggle semantics to configured action dimensions."""
-
-    toggle_dimensions: list[int]
-
-    def action(self, action: PolicyAction) -> PolicyAction:
-        if not self.toggle_dimensions:
-            return action
-        processed_action = action.clone()
-        action_dim = int(processed_action.shape[-1])
-        for dim in self.toggle_dimensions:
-            resolved_dim = dim if dim >= 0 else action_dim + dim
-            if resolved_dim < 0 or resolved_dim >= action_dim:
-                raise ValueError(
-                    f"FastWAM action toggle dimension {dim} is out of bounds for action dim {action_dim}."
-                )
-            value = processed_action[..., resolved_dim]
-            value = value * 2.0 - 1.0
-            processed_action[..., resolved_dim] = torch.sign(-value)
-        return processed_action
-
-    def get_config(self) -> dict[str, Any]:
-        return {"toggle_dimensions": self.toggle_dimensions}
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-
-def make_fastwam_pre_post_processors(
-    config: FastWAMConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
-    """Create LeRobot pre- and post-processing pipelines for FastWAM.
-
-    Args:
-        config (FastWAMConfig): Policy configuration controlling device and
-            normalization feature metadata.
-        dataset_stats (dict[str, dict[str, torch.Tensor]] | None): Optional
-            LeRobot dataset statistics used by normalization processors.
-
-    Returns:
-        tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]: Input and
-        output processor pipelines discoverable by LeRobot.
-    """
-
-    # NOTE: no visual normalization here. VISUAL is IDENTITY (see configuration_fastwam.normalization_mapping)
-    # — images pass through in [0, 1] and the model maps them to the Wan VAE's [-1, 1] at the encode
-    # boundary. This is deliberate: `lerobot_train.py` overrides the normalizer stats with
-    # `dataset.meta.stats` when fine-tuning, and a real dataset's per-channel image std is the tiny
-    # frame-to-frame brightness variance, which would blow images far outside [-1,1] and saturate them.
-    # STATE/ACTION still normalize with dataset stats below.
-    normalization_stats: dict[str, dict[str, Any]] = dict(dataset_stats or {})
-
-    # NOTE: no resize step here. The model is the single authority on input resolution: it resizes
-    # each camera to the per-camera target (image_size split across cameras) in
-    # `_stack_video_from_images` / `_prepare_infer_image`, on every path (train forward, rollout and
-    # eval select_action). A preprocessor resize step would be both redundant (the model re-resizes
-    # anyway) and unsafe across fine-tuning: its `resize_size` would be inherited from the base
-    # checkpoint's camera geometry, not this dataset's, making the concatenation N_cameras x too wide.
-
-    input_steps = [
-        RenameObservationsProcessorStep(rename_map={}),
-        AddBatchDimensionProcessorStep(),
-        DeviceProcessorStep(device=config.device),
-        NormalizerProcessorStep(
-            features={**config.input_features, **config.output_features},
-            norm_map=config.normalization_mapping,
-            stats=normalization_stats,
-            device=config.device,
-        ),
-    ]
-    output_steps = [
-        UnnormalizerProcessorStep(
-            features=config.output_features,
-            norm_map=config.normalization_mapping,
-            stats=normalization_stats,
-        ),
-    ]
-    if config.toggle_action_dimensions:
-        output_steps.append(
-            FastWAMActionToggleProcessorStep(toggle_dimensions=config.toggle_action_dimensions)
-        )
-    output_steps.append(DeviceProcessorStep(device="cpu"))
-    return (
-        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-            steps=input_steps,
-            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        ),
-        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/fastwam/wan/README.md

@@ -1,34 +0,0 @@
-# FastWAM `wan` package
-
-This package holds FastWAM's model implementation. It mixes a small **vendored
-subset of the official Wan2.2 source tree** with FastWAM's own code, kept flat in
-a single directory.
-
-## Vendored from Wan2.2
-
-- Upstream repository: https://github.com/Wan-Video/Wan2.2
-- Upstream commit: `42bf4cfaa384bc21833865abc2f9e6c0e67233dc`
-- License: Apache-2.0, matching the license in `LICENSE.txt` from the upstream repository
-
-Copied files:
-
-- `model.py` (was `wan/modules/model.py`), trimmed: the flash-attention path
-  (the vendored `attention.py` and the block/model `forward`s) was removed.
-  FastWAM's DiT uses SDPA instead (see `video_dit.py`).
-- `get_sampling_sigmas` in `video_dit.py` (was `wan/utils/fm_solvers.py`), inlined
-  next to its only caller.
-
-This subset only backs FastWAM's **custom MoT video DiT**. The Wan2.2 VAE,
-UMT5 text encoder, and tokenizer are no longer vendored - they come from
-`diffusers.AutoencoderKLWan`, `transformers.UMT5EncoderModel`, and
-`transformers.AutoTokenizer` (see `components.py` and `adapters.py`).
-
-## FastWAM's own code
-
-- `video_dit.py` builds on `model` (`sinusoidal_embedding_1d`, `rope_params`,
-  `rope_apply`, …) and computes attention with SDPA (`fastwam_masked_attention`). Its
-  `WanContinuousFlowMatchScheduler` uses `get_sampling_sigmas` for Wan-compatible
-  inference timesteps.
-- `components.py` / `adapters.py` load the VAE, text encoder, tokenizer, and the
-  custom DiT weights.
-- `modular.py` defines the FastWAM model (`ActionDiT`, `MoT`, `FastWAM`, …).

src/lerobot/policies/fastwam/wan/__init__.py

@@ -1,33 +0,0 @@
-# Copyright 2024 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 .adapters import WanVideoVAE38
-from .components import (
-    build_wan_tokenizer,
-    load_pretrained_wan_text_encoder,
-    load_pretrained_wan_vae,
-)
-from .modular import ActionDiT, FastWAM, MoT
-from .video_dit import WanVideoDiT
-
-__all__ = [
-    "ActionDiT",
-    "FastWAM",
-    "MoT",
-    "WanVideoDiT",
-    "WanVideoVAE38",
-    "build_wan_tokenizer",
-    "load_pretrained_wan_text_encoder",
-    "load_pretrained_wan_vae",
-]

src/lerobot/policies/fastwam/wan/adapters.py

@@ -1,108 +0,0 @@
-# Copyright 2024 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 typing import TYPE_CHECKING
-
-import torch
-
-if TYPE_CHECKING:
-    from diffusers import AutoencoderKLWan
-
-
-class WanVideoVAE38(torch.nn.Module):
-    """FastWAM VAE contract over `diffusers.AutoencoderKLWan` (Wan2.2-TI2V-5B).
-
-    16x spatial / 4x temporal compression, 48 latent channels. diffusers'
-    `AutoencoderKLWan` returns *raw* latents (it does not apply `latents_mean`/
-    `latents_std`), so `encode`/`decode` here apply the same standardization the
-    Wan reference uses — `(latents - mean) / std` — done in fp32 for stability.
-    `encode` uses the deterministic posterior mode, matching the original VAE
-    which returned the latent mean `mu`.
-    """
-
-    upsampling_factor = 16
-    temporal_downsample_factor = 4
-    z_dim = 48
-
-    def __init__(
-        self,
-        dtype: torch.dtype = torch.float32,
-        device: str | torch.device = "cuda",
-        *,
-        pretrained: AutoencoderKLWan,
-    ) -> None:
-        super().__init__()
-        # The Wan2.2 VAE is a fixed pretrained model — it is never trained from scratch,
-        # so a real `AutoencoderKLWan` (with weights) must always be supplied (loaded from
-        # the diffusers repo by `load_pretrained_wan_vae`). No random/offline build path.
-        self.vae = pretrained.to(device=device, dtype=dtype)
-
-        # Read the standardization stats from the VAE's own config (diffusers populates
-        # these from vae/config.json) — single source of truth, no local copy. diffusers'
-        # encode/decode return *raw* latents, so we apply (latent - mean) / std ourselves.
-        # Non-persistent: kept out of state_dict.
-        self.register_buffer(
-            "latents_mean",
-            torch.tensor(self.vae.config.latents_mean).view(1, self.z_dim, 1, 1, 1),
-            persistent=False,
-        )
-        self.register_buffer(
-            "latents_std",
-            torch.tensor(self.vae.config.latents_std).view(1, self.z_dim, 1, 1, 1),
-            persistent=False,
-        )
-
-    def _device_dtype(self) -> tuple[torch.device, torch.dtype]:
-        param = next(self.vae.parameters())
-        return param.device, param.dtype
-
-    def encode(
-        self,
-        videos: list[torch.Tensor] | torch.Tensor,
-        device: str | torch.device | None = None,
-        tiled: bool = False,
-        tile_size: tuple[int, int] = (34, 34),
-        tile_stride: tuple[int, int] = (18, 16),
-    ) -> torch.Tensor:
-        del device, tile_size, tile_stride
-        if tiled:
-            raise NotImplementedError("Tiled Wan2.2 VAE encoding is not supported by the FastWAM adapter.")
-        if isinstance(videos, (list, tuple)):
-            videos = torch.stack(list(videos))
-        dev, dtype = self._device_dtype()
-        mu = self.vae.encode(videos.to(device=dev, dtype=dtype)).latent_dist.mode().float()
-        mean = self.latents_mean.float().to(mu.device)
-        std = self.latents_std.float().to(mu.device)
-        return (mu - mean) / std
-
-    def decode(
-        self,
-        hidden_states: list[torch.Tensor] | torch.Tensor,
-        device: str | torch.device | None = None,
-        tiled: bool = False,
-        tile_size: tuple[int, int] = (34, 34),
-        tile_stride: tuple[int, int] = (18, 16),
-    ) -> torch.Tensor:
-        del device, tile_size, tile_stride
-        if tiled:
-            raise NotImplementedError("Tiled Wan2.2 VAE decoding is not supported by the FastWAM adapter.")
-        if isinstance(hidden_states, (list, tuple)):
-            hidden_states = torch.stack(list(hidden_states))
-        dev, dtype = self._device_dtype()
-        z = hidden_states.float()
-        z = z * self.latents_std.float().to(z.device) + self.latents_mean.float().to(z.device)
-        out = self.vae.decode(z.to(device=dev, dtype=dtype)).sample
-        return out.float().clamp_(-1.0, 1.0)

src/lerobot/policies/fastwam/wan/components.py

@@ -1,175 +0,0 @@
-# Copyright 2024 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 pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-import torch
-from huggingface_hub import snapshot_download
-from safetensors.torch import load_file
-
-from lerobot.utils.import_utils import _diffusers_available, _transformers_available, require_package
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoTokenizer, UMT5EncoderModel
-else:
-    AutoTokenizer = None
-    UMT5EncoderModel = None
-
-if TYPE_CHECKING or _diffusers_available:
-    from diffusers import AutoencoderKLWan
-else:
-    AutoencoderKLWan = None
-
-from .adapters import WanVideoVAE38
-from .video_dit import WanVideoDiT
-
-logger = logging.getLogger(__name__)
-
-# The custom MoT video DiT still ships in the original (non-diffusers) Wan2.2
-# repo as sharded `diffusion_pytorch_model*.safetensors`; the VAE and UMT5 text
-# encoder come from the diffusers conversion. Tokenizer is the stock UMT5 one.
-WAN_DIT_PATTERN = "diffusion_pytorch_model*.safetensors"
-WAN_T5_TOKENIZER = "google/umt5-xxl"
-WAN22_DIFFUSERS_MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-
-
-class WanTextEncoder(torch.nn.Module):
-    """FastWAM text-encoder contract over `transformers.UMT5EncoderModel`.
-
-    Exposes `.dim` (hidden size) and `forward(ids, mask) -> [B, L, dim]`, matching
-    the call in `FastWAM.encode_prompt`.
-    """
-
-    def __init__(
-        self,
-        dtype: torch.dtype = torch.bfloat16,
-        device: str | torch.device = "cuda",
-        *,
-        pretrained: torch.nn.Module,
-    ) -> None:
-        super().__init__()
-        # UMT5-XXL is a fixed pretrained encoder — never trained from scratch, so a real
-        # `UMT5EncoderModel` (with weights) must always be supplied (loaded from the
-        # diffusers repo by `load_pretrained_wan_text_encoder`). No random/offline build.
-        self.model = pretrained.to(device=device, dtype=dtype)
-        self.dim = int(self.model.config.d_model)
-
-    def forward(self, ids: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        return self.model(input_ids=ids, attention_mask=mask.long()).last_hidden_state
-
-
-class WanTokenizer:
-    """UMT5 tokenizer wrapper returning `(input_ids, attention_mask)` like the
-    FastWAM call site expects."""
-
-    def __init__(self, name: str = WAN_T5_TOKENIZER, seq_len: int = 512) -> None:
-        require_package("transformers", extra="fastwam")
-        self.tokenizer = AutoTokenizer.from_pretrained(name)
-        self.seq_len = int(seq_len)
-
-    def __call__(
-        self,
-        sequence: str | Sequence[str],
-        return_mask: bool = False,
-        add_special_tokens: bool = True,
-        **_: Any,
-    ):
-        if isinstance(sequence, str):
-            sequence = [sequence]
-        out = self.tokenizer(
-            list(sequence),
-            padding="max_length",
-            truncation=True,
-            max_length=self.seq_len,
-            add_special_tokens=add_special_tokens,
-            return_tensors="pt",
-        )
-        if return_mask:
-            return out.input_ids, out.attention_mask
-        return out.input_ids
-
-
-def build_wan_tokenizer(*, model_id: str = WAN_T5_TOKENIZER, tokenizer_max_len: int) -> WanTokenizer:
-    return WanTokenizer(name=model_id, seq_len=int(tokenizer_max_len))
-
-
-def load_pretrained_wan_vae(*, torch_dtype: torch.dtype, device: str) -> WanVideoVAE38:
-    """Load real Wan2.2 VAE weights from the diffusers repo (offline base creation)."""
-    require_package("diffusers", extra="fastwam")
-    vae = AutoencoderKLWan.from_pretrained(WAN22_DIFFUSERS_MODEL_ID, subfolder="vae", torch_dtype=torch_dtype)
-    return WanVideoVAE38(dtype=torch_dtype, device=device, pretrained=vae)
-
-
-def load_pretrained_wan_text_encoder(
-    *,
-    model_id: str = WAN22_DIFFUSERS_MODEL_ID,
-    subfolder: str | None = "text_encoder",
-    torch_dtype: torch.dtype,
-    device: str,
-) -> WanTextEncoder:
-    """Load UMT5-XXL encoder weights (defaults to the Wan2.2 diffusers repo).
-
-    Must stay compatible with the tokenizer (see `build_wan_tokenizer`): the encoder's
-    embedding table is indexed by the tokenizer's vocabulary.
-    """
-    require_package("transformers", extra="fastwam")
-    encoder = UMT5EncoderModel.from_pretrained(model_id, subfolder=subfolder, torch_dtype=torch_dtype)
-    return WanTextEncoder(dtype=torch_dtype, device=device, pretrained=encoder)
-
-
-def resolve_wan_dit_paths(
-    model_id_or_path: str | Path,
-    *,
-    cache_dir: str | Path | None = None,
-    local_files_only: bool = False,
-    revision: str | None = None,
-) -> list[Path]:
-    """Resolve the custom MoT DiT shards from the original Wan2.2 repo or a local dir."""
-    path = Path(model_id_or_path).expanduser()
-    if path.is_dir():
-        return sorted(path.glob(WAN_DIT_PATTERN))
-
-    snapshot_path = snapshot_download(
-        repo_id=str(model_id_or_path),
-        revision=revision,
-        cache_dir=cache_dir,
-        local_files_only=local_files_only,
-        allow_patterns=[WAN_DIT_PATTERN],
-    )
-    return sorted(Path(snapshot_path).glob(WAN_DIT_PATTERN))
-
-
-def load_wan_video_dit(
-    paths: list[str | Path],
-    *,
-    dit_config: dict[str, Any],
-    torch_dtype: torch.dtype,
-    device: str,
-) -> WanVideoDiT:
-    model = WanVideoDiT(**dit_config)
-    state_dict = _read_wan_dit_safetensors(paths)
-    model.load_state_dict(state_dict, strict=False)
-    return model.to(device=device, dtype=torch_dtype)
-
-
-def _read_wan_dit_safetensors(paths: list[str | Path]) -> dict[str, torch.Tensor]:
-    state_dict = {}
-    for path in paths:
-        state_dict.update(load_file(str(path), device="cpu"))
-    return state_dict

src/lerobot/policies/fastwam/wan/model.py

@@ -1,341 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-
-
-def sinusoidal_embedding_1d(dim, position):
-    # preprocess
-    if dim % 2 != 0:
-        raise ValueError(f"dim must be even, got {dim}.")
-    half = dim // 2
-    position = position.type(torch.float64)
-
-    # calculation
-    sinusoid = torch.outer(position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
-    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
-    return x
-
-
-@torch.amp.autocast("cuda", enabled=False)
-def rope_params(max_seq_len, dim, theta=10000):
-    if dim % 2 != 0:
-        raise ValueError(f"dim must be even, got {dim}.")
-    freqs = torch.outer(
-        torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))
-    )
-    freqs = torch.polar(torch.ones_like(freqs), freqs)
-    return freqs
-
-
-@torch.amp.autocast("cuda", enabled=False)
-def rope_apply(x, grid_sizes, freqs):
-    n, c = x.size(2), x.size(3) // 2
-
-    # split freqs
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
-
-    # loop over samples
-    output = []
-    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
-        seq_len = f * h * w
-
-        # precompute multipliers
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2))
-        freqs_i = torch.cat(
-            [
-                freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
-                freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
-                freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
-            ],
-            dim=-1,
-        ).reshape(seq_len, 1, -1)
-
-        # apply rotary embedding
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
-
-        # append to collection
-        output.append(x_i)
-    return torch.stack(output).float()
-
-
-class WanRMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L, C]
-        """
-        return self._norm(x.float()).type_as(x) * self.weight
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
-
-
-class WanLayerNorm(nn.LayerNorm):
-    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
-        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(self, x):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L, C]
-        """
-        return super().forward(x.float()).type_as(x)
-
-
-class WanSelfAttention(nn.Module):
-    def __init__(self, dim, num_heads, qk_norm=True, eps=1e-6):
-        if dim % num_heads != 0:
-            raise ValueError(f"dim ({dim}) must be divisible by num_heads ({num_heads}).")
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        # layers
-        self.q = nn.Linear(dim, dim)
-        self.k = nn.Linear(dim, dim)
-        self.v = nn.Linear(dim, dim)
-        self.o = nn.Linear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    # NOTE: FastWAM never runs the upstream Wan attention forward. FastWAMAttentionBlock
-    # reuses only the q/k/v/o/norm submodules defined above and computes attention via
-    # `fastwam_masked_attention` (SDPA). The original flash-attention forward was removed,
-    # which also collapsed the former WanCrossAttention subclass into this class (it only
-    # differed by its forward): self- and cross-attention now share the same projection module.
-
-
-class WanAttentionBlock(nn.Module):
-    def __init__(self, dim, ffn_dim, num_heads, qk_norm=True, cross_attn_norm=False, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.num_heads = num_heads
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # layers
-        self.norm1 = WanLayerNorm(dim, eps)
-        self.self_attn = WanSelfAttention(dim, num_heads, qk_norm, eps)
-        self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
-        self.cross_attn = WanSelfAttention(dim, num_heads, qk_norm, eps)
-        self.norm2 = WanLayerNorm(dim, eps)
-        self.ffn = nn.Sequential(
-            nn.Linear(dim, ffn_dim), nn.GELU(approximate="tanh"), nn.Linear(ffn_dim, dim)
-        )
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
-
-    # NOTE: The upstream Wan block forward (self-attention + cross-attention + FFN via
-    # flash-attention) was removed. FastWAM subclasses this block as FastWAMAttentionBlock
-    # and overrides forward to use SDPA with explicit boolean masks; only __init__ (the
-    # norm/attention/ffn submodules) is reused here.
-
-
-class Head(nn.Module):
-    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.out_dim = out_dim
-        self.patch_size = patch_size
-        self.eps = eps
-
-        # layers
-        out_dim = math.prod(patch_size) * out_dim
-        self.norm = WanLayerNorm(dim, eps)
-        self.head = nn.Linear(dim, out_dim)
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
-
-    def forward(self, x, e):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L1, C]
-            e(Tensor): Shape [B, L1, C]
-        """
-        with torch.amp.autocast("cuda", dtype=torch.float32):
-            e = (self.modulation.unsqueeze(0) + e.unsqueeze(2)).chunk(2, dim=2)
-            x = self.head(self.norm(x) * (1 + e[1].squeeze(2)) + e[0].squeeze(2))
-        return x
-
-
-class WanModel(nn.Module):
-    r"""
-    Wan diffusion backbone supporting both text-to-video and image-to-video.
-    """
-
-    def __init__(
-        self,
-        model_type="t2v",
-        patch_size=(1, 2, 2),
-        text_len=512,
-        in_dim=16,
-        dim=2048,
-        ffn_dim=8192,
-        freq_dim=256,
-        text_dim=4096,
-        out_dim=16,
-        num_heads=16,
-        num_layers=32,
-        qk_norm=True,
-        cross_attn_norm=True,
-        eps=1e-6,
-    ):
-        r"""
-        Initialize the diffusion model backbone.
-
-        Args:
-            model_type (`str`, *optional*, defaults to 't2v'):
-                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
-            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
-                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
-            text_len (`int`, *optional*, defaults to 512):
-                Fixed length for text embeddings
-            in_dim (`int`, *optional*, defaults to 16):
-                Input video channels (C_in)
-            dim (`int`, *optional*, defaults to 2048):
-                Hidden dimension of the transformer
-            ffn_dim (`int`, *optional*, defaults to 8192):
-                Intermediate dimension in feed-forward network
-            freq_dim (`int`, *optional*, defaults to 256):
-                Dimension for sinusoidal time embeddings
-            text_dim (`int`, *optional*, defaults to 4096):
-                Input dimension for text embeddings
-            out_dim (`int`, *optional*, defaults to 16):
-                Output video channels (C_out)
-            num_heads (`int`, *optional*, defaults to 16):
-                Number of attention heads
-            num_layers (`int`, *optional*, defaults to 32):
-                Number of transformer blocks
-            qk_norm (`bool`, *optional*, defaults to True):
-                Enable query/key normalization
-            cross_attn_norm (`bool`, *optional*, defaults to False):
-                Enable cross-attention normalization
-            eps (`float`, *optional*, defaults to 1e-6):
-                Epsilon value for normalization layers
-        """
-
-        super().__init__()
-
-        if model_type not in ["t2v", "i2v", "ti2v", "s2v"]:
-            raise ValueError(f"model_type must be one of ['t2v', 'i2v', 'ti2v', 's2v'], got {model_type!r}.")
-        self.model_type = model_type
-
-        self.patch_size = patch_size
-        self.text_len = text_len
-        self.in_dim = in_dim
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.freq_dim = freq_dim
-        self.text_dim = text_dim
-        self.out_dim = out_dim
-        self.num_heads = num_heads
-        self.num_layers = num_layers
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # embeddings
-        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
-        self.text_embedding = nn.Sequential(
-            nn.Linear(text_dim, dim), nn.GELU(approximate="tanh"), nn.Linear(dim, dim)
-        )
-
-        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
-        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
-
-        # blocks
-        self.blocks = nn.ModuleList(
-            [
-                WanAttentionBlock(dim, ffn_dim, num_heads, qk_norm, cross_attn_norm, eps)
-                for _ in range(num_layers)
-            ]
-        )
-
-        # head
-        self.head = Head(dim, out_dim, patch_size, eps)
-
-        # buffers (don't use register_buffer otherwise dtype will be changed in to())
-        if (dim % num_heads) != 0 or (dim // num_heads) % 2 != 0:
-            raise ValueError(
-                f"dim ({dim}) must be divisible by num_heads ({num_heads}) with an even head dim."
-            )
-        d = dim // num_heads
-        self.freqs = torch.cat(
-            [
-                rope_params(1024, d - 4 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-            ],
-            dim=1,
-        )
-
-        # initialize weights
-        self.init_weights()
-
-    # NOTE: The upstream Wan diffusion forward (flash-attention based) was removed.
-    # FastWAM's WanVideoDiT subclasses this model, rebuilds `self.blocks` with
-    # FastWAMAttentionBlock, and provides its own SDPA-based forward. Only the
-    # constructor (embeddings, blocks, head, rope buffers) and the helpers below
-    # (unpatchify / init_weights) are reused. WanModel is never run directly.
-
-    def unpatchify(self, x, grid_sizes):
-        r"""
-        Reconstruct video tensors from patch embeddings.
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
-
-        Returns:
-            List[Tensor]:
-                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_dim
-        out = []
-        for u, v in zip(x, grid_sizes.tolist(), strict=False):
-            u = u[: math.prod(v)].view(*v, *self.patch_size, c)
-            u = torch.einsum("fhwpqrc->cfphqwr", u)
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size, strict=False)])
-            out.append(u)
-        return out
-
-    def init_weights(self):
-        r"""
-        Initialize model parameters using Xavier initialization.
-        """
-
-        # basic init
-        for m in self.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.xavier_uniform_(m.weight)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-        # init embeddings
-        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
-        for m in self.text_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=0.02)
-        for m in self.time_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=0.02)
-
-        # init output layer
-        nn.init.zeros_(self.head.head.weight)

src/lerobot/policies/fastwam/wan/video_dit.py

@@ -1,800 +0,0 @@
-# Copyright 2024 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.
-
-import logging
-from typing import Any
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as functional
-from einops import rearrange
-
-from .model import (
-    WanAttentionBlock,
-    WanLayerNorm,
-    WanModel,
-    WanRMSNorm,
-    rope_apply,
-    rope_params,
-    sinusoidal_embedding_1d,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def get_sampling_sigmas(sampling_steps, shift):
-    # Vendored from Wan2.2 (formerly wan/utils/fm_solvers.py); computes the
-    # noise-level (sigma) schedule for Wan-compatible flow-matching inference.
-    sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps]
-    sigma = shift * sigma / (1 + (shift - 1) * sigma)
-    return sigma
-
-
-def create_custom_forward(module):
-    def custom_forward(*inputs, **kwargs):
-        return module(*inputs, **kwargs)
-
-    return custom_forward
-
-
-def gradient_checkpoint_forward(
-    model,
-    use_gradient_checkpointing,
-    *args,
-    **kwargs,
-):
-    if use_gradient_checkpointing:
-        model_output = torch.utils.checkpoint.checkpoint(
-            create_custom_forward(model),
-            *args,
-            **kwargs,
-            use_reentrant=False,
-        )
-    else:
-        model_output = model(*args, **kwargs)
-    return model_output
-
-
-def fastwam_masked_attention(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    num_heads: int,
-    ctx_mask: torch.Tensor | None = None,
-    fp32_attention: bool = True,
-) -> torch.Tensor:
-    """FastWAM masked attention wrapper for MoT masks and CPU test coverage.
-
-    The official Wan attention implementation is still used as the source of
-    the projection/norm modules. This wrapper only replaces the final attention
-    kernel because FastWAM needs explicit boolean masks for video/action MoT
-    routing, while the upstream FlashAttention path accepts sequence lengths
-    but not arbitrary [query, key] masks.
-    """
-
-    q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-    k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-    v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-    if fp32_attention:
-        q = q.float()
-        k = k.float()
-        v = v.float()
-    else:
-        q = q.to(dtype=v.dtype)
-        k = k.to(dtype=v.dtype)
-    x = functional.scaled_dot_product_attention(q, k, v, attn_mask=ctx_mask)
-    return rearrange(x, "b n s d -> b s (n d)", n=num_heads)
-
-
-def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
-    return x * (1 + scale) + shift
-
-
-class WanContinuousFlowMatchScheduler:
-    """Continuous-time Flow-Matching scheduler with shift-based Wan sampling."""
-
-    def __init__(self, num_train_timesteps: int = 1000, shift: float = 5.0, eps: float = 1e-10):
-        if num_train_timesteps <= 0:
-            raise ValueError(f"`num_train_timesteps` must be positive, got {num_train_timesteps}")
-        if shift <= 0:
-            raise ValueError(f"`shift` must be positive, got {shift}")
-        self.num_train_timesteps = int(num_train_timesteps)
-        self.shift = float(shift)
-        self.eps = float(eps)
-        self._y_min, self._weight_norm_const = self._precompute_training_weight_stats()
-
-    @staticmethod
-    def _phi(u: torch.Tensor, shift: float) -> torch.Tensor:
-        return shift * u / (1.0 + (shift - 1.0) * u)
-
-    def _precompute_training_weight_stats(self) -> tuple[float, float]:
-        steps = self.num_train_timesteps
-        u_grid = torch.linspace(1.0, 0.0, steps + 1, dtype=torch.float64)[:-1]
-        t_grid = self._phi(u_grid, self.shift) * float(steps)
-        y_grid = torch.exp(-2.0 * ((t_grid - (steps / 2.0)) / steps) ** 2)
-        y_min = float(y_grid.min().item())
-        y_shifted_grid = y_grid - y_min
-        norm_const = float(y_shifted_grid.mean().item())
-        return y_min, norm_const
-
-    def sample_training_t(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-        if batch_size <= 0:
-            raise ValueError(f"`batch_size` must be positive, got {batch_size}")
-        u = torch.rand((batch_size,), device=device, dtype=torch.float32)
-        sigma = self._phi(u, self.shift)
-        timestep = sigma * float(self.num_train_timesteps)
-        return timestep.to(dtype=dtype)
-
-    def training_weight(self, timestep: torch.Tensor) -> torch.Tensor:
-        t = timestep.to(dtype=torch.float32)
-        steps = float(self.num_train_timesteps)
-        y = torch.exp(-2.0 * ((t - (steps / 2.0)) / steps) ** 2)
-        y_shifted = y - self._y_min
-        weight = y_shifted / (self._weight_norm_const + self.eps)
-        if weight.numel() == 1:
-            return weight.reshape(())
-        return weight
-
-    def add_noise(
-        self, original_samples: torch.Tensor, noise: torch.Tensor, timestep: torch.Tensor
-    ) -> torch.Tensor:
-        sigma = (timestep / float(self.num_train_timesteps)).to(
-            original_samples.device, dtype=original_samples.dtype
-        )
-        if sigma.ndim == 0:
-            return (1 - sigma) * original_samples + sigma * noise
-        sigma = sigma.view(-1, *([1] * (original_samples.ndim - 1)))
-        return (1 - sigma) * original_samples + sigma * noise
-
-    @staticmethod
-    def training_target(sample: torch.Tensor, noise: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
-        del timestep
-        return noise - sample
-
-    def build_inference_schedule(
-        self,
-        num_inference_steps: int,
-        device: torch.device,
-        dtype: torch.dtype,
-        shift_override: float | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if num_inference_steps <= 0:
-            raise ValueError(f"`num_inference_steps` must be positive, got {num_inference_steps}")
-        shift = self.shift if shift_override is None else float(shift_override)
-        if shift <= 0:
-            raise ValueError(f"`shift` must be positive, got {shift}")
-
-        sigma_steps = torch.as_tensor(
-            get_sampling_sigmas(num_inference_steps, shift),
-            device=device,
-            dtype=torch.float32,
-        )
-        timesteps = sigma_steps * float(self.num_train_timesteps)
-        sigma_next = torch.cat([sigma_steps[1:], sigma_steps.new_zeros(1)])
-        deltas = sigma_next - sigma_steps
-        return timesteps.to(dtype=dtype), deltas.to(dtype=dtype)
-
-    @staticmethod
-    def step(model_output: torch.Tensor, delta: torch.Tensor, sample: torch.Tensor) -> torch.Tensor:
-        delta = delta.to(sample.device, dtype=sample.dtype)
-        if delta.ndim == 0:
-            return sample + model_output * delta
-        delta = delta.view(-1, *([1] * (sample.ndim - 1)))
-        return sample + model_output * delta
-
-
-def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
-    return rope_params(end, dim, theta)
-
-
-def apply_dense_rope(x: torch.Tensor, freqs: torch.Tensor, num_heads: int) -> torch.Tensor:
-    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
-    x_out = torch.view_as_complex(x.to(torch.float32).reshape(x.shape[0], x.shape[1], x.shape[2], -1, 2))
-    freqs = freqs.to(torch.complex64) if freqs.device.type == "npu" else freqs
-    x_out = torch.view_as_real(x_out * freqs).flatten(2)
-    return x_out.to(x.dtype)
-
-
-def _linear_input(linear: nn.Linear, x: torch.Tensor) -> torch.Tensor:
-    return x.to(dtype=linear.weight.dtype)
-
-
-def _wan_layer_norm(norm: nn.Module, x: torch.Tensor) -> torch.Tensor:
-    if isinstance(norm, WanLayerNorm) and norm.weight is not None:
-        weight = norm.weight.float()
-        bias = norm.bias.float() if norm.bias is not None else None
-        return functional.layer_norm(x.float(), norm.normalized_shape, weight, bias, norm.eps).to(
-            dtype=x.dtype
-        )
-    return norm(x)
-
-
-def create_group_causal_attn_mask(
-    num_temporal_groups: int, num_query_per_group: int, num_key_per_group: int, mode: str = "causal"
-) -> torch.Tensor:
-    if mode not in ["causal", "group_diagonal"]:
-        raise ValueError(f"`mode` must be 'causal' or 'group_diagonal', got {mode}.")
-    if num_temporal_groups <= 0:
-        raise ValueError(f"`num_temporal_groups` must be positive, got {num_temporal_groups}.")
-    if num_query_per_group <= 0:
-        raise ValueError(f"`num_query_per_group` must be positive, got {num_query_per_group}.")
-    if num_key_per_group <= 0:
-        raise ValueError(f"`num_key_per_group` must be positive, got {num_key_per_group}.")
-
-    total_num_query_tokens = num_temporal_groups * num_query_per_group
-    total_num_key_tokens = num_temporal_groups * num_key_per_group
-    query_time_indices = torch.arange(num_temporal_groups).repeat_interleave(num_query_per_group).unsqueeze(1)
-    key_time_indices = torch.arange(num_temporal_groups).repeat_interleave(num_key_per_group).unsqueeze(0)
-
-    if mode == "causal":
-        attn_mask = query_time_indices >= key_time_indices
-    else:
-        attn_mask = query_time_indices == key_time_indices
-
-    if attn_mask.shape != (total_num_query_tokens, total_num_key_tokens):
-        raise RuntimeError("Attention mask shape mismatch.")
-    return attn_mask
-
-
-class FastWAMAttentionBlock(WanAttentionBlock):
-    """Wan attention block with FastWAM's arbitrary boolean mask support."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        attn_head_dim: int,
-        num_heads: int,
-        ffn_dim: int,
-        eps: float = 1e-6,
-        fp32_attention: bool = True,
-    ):
-        attention_dim = attn_head_dim * num_heads
-        if hidden_dim == attention_dim:
-            super().__init__(
-                dim=hidden_dim,
-                ffn_dim=ffn_dim,
-                num_heads=num_heads,
-                qk_norm=True,
-                cross_attn_norm=True,
-                eps=eps,
-            )
-        else:
-            nn.Module.__init__(self)
-            self.dim = hidden_dim
-            self.ffn_dim = ffn_dim
-            self.num_heads = num_heads
-            self.qk_norm = True
-            self.cross_attn_norm = True
-            self.eps = eps
-            self.norm1 = WanLayerNorm(hidden_dim, eps)
-            self.self_attn = _FastWAMProjectedAttention(hidden_dim, attention_dim, num_heads, eps)
-            self.norm3 = WanLayerNorm(hidden_dim, eps, elementwise_affine=True)
-            self.cross_attn = _FastWAMProjectedAttention(hidden_dim, attention_dim, num_heads, eps)
-            self.norm2 = WanLayerNorm(hidden_dim, eps)
-            self.ffn = nn.Sequential(
-                nn.Linear(hidden_dim, ffn_dim),
-                nn.GELU(approximate="tanh"),
-                nn.Linear(ffn_dim, hidden_dim),
-            )
-            self.modulation = nn.Parameter(torch.randn(1, 6, hidden_dim) / hidden_dim**0.5)
-        self.attn_head_dim = attn_head_dim
-        self.fp32_attention = bool(fp32_attention)
-
-    @staticmethod
-    def split_modulation(block, t_mod: torch.Tensor):
-        has_seq = len(t_mod.shape) == 4
-        chunk_dim = 2 if has_seq else 1
-
-        base_mod = block.modulation.to(dtype=t_mod.dtype, device=t_mod.device)
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (base_mod + t_mod).chunk(
-            6, dim=chunk_dim
-        )
-        if has_seq:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                shift_msa.squeeze(2),
-                scale_msa.squeeze(2),
-                gate_msa.squeeze(2),
-                shift_mlp.squeeze(2),
-                scale_mlp.squeeze(2),
-                gate_mlp.squeeze(2),
-            )
-        return shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp
-
-    def project_self_attention(
-        self, x: torch.Tensor, freqs: torch.Tensor | dict[str, torch.Tensor]
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        q = self.self_attn.norm_q(self.self_attn.q(x))
-        k = self.self_attn.norm_k(self.self_attn.k(x))
-        v = self.self_attn.v(x)
-        if isinstance(freqs, dict):
-            b, s = x.shape[:2]
-            q = rope_apply(
-                q.view(b, s, self.num_heads, self.attn_head_dim),
-                freqs["grid_sizes"],
-                freqs["freqs"],
-            ).flatten(2)
-            k = rope_apply(
-                k.view(b, s, self.num_heads, self.attn_head_dim),
-                freqs["grid_sizes"],
-                freqs["freqs"],
-            ).flatten(2)
-        else:
-            q = apply_dense_rope(q, freqs, self.num_heads)
-            k = apply_dense_rope(k, freqs, self.num_heads)
-        return q, k, v
-
-    def apply_cross_attention(
-        self, x: torch.Tensor, context: torch.Tensor, context_mask: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        if context_mask is not None and context_mask.dim() == 3:
-            context_mask = context_mask.unsqueeze(1)
-        attn = self.cross_attn
-        b, n, d = x.size(0), attn.num_heads, attn.head_dim
-        q = attn.norm_q(attn.q(x)).view(b, -1, n * d)
-        k = attn.norm_k(attn.k(context)).view(b, -1, n * d)
-        v = attn.v(context).view(b, -1, n * d)
-        x = fastwam_masked_attention(
-            q=q,
-            k=k,
-            v=v,
-            num_heads=n,
-            ctx_mask=context_mask,
-            fp32_attention=self.fp32_attention,
-        )
-        return attn.o(_linear_input(attn.o, x))
-
-    def project_self_attention_output(self, x: torch.Tensor) -> torch.Tensor:
-        return self.self_attn.o(_linear_input(self.self_attn.o, x))
-
-    def apply_norm1(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm1, x)
-
-    def apply_norm2(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm2, x)
-
-    def apply_norm3(self, x: torch.Tensor) -> torch.Tensor:
-        return _wan_layer_norm(self.norm3, x)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: torch.Tensor,
-        t_mod: torch.Tensor,
-        freqs: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        self_attn_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.split_modulation(self, t_mod)
-        residual_x = x
-        attn_input = modulate(self.apply_norm1(x), shift_msa, scale_msa)
-        q, k, v = self.project_self_attention(attn_input, freqs)
-        y = fastwam_masked_attention(
-            q=q,
-            k=k,
-            v=v,
-            num_heads=self.num_heads,
-            ctx_mask=self_attn_mask,
-            fp32_attention=self.fp32_attention,
-        )
-        x = residual_x + gate_msa * self.project_self_attention_output(y)
-        x = x + self.apply_cross_attention(self.apply_norm3(x), context, context_mask=context_mask)
-        mlp_input = modulate(self.apply_norm2(x), shift_mlp, scale_mlp)
-        return x + gate_mlp * self.ffn(mlp_input)
-
-
-class _FastWAMProjectedAttention(nn.Module):
-    def __init__(self, hidden_dim: int, attention_dim: int, num_heads: int, eps: float):
-        super().__init__()
-        self.dim = hidden_dim
-        self.num_heads = num_heads
-        self.head_dim = attention_dim // num_heads
-        self.q = nn.Linear(hidden_dim, attention_dim)
-        self.k = nn.Linear(hidden_dim, attention_dim)
-        self.v = nn.Linear(hidden_dim, attention_dim)
-        self.o = nn.Linear(attention_dim, hidden_dim)
-        self.norm_q = WanRMSNorm(attention_dim, eps=eps)
-        self.norm_k = WanRMSNorm(attention_dim, eps=eps)
-
-
-class WanVideoDiT(WanModel):
-    def __init__(
-        self,
-        hidden_dim: int,
-        in_dim: int,
-        ffn_dim: int,
-        out_dim: int,
-        text_dim: int,
-        freq_dim: int,
-        eps: float,
-        patch_size: tuple[int, int, int],
-        num_heads: int,
-        attn_head_dim: int,
-        num_layers: int,
-        has_image_input: bool = False,
-        has_image_pos_emb: bool = False,
-        has_ref_conv: bool = False,
-        add_control_adapter: bool = False,
-        in_dim_control_adapter: int = 24,
-        seperated_timestep: bool = False,
-        require_vae_embedding: bool = False,
-        require_clip_embedding: bool = False,
-        fuse_vae_embedding_in_latents: bool = True,
-        action_conditioned: bool = False,
-        action_dim: int = 7,
-        action_group_causal_mask_mode="causal",
-        video_attention_mask_mode: str = "bidirectional",
-        use_gradient_checkpointing: bool = False,
-        fp32_attention: bool = True,
-    ):
-        del in_dim_control_adapter
-        if has_image_input:
-            raise ValueError("FastWAM currently expects Wan2.2 TI2V latents with fused image conditioning.")
-        if has_image_pos_emb:
-            raise ValueError("FastWAM does not support extra image positional embeddings in WanVideoDiT.")
-        if has_ref_conv:
-            raise ValueError("FastWAM does not support reference convolutions in WanVideoDiT.")
-        if add_control_adapter:
-            raise ValueError("FastWAM does not support control adapters in WanVideoDiT.")
-        if require_clip_embedding:
-            raise ValueError("FastWAM does not support CLIP embedding conditioning in WanVideoDiT.")
-        if require_vae_embedding or not fuse_vae_embedding_in_latents:
-            raise ValueError("FastWAM expects VAE conditioning to be fused in latents.")
-        if attn_head_dim != hidden_dim // num_heads:
-            raise ValueError(
-                "`attn_head_dim` must match the upstream Wan head dimension `hidden_dim // num_heads`; "
-                f"got {attn_head_dim} vs {hidden_dim // num_heads}."
-            )
-
-        super().__init__(
-            model_type="ti2v",
-            patch_size=patch_size,
-            text_len=512,
-            in_dim=in_dim,
-            dim=hidden_dim,
-            ffn_dim=ffn_dim,
-            freq_dim=freq_dim,
-            text_dim=text_dim,
-            out_dim=out_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            qk_norm=True,
-            cross_attn_norm=True,
-            eps=eps,
-        )
-        self.blocks = torch.nn.ModuleList(
-            [
-                FastWAMAttentionBlock(
-                    hidden_dim=hidden_dim,
-                    attn_head_dim=attn_head_dim,
-                    num_heads=num_heads,
-                    ffn_dim=ffn_dim,
-                    eps=eps,
-                    fp32_attention=fp32_attention,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-        self.init_weights()
-
-        self.hidden_dim = hidden_dim
-        self.attn_head_dim = attn_head_dim
-        self.seperated_timestep = seperated_timestep
-        self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
-        self.video_attention_mask_mode = str(video_attention_mask_mode)
-        self.action_conditioned = action_conditioned
-        self.action_dim = action_dim
-        self.fp32_attention = bool(fp32_attention)
-
-        if self.action_conditioned:
-            self.action_embedding = torch.nn.Linear(action_dim, hidden_dim)
-            self.action_group_causal_mask_mode = action_group_causal_mask_mode
-
-        self.use_gradient_checkpointing = use_gradient_checkpointing
-        if self.use_gradient_checkpointing:
-            logger.info(
-                "Using gradient checkpointing for DiT blocks. This will save memory but use more computation."
-            )
-
-    def patchify(self, x: torch.Tensor):
-        return self.patch_embedding(x)
-
-    def _validate_forward_inputs(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None,
-        action: torch.Tensor | None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        if x.ndim != 5:
-            raise ValueError(f"`latents` must be 5D [B, C, T, H, W], got shape {tuple(x.shape)}")
-        num_latent_frames = x.shape[2]
-        if context.ndim != 3:
-            raise ValueError(f"`context` must be 3D [B, L, D], got shape {tuple(context.shape)}")
-        if timestep.ndim != 1:
-            raise ValueError(f"`timestep` must be 1D [B] or [1], got shape {tuple(timestep.shape)}")
-        if self.action_conditioned:
-            allow_text_only_single_frame = num_latent_frames == 1 and action is None
-            if not allow_text_only_single_frame:
-                if action is None:
-                    raise ValueError("Action input is required for action-conditioned model.")
-                if action.ndim != 3:
-                    raise ValueError(
-                        f"`action` must be 3D [B, action_horizon, action_dim], got shape {tuple(action.shape)}"
-                    )
-                if action.shape[2] != self.action_dim:
-                    raise ValueError(
-                        f"`action` last dimension must be {self.action_dim}, got {action.shape[2]}"
-                    )
-                if num_latent_frames <= 1:
-                    raise ValueError(
-                        f"video length must be > 1 for action-conditioned model, got {num_latent_frames}"
-                    )
-                if action.shape[1] % (num_latent_frames - 1) != 0:
-                    raise ValueError(
-                        "action horizon must be divisible by (num_latent_frames - 1), "
-                        f"got action_horizon={action.shape[1]}"
-                    )
-        if context_mask is None:
-            context_mask = torch.ones(
-                (context.shape[0], context.shape[1]), dtype=torch.bool, device=context.device
-            )
-        else:
-            if context_mask.ndim != 2:
-                raise ValueError(f"`context_mask` must be 2D [B, L], got shape {tuple(context_mask.shape)}")
-            if context_mask.shape[0] != context.shape[0] or context_mask.shape[1] != context.shape[1]:
-                raise ValueError(
-                    "`context_mask` shape must match `context` shape [B, L], "
-                    f"got {tuple(context_mask.shape)} vs {tuple(context.shape)}"
-                )
-
-        batch_size = x.shape[0]
-        if batch_size != context.shape[0]:
-            if not self.training and batch_size == 1:
-                x = x.expand(context.shape[0], -1, -1, -1, -1)
-                batch_size = context.shape[0]
-            else:
-                raise ValueError(
-                    f"Batch mismatch between latents and context: {batch_size} vs {context.shape[0]}."
-                )
-
-        if timestep.shape[0] not in (1, batch_size):
-            raise ValueError(
-                f"`timestep` length must be 1 or batch_size({batch_size}), got {timestep.shape[0]}"
-            )
-        if timestep.shape[0] == 1 and batch_size > 1:
-            if self.training:
-                raise ValueError("During training, timestep length must match batch_size.")
-            timestep = timestep.expand(batch_size)
-        return x, timestep, context_mask
-
-    def build_video_to_video_mask(
-        self,
-        video_seq_len: int,
-        video_tokens_per_frame: int,
-        device: torch.device,
-    ) -> torch.Tensor:
-        if video_seq_len <= 0:
-            raise ValueError(f"`video_seq_len` must be positive, got {video_seq_len}")
-        if video_tokens_per_frame <= 0:
-            raise ValueError(f"`video_tokens_per_frame` must be positive, got {video_tokens_per_frame}")
-
-        if self.video_attention_mask_mode == "bidirectional":
-            return torch.ones((video_seq_len, video_seq_len), dtype=torch.bool, device=device)
-
-        if self.video_attention_mask_mode == "per_frame_causal":
-            if video_seq_len % video_tokens_per_frame != 0:
-                raise ValueError(
-                    "`video_seq_len` must be divisible by `video_tokens_per_frame` in `per_frame_causal` mode, "
-                    f"got {video_seq_len} and {video_tokens_per_frame}"
-                )
-            num_video_frames = video_seq_len // video_tokens_per_frame
-            frame_causal = torch.tril(
-                torch.ones((num_video_frames, num_video_frames), dtype=torch.bool, device=device)
-            )
-            return frame_causal.repeat_interleave(video_tokens_per_frame, dim=0).repeat_interleave(
-                video_tokens_per_frame, dim=1
-            )
-
-        if self.video_attention_mask_mode == "first_frame_causal":
-            video_mask = torch.ones((video_seq_len, video_seq_len), dtype=torch.bool, device=device)
-            first_frame_tokens = min(video_tokens_per_frame, video_seq_len)
-            video_mask[:first_frame_tokens, first_frame_tokens:] = False
-            return video_mask
-
-        raise ValueError(f"Unsupported video attention mask mode: {self.video_attention_mask_mode}")
-
-    def pre_dit(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        action: torch.Tensor | None = None,
-        fuse_vae_embedding_in_latents: bool = False,
-    ) -> dict[str, Any]:
-        x, timestep, context_mask = self._validate_forward_inputs(
-            x=x,
-            timestep=timestep,
-            context=context,
-            context_mask=context_mask,
-            action=action,
-        )
-        model_dtype = self.patch_embedding.weight.dtype
-        x = x.to(dtype=model_dtype)
-        context = context.to(dtype=model_dtype)
-        if action is not None:
-            action = action.to(dtype=model_dtype)
-
-        batch_size = x.shape[0]
-        patch_h = int(self.patch_size[1])
-        patch_w = int(self.patch_size[2])
-        if x.shape[3] % patch_h != 0 or x.shape[4] % patch_w != 0:
-            raise ValueError(
-                "Latent spatial shape must be divisible by DiT patch size, "
-                f"got HxW=({x.shape[3]}, {x.shape[4]}), patch=({patch_h}, {patch_w})"
-            )
-        tokens_per_frame = (x.shape[3] // patch_h) * (x.shape[4] // patch_w)
-
-        if not (self.seperated_timestep and fuse_vae_embedding_in_latents):
-            raise NotImplementedError(
-                "FastWAM currently requires separated timesteps with fused VAE latents."
-            )
-
-        token_timesteps = torch.ones(
-            (batch_size, x.shape[2], tokens_per_frame),
-            dtype=model_dtype,
-            device=timestep.device,
-        ) * timestep.to(dtype=model_dtype).view(batch_size, 1, 1)
-        token_timesteps[:, 0, :] = 0
-        token_timesteps = token_timesteps.reshape(batch_size, -1)
-        # Wan keeps the time embedding in fp32: the AdaLN modulation in the vendored
-        # Head/Block asserts e.dtype == float32 (numerical stability of the scale/shift).
-        # Upstream guarantees this via an fp32 autocast region, so it holds even when the
-        # model runs in bf16. Mirror that here, then cast the per-block modulation back to
-        # model_dtype so the bf16 attention blocks are not upcast to fp32.
-        with torch.amp.autocast("cuda", dtype=torch.float32):
-            token_t_emb = sinusoidal_embedding_1d(self.freq_dim, token_timesteps.reshape(-1)).float()
-            t = self.time_embedding(token_t_emb).reshape(batch_size, -1, self.hidden_dim)
-            t_mod = self.time_projection(t).unflatten(2, (6, self.hidden_dim))
-        t_mod = t_mod.to(dtype=model_dtype)
-
-        x = self.patchify(x)
-        f, h, w = x.shape[2:]
-
-        context = self.text_embedding(context)
-        context_len = context.shape[1]
-        if self.action_conditioned and action is not None:
-            action_len = action.shape[1]
-            action_emb = self.action_embedding(action)
-            action_pos_embed = sinusoidal_embedding_1d(
-                self.hidden_dim, torch.arange(action_len, device=action_emb.device)
-            ).to(dtype=action_emb.dtype)
-            action_emb = action_emb + action_pos_embed.unsqueeze(0)
-            context = torch.cat([context, action_emb], dim=1)
-
-            num_temporal_groups = f - 1
-            if num_temporal_groups <= 0:
-                raise ValueError(
-                    "Action-conditioned context mask requires at least 2 latent frames when `action` is provided."
-                )
-            if action_emb.shape[1] % num_temporal_groups != 0:
-                raise ValueError(
-                    f"Action embedding length {action_emb.shape[1]} must be divisible by "
-                    f"number of temporal groups {num_temporal_groups}"
-                )
-            action_group_mask = create_group_causal_attn_mask(
-                num_temporal_groups=num_temporal_groups,
-                num_query_per_group=tokens_per_frame,
-                num_key_per_group=action_len // num_temporal_groups,
-                mode=self.action_group_causal_mask_mode,
-            ).to(context.device)
-
-            seq_len = f * h * w
-            final_context_mask = torch.zeros(
-                (batch_size, seq_len, context.shape[1]), dtype=torch.bool, device=context.device
-            )
-            final_context_mask[:, :, :context_len] = context_mask.unsqueeze(1).expand(-1, seq_len, -1)
-            final_context_mask[:, tokens_per_frame:, context_len:] = action_group_mask.unsqueeze(0).expand(
-                batch_size, -1, -1
-            )
-            context_mask = final_context_mask
-        elif self.action_conditioned and action is None:
-            if f != 1:
-                raise ValueError(
-                    "Action-conditioned model requires `action` unless running single-frame text-only mode "
-                    "with num_latent_frames=1."
-                )
-            context_mask = context_mask.unsqueeze(1).expand(-1, f * h * w, -1)
-        else:
-            context_mask = context_mask.unsqueeze(1).expand(-1, f * h * w, -1)
-
-        x_tokens = rearrange(x, "b c f h w -> b (f h w) c").contiguous()
-        grid_sizes = torch.tensor([[f, h, w]] * batch_size, dtype=torch.long, device=x_tokens.device)
-        freqs = {"grid_sizes": grid_sizes, "freqs": self.freqs.to(x_tokens.device)}
-
-        return {
-            "tokens": x_tokens,
-            "freqs": freqs,
-            "t": t,
-            "t_mod": t_mod,
-            "context": context,
-            "context_mask": context_mask,
-            "meta": {
-                "grid_sizes": grid_sizes,
-                "tokens_per_frame": tokens_per_frame,
-                "batch_size": batch_size,
-            },
-        }
-
-    def post_dit(self, x_tokens: torch.Tensor, pre_state: dict[str, Any]) -> torch.Tensor:
-        x = self.head(x_tokens, pre_state["t"])
-        return torch.stack(super().unpatchify(x, pre_state["meta"]["grid_sizes"]))
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        timestep: torch.Tensor,
-        context: torch.Tensor,
-        context_mask: torch.Tensor | None = None,
-        action: torch.Tensor | None = None,
-        fuse_vae_embedding_in_latents: bool = False,
-    ):
-        pre_state = self.pre_dit(
-            x=x,
-            timestep=timestep,
-            context=context,
-            context_mask=context_mask,
-            action=action,
-            fuse_vae_embedding_in_latents=fuse_vae_embedding_in_latents,
-        )
-        x_tokens = pre_state["tokens"]
-        context_emb = pre_state["context"]
-        t_mod = pre_state["t_mod"]
-        freqs = pre_state["freqs"]
-        context_attn_mask = pre_state["context_mask"]
-        self_attn_mask = (
-            self.build_video_to_video_mask(
-                video_seq_len=x_tokens.shape[1],
-                video_tokens_per_frame=int(pre_state["meta"]["tokens_per_frame"]),
-                device=x_tokens.device,
-            )
-            if self.video_attention_mask_mode != "bidirectional"
-            else None
-        )
-
-        for block in self.blocks:
-            if self.use_gradient_checkpointing:
-                x_tokens = gradient_checkpoint_forward(
-                    block,
-                    self.use_gradient_checkpointing,
-                    x_tokens,
-                    context_emb,
-                    t_mod,
-                    freqs,
-                    context_mask=context_attn_mask,
-                    self_attn_mask=self_attn_mask,
-                )
-            else:
-                x_tokens = block(
-                    x_tokens,
-                    context_emb,
-                    t_mod,
-                    freqs,
-                    context_mask=context_attn_mask,
-                    self_attn_mask=self_attn_mask,
-                )
-
-        return self.post_dit(x_tokens, pre_state)

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

@@ -0,0 +1,54 @@
+# 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

@@ -1,12 +1,11 @@
-#!/usr/bin/env python
-
-# Copyright 2025 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+# 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
+# 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,
@@ -15,7 +14,6 @@
 # limitations under the License.
 
 
-import logging
 from typing import TYPE_CHECKING
 
 import torch
@@ -44,9 +42,6 @@ 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")
@@ -186,7 +181,8 @@ class BasicTransformerBlock(nn.Module):
         attn_output = self.attn1(
             norm_hidden_states,
             encoder_hidden_states=encoder_hidden_states,
-            attention_mask=encoder_attention_mask if encoder_hidden_states is not None else attention_mask,
+            attention_mask=attention_mask,
+            # encoder_attention_mask=encoder_attention_mask,
         )
         if self.final_dropout:
             attn_output = self.final_dropout(attn_output)
@@ -270,8 +266,8 @@ 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)
-        logger.debug(
-            "Total number of DiT parameters: %d",
+        print(
+            "Total number of DiT parameters: ",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 
@@ -322,71 +318,6 @@ 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
 
@@ -431,8 +362,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                 for _ in range(self.config.num_layers)
             ]
         )
-        logger.debug(
-            "Total number of SelfAttentionTransformer parameters: %d",
+        print(
+            "Total number of SelfAttentionTransformer parameters: ",
             sum(p.numel() for p in self.parameters() if p.requires_grad),
         )
 

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

@@ -0,0 +1,408 @@
+# 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

@@ -14,229 +14,12 @@
 # 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, DiffuserSchedulerConfig
+from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
 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
@@ -245,44 +28,35 @@ class GrootConfig(PreTrainedConfig):
 
     # Basic policy settings
     n_obs_steps: int = 1
-    chunk_size: int = 40
-    n_action_steps: int = 40
+    chunk_size: int = 50
+    n_action_steps: int = 50
 
     # Dimension settings (must match pretrained GR00T model expectations)
     # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 132
+    max_state_dim: int = 64
 
     # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 132
+    max_action_dim: int = 32
 
-    # 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 (start with identity, adjust as needed)
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.IDENTITY,
-            "ACTION": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MEAN_STD,
         }
     )
 
-    # Groot-specific model parameters
+    # Image preprocessing (adjust to match Groot's expected input)
+    image_size: tuple[int, int] = (224, 224)
 
-    # 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
+    # Groot-specific model parameters (from groot_finetune_script.py)
 
-    # 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
+    # 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"
 
     # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
     embodiment_tag: str = "new_embodiment"
@@ -301,67 +75,38 @@ class GrootConfig(PreTrainedConfig):
     # Whether to fine-tune the diffusion model
     tune_diffusion_model: bool = True
 
-    # Whether to fine-tune the VL LayerNorm + VL self-attention projector in the action head.
-    tune_vlln: 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
 
-    # 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
+    # Alpha value for the LORA model
+    lora_alpha: int = 16
 
-    # 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
+    # Dropout rate for the LORA model
+    lora_dropout: float = 0.1
 
-    # 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
+    # Whether to use the full model for LORA
+    lora_full_model: bool = False
 
-    # 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
+    # Training parameters (matching groot_finetune_script.py)
     optimizer_lr: float = 1e-4
-    # Isaac-GR00T N1.7 fine-tunes with AdamW betas (0.9, 0.999).
-    optimizer_betas: tuple[float, float] = (0.9, 0.999)
+    optimizer_betas: tuple[float, float] = (0.95, 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
 
-    # 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
+    # Dataset parameters
+    # Video backend to use for training ('decord' or 'torchvision_av')
     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
@@ -372,65 +117,6 @@ 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:
@@ -438,6 +124,9 @@ 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]
@@ -484,20 +173,15 @@ 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) -> 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),
+    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,
         )
 
     @property
@@ -508,15 +192,7 @@ class GrootConfig(PreTrainedConfig):
     @property
     def action_delta_indices(self) -> list[int]:
         """Return indices for delta actions."""
-        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)
+        return list(range(min(self.chunk_size, 16)))
 
     @property
     def reward_delta_indices(self) -> None:

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

@@ -0,0 +1,135 @@
+# 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

@@ -0,0 +1,503 @@
+# --------------------------------------------------------
+# 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

@@ -0,0 +1,396 @@
+# --------------------------------------------------------
+# 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

@@ -0,0 +1,541 @@
+# 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

@@ -0,0 +1,380 @@
+# 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

@@ -1,951 +0,0 @@
-#!/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)