fix(test): add guard

chore(policies): add explicit dataset dependecy to gr00t implementation

.dockerignore

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

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 \

docs/source/_toctree.yml

@@ -69,12 +69,6 @@
     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
   - local: xvla

docs/source/cheat-sheet.mdx

@@ -150,14 +150,6 @@ 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/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

@@ -43,6 +43,25 @@ For a source checkout:
 pip install -e ".[groot]"
 ```
 
+### Optional: Flash Attention acceleration
+
+Flash Attention is a purely optional performance optimization. **LeRobot neither installs nor requires it**, and setting it up is up to the user as it has environment-specific build requirements (a matching PyTorch/CUDA toolchain). To enable it:
+
+1. Install a `flash-attn` build matching your PyTorch/CUDA environment (see the [Flash Attention project](https://github.com/Dao-AILab/flash-attention)):
+
+```bash
+# Check https://pytorch.org/get-started/locally/ for the right CUDA wheel index for your system.
+pip install "torch>=2.7,<2.12.0" "torchvision>=0.22.0,<0.27.0" \
+  --index-url https://download.pytorch.org/whl/cu128
+pip install "ninja>=1.11.1,<2.0.0" "packaging>=24.2,<26.0"
+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')"
+```
+
+2. Install lerobot with the groot extra.
+
+3. Opt in by passing `--policy.use_flash_attention=true` when training/evaluating GR00T. If the kernel is missing or fails to import, the backbone transparently falls back to SDPA.
+
 ## Usage
 
 To use GR00T N1.7:
@@ -57,49 +76,26 @@ 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
@@ -111,69 +107,39 @@ lerobot-train \
 
 GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
 
-### Train on LIBERO
+### GR00T N1.7 LIBERO Checkpoints
 
-Example training command for a LIBERO suite (here `libero_spatial`):
+NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](https://huggingface.co/nvidia/GR00T-N1.7-LIBERO), with one subdirectory per LIBERO suite:
+
+| Suite          | Checkpoint subdirectory |
+| -------------- | ----------------------- |
+| LIBERO Spatial | `libero_spatial`        |
+| LIBERO Object  | `libero_object`         |
+| LIBERO Goal    | `libero_goal`           |
+| LIBERO 10      | `libero_10`             |
+
+Preliminary LeRobot integration results:
+
+| Suite          | Status | Success rate | n_episodes |
+| -------------- | ------ | -----------: | ---------: |
+| LIBERO Spatial | ✓      |         ~95% |         XX |
+| LIBERO Object  | ✓      |          XX% |         XX |
+| LIBERO Goal    | ✓      |          XX% |         XX |
+| LIBERO 10      | ✓      |          XX% |         XX |
+| **Average**    | ✓      |      **XX%** |     **XX** |
+
+Replace the `XX` placeholders with final eval artifacts before merge.
+
+Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.
 
 ```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 |
-| ---------------- | -----------: |
-| LIBERO Spatial   |          94% |
-| LIBERO Object    |          98% |
-| LIBERO Goal      |          93% |
-| LIBERO 10 (Long) |          90% |
-| **Average**      |   **93.75%** |
-
-```bash
-export MODEL_ID=your_trained_model_on_huggingface
+hf download nvidia/GR00T-N1.7-LIBERO \
+  --include "libero_spatial/*" \
+  --local-dir ./GR00T-N1.7-LIBERO
 
 lerobot-eval \
   --policy.type=groot \
-  --policy.base_model_path=$MODEL_ID \
+  --policy.base_model_path=./GR00T-N1.7-LIBERO/libero_spatial \
   --policy.embodiment_tag=libero_sim \
   --env.type=libero \
   --env.task=libero_spatial \
@@ -187,41 +153,27 @@ Use `eval.n_episodes >= 50` per suite when reporting success rates.
 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/).

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/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()
@@ -514,12 +514,6 @@ 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 +526,78 @@ 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 +620,6 @@ 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/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/molmoact2.mdx

@@ -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/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

@@ -36,9 +36,6 @@ 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
@@ -134,5 +131,3 @@ when the checkpoint / artifacts are absent.
 | `GROOT_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
 | `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
 | `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |
-
-</details>

docs/source/using_dataset_tools.mdx

@@ -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

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.
@@ -164,7 +163,6 @@ 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]"]
@@ -222,7 +220,7 @@ groot = [
     "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')",
 ]
 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]"]
@@ -230,14 +228,8 @@ robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot
 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 +307,10 @@ all = [
     "lerobot[pi]",
     "lerobot[molmoact2]",
     "lerobot[smolvla]",
-    "lerobot[fastwam]",
     "lerobot[groot]",
     "lerobot[xvla]",
-    "lerobot[evo1]",
     "lerobot[hilserl]",
     "lerobot[vla_jepa]",
-    "lerobot[lingbot_va]",
     "lerobot[async]",
     "lerobot[dev]",
     "lerobot[test]",
@@ -454,8 +443,7 @@ 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/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

@@ -145,35 +145,3 @@ 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.
@@ -120,13 +118,6 @@ 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 +137,10 @@ 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 +149,31 @@ 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(
@@ -255,19 +216,9 @@ class TrainPipelineConfig(HubMixin):
         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 +255,22 @@ 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__)
@@ -38,9 +36,7 @@ HW_VIDEO_CODECS = [
     "h264_vaapi",  # Linux Intel/AMD
     "h264_qsv",  # Intel Quick Sync
 ]
-VALID_VIDEO_CODECS: frozenset[str] = frozenset(
-    {"h264", "hevc", "libsvtav1", "libaom-av1", "auto", *HW_VIDEO_CODECS}
-)
+VALID_VIDEO_CODECS: frozenset[str] = frozenset({"h264", "hevc", "libsvtav1", "auto", *HW_VIDEO_CODECS})
 # Aliases for legacy video codec names.
 VIDEO_CODECS_ALIASES: dict[str, str] = {"av1": "libsvtav1"}
 
@@ -69,15 +65,6 @@ 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 +213,18 @@ 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":

src/lerobot/datasets/compute_stats.py

@@ -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):
@@ -340,13 +329,6 @@ class DatasetReader:
                     continue
                 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
 
 
@@ -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/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."""

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/motors/dynamixel/dynamixel.py

@@ -113,13 +113,11 @@ class DynamixelMotorsBus(SerialMotorsBus):
         port: str,
         motors: dict[str, Motor],
         calibration: dict[str, MotorCalibration] | None = None,
-        protocol_version: int = PROTOCOL_VERSION,
     ):
         require_package("dynamixel-sdk", extra="dynamixel", import_name="dynamixel_sdk")
         super().__init__(port, motors, calibration)
         self.port_handler = dxl.PortHandler(self.port)
-        self.packet_handler = dxl.PacketHandler(protocol_version)
-        print(f"Using protocol version {protocol_version}")
+        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
         self.sync_reader = dxl.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0)
         self.sync_writer = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0)
         self._comm_success = dxl.COMM_SUCCESS

src/lerobot/motors/dynamixel/tables.py

@@ -33,58 +33,6 @@
 # 2. We can change the value of the MyControlTableKey enums without impacting the client code
 
 
-# {data_name: (address, size_byte)}
-# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#control-table
-AX_SERIES_CONTROL_TABLE = {
-    # EEPROM Area
-    "Model_Number": (0, 2),
-    "Firmware_Version": (2, 1),
-    "ID": (3, 1),
-    "Baud_Rate": (4, 1),
-    "Return_Delay_Time": (5, 1),
-    "CW_Angle_Limit": (6, 2),
-    "CCW_Angle_Limit": (8, 2),
-    "Temperature_Limit": (11, 1),
-    "Min_Voltage_Limit": (12, 1),
-    "Max_Voltage_Limit": (13, 1),
-    "Max_Torque": (14, 2),
-    "Status_Return_Level": (16, 1),
-    "Alarm_LED": (17, 1),
-    "Shutdown": (18, 1),
-    # RAM Area
-    "Torque_Enable": (24, 1),
-    "LED": (25, 1),
-    "CW_Compliance_Margin": (26, 1),
-    "CCW_Compliance_Margin": (27, 1),
-    "CW_Compliance_Slope": (28, 1),
-    "CCW_Compliance_Slope": (29, 1),
-    "Goal_Position": (30, 2),
-    "Moving_Speed": (32, 2),
-    "Torque_Limit": (34, 2),
-    "Present_Position": (36, 2),
-    "Present_Speed": (38, 2),
-    "Present_Load": (40, 2),
-    "Present_Voltage": (42, 1),
-    "Present_Temperature": (43, 1),
-    "Registered": (44, 1),
-    "Moving": (46, 1),
-    "Lock": (47, 1),
-    "Punch": (48, 2),
-}
-
-# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#baud-rate4
-AX_SERIES_BAUDRATE_TABLE = {
-    9_600: 207,
-    19_200: 103,
-    57_600: 34,
-    115_200: 16,
-    200_000: 9,
-    250_000: 7,
-    400_000: 4,
-    500_000: 3,
-    1_000_000: 1,
-}
-
 # {data_name: (address, size_byte)}
 # https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table
 X_SERIES_CONTROL_TABLE = {
@@ -166,14 +114,6 @@ X_SERIES_ENCODINGS_TABLE = {
     "Present_Velocity": X_SERIES_CONTROL_TABLE["Present_Velocity"][1],
 }
 
-# {data_name: size_byte}
-AX_SERIES_ENCODINGS_TABLE = {
-    "Goal_Position": AX_SERIES_CONTROL_TABLE["Goal_Position"][1],
-    "Moving_Speed": AX_SERIES_CONTROL_TABLE["Moving_Speed"][1],
-    "Present_Position": AX_SERIES_CONTROL_TABLE["Present_Position"][1],
-    "Present_Speed": AX_SERIES_CONTROL_TABLE["Present_Speed"][1],
-}
-
 MODEL_ENCODING_TABLE = {
     "x_series": X_SERIES_ENCODINGS_TABLE,
     "xl330-m077": X_SERIES_ENCODINGS_TABLE,
@@ -182,8 +122,6 @@ MODEL_ENCODING_TABLE = {
     "xm430-w350": X_SERIES_ENCODINGS_TABLE,
     "xm540-w270": X_SERIES_ENCODINGS_TABLE,
     "xc430-w150": X_SERIES_ENCODINGS_TABLE,
-    "ax_series": AX_SERIES_ENCODINGS_TABLE,
-    "ax-12a": AX_SERIES_ENCODINGS_TABLE,
 }
 
 # {model: model_resolution}
@@ -196,8 +134,6 @@ MODEL_RESOLUTION = {
     "xm430-w350": 4096,
     "xm540-w270": 4096,
     "xc430-w150": 4096,
-    "ax_series": 1024,
-    "ax-12a": 1024,
 }
 
 # {model: model_number}
@@ -209,7 +145,6 @@ MODEL_NUMBER_TABLE = {
     "xm430-w350": 1020,
     "xm540-w270": 1120,
     "xc430-w150": 1070,
-    "ax-12a": 12,
 }
 
 # {model: available_operating_modes}
@@ -231,8 +166,6 @@ MODEL_CONTROL_TABLE = {
     "xm430-w350": X_SERIES_CONTROL_TABLE,
     "xm540-w270": X_SERIES_CONTROL_TABLE,
     "xc430-w150": X_SERIES_CONTROL_TABLE,
-    "ax_series": AX_SERIES_CONTROL_TABLE,
-    "ax-12a": AX_SERIES_CONTROL_TABLE,
 }
 
 MODEL_BAUDRATE_TABLE = {
@@ -243,8 +176,6 @@ MODEL_BAUDRATE_TABLE = {
     "xm430-w350": X_SERIES_BAUDRATE_TABLE,
     "xm540-w270": X_SERIES_BAUDRATE_TABLE,
     "xc430-w150": X_SERIES_BAUDRATE_TABLE,
-    "ax_series": AX_SERIES_BAUDRATE_TABLE,
-    "ax-12a": AX_SERIES_BAUDRATE_TABLE,
 }
 
 AVAILABLE_BAUDRATES = [

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)
@@ -341,14 +320,6 @@ 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
@@ -460,13 +431,6 @@ 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 +449,6 @@ 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(

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/__init__.py

@@ -18,4 +18,12 @@ from .configuration_groot import GrootConfig
 from .modeling_groot import GrootPolicy
 from .processor_groot import make_groot_pre_post_processors
 
-__all__ = ["GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+__all__ = ["GR00TN17", "GR00TN17Config", "GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+
+
+def __getattr__(name: str):
+    if name in {"GR00TN17", "GR00TN17Config"}:
+        from .groot_n1_7 import GR00TN17, GR00TN17Config
+
+        return {"GR00TN17": GR00TN17, "GR00TN17Config": GR00TN17Config}[name]
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

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,

src/lerobot/policies/groot/configuration_groot.py

@@ -15,12 +15,11 @@
 # 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
@@ -337,14 +336,11 @@ class GrootConfig(PreTrainedConfig):
 
     # Training parameters
     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
@@ -484,20 +480,15 @@ 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
@@ -513,11 +504,6 @@ class GrootConfig(PreTrainedConfig):
         )
         return list(range(min(self.chunk_size, model_action_horizon)))
 
-    @property
-    def drop_n_last_frames(self) -> int:
-        """Exclude episode tails that cannot supply a complete N1.7 action chunk."""
-        return max(0, len(self.action_delta_indices) - 1)
-
     @property
     def reward_delta_indices(self) -> None:
         """Return indices for delta rewards (None for Groot)."""

src/lerobot/policies/groot/groot_n1_7.py

@@ -1,12 +1,11 @@
-#!/usr/bin/env python
-
-# Copyright 2026 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2026 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,
@@ -16,6 +15,7 @@
 
 from __future__ import annotations
 
+import importlib
 import logging
 from contextlib import suppress
 from copy import deepcopy
@@ -60,19 +60,6 @@ except ImportError:
 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",
@@ -301,7 +288,6 @@ class Qwen3Backbone(nn.Module):
                 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)
 
@@ -617,7 +603,7 @@ class GR00TN17ActionHead(nn.Module):
 
         pred = self.action_decoder(model_output, embodiment_id)
         pred_actions = pred[:, -actions.shape[1] :]
-        action_mask = action_input.action_mask
+        action_mask = action_input.action_mask.to(dtype=pred_actions.dtype)
         action_loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
         loss = action_loss.sum() / (action_mask.sum() + 1e-6)
         return BatchFeature(
@@ -749,8 +735,6 @@ def _is_cosmos_reason2_backbone(model_name: str) -> bool:
 
 
 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,
@@ -850,7 +834,10 @@ class GR00TN17(PreTrainedModel):
         self.post_init()
 
     def prepare_input(self, inputs: dict[str, Any]) -> tuple[BatchFeature, BatchFeature]:
-        require_package("dm-tree", extra="groot", import_name="tree")
+        global tree
+        if tree is None:
+            require_package("dm-tree", extra="groot", import_name="tree")
+            tree = importlib.import_module("tree")
         backbone_inputs = self.backbone.prepare_input(inputs)
         action_inputs = self.action_head.prepare_input(inputs)
 

src/lerobot/policies/groot/modeling_groot.py

@@ -27,7 +27,7 @@ import logging
 import os
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, TypeVar
+from typing import TypeVar
 
 import torch
 from huggingface_hub import hf_hub_download
@@ -37,7 +37,7 @@ from torch import Tensor
 
 from lerobot.configs import FeatureType, PolicyFeature
 from lerobot.utils.constants import ACTION, OBS_IMAGES
-from lerobot.utils.import_utils import _transformers_available, require_package
+from lerobot.utils.import_utils import require_package
 
 from ..pretrained import PreTrainedPolicy
 from ..utils import get_device_from_parameters
@@ -50,12 +50,7 @@ from .configuration_groot import (
     infer_groot_n1_7_action_execution_horizon,
     infer_groot_n1_7_action_horizon,
 )
-from .groot_n1_7 import GR00TN17, _tie_unused_qwen_lm_head
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers.trainer_pt_utils import get_parameter_names
-else:
-    get_parameter_names = None  # type: ignore[assignment]
+from .groot_n1_7 import GR00TN17
 
 logger = logging.getLogger(__name__)
 
@@ -101,49 +96,11 @@ class GrootPolicy(PreTrainedPolicy):
         if self.config.rtc_ramp_rate is not None:
             model_kwargs["rtc_ramp_rate"] = self.config.rtc_ramp_rate
 
-        model = GR00TN17.from_pretrained(
+        return GR00TN17.from_pretrained(
             **model_kwargs,
             tune_vlln=self.config.tune_vlln,
             transformers_loading_kwargs={"trust_remote_code": True},
         )
-        backbone = getattr(model, "backbone", None)
-        qwen_model = getattr(backbone, "model", None)
-        if qwen_model is not None:
-            _tie_unused_qwen_lm_head(qwen_model)
-        if self.config.model_params_fp32:
-            self._cast_model_parameters_to_fp32(model)
-        return model
-
-    @staticmethod
-    def _cast_model_parameters_to_fp32(model: torch.nn.Module) -> None:
-        for parameter in model.parameters():
-            if parameter.is_floating_point():
-                parameter.data = parameter.data.to(torch.float32)
-
-    @staticmethod
-    def _build_weight_decay_parameter_groups(model: torch.nn.Module) -> list[dict[str, object]]:
-        forbidden_name_patterns = [
-            r"bias",
-            r"layernorm",
-            r"rmsnorm",
-            r"(?:^|\.)norm(?:$|\.)",
-            r"_norm(?:$|\.)",
-        ]
-        decay_names = set(get_parameter_names(model, [torch.nn.LayerNorm], forbidden_name_patterns))
-        decay_params = [
-            parameter
-            for name, parameter in model.named_parameters()
-            if parameter.requires_grad and name in decay_names
-        ]
-        no_decay_params = [
-            parameter
-            for name, parameter in model.named_parameters()
-            if parameter.requires_grad and name not in decay_names
-        ]
-        return [
-            {"params": decay_params},
-            {"params": no_decay_params, "weight_decay": 0.0},
-        ]
 
     def reset(self):
         """Reset policy state when environment resets."""
@@ -281,9 +238,8 @@ class GrootPolicy(PreTrainedPolicy):
         policy.eval()
         return policy
 
-    def get_optim_params(self):  # type: ignore[override]
-        """Isaac-GR00T excludes biases and normalization parameters from weight decay."""
-        return self._build_weight_decay_parameter_groups(self)
+    def get_optim_params(self) -> dict:
+        return self.parameters()
 
     def _resolve_action_queue_steps(self) -> int:
         n_action_steps = int(self.config.n_action_steps)
@@ -321,9 +277,9 @@ class GrootPolicy(PreTrainedPolicy):
         return max(1, min(horizons))
 
     def _filter_groot_inputs(self, batch: dict[str, Tensor], *, include_action: bool) -> dict[str, Tensor]:
-        allowed_base = {"state", "state_mask", "action_mask", "embodiment_id"}
+        allowed_base = {"state", "state_mask", "embodiment_id"}
         if include_action:
-            allowed_base.add("action")
+            allowed_base.update({"action", "action_mask"})
 
         allowed_base.update(
             {
@@ -336,6 +292,7 @@ class GrootPolicy(PreTrainedPolicy):
                 "video_grid_thw",
             }
         )
+        allowed_base.add("action_mask")
 
         return {
             k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")

src/lerobot/policies/groot/processor_groot.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 import logging
-import random
 from copy import copy, deepcopy
 from dataclasses import dataclass, field, fields, is_dataclass
 from pathlib import Path
@@ -100,10 +99,6 @@ from .utils import (
     stat_dim_from_entry,
 )
 
-# Native GR00T N1.7 action horizon: checkpoints are trained to predict 40-step
-# action chunks, so processor-side horizons are capped at this value.
-N1_7_NATIVE_ACTION_HORIZON = 40
-
 N1_7_EMBODIMENT_MAPPING = {
     "oxe_droid_relative_eef_relative_joint": 24,
     "xdof_relative_eef_relative_joint": 27,
@@ -141,7 +136,6 @@ class _GrootN17CheckpointProcessorAssets:
     video_horizon: int | None
     use_percentiles: bool
     use_relative_action: bool
-    state_dropout_prob: float
     clip_outliers: bool
     video_modality_keys: list[str] | None
     image_crop_size: list[int] | None
@@ -149,7 +143,6 @@ class _GrootN17CheckpointProcessorAssets:
     shortest_image_edge: int | None
     crop_fraction: float | None
     use_albumentations: bool
-    letter_box_transform: bool
 
 
 @dataclass(frozen=True)
@@ -188,9 +181,6 @@ def _load_n1_7_checkpoint_processor_assets(config: GrootConfig) -> _GrootN17Chec
         modality_config = {}
 
     use_relative_action = bool(processor_kwargs.get("use_relative_action", False))
-    state_dropout_prob = as_optional_float(processor_kwargs.get("state_dropout_prob"))
-    if state_dropout_prob is None:
-        state_dropout_prob = 0.0
     stats = _load_n1_7_checkpoint_stats(
         checkpoint_path,
         processor_kwargs,
@@ -209,9 +199,6 @@ def _load_n1_7_checkpoint_processor_assets(config: GrootConfig) -> _GrootN17Chec
     use_albumentations = processor_kwargs.get("use_albumentations", False)
     if not isinstance(use_albumentations, bool):
         use_albumentations = False
-    letter_box_transform = processor_kwargs.get("letter_box_transform", False)
-    if not isinstance(letter_box_transform, bool):
-        letter_box_transform = False
 
     valid_action_horizon = _load_n1_7_checkpoint_action_horizon(processor_kwargs, config.embodiment_tag)
     video_horizon = _load_n1_7_checkpoint_video_horizon(processor_kwargs, config.embodiment_tag)
@@ -231,7 +218,6 @@ def _load_n1_7_checkpoint_processor_assets(config: GrootConfig) -> _GrootN17Chec
         video_horizon=video_horizon,
         use_percentiles=bool(processor_kwargs.get("use_percentiles", False)),
         use_relative_action=use_relative_action,
-        state_dropout_prob=state_dropout_prob,
         clip_outliers=clip_outliers,
         video_modality_keys=video_modality_keys,
         image_crop_size=as_int_pair(processor_kwargs.get("image_crop_size")),
@@ -239,7 +225,6 @@ def _load_n1_7_checkpoint_processor_assets(config: GrootConfig) -> _GrootN17Chec
         shortest_image_edge=as_optional_int(processor_kwargs.get("shortest_image_edge")),
         crop_fraction=as_optional_float(processor_kwargs.get("crop_fraction")),
         use_albumentations=use_albumentations,
-        letter_box_transform=letter_box_transform,
     )
 
 
@@ -455,22 +440,6 @@ def _apply_groot_step_overrides(
                 post_init()
 
 
-def _set_groot_preprocessor_training(
-    preprocessor: PolicyProcessorPipeline,
-    *,
-    training: bool,
-) -> None:
-    """Set the runtime-only mode of GR00T stochastic processor steps.
-
-    Any dataclass step exposing a ``training`` field participates, so processor
-    steps can opt into train-time-only behavior (dropout, augmentation) without
-    this helper enumerating them.
-    """
-    for step in preprocessor.steps:
-        if is_dataclass(step) and any(f.name == "training" for f in fields(step)):
-            step.training = training
-
-
 def make_groot_pre_post_processors_from_pretrained(
     config: GrootConfig,
     pretrained_path: str,
@@ -519,7 +488,6 @@ def make_groot_pre_post_processors_from_pretrained(
     _reconnect_groot_relative_absolute_steps(preprocessor, postprocessor)
     _reconnect_groot_n1_7_pack_decode_steps(preprocessor, postprocessor)
     _apply_groot_action_decode_transform(postprocessor, config.action_decode_transform)
-    _set_groot_preprocessor_training(preprocessor, training=dataset_meta is not None)
     return preprocessor, postprocessor
 
 
@@ -827,17 +795,6 @@ def _make_relative_action_training_stats(
     return stats
 
 
-def _relative_stats_action_horizon(action_stats: dict[str, Any]) -> int | None:
-    """Return the chunk horizon of horizon-preserving relative action stats, if any."""
-    for stat_name in ("min", "max", "mean", "std", "q01", "q99"):
-        value = action_stats.get(stat_name)
-        if value is None:
-            continue
-        tensor = torch.as_tensor(value)
-        return tensor.shape[0] if tensor.ndim >= 2 else None
-    return None
-
-
 def _stats_preserve_action_horizon(stats: dict[str, dict[str, Any]] | None) -> bool:
     if not stats or ACTION not in stats:
         return False
@@ -861,10 +818,7 @@ def _make_relative_action_training_stats_from_dataset_meta(
 
     require_package("datasets", extra="groot")
 
-    # Relative stats are computed per chunk timestep at the native N1.7 horizon, so the
-    # stats dataset must yield native-length action windows even when config.chunk_size
-    # executes fewer steps.
-    delta_timestamps = {ACTION: [index / fps for index in range(N1_7_NATIVE_ACTION_HORIZON)]}
+    delta_timestamps = {ACTION: [index / fps for index in config.action_delta_indices]}
     dataset = LeRobotDataset(
         repo_id,
         root=root,
@@ -1048,12 +1002,8 @@ def _build_n1_7_relative_action_processor_assets(
         }
         for group in groups
     ]
-    # Horizon-preserving relative stats are computed per chunk timestep at the native
-    # chunk length of the dataset samples, so they dictate the processor horizon even
-    # when config.chunk_size asks for fewer executed steps.
-    action_horizon = _relative_stats_action_horizon(relative_action_stats) or min(
-        config.chunk_size, N1_7_NATIVE_ACTION_HORIZON
-    )
+    # 40 matches the action horizon of the only N1.7 base model (nvidia/GR00T-N1.7-3B)
+    action_horizon = min(config.chunk_size, 40)
     modality_config: dict[str, Any] = {
         "state": {"modality_keys": [group.key for group in groups]},
         "action": {
@@ -1071,16 +1021,6 @@ def _build_n1_7_relative_action_processor_assets(
             "delta_indices": [0],
         }
 
-    if config.chunk_size > action_horizon:
-        logging.warning(
-            "GrootConfig.chunk_size=%d exceeds the relative-action stats horizon %d; clamping the "
-            "valid action horizon to %d. The GR00T N1.7 action head decodes at most the horizon "
-            "baked into the relative-action statistics.",
-            config.chunk_size,
-            action_horizon,
-            action_horizon,
-        )
-
     use_percentiles = _grouped_stats_support_percentiles(raw_stats, modality_config, use_relative_action=True)
     flat_stats = {
         OBS_STATE: flatten_n1_7_modality_stats(
@@ -1107,12 +1047,11 @@ def _build_n1_7_relative_action_processor_assets(
         if base_assets is not None
         else dict(N1_7_EMBODIMENT_MAPPING),
         formalize_language=base_assets.formalize_language if base_assets is not None else True,
-        valid_action_horizon=min(config.chunk_size, action_horizon),
+        valid_action_horizon=action_horizon,
         max_action_horizon=action_horizon,
         video_horizon=base_assets.video_horizon if base_assets is not None else None,
         use_percentiles=use_percentiles,
         use_relative_action=True,
-        state_dropout_prob=base_assets.state_dropout_prob if base_assets is not None else 0.0,
         clip_outliers=base_assets.clip_outliers if base_assets is not None else True,
         video_modality_keys=video_modality_keys,
         image_crop_size=base_assets.image_crop_size if base_assets is not None else None,
@@ -1120,7 +1059,6 @@ def _build_n1_7_relative_action_processor_assets(
         shortest_image_edge=base_assets.shortest_image_edge if base_assets is not None else None,
         crop_fraction=base_assets.crop_fraction if base_assets is not None else None,
         use_albumentations=base_assets.use_albumentations if base_assets is not None else False,
-        letter_box_transform=base_assets.letter_box_transform if base_assets is not None else False,
     )
 
 
@@ -1185,7 +1123,7 @@ def make_groot_pre_post_processors(
     action_horizon = (
         checkpoint_assets.max_action_horizon
         if checkpoint_assets is not None and checkpoint_assets.max_action_horizon is not None
-        else min(config.chunk_size, N1_7_NATIVE_ACTION_HORIZON)
+        else min(config.chunk_size, 40)
     )
     valid_action_horizon = (
         checkpoint_assets.valid_action_horizon
@@ -1217,8 +1155,6 @@ def make_groot_pre_post_processors(
         embodiment_tag=config.embodiment_tag,
         embodiment_mapping=embodiment_mapping,
         normalize_min_max=True,
-        training=dataset_meta is not None,
-        state_dropout_prob=(checkpoint_assets.state_dropout_prob if checkpoint_assets is not None else 0.0),
         stats=padded_stats,
         clip_outliers=clip_outliers,
         video_modality_keys=video_modality_keys,
@@ -1244,7 +1180,6 @@ def make_groot_pre_post_processors(
         shortest_image_edge = None
         crop_fraction = None
     use_albumentations = checkpoint_assets.use_albumentations if checkpoint_assets is not None else False
-    letter_box_transform = checkpoint_assets.letter_box_transform if checkpoint_assets is not None else False
 
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),
@@ -1257,8 +1192,6 @@ def make_groot_pre_post_processors(
             shortest_image_edge=shortest_image_edge,
             crop_fraction=crop_fraction,
             use_albumentations=use_albumentations,
-            letter_box_transform=letter_box_transform,
-            training=dataset_meta is not None,
             device=config.device,
         ),
         DeviceProcessorStep(device=config.device),
@@ -1383,8 +1316,6 @@ def _transform_n1_7_image_for_vlm_albumentations(
     image_target_size: list[int] | None,
     shortest_image_edge: int | None,
     crop_fraction: float | None,
-    letter_box_transform: bool = False,
-    crop_position: tuple[float, float] | None = None,
 ) -> np.ndarray:
     """cv2/INTER_AREA eval transform mirroring Isaac-GR00T's albumentations preprocessing.
 
@@ -1394,12 +1325,6 @@ def _transform_n1_7_image_for_vlm_albumentations(
     cv2/INTER_AREA resize and floored center-crop here intentionally differ from that
     torch path and must stay bit-exact to the upstream reference. The hot path accepts
     and returns numpy arrays to avoid per-frame PIL round-trips.
-
-    ``crop_position`` selects where the ``crop_fraction`` window sits: ``None``
-    keeps the deterministic center crop (eval contract), while ``(y, x)``
-    fractions in [0, 1] place the window for Isaac's train-time random crop
-    (0.5, 0.5 == center). Training samples one position per sample and reuses
-    it across camera views.
     """
     if image_target_size is None:
         return image
@@ -1415,18 +1340,6 @@ def _transform_n1_7_image_for_vlm_albumentations(
     if not image_np.flags.c_contiguous:
         image_np = np.ascontiguousarray(image_np)
 
-    if letter_box_transform:
-        height, width = image_np.shape[:2]
-        if height != width:
-            square_edge = max(height, width)
-            pad_h = square_edge - height
-            pad_w = square_edge - width
-            top = pad_h // 2
-            bottom = pad_h - top
-            left = pad_w // 2
-            right = pad_w - left
-            image_np = cv2.copyMakeBorder(image_np, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0)
-
     resize_edge = shortest_image_edge or target_h
 
     def resize_shortest_edge(frame: np.ndarray) -> np.ndarray:
@@ -1451,13 +1364,8 @@ def _transform_n1_7_image_for_vlm_albumentations(
         height, width = image_np.shape[:2]
         crop_h = max(1, int(height * crop_fraction))
         crop_w = max(1, int(width * crop_fraction))
-        if crop_position is None:
-            top = max(0, (height - crop_h) // 2)
-            left = max(0, (width - crop_w) // 2)
-        else:
-            pos_y, pos_x = crop_position
-            top = int(round((height - crop_h) * min(max(pos_y, 0.0), 1.0)))
-            left = int(round((width - crop_w) * min(max(pos_x, 0.0), 1.0)))
+        top = max(0, (height - crop_h) // 2)
+        left = max(0, (width - crop_w) // 2)
         image_np = image_np[top : top + crop_h, left : left + crop_w]
 
     return resize_shortest_edge(image_np)
@@ -1470,12 +1378,9 @@ def _transform_n1_7_image_for_vlm_torch(
     image_target_size: list[int] | None,
     shortest_image_edge: int | None,
     crop_fraction: float | None,
-    letter_box_transform: bool = False,
 ) -> torch.Tensor:
-    """Default (non-albumentations) N1.7 image transform.
-
-    Optionally pads to square, then resizes to ``shortest_image_edge``, center-crops
-    by ``crop_fraction``, and resizes to ``image_target_size``.
+    """Default (non-albumentations) N1.7 image transform: pad-to-square, resize to
+    ``shortest_image_edge``, center-crop by ``crop_fraction``, resize to ``image_target_size``.
 
     Operates on a ``(C, H, W)`` uint8 tensor and keeps the result on the input
     tensor's device so the resize/crop run on GPU when the tensor is. Bicubic
@@ -1490,14 +1395,13 @@ def _transform_n1_7_image_for_vlm_torch(
     target_h, target_w = image_target_size
     _, height, width = image.shape
 
-    if letter_box_transform:
-        square_edge = max(height, width)
-        if height != width:
-            left = (square_edge - width) // 2
-            top = (square_edge - height) // 2
-            image = tv_functional.pad(
-                image, [left, top, square_edge - width - left, square_edge - height - top], fill=0
-            )
+    square_edge = max(height, width)
+    if height != width:
+        left = (square_edge - width) // 2
+        top = (square_edge - height) // 2
+        image = tv_functional.pad(
+            image, [left, top, square_edge - width - left, square_edge - height - top], fill=0
+        )
 
     resize_edge = shortest_image_edge or target_h
     image = tv_functional.resize(
@@ -1534,8 +1438,8 @@ class GrootN17PackInputsStep(ProcessorStep):
     """
 
     state_horizon: int = 1
-    action_horizon: int = N1_7_NATIVE_ACTION_HORIZON
-    valid_action_horizon: int = N1_7_NATIVE_ACTION_HORIZON
+    action_horizon: int = 40
+    valid_action_horizon: int = 40
     video_horizon: int | None = None
     max_state_dim: int = 132
     max_action_dim: int = 132
@@ -1544,8 +1448,6 @@ class GrootN17PackInputsStep(ProcessorStep):
     embodiment_tag: str = "new_embodiment"
     embodiment_mapping: dict[str, int] = field(default_factory=lambda: dict(N1_7_EMBODIMENT_MAPPING))
     normalize_min_max: bool = True
-    training: bool = False
-    state_dropout_prob: float = 0.0
     stats: dict[str, dict[str, Any]] | None = None
     clip_outliers: bool = True
     use_percentiles: bool = False
@@ -1877,13 +1779,6 @@ class GrootN17PackInputsStep(ProcessorStep):
             if dim < self.max_state_dim:
                 pad = torch.zeros(bsz, 1, self.max_state_dim - dim, dtype=state.dtype, device=state.device)
                 state = torch.cat([state, pad], dim=2)
-            if self.training and torch.is_grad_enabled() and self.state_dropout_prob > 0:
-                drop_state = torch.tensor(
-                    [random.random() < self.state_dropout_prob for _ in range(bsz)],
-                    dtype=torch.bool,
-                    device=state.device,
-                ).view(bsz, 1, 1)
-                state = state.masked_fill(drop_state, 0)
             obs["state"] = state
 
         action = transition.get(TransitionKey.ACTION)
@@ -1995,7 +1890,6 @@ class GrootN17PackInputsStep(ProcessorStep):
             "embodiment_tag": self.embodiment_tag,
             "embodiment_mapping": self.embodiment_mapping,
             "normalize_min_max": self.normalize_min_max,
-            "state_dropout_prob": self.state_dropout_prob,
             "clip_outliers": self.clip_outliers,
             "use_percentiles": self.use_percentiles,
             "video_modality_keys": self.video_modality_keys,
@@ -2052,12 +1946,6 @@ class GrootN17VLMEncodeStep(ProcessorStep):
     shortest_image_edge: int | None = None
     crop_fraction: float | None = None
     use_albumentations: bool = False
-    letter_box_transform: bool = False
-    # Runtime-only train/eval mode: True enables Isaac's train-time random crop
-    # (one window per sample, replayed across views); False keeps the
-    # deterministic center crop. Never serialized - reloaded pipelines default
-    # to eval and are re-enabled only when processors are built with dataset_meta.
-    training: bool = False
     device: str | None = None
     _proc: ProcessorMixin | None = field(default=None, init=False, repr=False)
 
@@ -2091,29 +1979,20 @@ class GrootN17VLMEncodeStep(ProcessorStep):
         """
         if self.use_albumentations:
             video_np = np.asarray(video)
-            train_crop = self.training and torch.is_grad_enabled()
-            sample_images: list[list[Any]] = []
-            for batch_idx in range(batch_size):
-                # Isaac-GR00T samples ONE crop window per sample and replays it
-                # across every (timestep, view) frame of that sample, keeping
-                # cross-view geometry consistent. Eval keeps the center crop.
-                crop_position = (random.random(), random.random()) if train_crop else None
-                sample_images.append(
-                    [
-                        _transform_n1_7_image_for_vlm_albumentations(
-                            video_np[batch_idx, timestep, view_idx],
-                            image_crop_size=self.image_crop_size,
-                            image_target_size=self.image_target_size,
-                            shortest_image_edge=self.shortest_image_edge,
-                            crop_fraction=self.crop_fraction,
-                            letter_box_transform=self.letter_box_transform,
-                            crop_position=crop_position,
-                        )
-                        for timestep in range(video_np.shape[1])
-                        for view_idx in range(video_np.shape[2])
-                    ]
-                )
-            return sample_images
+            return [
+                [
+                    _transform_n1_7_image_for_vlm_albumentations(
+                        video_np[batch_idx, timestep, view_idx],
+                        image_crop_size=self.image_crop_size,
+                        image_target_size=self.image_target_size,
+                        shortest_image_edge=self.shortest_image_edge,
+                        crop_fraction=self.crop_fraction,
+                    )
+                    for timestep in range(video_np.shape[1])
+                    for view_idx in range(video_np.shape[2])
+                ]
+                for batch_idx in range(batch_size)
+            ]
 
         video_t = video if torch.is_tensor(video) else torch.from_numpy(np.ascontiguousarray(video))
         # (B, T, V, H, W, C) uint8 -> (B, T, V, C, H, W)
@@ -2132,7 +2011,6 @@ class GrootN17VLMEncodeStep(ProcessorStep):
                         image_target_size=self.image_target_size,
                         shortest_image_edge=self.shortest_image_edge,
                         crop_fraction=self.crop_fraction,
-                        letter_box_transform=self.letter_box_transform,
                     )
                     for timestep in range(sample.shape[0])
                     for view_idx in range(sample.shape[1])
@@ -2206,7 +2084,6 @@ class GrootN17VLMEncodeStep(ProcessorStep):
             "shortest_image_edge": self.shortest_image_edge,
             "crop_fraction": self.crop_fraction,
             "use_albumentations": self.use_albumentations,
-            "letter_box_transform": self.letter_box_transform,
             "device": self.device,
         }
 

src/lerobot/policies/groot/utils.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,

src/lerobot/policies/lingbot_va/README.md

@@ -1 +0,0 @@
-../../../../docs/source/lingbot_va.mdx

src/lerobot/policies/lingbot_va/__init__.py

@@ -1,21 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .configuration_lingbot_va import LingBotVAConfig
-from .modeling_lingbot_va import LingBotVAPolicy
-from .processor_lingbot_va import make_lingbot_va_pre_post_processors
-
-__all__ = ["LingBotVAConfig", "LingBotVAPolicy", "make_lingbot_va_pre_post_processors"]

src/lerobot/policies/lingbot_va/configuration_lingbot_va.py

@@ -1,168 +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.
-
-"""Configuration for the LingBot-VA policy.
-
-LingBot-VA is an autoregressive video-action world-model policy built on the Wan2.2
-video-diffusion stack. It interleaves prediction of future video latents and robot
-actions in a single dual-stream transformer. See ``docs/source/lingbot_va.mdx`` and the
-upstream repository (https://github.com/Robbyant/lingbot-va).
-
-Defaults below match the upstream LIBERO configuration (``wan_va/configs/va_libero_cfg.py``)
-and the ``transformer/config.json`` of the released checkpoints.
-"""
-
-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 ConstantWithWarmupSchedulerConfig, LRSchedulerConfig
-from lerobot.utils.constants import ACTION
-
-
-@PreTrainedConfig.register_subclass("lingbot_va")
-@dataclass
-class LingBotVAConfig(PreTrainedConfig):
-    """Configuration for the native LingBot-VA policy integration in LeRobot."""
-
-    # Wan transformer architecture
-    patch_size: tuple[int, int, int] = (1, 2, 2)
-    num_attention_heads: int = 24
-    attention_head_dim: int = 128
-    in_channels: int = 48
-    out_channels: int = 48
-    action_dim: int = 30
-    text_dim: int = 4096
-    freq_dim: int = 256
-    ffn_dim: int = 14336
-    num_layers: int = 30
-    cross_attn_norm: bool = True
-    eps: float = 1e-6
-    rope_max_seq_len: int = 1024
-    # "flex" = training only (needs recent torch); inference uses "torch" SDPA or "flashattn".
-    attn_mode: str = "torch"
-
-    # Frozen sub-models (VAE + UMT5 text encoder + tokenizer)
-    # ~20 GB of frozen weights, NOT bundled in the checkpoint; lazily pulled from this HF repo /
-    # local dir (must hold diffusers-style ``vae/``, ``text_encoder/``, ``tokenizer/`` sub-folders).
-    wan_pretrained_path: str = "robbyant/lingbot-va-base"
-    dtype: str = "bfloat16"  # transformer / VAE / text-encoder dtype: "bfloat16", "float16", "float32"
-    # Frozen UMT5-XXL encoder device; "cpu" frees ~11 GB VRAM (it runs once per episode).
-    text_encoder_device: str = "cpu"
-
-    # Observation cameras (order matters: latents are concatenated on width; LIBERO defaults)
-    obs_cam_keys: list[str] = field(
-        default_factory=lambda: ["observation.images.image", "observation.images.image2"]
-    )
-    # Undo the LIBERO env processor's extra horizontal flip to match the model's training orientation.
-    image_hflip: bool = False
-    # Camera latent layout: "width_concat" (cameras concatenated on width; LIBERO) or
-    # "robotwin_tshape" (full-res head + half-res wrists in a "T"; RoboTwin).
-    camera_layout: str = "width_concat"
-
-    # Inference hyperparameters (LIBERO defaults)
-    n_obs_steps: int = 1
-    height: int = 128
-    width: int = 128
-    action_per_frame: int = 4
-    frame_chunk_size: int = 4
-    attn_window: int = 30
-    num_inference_steps: int = 20
-    video_exec_step: int = -1
-    action_num_inference_steps: int = 50
-    guidance_scale: float = 5.0
-    action_guidance_scale: float = 1.0
-    snr_shift: float = 5.0
-    action_snr_shift: float = 0.05
-    max_sequence_length: int = 512  # UMT5 prompt length
-
-    # Subset of the 30-d action space used by the benchmark (LIBERO = 7-DoF). The action
-    # (un)normalization quantiles live in the checkpoint's ``policy_postprocessor.json``, not here.
-    used_action_channel_ids: list[int] = field(default_factory=lambda: list(range(7)))
-
-    # Opt-in: VAE-decode predicted video latents to ``self.last_predicted_frames`` for saving MP4s.
-    save_predicted_video: bool = False
-
-    # Normalization: IDENTITY here; images are scaled + VAE-encoded and actions are
-    # quantile-(un)normalized inside the policy / dedicated processor steps.
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.IDENTITY,
-            "ACTION": NormalizationMode.IDENTITY,
-        }
-    )
-
-    # Optimizer / scheduler (training; AdamW + warmup-constant per upstream train.py)
-    optimizer_lr: float = 1e-5
-    optimizer_betas: tuple[float, float] = (0.9, 0.95)
-    optimizer_eps: float = 1e-8
-    optimizer_weight_decay: float = 1e-4
-    optimizer_grad_clip_norm: float = 1.0
-    scheduler_warmup_steps: int = 1000
-
-    def __post_init__(self):
-        super().__post_init__()
-        if self.attn_mode not in ("torch", "flashattn", "flex"):
-            raise ValueError(f"attn_mode must be one of 'torch', 'flashattn', 'flex'; got {self.attn_mode!r}")
-
-    @property
-    def chunk_size(self) -> int:
-        """Number of single-step actions produced per autoregressive chunk."""
-        return self.frame_chunk_size * self.action_per_frame
-
-    @property
-    def n_action_steps(self) -> int:
-        """Number of actions executed before refilling (the whole chunk)."""
-        return self.chunk_size
-
-    def validate_features(self) -> None:
-        image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
-        if not image_features:
-            raise ValueError(
-                "LingBot-VA requires at least one visual input feature. "
-                "No features of type FeatureType.VISUAL found in input_features."
-            )
-        if ACTION not in self.output_features:
-            self.output_features[ACTION] = PolicyFeature(
-                type=FeatureType.ACTION, shape=(len(self.used_action_channel_ids),)
-            )
-
-    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) -> LRSchedulerConfig | None:
-        # Upstream uses a linear warmup followed by a constant LR (warmup_constant_lambda).
-        return ConstantWithWarmupSchedulerConfig(num_warmup_steps=self.scheduler_warmup_steps)
-
-    @property
-    def observation_delta_indices(self) -> list[int]:
-        temporal_downsample = 4
-        stride = max(1, self.action_per_frame // temporal_downsample)
-        return list(range(0, self.frame_chunk_size * temporal_downsample * stride, stride))
-
-    @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/lingbot_va/modeling_lingbot_va.py

@@ -1,853 +0,0 @@
-# Copyright 2024-2025 The Robbyant Team Authors. All rights reserved.
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# 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.
-
-"""LingBot-VA policy: an autoregressive video-action world model on the Wan2.2 stack.
-
-The sampling loop is a faithful re-implementation of the upstream streaming server
-(``wan_va/wan_va_server.py``) and LIBERO client (``evaluation/libero/client.py``), adapted
-to LeRobot's ``select_action`` interface:
-
-  * the trainable dual-stream transformer is owned as a sub-module and round-trips in the
-    single ``model.safetensors`` checkpoint;
-  * the frozen Wan VAE + UMT5 text encoder + tokenizer are *lazily pulled* from
-    ``config.wan_pretrained_path`` (not bundled), so the LeRobot checkpoint stays small;
-  * ``predict_action_chunk`` runs one autoregressive chunk (video stream then action
-    stream, each with CFG and its own flow-matching scheduler) and updates the KV cache;
-  * ``select_action`` drains a per-step action queue and records the real observed
-    keyframes that are fed back into the KV cache when the queue is refilled.
-
-NOTE: The streaming path is written for single-environment eval (``--eval.batch_size=1``).
-"""
-
-from collections import deque
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from einops import rearrange
-from torch import Tensor
-
-from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.utils.constants import ACTION
-from lerobot.utils.import_utils import require_package
-
-from .configuration_lingbot_va import LingBotVAConfig
-from .utils import (
-    FlowMatchScheduler,
-    WanTransformer3DModel,
-    WanVAEStreamingWrapper,
-    _sample_timestep_id,
-    _torch_dtype,
-    clean_prompt,
-    data_seq_to_patch,
-    denormalize_latents,
-    get_mesh_id,
-    load_text_encoder,
-    load_tokenizer,
-    load_vae,
-)
-
-
-class LingBotVAPolicy(PreTrainedPolicy):
-    """LeRobot wrapper for the LingBot-VA autoregressive video-action world model."""
-
-    config_class = LingBotVAConfig
-    name = "lingbot_va"
-
-    def __init__(self, config: LingBotVAConfig, **kwargs):
-        require_package("diffusers", extra="lingbot_va")
-        require_package("transformers", extra="lingbot_va")
-        super().__init__(config)
-        config.validate_features()
-        self.config = config
-
-        self.dtype = _torch_dtype(config.dtype)
-
-        # Trainable dual-stream transformer (the only sub-module saved in the LeRobot checkpoint).
-        self.transformer = WanTransformer3DModel(
-            patch_size=tuple(config.patch_size),
-            num_attention_heads=config.num_attention_heads,
-            attention_head_dim=config.attention_head_dim,
-            in_channels=config.in_channels,
-            out_channels=config.out_channels,
-            action_dim=config.action_dim,
-            text_dim=config.text_dim,
-            freq_dim=config.freq_dim,
-            ffn_dim=config.ffn_dim,
-            num_layers=config.num_layers,
-            cross_attn_norm=config.cross_attn_norm,
-            eps=config.eps,
-            rope_max_seq_len=config.rope_max_seq_len,
-            attn_mode=config.attn_mode,
-        )
-        # Run the transformer in config.dtype (bf16); norm/modulation paths upcast to fp32 internally.
-        self.transformer = self.transformer.to(self.dtype)
-
-        # Frozen modules are stored OUTSIDE the nn.Module registry (plain dict) so they are
-        # neither saved into model.safetensors nor moved by ``.to()``. They are lazily loaded
-        # from ``config.wan_pretrained_path`` the first time inference runs.
-        self._frozen: dict = {}
-
-        self.last_predicted_frames: Tensor | None = None
-        self.last_predicted_latents: Tensor | None = None
-        self.reset()
-
-    # Frozen-module lazy loading (VAE + UMT5 + tokenizer)
-    def _ensure_frozen_modules(self):
-        if self._frozen:
-            return
-        path = self.config.wan_pretrained_path
-        device = self.config.device
-
-        # The frozen modules always live in ``vae/``, ``text_encoder/`` and ``tokenizer/``
-        # sub-folders -- both in the released diffusers-style HF repos and in the local
-        # ``--bundle-frozen`` output dir. ``from_pretrained(path, subfolder=...)`` resolves
-        # them for either a HF repo id or a local directory.
-        vae = load_vae(path, torch_dtype=self.dtype, torch_device=device, subfolder="vae")
-        # The UMT5-XXL text encoder (~11 GB) runs once per episode; keep it on its own
-        # (CPU by default) device so the 5B transformer + VAE fit on a single GPU.
-        text_encoder = load_text_encoder(
-            path,
-            torch_dtype=self.dtype,
-            torch_device=self.config.text_encoder_device,
-            subfolder="text_encoder",
-        )
-        tokenizer = load_tokenizer(path, subfolder="tokenizer")
-        self._frozen = {
-            "vae": vae.eval(),
-            "streaming_vae": WanVAEStreamingWrapper(vae),
-            "text_encoder": text_encoder.eval(),
-            "tokenizer": tokenizer,
-        }
-        # RoboTwin's T-shape layout encodes the half-resolution wrist cameras through a second
-        # streaming VAE (separate causal cache) alongside the full-res head camera.
-        if self.config.camera_layout == "robotwin_tshape":
-            vae_half = load_vae(path, torch_dtype=self.dtype, torch_device=device, subfolder="vae")
-            self._frozen["streaming_vae_half"] = WanVAEStreamingWrapper(vae_half.eval())
-
-    @property
-    def _vae(self):
-        return self._frozen["vae"]
-
-    @property
-    def _streaming_vae(self):
-        return self._frozen["streaming_vae"]
-
-    # PreTrainedPolicy API
-    def get_optim_params(self) -> dict:
-        # Only the transformer is trainable; the VAE / text encoder stay frozen (kept outside the
-        # nn.Module registry). With PEFT/LoRA this naturally returns just the adapter params.
-        return [p for p in self.transformer.parameters() if p.requires_grad]
-
-    def reset(self):
-        """Reset all per-episode streaming state (KV cache, queues, frame counter)."""
-        cfg = self.config
-        self._action_queue: deque = deque(maxlen=cfg.n_action_steps)
-        self._obs_buffer: list = []  # raw keyframe obs (one per env substep) observed this chunk
-        self._executed_actions: Tensor | None = (
-            None  # last chunk's actions (model-normalized) for KV feedback
-        )
-        self._started = False  # first select_action call uses the obs as the conditioning frame
-        self._exec_step = 0  # index of the action being executed within the current chunk
-        self._prev_j = 0  # sub-step index (within a predicted frame) of the last executed action
-        # Sample one keyframe every ``action_per_frame / temporal_downsample`` executed sub-steps so
-        # that exactly ``frame_chunk_size * temporal_downsample`` frames are VAE-encoded per chunk
-        # (the Wan2.2 VAE temporal downsample is 4 -> ``frame_chunk_size`` latent frames).
-        self._keyframe_stride = max(1, cfg.action_per_frame // 4)
-        self._frame_st_id = 0
-        self._first_chunk = True
-        self._prompt: str | None = None
-        self._prompt_embeds = None
-        self._negative_prompt_embeds = None
-        self.last_predicted_frames = None
-        self.last_predicted_latents = None
-        self._use_cfg = (cfg.guidance_scale > 1) or (cfg.action_guidance_scale > 1)
-        # Two independent flow-matching schedulers (video latent + action streams).
-        self._scheduler = FlowMatchScheduler(shift=cfg.snr_shift, sigma_min=0.0, extra_one_step=True)
-        self._action_scheduler = FlowMatchScheduler(
-            shift=cfg.action_snr_shift, sigma_min=0.0, extra_one_step=True
-        )
-        self._scheduler.set_timesteps(1000, training=True)
-        self._action_scheduler.set_timesteps(1000, training=True)
-        self._cache_initialised = False
-        # Clear KV cache on the (already-built) transformer, if present.
-        if hasattr(self, "transformer"):
-            self.transformer.clear_cache("pos")
-        # Reset the causal streaming-VAE feat cache between episodes (mirrors upstream ``_reset``).
-        # Without this the encoder carries over the previous episode's temporal state, corrupting the
-        # latent frame counts on the next episode's first encode.
-        if self._frozen:
-            self._frozen["streaming_vae"].clear_cache()
-            if "streaming_vae_half" in self._frozen:
-                self._frozen["streaming_vae_half"].clear_cache()
-
-    # Training (flow-matching dual-stream loss). Requires attn_mode="flex".
-    def _ensure_train_schedulers(self):
-        if getattr(self, "_train_sched_latent", None) is None:
-            cfg = self.config
-            self._train_sched_latent = FlowMatchScheduler(
-                shift=cfg.snr_shift, sigma_min=0.0, extra_one_step=True
-            )
-            self._train_sched_latent.set_timesteps(1000, training=True)
-            self._train_sched_action = FlowMatchScheduler(
-                shift=cfg.action_snr_shift, sigma_min=0.0, extra_one_step=True
-            )
-            self._train_sched_action.set_timesteps(1000, training=True)
-
-    @torch.no_grad()
-    def _add_noise_stream(self, latent, scheduler, action_mask, action_mode, noisy_cond_prob):
-        """Flow-matching noising of one stream (port of upstream ``Trainer._add_noise``)."""
-        device = latent.device
-        b, _c, f, _h, _w = latent.shape
-        p = self.config.patch_size
-        patch_f, patch_h, patch_w = (1, 1, 1) if action_mode else (p[0], p[1], p[2])
-
-        ts_ids = _sample_timestep_id(f, num_train_timesteps=scheduler.num_train_timesteps)
-        noise = torch.zeros_like(latent).normal_()
-        timesteps = scheduler.timesteps[ts_ids].to(device)
-        noisy_latents = scheduler.add_noise(latent, noise, timesteps, t_dim=2)
-        targets = scheduler.training_target(latent, noise, timesteps)
-
-        grid_id = (
-            get_mesh_id(
-                latent.shape[-3] // patch_f,
-                latent.shape[-2] // patch_h,
-                latent.shape[-1] // patch_w,
-                t=1 if action_mode else 0,
-                f_w=1,
-                f_shift=0,
-                action=action_mode,
-            )
-            .to(device)[None]
-            .repeat(b, 1, 1)
-        )
-
-        if torch.rand(1).item() < noisy_cond_prob:
-            cond_ids = _sample_timestep_id(
-                f, min_timestep_bd=0.5, max_timestep_bd=1.0, num_train_timesteps=scheduler.num_train_timesteps
-            )
-            cond_noise = torch.zeros_like(latent).normal_()
-            cond_timesteps = scheduler.timesteps[cond_ids].to(device)
-            latent = scheduler.add_noise(latent, cond_noise, cond_timesteps, t_dim=2)
-        else:
-            cond_timesteps = torch.zeros_like(timesteps)
-
-        if action_mask is not None:
-            noisy_latents = noisy_latents * action_mask.float()
-            targets = targets * action_mask.float()
-            latent = latent * action_mask.float()
-
-        return {
-            "timesteps": timesteps[None].repeat(b, 1),
-            "noisy_latents": noisy_latents,
-            "targets": targets,
-            "latent": latent,
-            "cond_timesteps": cond_timesteps[None].repeat(b, 1),
-            "grid_id": grid_id,
-        }
-
-    def _flow_matching_loss(self, input_dict, pred):
-        """Dual-stream flow-matching loss (port of upstream ``Trainer.compute_loss``)."""
-        latent_pred, action_pred = pred
-        ld, ad = input_dict["latent_dict"], input_dict["action_dict"]
-        action_pred = rearrange(action_pred, "b (f n) c -> b c f n 1", f=ad["targets"].shape[-3])
-        latent_pred = data_seq_to_patch(
-            self.config.patch_size,
-            latent_pred,
-            ld["targets"].shape[-3],
-            ld["targets"].shape[-2],
-            ld["targets"].shape[-1],
-            batch_size=latent_pred.shape[0],
-        )
-        bn, fn = ld["timesteps"].shape
-        lw = self._train_sched_latent.training_weight(ld["timesteps"].flatten()).reshape(bn, fn)
-        aw = self._train_sched_action.training_weight(ad["timesteps"].flatten()).reshape(bn, fn)
-
-        latent_loss = F.mse_loss(latent_pred.float(), ld["targets"].float().detach(), reduction="none")
-        latent_loss = (
-            (latent_loss * lw[:, None, :, None, None]).permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
-        )
-        latent_loss = (latent_loss.sum(dim=1) / (torch.ones_like(latent_loss).sum(dim=1) + 1e-6)).mean()
-
-        amask = ad["actions_mask"].float()
-        action_loss = F.mse_loss(action_pred.float(), ad["targets"].float().detach(), reduction="none")
-        action_loss = (
-            (action_loss * aw[:, None, :, None, None] * amask).permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
-        )
-        amask_f = amask.permute(0, 2, 3, 4, 1).flatten(0, 1).flatten(1)
-        action_loss = (action_loss.sum(dim=1) / (amask_f.sum(dim=1) + 1e-6)).mean()
-        return latent_loss, action_loss
-
-    def training_loss_from_streams(self, latents, actions, actions_mask, text_emb):
-        """Core dual-stream training loss given prepared latents / actions / text embeddings.
-
-        ``latents``: ``[B, in_channels, F, h, w]`` (normalized video latents).
-        ``actions`` / ``actions_mask``: ``[B, action_dim, F, action_per_frame, 1]``.
-        ``text_emb``: ``[B, seq_len, text_dim]``. Returns ``(loss, {latent_loss, action_loss})``.
-        """
-        if self.config.attn_mode != "flex":
-            raise ValueError(
-                "LingBot-VA training requires attn_mode='flex' (block-causal flow-matching masks). "
-                "Load/convert the policy with --policy.attn_mode=flex for training/fine-tuning."
-            )
-        self._ensure_train_schedulers()
-        latent_dict = self._add_noise_stream(
-            latents, self._train_sched_latent, action_mask=None, action_mode=False, noisy_cond_prob=0.5
-        )
-        action_dict = self._add_noise_stream(
-            actions, self._train_sched_action, action_mask=actions_mask, action_mode=True, noisy_cond_prob=0.0
-        )
-        latent_dict["text_emb"] = text_emb
-        action_dict["text_emb"] = text_emb
-        action_dict["actions_mask"] = actions_mask
-        input_dict = {
-            "latent_dict": latent_dict,
-            "action_dict": action_dict,
-            "chunk_size": int(torch.randint(1, 5, (1,)).item()),
-            "window_size": int(torch.randint(4, 65, (1,)).item()),
-        }
-        pred = self.transformer(input_dict, train_mode=True)
-        latent_loss, action_loss = self._flow_matching_loss(input_dict, pred)
-        loss = latent_loss + action_loss
-        return loss, {"latent_loss": latent_loss.item(), "action_loss": action_loss.item()}
-
-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict | None]:
-        """Training forward: dual-stream flow-matching loss.
-
-        Builds the (video-latent, action, text) training streams from a LeRobot batch
-        (VAE-encoding the camera frames and UMT5-encoding the task), then runs the flow-matching
-        dual-stream loss. Requires the policy to be built with ``attn_mode='flex'``.
-        """
-        self._ensure_frozen_modules()
-        latents, actions, actions_mask, text_emb = self._build_training_streams(batch)
-        return self.training_loss_from_streams(latents, actions, actions_mask, text_emb)
-
-    @torch.no_grad()
-    def _build_training_streams(self, batch):
-        """Build (latents, actions, actions_mask, text_emb) from a LeRobot training batch.
-
-        Camera frames per ``obs_cam_keys`` are expected as a temporal clip ``[B, C, T, H, W]`` (or
-        ``[B, T, C, H, W]``); they are VAE-encoded into ``F = T / temporal_downsample`` latent frames.
-        Actions ``[B, F*action_per_frame, n_used]`` are scattered into the model's ``action_dim`` space.
-        """
-        cfg = self.config
-        device = cfg.device
-        # text embeddings
-        task = batch.get("task")
-        if isinstance(task, str):
-            task = [task]
-        text_emb = self._get_t5_prompt_embeds(list(task), cfg.max_sequence_length)
-
-        # video latents (VAE-encode the camera clips)
-        latents = self._encode_training_latents(batch)
-
-        # actions -> [B, action_dim, F, action_per_frame, 1]
-        act = batch[ACTION].to(device)  # [B, F*apf, n_used]
-        b = act.shape[0]
-        used = cfg.used_action_channel_ids
-        apf, fc = cfg.action_per_frame, cfg.frame_chunk_size
-        act = act[:, : fc * apf].reshape(b, fc, apf, len(used)).permute(0, 3, 1, 2)  # [B, n_used, F, apf]
-        full = act.new_zeros(b, cfg.action_dim, fc, apf)
-        idx = torch.as_tensor(used, device=device)
-        full[:, idx] = act
-        actions = full.unsqueeze(-1).to(self.dtype)  # [B, action_dim, F, apf, 1]
-        mask = torch.zeros(cfg.action_dim, device=device, dtype=self.dtype)
-        mask[idx] = 1.0
-        actions_mask = mask.view(1, -1, 1, 1, 1).expand_as(actions)
-        return latents, actions, actions_mask, text_emb
-
-    @torch.no_grad()
-    def _encode_training_latents(self, batch) -> Tensor:
-        """VAE-encode the per-camera training clips into normalized video latents [B, C, F, h, w]."""
-        vae_device = next(self._vae.parameters()).device
-
-        def _clip(key):
-            x = batch[key].to(vae_device)
-            if x.dim() == 4:  # [B, C, H, W] -> single frame clip
-                x = x.unsqueeze(2)
-            elif x.shape[1] not in (1, 3) and x.shape[2] in (1, 3):  # [B, T, C, H, W] -> [B, C, T, H, W]
-                x = x.permute(0, 2, 1, 3, 4)
-            return x.contiguous()
-
-        def _encode(x, size):
-            b, c, t = x.shape[:3]
-            x = F.interpolate(x.flatten(0, 1).float(), size=size, mode="bilinear", align_corners=False)
-            x = (x.view(b, c, t, *size) * 2.0 - 1.0).to(self.dtype)
-            mu = self._vae.encode(x).latent_dist.mode()  # [B, z_dim, F, h, w]
-            mean = torch.tensor(self._vae.config.latents_mean).view(1, -1, 1, 1, 1).to(mu.device)
-            inv_std = (1.0 / torch.tensor(self._vae.config.latents_std)).view(1, -1, 1, 1, 1).to(mu.device)
-            return ((mu.float() - mean) * inv_std).to(mu)
-
-        keys = self.config.obs_cam_keys
-        if self.config.camera_layout == "robotwin_tshape":
-            h, w = self.config.height, self.config.width
-            head = _encode(_clip(keys[0]), (h, w))
-            left = _encode(_clip(keys[1]), (h // 2, w // 2))
-            right = _encode(_clip(keys[2]), (h // 2, w // 2))
-            return torch.cat([torch.cat([left, right], dim=-1), head], dim=-2).to(self.config.device)
-        per_cam = [_encode(_clip(k), (self.config.height, self.config.width)) for k in keys]
-        return torch.cat(per_cam, dim=-1).to(self.config.device)
-
-    @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
-        """Return one action, refilling the chunk (and feeding back observed keyframes) as needed.
-
-        Mirrors the upstream LIBERO client loop (``evaluation/libero/client.py``): the first obs is
-        the conditioning frame; every observation produced afterwards is buffered as a keyframe and,
-        once the chunk's actions are exhausted, the buffered frames + executed actions are fed back
-        into the KV cache before the next chunk is predicted.
-        """
-        self.eval()
-        self._ensure_frozen_modules()
-        self._maybe_init_prompt(batch)
-
-        if not self._started:
-            # First call: this observation conditions the first chunk (it is *not* a keyframe).
-            self._started = True
-            actions = self.predict_action_chunk(batch)  # [B, chunk_size, n_used]
-            self._action_queue.extend(actions.transpose(0, 1))  # [chunk_size, B, n_used]
-            self._obs_buffer = []
-            self._exec_step = 0
-        else:
-            # This observation is the result of the previously executed action -> a candidate
-            # keyframe. Buffer it on the sub-step boundary the upstream client samples on.
-            if (self._prev_j + 1) % self._keyframe_stride == 0:
-                self._obs_buffer.append(self._extract_raw_obs(batch))
-            if len(self._action_queue) == 0:
-                # All actions for the current chunk have been executed; feed the observed
-                # keyframes + executed actions back and predict the next chunk.
-                actions = self.predict_action_chunk(None)
-                self._action_queue.extend(actions.transpose(0, 1))
-                self._exec_step = 0
-
-        self._prev_j = self._exec_step % self.config.action_per_frame
-        self._exec_step += 1
-        return self._action_queue.popleft()
-
-    @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs) -> Tensor:
-        """Run one autoregressive chunk and return actions ``[B, chunk_size, n_used]`` (normalized)."""
-        self.eval()
-        self._ensure_frozen_modules()
-        self._maybe_init_prompt(batch)
-
-        is_first = self._first_chunk
-        if is_first:
-            init_latent = self._encode_frames([self._extract_raw_obs(batch)])
-            self._init_latent = init_latent
-            self._init_streaming_cache(init_latent)
-            self._obs_buffer = []  # frame 0 (the init obs) conditions the chunk; it is not fed back
-            actions, latents = self._infer(init_latent, frame_st_id=0)
-            self._first_chunk = False
-        else:
-            # Feed the real observed keyframes + the executed actions back into the KV cache.
-            self._compute_kv_cache(self._obs_buffer, self._executed_actions)
-            self._obs_buffer = []
-            actions, latents = self._infer(None, frame_st_id=self._frame_st_id)
-
-        # actions: [B, action_dim, F, action_per_frame, 1] (model-normalized). Keep for KV feedback.
-        self._executed_actions = actions
-
-        if self.config.save_predicted_video:
-            # Match upstream LingBot-VA visualization: collect chunk latents and decode the
-            # concatenated latent sequence once after the rollout finishes.
-            self.last_predicted_frames = None
-            self.last_predicted_latents = latents.detach().to("cpu")
-
-        # On the first chunk, frame 0 is the conditioning frame (already "known"): the upstream
-        # LIBERO client skips it (start_idx=1), so we drop the first frame's actions here.
-        used = self.config.used_action_channel_ids
-        a = actions[:, used]  # [B, n_used, F, action_per_frame, 1]
-        if is_first:
-            a = a[:, :, 1:]  # drop frame 0 -> (F-1) frames of actions
-        a = a.squeeze(-1).flatten(2)  # [B, n_used, n_steps]
-        a = a.transpose(1, 2).contiguous()  # [B, n_steps, n_used]
-        return a.to(torch.float32)
-
-    # Prompt / text encoding
-    def _maybe_init_prompt(self, batch):
-        if self._prompt_embeds is not None or batch is None:
-            return
-        task = batch.get("task")
-        prompt = task[0] if isinstance(task, list | tuple) else task
-        self._prompt = prompt or ""
-        self._prompt_embeds, self._negative_prompt_embeds = self._encode_prompt(self._prompt)
-
-    def _get_t5_prompt_embeds(self, prompt, max_sequence_length):
-        tokenizer = self._frozen["tokenizer"]
-        text_encoder = self._frozen["text_encoder"]
-        device = self.config.device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        prompt = [clean_prompt(u) for u in prompt]
-
-        text_inputs = tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_attention_mask=True,
-            return_tensors="pt",
-        )
-        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
-        seq_lens = mask.gt(0).sum(dim=1).long()
-
-        te_device = next(text_encoder.parameters()).device
-        prompt_embeds = text_encoder(text_input_ids.to(te_device), mask.to(te_device)).last_hidden_state
-        prompt_embeds = prompt_embeds.to(dtype=self.dtype, device=device)
-        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens, strict=False)]
-        prompt_embeds = torch.stack(
-            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds],
-            dim=0,
-        )
-        return prompt_embeds.to(device)
-
-    def _encode_prompt(self, prompt):
-        max_len = self.config.max_sequence_length
-        prompt_embeds = self._get_t5_prompt_embeds(prompt, max_len)
-        negative_prompt_embeds = None
-        if self._use_cfg:
-            negative_prompt_embeds = self._get_t5_prompt_embeds("", max_len)
-        return prompt_embeds, negative_prompt_embeds
-
-    # Observation (image) encoding -> normalized video latents
-    def _extract_raw_obs(self, batch) -> dict[str, Tensor]:
-        """Snapshot the configured camera images from a batch (kept raw for later VAE encoding)."""
-        return {k: batch[k].detach() for k in self.config.obs_cam_keys}
-
-    def _camera_frame(self, raw_obs, key, size=None) -> Tensor:
-        """Return a single-frame camera tensor [1, C, 1, H, W] resized + scaled to [-1, 1]."""
-        img = raw_obs[key]
-        if img.dim() == 3:  # [C, H, W]
-            img = img.unsqueeze(0)
-        # LeRobot images arrive as float in [0, 1], shape [B, C, H, W].
-        img = img.to(self.config.device, torch.float32)
-        if self.config.image_hflip:
-            img = torch.flip(img, dims=[-1])  # undo the env processor's horizontal flip
-        if size is None:
-            size = (self.config.height, self.config.width)
-        img = F.interpolate(img, size=size, mode="bilinear", align_corners=False)
-        img = img * 2.0 - 1.0
-        return img.unsqueeze(2).to(self.dtype)  # [1, C, F=1, H, W]
-
-    def _normalize_vae_latent(self, enc_out: Tensor) -> Tensor:
-        """Take the mean of a VAE encoder output and channel-normalize it (matches upstream)."""
-        mu, _logvar = torch.chunk(enc_out, 2, dim=1)
-        latents_mean = torch.tensor(self._vae.config.latents_mean).to(mu.device)
-        latents_std = torch.tensor(self._vae.config.latents_std).to(mu.device)
-        mean = latents_mean.view(1, -1, 1, 1, 1)
-        inv_std = (1.0 / latents_std).view(1, -1, 1, 1, 1)
-        return ((mu.float() - mean) * inv_std).to(mu)
-
-    @torch.no_grad()
-    def _encode_frames(self, raw_frames: list) -> Tensor:
-        """VAE-encode a temporal clip of observed frames and concat the per-camera latents on width.
-
-        ``raw_frames`` is a list of per-frame obs dicts (one per env sub-step). Each configured
-        camera is stacked along the temporal axis into a ``[1, C, F, H, W]`` clip and encoded in a
-        single streaming ``encode_chunk`` call so the VAE temporal downsample (x4) collapses the F
-        input frames into ``F / 4`` latent frames, with the causal ``feat_cache`` carried across
-        chunks (mirrors upstream ``_encode_obs``).
-        """
-        vae_device = next(self._vae.parameters()).device
-        if self.config.camera_layout == "robotwin_tshape":
-            return self._encode_frames_tshape(raw_frames, vae_device)
-        per_cam_videos = []
-        for k in self.config.obs_cam_keys:
-            frames = [self._camera_frame(fb, k) for fb in raw_frames]
-            per_cam_videos.append(torch.cat(frames, dim=2))  # [1, C, F, H, W]
-        videos = torch.cat(per_cam_videos, dim=0)  # [num_cam, C, F, H, W]
-        enc_out = self._streaming_vae.encode_chunk(videos.to(vae_device).to(self.dtype))
-        mu_norm = self._normalize_vae_latent(enc_out)
-        # Concatenate the per-camera latents along width.
-        video_latent = torch.cat(mu_norm.split(1, dim=0), dim=-1)
-        return video_latent.to(self.config.device)
-
-    @torch.no_grad()
-    def _encode_frames_tshape(self, raw_frames: list, vae_device) -> Tensor:
-        """RoboTwin T-shape latent assembly: full-res head + half-res wrists (second streaming VAE).
-
-        The two wrist latents are concatenated on width and stacked (on the height axis) on top of
-        the head latent, mirroring upstream ``_encode_obs`` for ``env_type='robotwin_tshape'``.
-        """
-        cfg = self.config
-        h, w = cfg.height, cfg.width
-        head_key, left_key, right_key = cfg.obs_cam_keys[0], cfg.obs_cam_keys[1], cfg.obs_cam_keys[2]
-        head = torch.cat([self._camera_frame(fb, head_key, size=(h, w)) for fb in raw_frames], dim=2)
-        left = torch.cat(
-            [self._camera_frame(fb, left_key, size=(h // 2, w // 2)) for fb in raw_frames], dim=2
-        )
-        right = torch.cat(
-            [self._camera_frame(fb, right_key, size=(h // 2, w // 2)) for fb in raw_frames], dim=2
-        )
-        wrists = torch.cat([left, right], dim=0)  # [2, C, F, H/2, W/2]
-        enc_high = self._streaming_vae.encode_chunk(head.to(vae_device).to(self.dtype))
-        enc_lr = self._frozen["streaming_vae_half"].encode_chunk(wrists.to(vae_device).to(self.dtype))
-        # wrists side-by-side on width, then stacked on top of the head latent on the height axis.
-        enc_out = torch.cat([torch.cat(enc_lr.split(1, dim=0), dim=-1), enc_high], dim=-2)
-        video_latent = self._normalize_vae_latent(enc_out)
-        return video_latent.to(self.config.device)
-
-    # KV cache management
-    @property
-    def _latent_hw(self):
-        if self.config.camera_layout == "robotwin_tshape":
-            # head (full) on the bottom, two half-res wrists side-by-side on top -> 1.5x height.
-            return ((self.config.height // 16) * 3) // 2, self.config.width // 16
-        h = self.config.height // 16
-        w = (self.config.width // 16) * len(self.config.obs_cam_keys)
-        return h, w
-
-    def _init_streaming_cache(self, init_latent):
-        cfg = self.config
-        latent_h, latent_w = self._latent_hw
-        p = cfg.patch_size
-        latent_token_per_chunk = (cfg.frame_chunk_size * latent_h * latent_w) // (p[0] * p[1] * p[2])
-        action_token_per_chunk = cfg.frame_chunk_size * cfg.action_per_frame
-        self.transformer.create_empty_cache(
-            "pos",
-            cfg.attn_window,
-            latent_token_per_chunk,
-            action_token_per_chunk,
-            device=self.config.device,
-            dtype=self.dtype,
-            batch_size=2 if self._use_cfg else 1,
-        )
-        self._cache_initialised = True
-
-    def _repeat_input_for_cfg(self, input_dict):
-        if self._use_cfg:
-            input_dict["noisy_latents"] = input_dict["noisy_latents"].repeat(2, 1, 1, 1, 1)
-            input_dict["text_emb"] = torch.cat(
-                [
-                    self._prompt_embeds.to(self.dtype).clone(),
-                    self._negative_prompt_embeds.to(self.dtype).clone(),
-                ],
-                dim=0,
-            )
-            input_dict["grid_id"] = input_dict["grid_id"][None].repeat(2, 1, 1)
-            input_dict["timesteps"] = input_dict["timesteps"][None].repeat(2, 1)
-        else:
-            input_dict["grid_id"] = input_dict["grid_id"][None]
-            input_dict["timesteps"] = input_dict["timesteps"][None]
-        return input_dict
-
-    def _prepare_latent_input(
-        self,
-        latent_model_input,
-        action_model_input,
-        latent_t=0,
-        action_t=0,
-        latent_cond=None,
-        action_cond=None,
-        frame_st_id=0,
-    ):
-        cfg = self.config
-        device = self.config.device
-        p = cfg.patch_size
-        out = {}
-        if latent_model_input is not None:
-            out["latent_res_lst"] = {
-                "noisy_latents": latent_model_input,
-                "timesteps": torch.ones([latent_model_input.shape[2]], dtype=torch.float32, device=device)
-                * latent_t,
-                "grid_id": get_mesh_id(
-                    latent_model_input.shape[-3] // p[0],
-                    latent_model_input.shape[-2] // p[1],
-                    latent_model_input.shape[-1] // p[2],
-                    0,
-                    1,
-                    frame_st_id,
-                ).to(device),
-                "text_emb": self._prompt_embeds.to(self.dtype).clone(),
-            }
-            if latent_cond is not None:
-                out["latent_res_lst"]["noisy_latents"][:, :, 0:1] = latent_cond[:, :, 0:1]
-                out["latent_res_lst"]["timesteps"][0:1] *= 0
-        if action_model_input is not None:
-            out["action_res_lst"] = {
-                "noisy_latents": action_model_input,
-                "timesteps": torch.ones([action_model_input.shape[2]], dtype=torch.float32, device=device)
-                * action_t,
-                "grid_id": get_mesh_id(
-                    action_model_input.shape[-3],
-                    action_model_input.shape[-2],
-                    action_model_input.shape[-1],
-                    1,
-                    1,
-                    frame_st_id,
-                    action=True,
-                ).to(device),
-                "text_emb": self._prompt_embeds.to(self.dtype).clone(),
-            }
-            if action_cond is not None:
-                out["action_res_lst"]["noisy_latents"][:, :, 0:1] = action_cond[:, :, 0:1]
-                out["action_res_lst"]["timesteps"][0:1] *= 0
-            out["action_res_lst"]["noisy_latents"][:, ~self._action_mask] *= 0
-        return out
-
-    @property
-    def _action_mask(self):
-        mask = torch.zeros([self.config.action_dim], dtype=torch.bool)
-        mask[self.config.used_action_channel_ids] = True
-        return mask
-
-    # Action conditioning (executed action history) (de)normalization
-    def _preprocess_action_state(self, action_norm: Tensor) -> Tensor:
-        """Build the action-conditioning tensor from the already-normalized executed actions.
-
-        ``action_norm`` is the model-space action chunk ``[B, action_dim, F, action_per_frame, 1]``.
-        Upstream re-derives the conditioning from the raw executed action via quantile norm; here
-        the executed actions are already in the model-normalized space, so we pass them through.
-        """
-        return action_norm.to(self.config.device, self.dtype)
-
-    def _compute_kv_cache(self, obs_buffer, executed_actions):
-        """Feed real observed keyframes + executed actions back into the KV cache."""
-        if not obs_buffer or executed_actions is None:
-            return
-        self.transformer.clear_pred_cache("pos")
-        # Encode the buffered keyframe clip in one streaming call (carries the causal VAE cache).
-        latent_model_input = self._encode_frames(obs_buffer)
-        # On the first feedback, prepend the init latent so the latent/action frame counts align
-        # (upstream prepends ``init_latent`` to the observed keyframes when frame_st_id == 0).
-        if self._frame_st_id == 0 and getattr(self, "_init_latent", None) is not None:
-            latent_model_input = torch.cat([self._init_latent, latent_model_input], dim=2)
-        action_model_input = self._preprocess_action_state(executed_actions)
-        action_model_input = action_model_input.to(latent_model_input)
-        input_dict = self._prepare_latent_input(
-            latent_model_input, action_model_input, frame_st_id=self._frame_st_id
-        )
-        with torch.no_grad():
-            self.transformer(
-                self._repeat_input_for_cfg(input_dict["latent_res_lst"]),
-                update_cache=2,
-                cache_name="pos",
-                action_mode=False,
-            )
-            self.transformer(
-                self._repeat_input_for_cfg(input_dict["action_res_lst"]),
-                update_cache=2,
-                cache_name="pos",
-                action_mode=True,
-            )
-        self._frame_st_id += latent_model_input.shape[2]
-
-    # The core dual-stream denoising loop (one chunk)
-    @torch.no_grad()
-    def _infer(self, init_latent, frame_st_id=0):
-        cfg = self.config
-        device = self.config.device
-        latent_h, latent_w = self._latent_hw
-        frame_chunk_size = cfg.frame_chunk_size
-
-        latents = torch.randn(1, 48, frame_chunk_size, latent_h, latent_w, device=device, dtype=self.dtype)
-        actions = torch.randn(
-            1, cfg.action_dim, frame_chunk_size, cfg.action_per_frame, 1, device=device, dtype=self.dtype
-        )
-
-        self._scheduler.set_timesteps(cfg.num_inference_steps)
-        self._action_scheduler.set_timesteps(cfg.action_num_inference_steps)
-        timesteps = F.pad(self._scheduler.timesteps, (0, 1), mode="constant", value=0)
-        if cfg.video_exec_step != -1:
-            timesteps = timesteps[: cfg.video_exec_step]
-        action_timesteps = F.pad(self._action_scheduler.timesteps, (0, 1), mode="constant", value=0)
-
-        # 1. Video-latent denoising loop
-        for i, t in enumerate(timesteps):
-            last_step = i == len(timesteps) - 1
-            latent_cond = (
-                init_latent[:, :, 0:1].to(self.dtype)
-                if frame_st_id == 0 and init_latent is not None
-                else None
-            )
-            input_dict = self._prepare_latent_input(
-                latents, None, t, t, latent_cond, None, frame_st_id=frame_st_id
-            )
-            video_noise_pred = self.transformer(
-                self._repeat_input_for_cfg(input_dict["latent_res_lst"]),
-                update_cache=1 if last_step else 0,
-                cache_name="pos",
-                action_mode=False,
-            )
-            if not last_step or cfg.video_exec_step != -1:
-                video_noise_pred = data_seq_to_patch(
-                    cfg.patch_size,
-                    video_noise_pred,
-                    frame_chunk_size,
-                    latent_h,
-                    latent_w,
-                    batch_size=2 if self._use_cfg else 1,
-                )
-                if cfg.guidance_scale > 1:
-                    video_noise_pred = video_noise_pred[1:] + cfg.guidance_scale * (
-                        video_noise_pred[:1] - video_noise_pred[1:]
-                    )
-                else:
-                    video_noise_pred = video_noise_pred[:1]
-                latents = self._scheduler.step(video_noise_pred, t, latents, return_dict=False)
-            if frame_st_id == 0 and latent_cond is not None:
-                latents[:, :, 0:1] = latent_cond
-
-        # 2. Action denoising loop
-        for i, t in enumerate(action_timesteps):
-            last_step = i == len(action_timesteps) - 1
-            action_cond = (
-                torch.zeros([1, cfg.action_dim, 1, cfg.action_per_frame, 1], device=device, dtype=self.dtype)
-                if frame_st_id == 0
-                else None
-            )
-            input_dict = self._prepare_latent_input(
-                None, actions, t, t, None, action_cond, frame_st_id=frame_st_id
-            )
-            action_noise_pred = self.transformer(
-                self._repeat_input_for_cfg(input_dict["action_res_lst"]),
-                update_cache=1 if last_step else 0,
-                cache_name="pos",
-                action_mode=True,
-            )
-            if not last_step:
-                action_noise_pred = rearrange(action_noise_pred, "b (f n) c -> b c f n 1", f=frame_chunk_size)
-                if cfg.action_guidance_scale > 1:
-                    action_noise_pred = action_noise_pred[1:] + cfg.action_guidance_scale * (
-                        action_noise_pred[:1] - action_noise_pred[1:]
-                    )
-                else:
-                    action_noise_pred = action_noise_pred[:1]
-                actions = self._action_scheduler.step(action_noise_pred, t, actions, return_dict=False)
-            if frame_st_id == 0 and action_cond is not None:
-                actions[:, :, 0:1] = action_cond
-
-        actions[:, ~self._action_mask] *= 0
-        return actions, latents
-
-    # Predicted-video decoding (opt-in)
-    @torch.no_grad()
-    def decode_predicted_latents(self, latents) -> Tensor:
-        """Decode a concatenated predicted-latent sequence into ``[T, H, W, 3]`` uint8 frames."""
-        return self._decode_predicted_video(latents)
-
-    @torch.no_grad()
-    def _decode_predicted_video(self, latents) -> Tensor:
-        """VAE-decode predicted latents into a uint8 frame stack ``[T, H, W, 3]`` on CPU."""
-        vae = self._vae
-        z_dim = vae.config.z_dim
-        vae_device = next(vae.parameters()).device
-        latents = latents.to(device=vae_device, dtype=vae.dtype)
-        latents = denormalize_latents(latents, vae.config.latents_mean, vae.config.latents_std, z_dim)
-        video = vae.decode(latents, return_dict=False)[0]  # [B, C, F, H, W] in [-1, 1]
-        video = (video.float().clamp(-1, 1) + 1.0) / 2.0
-        video = (video[0].permute(1, 2, 3, 0) * 255.0).round().to(torch.uint8)  # [F, H, W, C]
-        return video.cpu()

src/lerobot/policies/lingbot_va/processor_lingbot_va.py

@@ -1,87 +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.
-
-"""Pre/post-processor pipelines for the LingBot-VA policy.
-
-The preprocessor passes inputs through (IDENTITY) and the postprocessor maps the policy's
-``[-1, 1]`` actions back to physical units with the built-in ``UnnormalizerProcessorStep``
-(QUANTILES) using per-channel q01/q99 restored from the checkpoint.
-"""
-
-from typing import Any
-
-import torch
-
-from lerobot.configs.types import FeatureType, NormalizationMode
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    ProcessorStep,
-    RenameObservationsProcessorStep,
-    UnnormalizerProcessorStep,
-)
-from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
-from lerobot.utils.constants import (
-    POLICY_POSTPROCESSOR_DEFAULT_NAME,
-    POLICY_PREPROCESSOR_DEFAULT_NAME,
-)
-
-from .configuration_lingbot_va import LingBotVAConfig
-
-
-def make_lingbot_va_pre_post_processors(
-    config: LingBotVAConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Build the pre/post processor pipelines for LingBot-VA."""
-
-    input_steps: list[ProcessorStep] = [
-        RenameObservationsProcessorStep(rename_map={}),
-        AddBatchDimensionProcessorStep(),
-        NormalizerProcessorStep(
-            features={**config.input_features, **config.output_features},
-            norm_map=config.normalization_mapping,
-            stats=dataset_stats,
-        ),
-        DeviceProcessorStep(device=config.device),
-    ]
-
-    # Unnormalize actions from [-1, 1] to physical units (QUANTILES) using q01/q99 restored from the checkpoint.
-    output_steps: list[ProcessorStep] = [
-        UnnormalizerProcessorStep(
-            features=config.output_features,
-            norm_map={FeatureType.ACTION: NormalizationMode.QUANTILES},
-            stats=dataset_stats,
-        ),
-        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/molmoact2/configuration_molmoact2.py

@@ -79,15 +79,6 @@ class MolmoAct2Config(PreTrainedConfig):
     eval_seed: int | None = None
     rtc_config: RTCConfig | None = None
 
-    # Joint frame transform for cross-calibration compatibility.
-    # Some MolmoAct2 checkpoints were trained on data using a different joint
-    # convention than the current LeRobot calibration. Set both to apply a
-    # sign/offset correction at runtime (state before model, action after).
-    # See: https://huggingface.co/docs/lerobot/backwardcomp
-    # Default is None (no transform). Both must be set together.
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
     # Default is full finetuning with gradients from the action expert flowing into the VLM.
     enable_lora_vlm: bool = False
     lora_rank: int = 64
@@ -132,10 +123,6 @@ class MolmoAct2Config(PreTrainedConfig):
 
     def __post_init__(self) -> None:
         super().__post_init__()
-        if (self.joint_signs is None) != (self.joint_offsets is None):
-            raise ValueError("joint_signs and joint_offsets must both be set or both be None.")
-        if self.joint_signs is not None and len(self.joint_signs) != len(self.joint_offsets):
-            raise ValueError("joint_signs and joint_offsets must have the same length.")
         if self.action_mode not in {"continuous", "discrete", "both"}:
             raise ValueError(
                 f"Unsupported action_mode={self.action_mode!r}. "

src/lerobot/policies/molmoact2/processor_molmoact2.py

@@ -1005,93 +1005,6 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
         return features
 
 
-@ProcessorStepRegistry.register(name="molmoact2_state_frame_transform")
-@dataclass
-class MolmoAct2StateFrameTransformStep(ProcessorStep):
-    """Convert robot state from arm frame to model frame before normalization.
-
-    Required for zero-shot deployment of MolmoAct2-SO100_101 on SO-100/101
-    arms calibrated with LeRobot >= 0.5.0 (v3.0 convention). The checkpoint
-    was trained on data using a different joint convention (sign flip on
-    shoulder_lift, 90 deg offset on shoulder_lift and elbow_flex).
-
-    No-op when joint_signs and joint_offsets are None (default), so this
-    step has no effect on fine-tuned models or other embodiments.
-
-    state_model = signs * arm_state + offsets
-
-    See: https://huggingface.co/docs/lerobot/backwardcomp
-    """
-
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if not isinstance(observation, dict) or OBS_STATE not in observation:
-            return transition
-        transition = transition.copy()
-        observation = observation.copy()
-        state = torch.as_tensor(observation[OBS_STATE], dtype=torch.float32).clone()
-        n = len(self.joint_signs)
-        signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=state.device)
-        offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=state.device)
-        state[..., :n] = signs * state[..., :n] + offsets
-        observation[OBS_STATE] = state
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {"joint_signs": self.joint_signs, "joint_offsets": self.joint_offsets}
-
-
-@ProcessorStepRegistry.register(name="molmoact2_action_frame_transform")
-@dataclass
-class MolmoAct2ActionFrameTransformStep(ProcessorStep):
-    """Convert model action from model frame back to arm frame after unnormalization.
-
-    Inverse of MolmoAct2StateFrameTransformStep. Required for zero-shot
-    MolmoAct2-SO100_101 on SO-100/101 arms. No-op when both fields are None.
-
-    action_arm = signs * (model_action - offsets)
-
-    See: https://huggingface.co/docs/lerobot/backwardcomp
-    """
-
-    joint_signs: list[float] | None = None
-    joint_offsets: list[float] | None = None
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        action = transition.get(TransitionKey.ACTION)
-        if action is None:
-            return transition
-        transition = transition.copy()
-        action = torch.as_tensor(action, dtype=torch.float32).clone()
-        n = len(self.joint_signs)
-        signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=action.device)
-        offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=action.device)
-        action[..., :n] = signs * (action[..., :n] - offsets)
-        transition[TransitionKey.ACTION] = action
-        return transition
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-    def get_config(self) -> dict[str, Any]:
-        return {"joint_signs": self.joint_signs, "joint_offsets": self.joint_offsets}
-
-
 @ProcessorStepRegistry.register(name="molmoact2_clamp_action")
 @dataclass
 class MolmoAct2ClampActionProcessorStep(ProcessorStep):
@@ -1154,10 +1067,6 @@ def make_molmoact2_pre_post_processors(
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),
         AddBatchDimensionProcessorStep(),
-        MolmoAct2StateFrameTransformStep(
-            joint_signs=config.joint_signs,
-            joint_offsets=config.joint_offsets,
-        ),
         MolmoAct2MaskedNormalizerProcessorStep(
             features={**config.input_features, **config.output_features},
             norm_map=config.normalization_mapping,
@@ -1193,10 +1102,6 @@ def make_molmoact2_pre_post_processors(
             norm_map=config.normalization_mapping,
             stats=masked_dataset_stats,
         ),
-        MolmoAct2ActionFrameTransformStep(
-            joint_signs=config.joint_signs,
-            joint_offsets=config.joint_offsets,
-        ),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pretrained.py

@@ -11,8 +11,6 @@
 # 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 abc
 import builtins
 import dataclasses
@@ -21,7 +19,7 @@ import os
 from importlib.resources import files
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, TypedDict, TypeVar, Unpack
+from typing import TypedDict, TypeVar, Unpack
 
 import packaging
 import safetensors
@@ -40,13 +38,10 @@ from .utils import log_model_loading_keys
 
 T = TypeVar("T", bound="PreTrainedPolicy")
 
-if TYPE_CHECKING:
-    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
-
 
 def _build_card_context(
     cfg: TrainPipelineConfig | None,
-    dataset_meta: LeRobotDatasetMetadata | None,
+    dataset_repo_id: str | None,
     input_features: dict | None,
     output_features: dict | None,
 ) -> dict:
@@ -77,16 +72,30 @@ def _build_card_context(
             "lerobot_version": __version__,
         }
 
-    if dataset_meta is not None:
-        context["dataset"] = {
-            "repo_id": dataset_meta.repo_id,
-            "episodes": dataset_meta.total_episodes,
-            "frames": dataset_meta.total_frames,
-            "fps": dataset_meta.fps,
-            "tasks": [str(task) for task in dataset_meta.tasks.index],
-        }
-        context["robot_type"] = dataset_meta.robot_type
-        context["cameras"] = [key.split(".")[-1] for key in dataset_meta.camera_keys]
+    if dataset_repo_id:
+        dataset_cfg = getattr(cfg, "dataset", None)
+        try:
+            from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+
+            meta = LeRobotDatasetMetadata(
+                dataset_repo_id,
+                root=getattr(dataset_cfg, "root", None),
+                revision=getattr(dataset_cfg, "revision", None),
+            )
+            context["dataset"] = {
+                "repo_id": dataset_repo_id,
+                "episodes": meta.total_episodes,
+                "frames": meta.total_frames,
+                "fps": meta.fps,
+                "tasks": [str(task) for task in meta.tasks.index],
+            }
+            context["robot_type"] = meta.robot_type
+            context["cameras"] = [key.split(".")[-1] for key in meta.camera_keys]
+        except Exception as e:  # noqa: BLE001 — dataset details are optional, never fail the push
+            logging.warning(
+                f"Could not load dataset metadata for '{dataset_repo_id}'; those sections will be "
+                f"omitted from the model card. ({e})"
+            )
 
     return context
 
@@ -295,7 +304,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         cfg: TrainPipelineConfig,
         peft_model=None,
         state_dict: dict[str, Tensor] | None = None,
-        dataset_meta: LeRobotDatasetMetadata | None = None,
     ):
         api = HfApi()
         repo_id = api.create_repo(
@@ -317,12 +325,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
                 self.save_pretrained(saved_path, state_dict=state_dict)
 
             card = self.generate_model_card(
-                cfg.dataset.repo_id,
-                self.config.type,
-                self.config.license,
-                self.config.tags,
-                cfg=cfg,
-                dataset_meta=dataset_meta,
+                cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags, cfg=cfg
             )
             card.save(str(saved_path / "README.md"))
 
@@ -337,9 +340,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
                 ignore_patterns=["*.tmp", "*.log"],
             )
 
-            # Contract: lerobot.jobs.hf.submit_to_hf watches for this exact
-            # "Model pushed to <url>" line to end a remote run early. Keep the wording
-            # and URL format in sync (it falls back to status polling if they drift).
             logging.info(f"Model pushed to {commit_info.repo_url.url}")
 
     def generate_model_card(
@@ -349,7 +349,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         license: str | None,
         tags: list[str] | None,
         cfg: TrainPipelineConfig | None = None,
-        dataset_meta: LeRobotDatasetMetadata | None = None,
     ) -> ModelCard:
         base_model_mapping = {
             "smolvla": "lerobot/smolvla_base",
@@ -370,7 +369,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         )
 
         context = _build_card_context(
-            cfg, dataset_meta, self.config.input_features, self.config.output_features
+            cfg, dataset_repo_id, self.config.input_features, self.config.output_features
         )
         # Used by the template to pre-fill commands and the "Fine-tuned from" line.
         context["policy_repo_id"] = getattr(self.config, "repo_id", None)
@@ -387,7 +386,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         self,
         peft_config=None,
         peft_cli_overrides: dict | None = None,
-    ) -> PreTrainedPolicy:
+    ) -> "PreTrainedPolicy":
         """
         Wrap this policy with PEFT adapters for parameter-efficient fine-tuning.
 

src/lerobot/policies/vla_jepa/modeling_vla_jepa.py

@@ -17,10 +17,12 @@ from __future__ import annotations
 import logging
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
+import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
+from PIL import Image
 from torch import Tensor, nn
 
 from lerobot.policies.pretrained import PreTrainedPolicy, T
@@ -53,13 +55,12 @@ class VLAJEPAModel(nn.Module):
       - DiT-B: flow-matching action head for future action prediction
       - V-JEPA: world model for video frame prediction
 
-    Inputs are batched tensors kept on the model device
-      - images: List[List[Tensor [C, H, W]]] (float [0,1]) — per sample, per view (Qwen messages)
-      - instructions: List[str]
-      - videos: Tensor [B, V, T, C, H, W] (float [0,1], world model only)
-      - actions: Tensor [B, T, action_dim] (optional, training only)
-      - state: Tensor [B, 1, state_dim] (optional)
-      - action_is_pad: Tensor [B, T] (optional)
+    Input: List[dict] native format (same as original starVLA)
+      - "image": List[PIL.Image] (multi-view images)
+      - "video": np.ndarray [V, T, H, W, 3]
+      - "lang": str (task instruction)
+      - "action": np.ndarray [T, action_dim] (optional, training only)
+      - "state": np.ndarray [1, state_dim] (optional)
     """
 
     def __init__(self, config: VLAJEPAConfig) -> None:
@@ -74,11 +75,6 @@ class VLAJEPAModel(nn.Module):
         self.action_tokens, self.action_token_ids, self.embodied_action_token_id = (
             self.qwen.expand_tokenizer()
         )
-        self.register_buffer(
-            "_action_token_ids_t",
-            torch.tensor(self.action_token_ids, dtype=torch.long),
-            persistent=False,
-        )
 
         # Action head (flow-matching DiT)
         self.action_model = VLAJEPAActionHead(config, cross_attention_dim=self.qwen.model.config.hidden_size)
@@ -165,123 +161,166 @@ class VLAJEPAModel(nn.Module):
 
     # ---- Native VLA-JEPA forward (follows original VLA_JEPA.py) ----
 
-    def _encode_qwen(
-        self, images: list[list[Tensor]], instructions: list[str], *, need_action_tokens: bool
-    ) -> tuple[Tensor, Tensor, Tensor | None]:
-        """Run Qwen and gather the embodied-action (and optionally action) token hidden states."""
+    def forward(self, examples: list[dict]) -> dict[str, Tensor]:
+        """
+        Native forward pass following original starVLA VLA_JEPA.forward.
+
+        Args:
+            examples: List of per-sample dicts with keys:
+                "image"  : List[PIL.Image]  — multi-view images
+                "video"  : np.ndarray [V, T, H, W, 3]
+                "lang"   : str — task instruction
+                "action" : np.ndarray [T, action_dim] (optional)
+                "state"  : np.ndarray [1, state_dim] (optional)
+
+        Returns:
+            dict with "action_loss" and "wm_loss" keys (scalar Tensors).
+        """
+        # Unpack native format (same pattern as original VLA_JEPA.py)
+        batch_images = [ex["image"] for ex in examples]  # List[List[PIL.Image]]
+        batch_videos = [ex["video"] for ex in examples]  # List[np.ndarray]
+        instructions = [ex["lang"] for ex in examples]  # List[str]
+        has_action = "action" in examples[0] and examples[0]["action"] is not None
+        actions = [ex["action"] for ex in examples] if has_action else None
+        has_state = "state" in examples[0] and examples[0]["state"] is not None
+        state = [ex["state"] for ex in examples] if has_state else None
+        action_is_pad = (
+            [ex["action_is_pad"] for ex in examples]
+            if has_action and "action_is_pad" in examples[0] and examples[0]["action_is_pad"] is not None
+            else None
+        )
+
+        # Stack videos: [B, V, T, H, W, 3] -> [B, V, T, 3, H, W]
+        batch_videos = np.stack(batch_videos)
+        batch_videos = batch_videos.transpose(0, 1, 2, 5, 3, 4)  # [B, V, T, 3, H, W]
+
+        # Adjust number of views for the world model:
+        # - fewer views than expected: duplicate the first view to fill up
+        # - more views than expected: keep only the first num_views_world_model views
+        num_views_world_model = self.config.jepa_tubelet_size
+        if batch_videos.shape[1] < num_views_world_model:
+            num_missing_views = num_views_world_model - batch_videos.shape[1]
+            first_view = np.repeat(batch_videos[:, :1], num_missing_views, axis=1)
+            batch_videos = np.concatenate([batch_videos, first_view], axis=1)
+        elif batch_videos.shape[1] > num_views_world_model:
+            batch_videos = batch_videos[:, :num_views_world_model]
+
+        # ---- Step 1: QwenVL encode (same as original) ----
         qwen_inputs = self.qwen.build_inputs(
-            images=images,
+            images=batch_images,
             instructions=instructions,
             action_prompt=self.replace_prompt,
             embodied_prompt=self.embodied_replace_prompt,
         )
-        input_ids = qwen_inputs["input_ids"]
-        embodied_idx = (input_ids == self.embodied_action_token_id).nonzero(as_tuple=True)
-        action_idx = None
-        if need_action_tokens:
-            action_mask = torch.isin(input_ids, self._action_token_ids_t)
-            action_idx = action_mask.nonzero(as_tuple=True)
+
+        # Locate embodied-action tokens (always needed for action head)
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        # Locate action tokens (only needed for world model predictor)
+        if self.config.enable_world_model:
+            action_mask = torch.isin(
+                qwen_inputs["input_ids"],
+                torch.tensor(self.action_token_ids, device=qwen_inputs["input_ids"].device),
+            )
+            action_indices = action_mask.nonzero(as_tuple=True)
 
         device_type = next(self.parameters()).device.type
+
         with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
             last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
             b, _, h = last_hidden.shape
-            embodied_action_tokens = last_hidden[embodied_idx[0], embodied_idx[1], :].view(b, -1, h)
-            action_tokens = (
-                last_hidden[action_idx[0], action_idx[1], :].view(b, -1, h)
-                if action_idx is not None
-                else None
-            )
-        return embodied_action_tokens, action_tokens
 
-    def _world_model_loss(self, videos: Tensor, action_tokens: Tensor) -> Tensor:
-        """JEPA encode + predictor L1 loss. `videos` is [B, V, T, C, H, W] float in [0, 1]."""
-        # Match the world model's expected view count: pad with the first view, or trim extras.
-        num_views = self.config.jepa_tubelet_size
-        if videos.shape[1] < num_views:
-            missing = num_views - videos.shape[1]
-            videos = torch.cat([videos, videos[:, :1].repeat(1, missing, 1, 1, 1, 1)], dim=1)
-        elif videos.shape[1] > num_views:
-            videos = videos[:, :num_views]
+            if self.config.enable_world_model:
+                action_tokens = last_hidden[action_indices[0], action_indices[1], :].view(b, -1, h)
 
-        b, v, t_frames, c, h_img, w_img = videos.shape
-        flat = videos.reshape(b * v, t_frames, c, h_img, w_img)
-        # Fast (torchvision) video processor on-device, do_rescale=False (frames already in [0, 1]).
-        video_pixels = self.video_processor(
-            videos=list(flat),
-            return_tensors="pt",
-            device=self.video_encoder.device,
-            do_rescale=False,
-        )["pixel_values_videos"]  # [B*V, T, C, H, W]
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
 
-        with torch.no_grad():
-            video_embeddings = self.video_encoder.get_vision_features(pixel_values_videos=video_pixels)
-            # Merge views: [B*V, ...] -> [B, ..., V*embed_dim]
-            video_embeddings = torch.cat(torch.chunk(video_embeddings, chunks=v, dim=0), dim=2)
-
-        tubelet_size = self.video_encoder.config.tubelet_size
-        # num_video_frames raw frames → t_enc_total temporal positions after tubelet compression
-        t_enc_total = self.config.num_video_frames // tubelet_size
-        if t_enc_total < 2:
-            return torch.zeros((), device=video_embeddings.device)
-
-        # Shift-by-one JEPA split: input_states = positions 0..T-2, gt_states = positions 1..T-1
-        t_enc_ctx = t_enc_total - 1
-        tokens_per_frame = video_embeddings.shape[1] // t_enc_total
-        input_states = video_embeddings[:, : tokens_per_frame * t_enc_ctx, :]
-        gt_states = video_embeddings[:, tokens_per_frame:, :]
-
-        expected_actions = t_enc_ctx * self.config.num_action_tokens_per_timestep
-        if action_tokens.shape[1] < expected_actions:
-            pad = action_tokens[:, -1:].repeat(1, expected_actions - action_tokens.shape[1], 1)
-            action_tokens = torch.cat([action_tokens, pad], dim=1)
-
-        predicted_states = self.video_predictor(
-            input_states.float(), action_tokens[:, :expected_actions].float()
-        )
-        return F.l1_loss(predicted_states, gt_states.float(), reduction="mean")
-
-    def _action_loss(
-        self,
-        embodied_action_tokens: Tensor,
-        actions: Tensor,
-        state: Tensor | None,
-        action_is_pad: Tensor | None,
-    ) -> Tensor:
-        """Flow-matching action-head loss, repeated over `repeated_diffusion_steps`."""
-        device_type = next(self.parameters()).device.type
-        with torch.autocast(device_type=device_type, dtype=torch.float32):
-            r = self.config.repeated_diffusion_steps
-            horizon = self.config.chunk_size
-            actions_target = actions[:, -horizon:, :].to(torch.float32).repeat(r, 1, 1)
-            embodied = embodied_action_tokens.repeat(r, 1, 1)
-            state_rep = state.to(embodied_action_tokens.dtype).repeat(r, 1, 1) if state is not None else None
-            pad_rep = action_is_pad[:, -horizon:].repeat(r, 1) if action_is_pad is not None else None
-            return self.action_model(embodied, actions_target, state_rep, pad_rep)
-
-    def forward(
-        self,
-        images: list[list[Tensor]],
-        instructions: list[str],
-        videos: Tensor | None = None,
-        actions: Tensor | None = None,
-        state: Tensor | None = None,
-        action_is_pad: Tensor | None = None,
-    ) -> dict[str, Tensor]:
-        """Native forward: Qwen encode → optional world-model loss → optional action-head loss."""
-        embodied_action_tokens, action_tokens = self._encode_qwen(
-            images, instructions, need_action_tokens=self.config.enable_world_model
-        )
-
-        if self.config.enable_world_model and videos is not None:
-            wm_loss = self._world_model_loss(videos, action_tokens)
+        # ---- Step 2+3: JEPA Encoder + Predictor ----
+        device_wm = last_hidden.device
+        if not self.config.enable_world_model:
+            wm_loss = torch.tensor(0.0, device=device_wm)
         else:
-            wm_loss = torch.zeros((), device=embodied_action_tokens.device)
+            b, v, t_frames, c, h_img, w_img = batch_videos.shape
+            batch_videos_flat = batch_videos.reshape(b * v, t_frames, c, h_img, w_img)
 
-        if actions is None:
+            video_pixels = self.video_processor(videos=list(batch_videos_flat), return_tensors="pt")[
+                "pixel_values_videos"
+            ].to(self.video_encoder.device)  # [B*V, T, C, H, W]
+
+            with torch.no_grad():
+                video_embeddings = self.video_encoder.get_vision_features(pixel_values_videos=video_pixels)
+                # Merge views: [B*V, ...] -> [B, ..., V*embed_dim]
+                video_embeddings = torch.cat(torch.chunk(video_embeddings, chunks=v, dim=0), dim=2)
+
+            tubelet_size = self.video_encoder.config.tubelet_size
+            device_wm = video_embeddings.device
+            # num_video_frames raw frames → t_enc_total temporal positions after tubelet compression
+            t_enc_total = self.config.num_video_frames // tubelet_size
+
+            if t_enc_total < 2:
+                wm_loss = torch.tensor(0.0, device=device_wm)
+            else:
+                # Shift-by-one JEPA split (matches original VLA_JEPA.py lines 231-232):
+                # input_states: positions 0..T-2, gt_states: positions 1..T-1
+                t_enc_ctx = t_enc_total - 1
+                tokens_per_frame = video_embeddings.shape[1] // t_enc_total
+
+                input_states = video_embeddings[:, : tokens_per_frame * t_enc_ctx, :]
+                gt_states = video_embeddings[:, tokens_per_frame:, :]
+
+                expected_actions = t_enc_ctx * self.config.num_action_tokens_per_timestep
+                if action_tokens.shape[1] < expected_actions:
+                    pad = action_tokens[:, -1:].repeat(1, expected_actions - action_tokens.shape[1], 1)
+                    action_tokens = torch.cat([action_tokens, pad], dim=1)
+
+                predicted_states = self.video_predictor(
+                    input_states.float(),
+                    action_tokens[:, :expected_actions].float(),
+                )
+
+                wm_loss = F.l1_loss(predicted_states, gt_states.float(), reduction="mean")
+
+        if not has_action:
             return {"wm_loss": wm_loss}
 
-        action_loss = self._action_loss(embodied_action_tokens, actions, state, action_is_pad)
+        # ---- Step 4: Action Head ----
+        with torch.autocast(device_type=device_type, dtype=torch.float32):
+            actions_tensor = torch.tensor(
+                np.array(actions), device=last_hidden.device, dtype=torch.float32
+            )  # [B, T_full, action_dim]
+            action_horizon = self.config.chunk_size
+            actions_target = actions_tensor[:, -action_horizon:, :]
+
+            state_tensor = None
+            if state is not None:
+                state_tensor = torch.tensor(
+                    np.array(state), device=last_hidden.device, dtype=last_hidden.dtype
+                )  # [B, 1, state_dim]
+
+            repeated_diffusion_steps = self.config.repeated_diffusion_steps
+            actions_target = actions_target.repeat(repeated_diffusion_steps, 1, 1)
+            embodied_action_tokens = embodied_action_tokens.repeat(repeated_diffusion_steps, 1, 1)
+            if state_tensor is not None:
+                state_tensor = state_tensor.repeat(repeated_diffusion_steps, 1, 1)
+
+            action_is_pad_rep = None
+            if action_is_pad is not None:
+                pad_tensor = torch.stack(
+                    [
+                        p.to(actions_target.device)
+                        if isinstance(p, Tensor)
+                        else torch.tensor(p, device=actions_target.device)
+                        for p in action_is_pad
+                    ]
+                )  # [B, T_full]
+                pad_tensor = pad_tensor[:, -action_horizon:]  # [B, action_horizon]
+                action_is_pad_rep = pad_tensor.repeat(repeated_diffusion_steps, 1)  # [B*R, action_horizon]
+
+            action_loss = self.action_model(
+                embodied_action_tokens, actions_target, state_tensor, action_is_pad_rep
+            )
+
         return {"action_loss": action_loss, "wm_loss": wm_loss * self.config.world_model_loss_weight}
 
     # ---- Native predict_action (follows original VLA_JEPA.predict_action) ----
@@ -289,23 +328,58 @@ class VLAJEPAModel(nn.Module):
     @torch.no_grad()
     def predict_action(
         self,
-        images: list[list[Tensor]],
+        batch_images: list[list[Image.Image]],
         instructions: list[str],
-        state: Tensor | None = None,
-    ) -> Tensor:
-        """Predict an action chunk. `images` is per-sample, per-view float [0,1] [C, H, W] tensors."""
+        state: np.ndarray | None = None,
+    ) -> np.ndarray:
+        """
+        Native action prediction following original VLA_JEPA.predict_action.
+
+        Args:
+            batch_images: List of samples; each is List[PIL.Image] (multi-view).
+            instructions: Task instructions, one per sample.
+            state: Optional [B, state_dim] numpy array.
+
+        Returns:
+            np.ndarray [B, action_horizon, action_dim] — predicted actions.
+        """
         if self.config.resize_images_to is not None:
             height, width = self.config.resize_images_to
-            images = [
-                [F.interpolate(img[None], size=(height, width), mode="area")[0] for img in views]
-                for views in images
+            resampling = getattr(Image, "Resampling", Image).BOX
+            batch_images = [
+                [image.resize((width, height), resample=resampling) for image in sample_images]
+                for sample_images in batch_images
             ]
 
-        embodied_action_tokens, _ = self._encode_qwen(images, instructions, need_action_tokens=False)
-        return self.action_model.predict_action(
-            embodied_action_tokens.float(), state.float() if state is not None else None
+        qwen_inputs = self.qwen.build_inputs(
+            images=batch_images,
+            instructions=instructions,
+            action_prompt=self.replace_prompt,
+            embodied_prompt=self.embodied_replace_prompt,
         )
 
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        device_type = next(self.parameters()).device.type
+
+        with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
+            last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
+            b, _, h = last_hidden.shape
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
+
+        state_tensor = None
+        if state is not None:
+            state_tensor = torch.from_numpy(np.array(state)).to(
+                device=last_hidden.device, dtype=last_hidden.dtype
+            )
+
+        pred_actions = self.action_model.predict_action(
+            embodied_action_tokens.float(), state_tensor.float() if state_tensor is not None else None
+        )  # [B, action_horizon, action_dim]
+
+        return pred_actions.detach().cpu().numpy()
+
 
 # ============================================================================
 # LeRobot Adapter Layer - converts between LeRobot batch format and native VLA-JEPA format
@@ -316,9 +390,9 @@ class VLAJEPAPolicy(PreTrainedPolicy):
     """
     LeRobot adapter for VLA-JEPA.
 
-    Converts LeRobot's standard batch format (dict[str, Tensor]) to the batched tensors
-    the native model expects (keeping everything on-device), calls the native model, and
-    converts outputs back to LeRobot format.
+    Converts LeRobot's standard batch format (dict[str, Tensor]) to the native
+    VLA-JEPA format (List[dict]), calls the native model, and converts outputs
+    back to LeRobot format.
     """
 
     config_class = VLAJEPAConfig
@@ -345,8 +419,9 @@ class VLAJEPAPolicy(PreTrainedPolicy):
 
     # ---- Format Conversion: LeRobot → Native ----
 
-    def _prepare_model_inputs(self, batch: dict[str, Tensor], training=True) -> dict[str, Any]:
-        """Convert a LeRobot batch to the model's batched, on-device inputs.
+    def _prepare_model_inputs(self, batch: dict[str, Tensor]) -> list[dict]:
+        """
+        Convert LeRobot batch format to native VLA-JEPA examples format.
 
         LeRobot format:
             batch = {
@@ -356,25 +431,65 @@ class VLAJEPAPolicy(PreTrainedPolicy):
                 "task": str | List[str],  (optional instruction)
             }
 
-        Returns the kwargs for `VLAJEPAModel.forward` / `.predict_action` (everything stays
-        on the batch device; no per-sample shredding): `images` (per-sample, per-view list for
-        Qwen messages), `instructions`, and the batched `videos` / `actions` / `state` /
-        `action_is_pad` when present.
+        Native format (List[dict]):
+            {
+                "image": List[PIL.Image],       # multi-view images per sample
+                "video": np.ndarray [V, T, H, W, 3],
+                "lang": str,                     # task instruction
+                "action": np.ndarray [T, action_dim],  # optional
+                "state": np.ndarray [1, state_dim],    # optional
+            }
         """
+        # Determine batch size from the first image feature
         image_keys = list(self.config.image_features.keys())
         if not image_keys:
             raise ValueError("VLAJEPA requires at least one image feature.")
-        batch_size = batch[image_keys[0]].shape[0]
+        first_key = image_keys[0]
+        first_tensor = batch[first_key]
+        batch_size = first_tensor.shape[0]
 
-        # Current-frame image per view ([B, C, H, W]); regroup per sample for Qwen messages.
-        frames = []
+        # ---- Collect images per sample ----
+        # images_per_sample[b][v] = PIL.Image for view v
+        images_per_sample: list[list[Image.Image]] = [[] for _ in range(batch_size)]
         for key in image_keys:
-            t = batch[key]
-            if t.ndim == 5:  # [B, T, C, H, W] -> current observation (delta=0)
-                t = t[:, 0]
-            frames.append(self.model.qwen.to_pixel_values(t))
-        images = [[frame[b] for frame in frames] for b in range(batch_size)]
+            tensor = batch[key]  # [B, C, H, W] or [B, T, C, H, W]
+            if tensor.ndim == 5:
+                # observation_delta_indices = [0, 1, ..., num_video_frames-1]
+                # index 0 is the current observation (delta=0)
+                tensor = tensor[:, 0]
+            for b in range(batch_size):
+                images_per_sample[b].append(self.model.qwen.tensor_to_pil(tensor[b]))
 
+        # ---- Collect videos per sample ----
+        # Build video arrays: for each sample, stack views as [V, T, H, W, 3]
+        # Check whether any image feature has a time dimension
+        video_source = None
+        for k in image_keys:
+            if k in batch:
+                video_source = batch[k]  # Use first available for shape inspection
+                break
+
+        if video_source is None:
+            raise ValueError("No image data found in batch for video construction.")
+
+        videos_per_sample = []
+        for b in range(batch_size):
+            sample_views = []
+            for k in image_keys:
+                t = batch[k][b]  # [C, H, W] or [T, C, H, W]
+                if t.ndim == 3:
+                    t = t.unsqueeze(0)  # [1, C, H, W]
+                # Convert to [T, H, W, 3] numpy
+                t_np = t.permute(0, 2, 3, 1).detach().cpu().float().numpy()
+                # Clamp to [0, 255]
+                if t_np.max() <= 1.0:
+                    t_np = t_np * 255.0
+                t_np = np.rint(t_np.clip(0, 255)).astype(np.uint8)
+                sample_views.append(t_np)
+            # Stack views: [V, T, H, W, 3]
+            videos_per_sample.append(np.stack(sample_views, axis=0))
+
+        # ---- Collect instructions ----
         tasks = batch.get("task")
         if tasks is None:
             instructions = ["Execute the robot action."] * batch_size
@@ -383,32 +498,52 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         else:
             instructions = list(tasks)
 
-        inputs: dict[str, Any] = {"images": images, "instructions": instructions}
+        # ---- Collect actions (training only) ----
+        actions_list = None
+        action_is_pad_list = None
+        actions_tensor = batch.get(ACTION)
+        if actions_tensor is not None:
+            if actions_tensor.ndim == 2:
+                actions_tensor = actions_tensor.unsqueeze(1)
+            actions_list = [actions_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
+            action_is_pad_tensor = batch.get("action_is_pad")
+            if action_is_pad_tensor is not None:
+                action_is_pad_list = [action_is_pad_tensor[b].detach().cpu() for b in range(batch_size)]
 
-        # Videos [B, V, T, C, H, W] - only assembled during training when the world model consumes them.
-        if self.model.config.enable_world_model and training:
-            views = [batch[k].unsqueeze(1) if batch[k].ndim == 4 else batch[k] for k in image_keys]
-            inputs["videos"] = self.model.qwen.to_pixel_values(torch.stack(views, dim=1))
+        # ---- Collect state ----
+        state_list = None
+        state_tensor = batch.get(OBS_STATE)
+        if state_tensor is not None:
+            if state_tensor.ndim > 2:
+                state_tensor = state_tensor[:, -1, :]
+            if state_tensor.ndim == 2:
+                state_tensor = state_tensor.unsqueeze(1)  # [B, 1, state_dim]
+            state_list = [state_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
 
-        actions = batch.get(ACTION)
-        if actions is not None:
-            inputs["actions"] = (actions.unsqueeze(1) if actions.ndim == 2 else actions).float()
-            if (pad := batch.get("action_is_pad")) is not None:
-                inputs["action_is_pad"] = pad
+        # ---- Assemble native examples ----
+        examples = []
+        for b in range(batch_size):
+            example = {
+                "image": images_per_sample[b],
+                "video": videos_per_sample[b],
+                "lang": instructions[b],
+            }
+            if actions_list is not None:
+                example["action"] = actions_list[b]
+            if action_is_pad_list is not None:
+                example["action_is_pad"] = action_is_pad_list[b]
+            if state_list is not None:
+                example["state"] = state_list[b]
+            examples.append(example)
 
-        state = batch.get(OBS_STATE)
-        if state is not None:
-            if state.ndim > 2:
-                state = state[:, -1, :]
-            inputs["state"] = (state.unsqueeze(1) if state.ndim == 2 else state).float()  # [B, 1, dim]
-
-        return inputs
+        return examples
 
     # ---- LeRobot Policy Interface ----
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
         """LeRobot train forward: convert → native forward → aggregate losses."""
-        native_output = self.model.forward(**self._prepare_model_inputs(batch, training=True))
+        examples = self._prepare_model_inputs(batch)
+        native_output = self.model.forward(examples)
 
         ref = next(iter(native_output.values()))
         zero = torch.zeros((), device=ref.device, dtype=ref.dtype)
@@ -426,9 +561,16 @@ class VLAJEPAPolicy(PreTrainedPolicy):
         self.eval()
         self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
 
-        inputs = self._prepare_model_inputs(batch, training=False)
-        actions = self.model.predict_action(inputs["images"], inputs["instructions"], inputs.get("state"))
-        return actions.to(device=self.config.device, dtype=torch.float32)
+        examples = self._prepare_model_inputs(batch)
+        batch_images = [ex["image"] for ex in examples]
+        instructions = [ex["lang"] for ex in examples]
+
+        state_np = None
+        if "state" in examples[0] and examples[0]["state"] is not None:
+            state_np = np.stack([ex["state"] for ex in examples])
+
+        actions_np = self.model.predict_action(batch_images, instructions, state_np)
+        return torch.from_numpy(actions_np).to(device=self.config.device, dtype=torch.float32)
 
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:

src/lerobot/policies/vla_jepa/qwen_interface.py

@@ -17,7 +17,9 @@ from __future__ import annotations
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
+import numpy as np
 import torch
+from PIL import Image
 
 from lerobot.utils.import_utils import _transformers_available
 
@@ -76,7 +78,7 @@ class Qwen3VLInterface(torch.nn.Module):
 
     def build_inputs(
         self,
-        images: Sequence[Sequence[torch.Tensor]],
+        images: Sequence[Sequence[Image.Image]],
         instructions: Sequence[str],
         action_prompt: str,
         embodied_prompt: str,
@@ -92,42 +94,24 @@ class Qwen3VLInterface(torch.nn.Module):
             content.append({"type": "text", "text": prompt})
             messages.append([{"role": "user", "content": content}])
 
-        # The Qwen image processor is a torchvision-backed fast processor: passing the
-        # images as GPU tensors (with `device`) keeps the whole vision pipeline on-device
-        # and avoids a GPU->CPU->GPU roundtrip. The image tensors are forwarded through
-        # apply_chat_template untouched into Qwen3VLProcessor.__call__.
-        # do_rescale=False: images already arrive as float in [0, 1] (the dataset decoder
-        # yields float32/255 and VISUAL normalization is IDENTITY), so we skip the
-        # processor's /255 rescale instead of round-tripping through uint8.
         batch_inputs = self.processor.apply_chat_template(
             messages,
             tokenize=True,
             add_generation_prompt=True,
             return_dict=True,
-            processor_kwargs={
-                "padding": True,
-                "return_tensors": "pt",
-                "device": self.model.device,
-                "do_rescale": False,
-            },
+            processor_kwargs={"padding": True, "return_tensors": "pt"},
         )
         return batch_inputs.to(self.model.device)
 
     @staticmethod
-    def to_pixel_values(image_tensor: torch.Tensor) -> torch.Tensor:
-        """Prepare an image/video tensor for the fast processors (used with do_rescale=False).
-
-        The dataset decoder yields float32 in [0, 1] (channels-first) and VISUAL
-        normalization is IDENTITY, so the tensor already arrives in [0, 1]; we pass it
-        through as float and let the processors normalize (no rescale, no uint8
-        quantization). A single channel is expanded to 3 to match the RGB processors.
-
-        Works for any channels-first layout (channel dim is -3): [C, H, W], [B, C, H, W],
-        [T, C, H, W], [B, V, T, C, H, W], ...
-        """
-        image = image_tensor.detach().float()
-        if image.shape[-3] == 1:
-            repeats = [1] * image.ndim
-            repeats[-3] = 3
-            image = image.repeat(*repeats)
-        return image
+    def tensor_to_pil(image_tensor: torch.Tensor) -> Image.Image:
+        image = image_tensor.detach().cpu()
+        if image.ndim == 3 and image.shape[0] in (1, 3):
+            image = image.permute(1, 2, 0)
+        image = image.float()
+        if image.max() <= 1.0:
+            image = image * 255.0
+        image = image.clamp(0, 255).round().to(torch.uint8).numpy()
+        if image.shape[-1] == 1:
+            image = np.repeat(image, 3, axis=-1)
+        return Image.fromarray(image)

src/lerobot/robots/bi_rebot_b601_follower/bi_rebot_b601_follower.py

@@ -65,13 +65,7 @@ class BiRebotB601Follower(BimanualMixin, Robot):
             cameras=left_arm_cameras,
             motor_can_ids=config.left_arm_config.motor_can_ids,
             pos_vel_velocity=config.left_arm_config.pos_vel_velocity,
-            control_mode=config.left_arm_config.control_mode,
-            mit_kp=config.left_arm_config.mit_kp,
-            mit_kd=config.left_arm_config.mit_kd,
-            gripper_control_mode=config.left_arm_config.gripper_control_mode,
             gripper_torque_ratio=config.left_arm_config.gripper_torque_ratio,
-            gripper_mit_kp=config.left_arm_config.gripper_mit_kp,
-            gripper_mit_kd=config.left_arm_config.gripper_mit_kd,
             joint_limits=config.left_arm_config.joint_limits,
         )
 
@@ -86,13 +80,7 @@ class BiRebotB601Follower(BimanualMixin, Robot):
             cameras=config.right_arm_config.cameras,
             motor_can_ids=config.right_arm_config.motor_can_ids,
             pos_vel_velocity=config.right_arm_config.pos_vel_velocity,
-            control_mode=config.right_arm_config.control_mode,
-            mit_kp=config.right_arm_config.mit_kp,
-            mit_kd=config.right_arm_config.mit_kd,
-            gripper_control_mode=config.right_arm_config.gripper_control_mode,
             gripper_torque_ratio=config.right_arm_config.gripper_torque_ratio,
-            gripper_mit_kp=config.right_arm_config.gripper_mit_kp,
-            gripper_mit_kd=config.right_arm_config.gripper_mit_kd,
             joint_limits=config.right_arm_config.joint_limits,
         )
 

src/lerobot/robots/rebot_b601_follower/config_rebot_b601_follower.py

@@ -65,33 +65,18 @@ class RebotB601FollowerConfig:
         }
     )
 
-    # Max speed (deg/s) per joint for POS_VEL arms and FORCE_POS gripper (motor order).
-    pos_vel_velocity: float | list[float] = field(
-        default_factory=lambda: [150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 900.0]
-    )
+    # Target velocity for joints running in POS_VEL mode, in degrees/s. A scalar is
+    # applied to every joint; a list provides one value per joint (in motor order).
+    pos_vel_velocity: float | list[float] = field(default_factory=lambda: [150.0] * 7)
 
-    # Arm control: "mit" or "pos_vel".
-    control_mode: str = "mit"
-
-    # MIT kp/kd per arm joint (motor order). Unused when control_mode="pos_vel".
-    mit_kp: float | list[float] = field(default_factory=lambda: [45.0, 45.0, 45.0, 8.0, 9.0, 8.0, 8.0])
-    mit_kd: float | list[float] = field(default_factory=lambda: [12.0, 12.0, 12.0, 1.0, 1.0, 1.0, 1.0])
-
-    # Gripper control: "force_pos" or "mit".
-    gripper_control_mode: str = "force_pos"
-
-    # FORCE_POS only: max grip force, in [0, 1].
-    gripper_torque_ratio: float = 0.07
-
-    # MIT only.
-    gripper_mit_kp: float = 8.0
-    gripper_mit_kd: float = 0.3
+    # Torque/current ratio for the gripper's FORCE_POS mode, in range [0, 1].
+    gripper_torque_ratio: float = 0.1
 
     # Soft joint limits (degrees). These are clipped against on every action.
     joint_limits: dict[str, tuple[float, float]] = field(
         default_factory=lambda: {
-            "shoulder_pan": (-150.0, 150.0),
-            "shoulder_lift": (-200.0, 1.0),
+            "shoulder_pan": (-145.0, 145.0),
+            "shoulder_lift": (-170.0, 1.0),
             "elbow_flex": (-200.0, 1.0),
             "wrist_flex": (-80.0, 90.0),
             "wrist_yaw": (-90.0, 90.0),

src/lerobot/robots/rebot_b601_follower/rebot_b601_follower.py

@@ -174,25 +174,11 @@ class RebotB601Follower(Robot):
         print(f"Calibration saved to {self.calibration_fpath}")
 
     def configure(self) -> None:
-        if self.config.control_mode not in ("pos_vel", "mit"):
-            raise ValueError(
-                f"Unsupported control_mode '{self.config.control_mode}'. Use 'pos_vel' or 'mit'."
-            )
-        if self.config.gripper_control_mode not in ("force_pos", "mit"):
-            raise ValueError(
-                f"Unsupported gripper_control_mode '{self.config.gripper_control_mode}'. "
-                "Use 'force_pos' or 'mit'."
-            )
-        use_mit = self.config.control_mode == "mit"
-        gripper_use_mit = self.config.gripper_control_mode == "mit"
         self.bus.enable_all()
         for motor_name, motor in self.motors.items():
-            if motor_name == GRIPPER_MOTOR:
-                target_mode = MotorBridgeMode.MIT if gripper_use_mit else MotorBridgeMode.FORCE_POS
-            elif use_mit:
-                target_mode = MotorBridgeMode.MIT
-            else:
-                target_mode = MotorBridgeMode.POS_VEL
+            target_mode = (
+                MotorBridgeMode.FORCE_POS if motor_name == GRIPPER_MOTOR else MotorBridgeMode.POS_VEL
+            )
             for attempt in range(_ENSURE_MODE_RETRIES + 1):
                 try:
                     motor.ensure_mode(target_mode)
@@ -278,34 +264,22 @@ class RebotB601Follower(Robot):
             goal_present_pos = {key: (g, present_pos.get(key, g)) for key, g in goal_pos.items()}
             goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
 
-        use_mit = self.config.control_mode == "mit"
         for motor_name, position_deg in goal_pos.items():
             motor = self.motors.get(motor_name)
             if motor is None:
                 continue
             idx = self.motor_names.index(motor_name)
+            vel_deg_s = (
+                self.config.pos_vel_velocity[idx]
+                if isinstance(self.config.pos_vel_velocity, list)
+                else self.config.pos_vel_velocity
+            )
             pos_rad = math.radians(position_deg)
+            vel_rad = math.radians(vel_deg_s)
             if motor_name == GRIPPER_MOTOR:
-                if self.config.gripper_control_mode == "mit":
-                    motor.send_mit(pos_rad, 0.0, self.config.gripper_mit_kp, self.config.gripper_mit_kd, 0.0)
-                else:
-                    vel_deg_s = (
-                        self.config.pos_vel_velocity[idx]
-                        if isinstance(self.config.pos_vel_velocity, list)
-                        else self.config.pos_vel_velocity
-                    )
-                    motor.send_force_pos(pos_rad, math.radians(vel_deg_s), self.config.gripper_torque_ratio)
-            elif use_mit:
-                kp = self.config.mit_kp[idx] if isinstance(self.config.mit_kp, list) else self.config.mit_kp
-                kd = self.config.mit_kd[idx] if isinstance(self.config.mit_kd, list) else self.config.mit_kd
-                motor.send_mit(pos_rad, 0.0, kp, kd, 0.0)
+                motor.send_force_pos(pos_rad, vel_rad, self.config.gripper_torque_ratio)
             else:
-                vel_deg_s = (
-                    self.config.pos_vel_velocity[idx]
-                    if isinstance(self.config.pos_vel_velocity, list)
-                    else self.config.pos_vel_velocity
-                )
-                motor.send_pos_vel(pos_rad, math.radians(vel_deg_s))
+                motor.send_pos_vel(pos_rad, vel_rad)
 
         return {f"{motor}.pos": val for motor, val in goal_pos.items()}
 

src/lerobot/rollout/configs.py

@@ -226,14 +226,11 @@ class RolloutConfig:
     device: str | None = None
     task: str = ""
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to display compressed images in Rerun
     display_compressed_images: bool = False
     # Use vocal synthesis to read events
     play_sounds: bool = True

src/lerobot/rollout/context.py

@@ -320,9 +320,7 @@ def build_rollout_context(
             raise ValueError(
                 f"Visual feature mismatch between policy and robot hardware.\n"
                 f"Policy expects: {expected_visuals}\n"
-                f"Robot provides: {provided_visuals}\n"
-                f"Use --rename_map to map camera names, e.g. "
-                f"""--rename_map='{{"observation.images.top": "observation.images.cam0"}}'"""
+                f"Robot provides: {provided_visuals}"
             )
 
     # --- 5. Dataset -------------

src/lerobot/rollout/strategies/core.py

@@ -26,7 +26,7 @@ from lerobot.utils.action_interpolator import ActionInterpolator
 from lerobot.utils.constants import OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.robot_utils import precise_sleep
-from lerobot.utils.visualization_utils import log_visualization_data
+from lerobot.utils.visualization_utils import log_rerun_data
 
 from ..inference import InferenceEngine
 
@@ -162,12 +162,11 @@ class RolloutStrategy(abc.ABC):
         action_dict: dict | None,
         runtime_ctx: RuntimeContext,
     ) -> None:
-        """Log observation/action telemetry to the visualization backend if display_data is enabled."""
+        """Log observation/action telemetry to Rerun if display_data is enabled."""
         cfg = runtime_ctx.cfg
         if not cfg.display_data:
             return
-        log_visualization_data(
-            cfg.display_mode,
+        log_rerun_data(
             observation=obs_processed,
             action=action_dict,
             compress_images=cfg.display_compressed_images,

src/lerobot/rollout/strategies/episodic.py

@@ -44,7 +44,7 @@ from lerobot.utils.feature_utils import build_dataset_frame
 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 log_visualization_data
+from lerobot.utils.visualization_utils import log_rerun_data
 
 from ..configs import EpisodicStrategyConfig
 from ..context import RolloutContext
@@ -171,7 +171,6 @@ class EpisodicStrategy(RolloutStrategy):
                             fps=fps,
                             control_time_s=reset_time_s,
                             display_data=cfg.display_data,
-                            display_mode=cfg.display_mode,
                             display_compressed=display_compressed,
                         )
 
@@ -260,7 +259,6 @@ class EpisodicStrategy(RolloutStrategy):
         fps: float,
         control_time_s: float,
         display_data: bool,
-        display_mode: str,
         display_compressed: bool,
     ) -> None:
         """Reset-phase loop: teleop drives the robot if available, no recording."""
@@ -290,8 +288,7 @@ class EpisodicStrategy(RolloutStrategy):
 
                 if display_data:
                     obs_processed = processors.robot_observation_processor(obs)
-                    log_visualization_data(
-                        display_mode,
+                    log_rerun_data(
                         observation=obs_processed,
                         action=act_teleop,
                         compress_images=display_compressed,

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -59,18 +59,6 @@ distant$ lerobot-dataset-viz \
 local$ rerun rerun+http://IP:GRPC_PORT/proxy
 ```
 
-- Visualize data in Foxglove with a seekable, scrubbable timeline:
-```
-local$ lerobot-dataset-viz \
-    --repo-id lerobot/pusht \
-    --episode-index 0 \
-    --display-mode foxglove
-
-# then open the Foxglove app and connect to ws://127.0.0.1:8765
-```
-This starts a Foxglove WebSocket server that serves the episode on demand from the on-disk dataset,
-so you can play/pause and scrub anywhere in the episode using Foxglove's playback controls.
-
 """
 
 import argparse
@@ -84,29 +72,10 @@ import torch
 import torch.utils.data
 import tqdm
 
-from lerobot.configs import DEPTH_MILLIMETER_UNIT
 from lerobot.datasets import LeRobotDataset
-from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
+from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
 from lerobot.utils.utils import init_logging
 
-DEFAULT_FOXGLOVE_PORT = 8765
-DEFAULT_RERUN_PORT = 9090
-
-
-def get_feature_names(dataset: LeRobotDataset, key: str) -> list[str]:
-    """Return per-dimension names for a feature from the dataset metadata.
-
-    Only flat-list ``names`` metadata is used. Dict-style ``names`` and missing names fall back to ``{key}_{i}`` indices.
-    """
-    feature = dataset.features[key]
-    dim = feature["shape"][-1]
-
-    names = feature.get("names")
-    if isinstance(names, list) and len(names) == dim:
-        return [str(name) for name in names]
-
-    return [f"{key}_{d}" for d in range(dim)]
-
 
 def check_chw_float32(frame: torch.Tensor) -> None:
     """
@@ -124,35 +93,10 @@ def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     return hwc_uint8_numpy
 
 
-def to_hwc_float32_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
+def to_hwc_uint16_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     check_chw_float32(chw_float32_torch)
-    hwc_float32_numpy = chw_float32_torch.permute(1, 2, 0).numpy()
-    return hwc_float32_numpy
-
-
-def build_blueprint_from_dataset(dataset: LeRobotDataset):
-    """Build a Rerun blueprint laying out camera images and time series for the given dataset.
-
-    Camera images and scalar signals (action, state, reward, done, success) are arranged in a grid.
-    The per-dimension series names for ``action`` and ``state`` are applied directly
-    via blueprint overrides.
-    """
-    import rerun as rr
-    import rerun.blueprint as rrb
-
-    views = [rrb.Spatial2DView(origin=key, name=key) for key in dataset.meta.camera_keys]
-
-    # Style multi-dimensional signals (action, state) with per-dimension names.
-    for origin, key in ((ACTION, ACTION), ("state", OBS_STATE)):
-        if key in dataset.features:
-            names = get_feature_names(dataset, key)
-            styling = rr.SeriesLines(names=names)
-            views.append(rrb.TimeSeriesView(origin=origin, name=origin, overrides={origin: styling}))
-    for key in (DONE, REWARD, SUCCESS):
-        if key in dataset.features:
-            views.append(rrb.TimeSeriesView(origin=key, name=key))
-
-    return rrb.Blueprint(rrb.Grid(*views))
+    hwc_uint16_numpy = chw_float32_torch.round().type(torch.uint16).permute(1, 2, 0).numpy()
+    return hwc_uint16_numpy
 
 
 def visualize_dataset(
@@ -161,30 +105,13 @@ def visualize_dataset(
     batch_size: int = 32,
     num_workers: int = 0,
     mode: str = "local",
-    web_port: int | None = None,
+    web_port: int = 9090,
     grpc_port: int = 9876,
     save: bool = False,
     output_dir: Path | None = None,
     display_compressed_images: bool = False,
-    display_mode: str = "rerun",
-    host: str = "127.0.0.1",
-    autoplay: bool = True,
     **kwargs,
 ) -> Path | None:
-    if display_mode == "foxglove":
-        from lerobot.utils.foxglove_visualization import serve_foxglove_dataset_playback
-
-        logging.info("Starting Foxglove server")
-        serve_foxglove_dataset_playback(
-            dataset,
-            episode_index,
-            host=host,
-            port=web_port if web_port is not None else DEFAULT_FOXGLOVE_PORT,
-            compress_images=display_compressed_images,
-            autoplay=autoplay,
-        )
-        return None
-
     if save:
         assert output_dir is not None, (
             "Set an output directory where to write .rrd files with `--output-dir path/to/directory`."
@@ -210,8 +137,7 @@ def visualize_dataset(
     import rerun as rr
 
     spawn_local_viewer = mode == "local" and not save
-    blueprint = build_blueprint_from_dataset(dataset)
-    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer, default_blueprint=blueprint)
+    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
 
     # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
     # when iterating on a dataloader with `num_workers` > 0
@@ -221,23 +147,14 @@ def visualize_dataset(
     if mode == "distant":
         server_uri = rr.serve_grpc(grpc_port=grpc_port)
         logging.info(f"Connect to a Rerun Server: rerun rerun+http://IP:{grpc_port}/proxy")
-        rr.serve_web_viewer(
-            open_browser=False,
-            web_port=web_port if web_port is not None else DEFAULT_RERUN_PORT,
-            connect_to=server_uri,
-        )
+        rr.serve_web_viewer(open_browser=False, web_port=web_port, connect_to=server_uri)
 
     logging.info("Logging to Rerun")
 
-    # Depth frames and stats are dequantized to the dataset's depth_output_unit on load.
-    depth_meter = 1000.0 if dataset.depth_output_unit == DEPTH_MILLIMETER_UNIT else 1.0
-
     # Use the dataset's q01/q99 depth statistics for robust depth range bounds
     depth_ranges = {}
     for key in dataset.meta.depth_keys:
-        stats = (dataset.meta.stats or {}).get(key)
-        if not stats:
-            continue
+        stats = dataset.meta.stats[key]
         lo = stats["q01"] if "q01" in stats else stats["min"]
         hi = stats["q99"] if "q99" in stats else stats["max"]
         depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
@@ -246,21 +163,19 @@ def visualize_dataset(
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
         if first_index is None:
             first_index = batch["index"][0].item()
-
         # iterate over the batch
         for i in range(len(batch["index"])):
             rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
             rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
-            # display each camera image (or depth map)
+            # display each camera image
             for key in dataset.meta.camera_keys:
                 if key in dataset.meta.depth_keys:
-                    depth = to_hwc_float32_numpy(batch[key][i])
+                    depth = to_hwc_uint16_numpy(batch[key][i])
                     depth_entity = rr.DepthImage(
                         depth,
-                        meter=depth_meter,
                         colormap=rr.components.Colormap.Viridis,
-                        depth_range=depth_ranges.get(key),
+                        depth_range=depth_ranges[key],
                     )
                     rr.log(key, entity=depth_entity)
                 else:
@@ -268,13 +183,15 @@ def visualize_dataset(
                     img_entity = rr.Image(img).compress() if display_compressed_images else rr.Image(img)
                     rr.log(key, entity=img_entity)
 
-            # display the action space (e.g. actuators command)
+            # display each dimension of action space (e.g. actuators command)
             if ACTION in batch:
-                rr.log(ACTION, rr.Scalars(batch[ACTION][i].numpy()))
+                for dim_idx, val in enumerate(batch[ACTION][i]):
+                    rr.log(f"{ACTION}/{dim_idx}", rr.Scalars(val.item()))
 
-            # display the observed state space (e.g. agent position in joint space)
+            # display each dimension of observed state space (e.g. agent position in joint space)
             if OBS_STATE in batch:
-                rr.log("state", rr.Scalars(batch[OBS_STATE][i].numpy()))
+                for dim_idx, val in enumerate(batch[OBS_STATE][i]):
+                    rr.log(f"state/{dim_idx}", rr.Scalars(val.item()))
 
             if DONE in batch:
                 rr.log(DONE, rr.Scalars(batch[DONE][i].item()))
@@ -282,11 +199,12 @@ def visualize_dataset(
             if REWARD in batch:
                 rr.log(REWARD, rr.Scalars(batch[REWARD][i].item()))
 
-            if SUCCESS in batch:
-                rr.log(SUCCESS, rr.Scalars(batch[SUCCESS][i].item()))
+            if "next.success" in batch:
+                rr.log("next.success", rr.Scalars(batch["next.success"][i].item()))
 
-    # save .rrd locally
     if mode == "local" and save:
+        # save .rrd locally
+        output_dir = Path(output_dir)
         output_dir.mkdir(parents=True, exist_ok=True)
         repo_id_str = repo_id.replace("/", "_")
         rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
@@ -294,7 +212,7 @@ def visualize_dataset(
         return rrd_path
 
     elif mode == "distant":
-        # Keep the process alive while it serves the gRPC/web connection.
+        # stop the process from exiting since it is serving the websocket connection
         try:
             while True:
                 time.sleep(1)
@@ -355,11 +273,13 @@ def main():
     parser.add_argument(
         "--web-port",
         type=int,
-        default=None,
-        help=(
-            "Web/WebSocket port. For rerun `--mode distant` it is the web viewer port (default 9090); "
-            "for `--display-mode foxglove` it is the server bind port (default 8765)."
-        ),
+        default=9090,
+        help="Web port for rerun.io when `--mode distant` is set.",
+    )
+    parser.add_argument(
+        "--ws-port",
+        type=int,
+        help="deprecated, please use --grpc-port instead.",
     )
     parser.add_argument(
         "--grpc-port",
@@ -392,61 +312,27 @@ def main():
     parser.add_argument(
         "--display-compressed-images",
         action="store_true",
-        help="If set, display compressed (JPEG) images instead of uncompressed ones.",
-    )
-
-    parser.add_argument(
-        "--display-mode",
-        type=str,
-        default="rerun",
-        choices=["rerun", "foxglove"],
-        help=(
-            "Visualization backend. 'rerun' uses the Rerun viewer (--mode/--save/--*-port apply). "
-            "'foxglove' starts a Foxglove WebSocket server that serves the episode as a seekable, "
-            "scrubbable timeline; connect the Foxglove app to ws://HOST:PORT (--host/--web-port)."
-        ),
-    )
-    parser.add_argument(
-        "--host",
-        type=str,
-        default="127.0.0.1",
-        help=(
-            "Host to bind the Foxglove WebSocket server to when `--display-mode foxglove` is set "
-            "(127.0.0.1 for local only, 0.0.0.0 for all interfaces)."
-        ),
-    )
-    parser.add_argument(
-        "--no-autoplay",
-        dest="autoplay",
-        action="store_false",
-        help=(
-            "For `--display-mode foxglove`: don't start playing automatically when a client "
-            "connects; wait for play to be pressed in the Foxglove app instead."
-        ),
+        help="If set, display compressed images in Rerun instead of uncompressed ones.",
     )
 
     args = parser.parse_args()
-
-    if args.display_mode == "foxglove":
-        rerun_only = ("mode", "save", "output_dir", "grpc_port", "batch_size", "num_workers")
-        ignored = [name for name in rerun_only if getattr(args, name) != parser.get_default(name)]
-        if ignored:
-            logging.warning(
-                "These flags only apply to `--display-mode rerun` and are ignored with "
-                "`--display-mode foxglove`: %s.",
-                ", ".join(f"--{name.replace('_', '-')}" for name in ignored),
-            )
-
     kwargs = vars(args)
     repo_id = kwargs.pop("repo_id")
     root = kwargs.pop("root")
     tolerance_s = kwargs.pop("tolerance_s")
 
+    if kwargs["ws_port"] is not None:
+        logging.warning(
+            "--ws-port is deprecated and will be removed in future versions. Please use --grpc-port instead."
+        )
+        logging.warning("Setting grpc_port to ws_port value.")
+        kwargs["grpc_port"] = kwargs.pop("ws_port")
+
     init_logging()
     logging.info("Loading dataset")
     dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], root=root, tolerance_s=tolerance_s)
 
-    visualize_dataset(dataset, **kwargs)
+    visualize_dataset(dataset, **vars(args))
 
 
 if __name__ == "__main__":

src/lerobot/scripts/lerobot_eval.py

@@ -169,7 +169,6 @@ def rollout(
     env_features: dict | None = None,
     recording_repo_id: str | None = None,
     recording_private: bool = False,
-    predicted_latents_callback: Callable[[PreTrainedPolicy], None] | None = None,
 ) -> dict:
     """Run a batched policy rollout once through a batch of environments.
 
@@ -199,9 +198,6 @@ def rollout(
             are returned optionally because they typically take more memory to cache. Defaults to False.
         render_callback: Optional rendering callback to be used after the environments are reset, and after
             every step.
-        predicted_latents_callback: Optional callback invoked after every ``select_action`` with the policy
-            itself. World-model policies (e.g. LingBot-VA) stash predicted video latents on
-            ``policy.last_predicted_latents``; this lets the caller concatenate chunks and decode once.
     Returns:
         The dictionary described above.
     """
@@ -280,8 +276,6 @@ def rollout(
             observation = preprocessor(observation)
             with torch.inference_mode():
                 action = policy.select_action(observation)
-            if predicted_latents_callback is not None:
-                predicted_latents_callback(policy)
             action = postprocessor(action)
 
             action_transition = {ACTION: action}
@@ -301,22 +295,12 @@ def rollout(
             # available if none of the envs finished.
             if "final_info" in info:
                 final_info = info["final_info"]
-                if isinstance(final_info, dict):
-                    is_success = final_info.get("is_success", [False] * env.num_envs)
-                    successes = (
-                        is_success.tolist()
-                        if hasattr(is_success, "tolist")
-                        else [bool(is_success)] * env.num_envs
+                if not isinstance(final_info, dict):
+                    raise RuntimeError(
+                        "Unsupported `final_info` format: expected dict (Gymnasium >= 1.0). "
+                        "You're likely using an older version of gymnasium (< 1.0). Please upgrade."
                     )
-                else:
-                    # Gymnasium < 1.0 returns final_info as a per-env sequence/object array,
-                    # with entries set to a dict only for envs that just finished.
-                    successes = []
-                    for item in final_info:
-                        if isinstance(item, dict) and "is_success" in item:
-                            successes.append(bool(item["is_success"]))
-                        else:
-                            successes.append(False)
+                successes = final_info["is_success"].tolist()
             elif "is_success" in info:
                 is_success = info["is_success"]
                 successes = (
@@ -416,7 +400,6 @@ def eval_policy(
     env_features: dict | None = None,
     recording_repo_id: str | None = None,
     recording_private: bool = False,
-    save_predicted_video: bool = False,
 ) -> dict:
     """
     Args:
@@ -435,11 +418,6 @@ def eval_policy(
     if max_episodes_rendered > 0 and not videos_dir:
         raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")
 
-    # World-model policies (e.g. LingBot-VA) opt into predicted-video saving via their config.
-    save_predicted_video = save_predicted_video or bool(
-        getattr(getattr(policy, "config", None), "save_predicted_video", False)
-    )
-
     if not isinstance(policy, PreTrainedPolicy):
         exc = ValueError(
             f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
@@ -483,22 +461,6 @@ def eval_policy(
     if max_episodes_rendered > 0:
         video_paths: list[str] = []
 
-    if save_predicted_video:
-        if not videos_dir:
-            raise ValueError("If save_predicted_video is True, videos_dir must be provided.")
-        predicted_video_paths: list[str] = []
-        n_predicted_rendered = 0
-
-    # Collect predicted-video latents across a rollout (world-model policies only). The latents are
-    # concatenated and decoded once after the rollout, matching upstream LingBot-VA's visualization path.
-    def collect_predicted_latents(policy: PreTrainedPolicy):
-        latents = getattr(policy, "last_predicted_latents", None)
-        if latents is not None:
-            pred_latents.append(
-                latents.detach().to("cpu") if hasattr(latents, "detach") else torch.as_tensor(latents).cpu()
-            )
-            policy.last_predicted_latents = None
-
     if return_episode_data:
         episode_data: dict | None = None
 
@@ -510,9 +472,6 @@ def eval_policy(
         if max_episodes_rendered > 0:
             ep_frames: list[np.ndarray] = []
 
-        if save_predicted_video:
-            pred_latents: list[torch.Tensor] = []
-
         if start_seed is None:
             seeds = None
         else:
@@ -533,7 +492,6 @@ def eval_policy(
             env_features=env_features,
             recording_repo_id=recording_repo_id,
             recording_private=recording_private,
-            predicted_latents_callback=collect_predicted_latents if save_predicted_video else None,
         )
 
         # Figure out where in each rollout sequence the first done condition was encountered (results after
@@ -599,35 +557,6 @@ def eval_policy(
                 threads.append(thread)
                 n_episodes_rendered += 1
 
-        # Maybe save the policy's predicted (imagined) video for this batch's rollout.
-        if save_predicted_video and len(pred_latents) > 0:
-            predicted_latent = torch.cat(pred_latents, dim=2)
-            decoder = getattr(policy, "decode_predicted_latents", None) or getattr(
-                policy, "_decode_predicted_video", None
-            )
-            if decoder is None:
-                raise AttributeError(
-                    "Policy config requested predicted-video saving, but the policy does not expose "
-                    "`decode_predicted_latents` or `_decode_predicted_video`."
-                )
-            predicted_video = decoder(predicted_latent)
-            if hasattr(predicted_video, "detach"):
-                predicted_video = predicted_video.detach().to("cpu").numpy()
-            videos_dir.mkdir(parents=True, exist_ok=True)
-            predicted_video_path = videos_dir / f"pred_episode_{n_predicted_rendered}.mp4"
-            predicted_video_paths.append(str(predicted_video_path))
-            thread = threading.Thread(
-                target=write_video,
-                args=(
-                    str(predicted_video_path),
-                    predicted_video,
-                    env.unwrapped.metadata["render_fps"],
-                ),
-            )
-            thread.start()
-            threads.append(thread)
-            n_predicted_rendered += 1
-
         progbar.set_postfix(
             {"running_success_rate": f"{np.mean(all_successes[:n_episodes]).item() * 100:.1f}%"}
         )
@@ -671,9 +600,6 @@ def eval_policy(
     if max_episodes_rendered > 0:
         info["video_paths"] = video_paths
 
-    if save_predicted_video:
-        info["predicted_video_paths"] = predicted_video_paths
-
     return info
 
 
@@ -814,10 +740,9 @@ class TaskMetrics(TypedDict):
     max_rewards: list[float]
     successes: list[bool]
     video_paths: list[str]
-    predicted_video_paths: list[str]
 
 
-ACC_KEYS = ("sum_rewards", "max_rewards", "successes", "video_paths", "predicted_video_paths")
+ACC_KEYS = ("sum_rewards", "max_rewards", "successes", "video_paths")
 
 
 def eval_one(
@@ -866,7 +791,6 @@ def eval_one(
         max_rewards=[ep["max_reward"] for ep in per_episode],
         successes=[ep["success"] for ep in per_episode],
         video_paths=task_result.get("video_paths", []),
-        predicted_video_paths=task_result.get("predicted_video_paths", []),
     )
 
 
@@ -927,7 +851,6 @@ def run_one(
 
     if max_episodes_rendered > 0:
         metrics.setdefault("video_paths", [])
-    metrics.setdefault("predicted_video_paths", [])
     return task_group, task_id, metrics
 
 
@@ -985,11 +908,11 @@ def eval_policy_all(
         _append("sum_rewards", metrics.get("sum_rewards"))
         _append("max_rewards", metrics.get("max_rewards"))
         _append("successes", metrics.get("successes"))
-        for key in ("video_paths", "predicted_video_paths"):
-            paths = metrics.get(key, [])
-            if paths:
-                group_acc[group][key].extend(paths)
-                overall[key].extend(paths)
+        # video_paths is list-like
+        paths = metrics.get("video_paths", [])
+        if paths:
+            group_acc[group]["video_paths"].extend(paths)
+            overall["video_paths"].extend(paths)
 
     # Choose runner (sequential vs threaded)
     task_runner = partial(
@@ -1061,7 +984,6 @@ def eval_policy_all(
             "pc_success": _agg_from_list(acc["successes"]) * 100 if acc["successes"] else float("nan"),
             "n_episodes": len(acc["sum_rewards"]),
             "video_paths": list(acc["video_paths"]),
-            "predicted_video_paths": list(acc["predicted_video_paths"]),
         }
 
     # overall aggregates
@@ -1073,7 +995,6 @@ def eval_policy_all(
         "eval_s": time.time() - start_t,
         "eval_ep_s": (time.time() - start_t) / max(1, len(overall["sum_rewards"])),
         "video_paths": list(overall["video_paths"]),
-        "predicted_video_paths": list(overall["predicted_video_paths"]),
     }
 
     return {

src/lerobot/scripts/lerobot_record.py

@@ -38,9 +38,6 @@ lerobot-record \\
     --display_data=true
 ```
 
-To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove`` (then connect the
-Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``).
-
 Example recording with bimanual so100:
 ```shell
 lerobot-record \\
@@ -160,11 +157,7 @@ from lerobot.utils.utils import (
     init_logging,
     log_say,
 )
-from lerobot.utils.visualization_utils import (
-    init_visualization,
-    log_visualization_data,
-    shutdown_visualization,
-)
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 
 @dataclass
@@ -175,14 +168,11 @@ class RecordConfig:
     teleop: TeleoperatorConfig | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to  display compressed images in Rerun
     display_compressed_images: bool = False
     # Use vocal synthesis to read events.
     play_sounds: bool = True
@@ -243,7 +233,6 @@ def record_loop(
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
-    display_mode: str = "rerun",
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -338,11 +327,8 @@ def record_loop(
             dataset.add_frame(frame)
 
         if display_data:
-            log_visualization_data(
-                display_mode,
-                observation=obs_processed,
-                action=action_values,
-                compress_images=display_compressed_images,
+            log_rerun_data(
+                observation=obs_processed, action=action_values, compress_images=display_compressed_images
             )
 
         dt_s = time.perf_counter() - start_loop_t
@@ -368,9 +354,7 @@ def record(
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_visualization(
-            cfg.display_mode, session_name="recording", ip=cfg.display_ip, port=cfg.display_port
-        )
+        init_rerun(session_name="recording", ip=cfg.display_ip, port=cfg.display_port)
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -480,7 +464,6 @@ def record(
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
-                    display_mode=cfg.display_mode,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -502,7 +485,6 @@ def record(
                         control_time_s=cfg.dataset.reset_time_s,
                         single_task=cfg.dataset.single_task,
                         display_data=cfg.display_data,
-                        display_mode=cfg.display_mode,
                     )
 
                 if events["rerecord_episode"]:
@@ -528,9 +510,6 @@ def record(
         if listener is not None:
             listener.stop()
 
-        if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
-
         if cfg.dataset.push_to_hub:
             if dataset and dataset.num_episodes > 0:
                 dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)

src/lerobot/scripts/lerobot_rollout.py

@@ -145,9 +145,6 @@ Usage examples
         --dataset.rgb_encoder.vcodec=h264 \\
         --dataset.rgb_encoder.preset=fast \\
         --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2}
-
-    # Stream to Foxglove instead of Rerun:
-    # add --display_mode=foxglove, then connect the Foxglove app to ws://127.0.0.1:8765.
 """
 
 import logging
@@ -193,7 +190,7 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.utils import init_logging
-from lerobot.utils.visualization_utils import init_visualization, shutdown_visualization
+from lerobot.utils.visualization_utils import init_rerun
 
 logger = logging.getLogger(__name__)
 
@@ -204,13 +201,8 @@ def rollout(cfg: RolloutConfig):
     init_logging()
 
     if cfg.display_data:
-        logger.info(
-            "Initializing %s visualization (ip=%s, port=%s)",
-            cfg.display_mode,
-            cfg.display_ip,
-            cfg.display_port,
-        )
-        init_visualization(cfg.display_mode, session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
+        logger.info("Initializing Rerun visualization (ip=%s, port=%s)", cfg.display_ip, cfg.display_port)
+        init_rerun(session_name="rollout", ip=cfg.display_ip, port=cfg.display_port)
 
     signal_handler = ProcessSignalHandler(use_threads=True, display_pid=False)
     shutdown_event = signal_handler.shutdown_event
@@ -235,8 +227,6 @@ def rollout(cfg: RolloutConfig):
         logger.info("Interrupted by user")
     finally:
         strategy.teardown(ctx)
-        if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
 
     logger.info("Rollout finished")
 

src/lerobot/scripts/lerobot_teleoperate.py

@@ -31,22 +31,6 @@ lerobot-teleoperate \
     --display_data=true
 ```
 
-To stream the data to Foxglove instead of Rerun, add ``--display_mode=foxglove``
-(then connect the Foxglove app to ``ws://127.0.0.1:8765``; override the port with ``--display_port=<port>``):
-
-```shell
-lerobot-teleoperate \
-    --robot.type=so101_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
-    --robot.id=black \
-    --teleop.type=so101_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=blue \
-    --display_data=true \
-    --display_mode=foxglove
-```
-
 Example teleoperation with bimanual so100:
 
 ```shell
@@ -124,11 +108,7 @@ from lerobot.teleoperators import (  # noqa: F401
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging, move_cursor_up
-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
 
 
 @dataclass
@@ -141,14 +121,11 @@ class TeleoperateConfig:
     teleop_time_s: float | None = None
     # Display all cameras on screen
     display_data: bool = False
-    # Visualization backend used when display_data is True: "rerun" or "foxglove".
-    display_mode: str = "rerun"
-    # For "rerun": IP of a remote server to send to. For "foxglove": interface to bind the WebSocket
-    # server to (127.0.0.1 for local only, 0.0.0.0 for all interfaces).
+    # Display data on a remote Rerun server
     display_ip: str | None = None
-    # For "rerun": port of the remote server. For "foxglove": port to bind the WebSocket server to.
+    # Port of the remote Rerun server
     display_port: int | None = None
-    # Whether to display compressed (JPEG) images instead of raw frames
+    # Whether to  display compressed images in Rerun
     display_compressed_images: bool = False
 
 
@@ -160,7 +137,6 @@ def teleop_loop(
     robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction],
     robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation],
     display_data: bool = False,
-    display_mode: str = "rerun",
     duration: float | None = None,
     display_compressed_images: bool = False,
 ):
@@ -173,10 +149,8 @@ def teleop_loop(
         teleop: The teleoperator device instance providing control actions.
         robot: The robot instance being controlled.
         fps: The target frequency for the control loop in frames per second.
-        display_data: If True, fetches robot observations and displays them in the console and the
-            visualization backend.
-        display_mode: Visualization backend to use when display_data is True ("rerun" or "foxglove").
-        display_compressed_images: If True, compresses images before sending them to the backend for display.
+        display_data: If True, fetches robot observations and displays them in the console and Rerun.
+        display_compressed_images: If True, compresses images before sending them to Rerun for display.
         duration: The maximum duration of the teleoperation loop in seconds. If None, the loop runs indefinitely.
         teleop_action_processor: An optional pipeline to process raw actions from the teleoperator.
         robot_action_processor: An optional pipeline to process actions before they are sent to the robot.
@@ -213,8 +187,7 @@ def teleop_loop(
             # Process robot observation through pipeline
             obs_transition = robot_observation_processor(obs)
 
-            log_visualization_data(
-                display_mode,
+            log_rerun_data(
                 observation=obs_transition,
                 action=teleop_action,
                 compress_images=display_compressed_images,
@@ -242,9 +215,7 @@ def teleoperate(cfg: TeleoperateConfig):
     init_logging()
     logging.info(pformat(asdict(cfg)))
     if cfg.display_data:
-        init_visualization(
-            cfg.display_mode, session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port
-        )
+        init_rerun(session_name="teleoperation", ip=cfg.display_ip, port=cfg.display_port)
     display_compressed_images = (
         True
         if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
@@ -264,7 +235,6 @@ def teleoperate(cfg: TeleoperateConfig):
             robot=robot,
             fps=cfg.fps,
             display_data=cfg.display_data,
-            display_mode=cfg.display_mode,
             duration=cfg.teleop_time_s,
             teleop_action_processor=teleop_action_processor,
             robot_action_processor=robot_action_processor,
@@ -275,7 +245,7 @@ def teleoperate(cfg: TeleoperateConfig):
         pass
     finally:
         if cfg.display_data:
-            shutdown_visualization(cfg.display_mode)
+            shutdown_rerun()
         teleop.disconnect()
         robot.disconnect()
 

src/lerobot/scripts/lerobot_train.py

@@ -20,7 +20,6 @@ Requires: pip install 'lerobot[training]'  (includes dataset + accelerate + wand
 
 import dataclasses
 import logging
-import sys
 import time
 from contextlib import nullcontext
 from pprint import pformat
@@ -42,17 +41,15 @@ from lerobot.common.train_utils import (
     load_training_batch_size,
     load_training_num_processes,
     load_training_state,
-    push_checkpoint_to_hub,
     save_checkpoint,
     update_last_checkpoint,
 )
 from lerobot.common.wandb_utils import WandBLogger
-from lerobot.configs import JobConfig, parser
+from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state
 from lerobot.datasets.factory import make_train_eval_datasets
 from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
-from lerobot.jobs import submit_to_hf
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.rewards import make_reward_pre_post_processors
@@ -191,9 +188,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         cfg: A `TrainPipelineConfig` object containing all training configurations.
         accelerator: Optional Accelerator instance. If None, one will be created automatically.
     """
-    if cfg.job.is_remote:
-        return submit_to_hf(cfg)
-
     from lerobot.utils.import_utils import require_package
 
     require_package("accelerate", extra="training")
@@ -211,12 +205,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
         # Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
         force_cpu = cfg.trainable_config.device == "cpu"
-        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32/absent -> launcher default).
-        policy_dtype = getattr(cfg.trainable_config, "dtype", None)
-        mixed_precision = {"bfloat16": "bf16", "float16": "fp16", "float32": "no"}.get(policy_dtype)
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
-            mixed_precision=mixed_precision,
             kwargs_handlers=[ddp_kwargs],
             cpu=force_cpu,
         )
@@ -665,12 +655,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     optim_state_dict=optim_state_dict,
                 )
                 update_last_checkpoint(checkpoint_dir)
-                if cfg.save_checkpoint_to_hub:
-                    push_checkpoint_to_hub(
-                        checkpoint_dir,
-                        cfg.policy.repo_id,
-                        private=cfg.policy.private,
-                    )
                 if wandb_logger:
                     wandb_logger.log_policy(checkpoint_dir)
 
@@ -740,9 +724,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             unwrapped_model = accelerator.unwrap_model(policy)
             # PEFT only applies when training a policy — reward models use the plain path.
             if not cfg.is_reward_model_training and cfg.policy.use_peft:
-                unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model, dataset_meta=dataset.meta)
+                unwrapped_model.push_model_to_hub(cfg, peft_model=unwrapped_model)
             else:
-                unwrapped_model.push_model_to_hub(cfg, state_dict=model_state_dict, dataset_meta=dataset.meta)
+                unwrapped_model.push_model_to_hub(cfg, state_dict=model_state_dict)
             preprocessor.push_to_hub(active_cfg.repo_id)
             postprocessor.push_to_hub(active_cfg.repo_id)
 
@@ -751,25 +735,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     accelerator.end_training()
 
 
-def _remote_target_in_argv() -> bool:
-    """True when the CLI requests a remote HF Jobs run (--job.target=<non-local>)."""
-    target = None
-    args = sys.argv[1:]
-    for i, tok in enumerate(args):
-        if tok == "--job.target" and i + 1 < len(args):
-            target = args[i + 1]
-        elif tok.startswith("--job.target="):
-            target = tok.split("=", 1)[1]
-    return JobConfig.is_remote_target(target)
-
-
 def main():
     register_third_party_plugins()
-    if _remote_target_in_argv():
-        # The policy device is resolved on the remote pod, not here, so silence the
-        # client-side "Device '...' is not available" warning PreTrainedConfig emits
-        # while parsing the config (it fires before train() can dispatch remotely).
-        logging.getLogger("lerobot.configs.policies").setLevel(logging.ERROR)
     train()
 
 

src/lerobot/teleoperators/rebot_102_leader/config_rebot_102_leader.py

@@ -65,7 +65,7 @@ class RebotArm102LeaderConfig:
     joint_ranges: dict[str, list[int]] = field(
         default_factory=lambda: {
             "shoulder_pan": [-150, 150],
-            "shoulder_lift": [-200, 1],
+            "shoulder_lift": [-170, 1],
             "elbow_flex": [-200, 1],
             "wrist_flex": [-80, 90],
             "wrist_yaw": [-90, 90],

src/lerobot/utils/constants.py

@@ -37,7 +37,6 @@ ACTION_TOKEN_MASK = ACTION + ".token_mask"
 REWARD = "next.reward"
 TRUNCATED = "next.truncated"
 DONE = "next.done"
-SUCCESS = "next.success"
 INFO = "info"
 
 ROBOTS = "robots"

src/lerobot/utils/foxglove_visualization.py

@@ -1,651 +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.
-
-"""Foxglove visualization backend.
-
-Live control-loop streaming (:func:`log_foxglove_data`) and seekable dataset playback
-(:func:`serve_foxglove_dataset_playback`) over a Foxglove WebSocket server. Callers usually select a
-backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
-importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
-"""
-
-import logging
-import numbers
-import time
-
-import cv2
-import numpy as np
-
-from lerobot.types import RobotAction, RobotObservation
-
-from .constants import (
-    ACTION,
-    ACTION_PREFIX,
-    DONE,
-    OBS_IMAGES,
-    OBS_PREFIX,
-    OBS_STATE,
-    OBS_STR,
-    REWARD,
-    SUCCESS,
-    TRUNCATED,
-)
-from .import_utils import require_package
-
-# Static schema shared by all scalar topics. Each message carries a flat list of ``{label, value}``
-# pairs rather than one field per feature, so the same schema fits any robot regardless of which
-# observation/action features it reports. The ``label`` field name is what Foxglove looks for to name
-# each series automatically, so a single filtered path plots every feature, e.g.
-# ``/observation/state.scalars[:]``.
-_SCALARS_SCHEMA = {
-    "type": "object",
-    "title": "lerobot.Scalars",
-    "properties": {
-        "scalars": {
-            "type": "array",
-            "items": {
-                "type": "object",
-                "properties": {
-                    "label": {"type": "string"},
-                    "value": {"type": "number"},
-                },
-            },
-        }
-    },
-}
-
-
-def _is_scalar(x):
-    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
-        isinstance(x, np.ndarray) and x.ndim == 0
-    )
-
-
-def init_foxglove(host: str = "127.0.0.1", port: int | None = 8765) -> None:
-    """
-    Starts a Foxglove WebSocket server for visualizing the control loop.
-
-    Connect to it from the Foxglove app at ``ws://<host>:<port>``. Calling this
-    more than once is a no-op while a server is already running.
-
-    Args:
-        host: Host interface to bind the WebSocket server to.
-        port: Port to bind the WebSocket server to (defaults to 8765).
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-    import foxglove
-
-    # Live-stream state lives as attributes on ``log_foxglove_data``:
-    # ``.server`` is the shared WebSocket server and
-    # ``.channels`` caches one Foxglove channel per topic
-    if getattr(log_foxglove_data, "server", None) is not None:
-        return
-    log_foxglove_data.server = foxglove.start_server(host=host, port=port or 8765)
-    log_foxglove_data.channels = {}
-
-
-def shutdown_foxglove() -> None:
-    """Stops the Foxglove WebSocket server and clears cached channels."""
-
-    server = getattr(log_foxglove_data, "server", None)
-    if server is not None:
-        server.stop()
-    log_foxglove_data.server = None
-    log_foxglove_data.channels = {}
-
-
-def _foxglove_safe_name(name: str) -> str:
-    """Replace ``.`` with ``_`` so a feature name is a single Foxglove topic-path segment.
-
-    Foxglove treats ``.`` as a path separator, so an unsanitized name like ``observation.images.front``
-    would split into nested segments instead of naming one topic.
-    """
-
-    return name.replace(".", "_")
-
-
-def _foxglove_topic(key: str, *, is_image: bool = False) -> str:
-    """Build the Foxglove topic for a feature ``key``.
-
-    Camera features map to a per-source image topic (``/observation/images/<name>``); scalar features
-    share one aggregate topic per source: ``/observation/state`` for observations, ``/action/state``
-    for actions.
-    """
-
-    if is_image:
-        name = str(key)
-        for prefix in (f"{OBS_IMAGES}.", OBS_PREFIX):
-            if name.startswith(prefix):
-                name = name[len(prefix) :]
-                break
-        return f"/{OBS_STR}/images/{_foxglove_safe_name(name)}"
-    source = ACTION if (str(key).startswith(ACTION_PREFIX) or str(key) == ACTION) else OBS_STR
-    return f"/{source}/state"
-
-
-def _log_foxglove_scalars(
-    topic: str, values: dict[str, float], *, channels: dict | None = None, log_time: int | None = None
-) -> None:
-    """Log scalars on a typed JSON channel using the static :data:`_SCALARS_SCHEMA`.
-
-    ``values`` is an ordered mapping of feature name to value; it is emitted as a ``scalars`` array of
-    ``{label, value}`` objects. Insertion order is preserved so series stay stable across messages.
-
-    ``channels`` is the per-topic channel cache to reuse (defaults to the live-stream cache on
-    :func:`log_foxglove_data`; dataset playback passes its own local cache to stay self-contained).
-    ``log_time`` is the message time in nanoseconds; when ``None`` the server's receive time is used.
-    """
-
-    if not values:
-        return
-
-    import foxglove
-
-    if channels is None:
-        channels = log_foxglove_data.channels
-    channel = channels.get(topic)
-    if channel is None:
-        channel = channels[topic] = foxglove.Channel(topic, schema=_SCALARS_SCHEMA, message_encoding="json")
-    msg = {"scalars": [{"label": label, "value": value} for label, value in values.items()]}
-    if log_time is None:
-        channel.log(msg)
-    else:
-        channel.log(msg, log_time=log_time)
-
-
-def _labeled_scalars(name: str, values, labels: list[str] | None = None) -> dict[str, float]:
-    """Expand a 1D sequence into ``{label: value}`` entries with a consistent fallback."""
-
-    flat = [float(v) for v in values]
-    if labels is None or len(labels) != len(flat):
-        labels = [f"{name}_{i}" for i in range(len(flat))]
-    return dict(zip(labels, flat, strict=True))
-
-
-def _log_foxglove_image(
-    topic: str,
-    frame_id: str,
-    arr: np.ndarray,
-    *,
-    compress_images: bool,
-    channels: dict | None = None,
-    log_time: int | None = None,
-    depth_range: tuple[float, float] | None = None,
-    raw_depth_values: bool = False,
-) -> None:
-    """Log an image on a cached per-topic channel.
-
-    The encoding is chosen from the channel count and dtype: a single-channel ``float`` or ``uint16``
-    frame is a depth map (``32FC1``/``16UC1``), single-channel ``uint8`` is ``mono8``, 3 => ``rgb8``
-    (float input assumed in [0, 1], cast to uint8), 4 => ``rgba8``; other counts are skipped with a
-    warning. When ``compress_images`` is set, ``rgb8`` is JPEG-encoded instead.
-
-    Args:
-        topic: Foxglove topic to log on.
-        frame_id: Frame id stamped on the message.
-        arr: Image as HWC or CHW (CHW is transposed to HWC), any dtype.
-        compress_images: JPEG-encode ``rgb8`` frames; ignored for other encodings.
-        channels: Per-topic channel cache to reuse (see :func:`_log_foxglove_scalars`).
-        log_time: Message time in nanoseconds, also written to the header timestamp; when ``None``
-            the server's receive time is used.
-        depth_range: ``(lo, hi)`` clip bounds in a depth frame's own input units. Depth frames
-            (``32FC1``/``16UC1``) are rescaled onto Foxglove's default display max for their encoding
-            (``1.0`` / ``10000``) so they show with sensible contrast; ``depth_range`` sets the source
-            range, else the frame's own min/max is used. Ignored for ``mono8``/``rgb8``/``rgba8``.
-        raw_depth_values: If True, depth values are not rescaled and are logged as is.
-    """
-
-    from foxglove.channels import CompressedImageChannel, RawImageChannel
-    from foxglove.messages import CompressedImage, RawImage, Timestamp
-
-    if channels is None:
-        channels = log_foxglove_data.channels
-    time_ns = time.time_ns() if log_time is None else log_time
-    timestamp = Timestamp(sec=time_ns // 1_000_000_000, nsec=time_ns % 1_000_000_000)
-    log_kwargs = {} if log_time is None else {"log_time": log_time}
-
-    # Convert CHW -> HWC when needed (mirrors log_rerun_data).
-    if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
-        arr = np.transpose(arr, (1, 2, 0))
-    height, width = arr.shape[0], arr.shape[1]
-    n_channels = 1 if arr.ndim == 2 else arr.shape[2]
-
-    if n_channels == 1 and arr.dtype != np.uint8:
-        # Depth map: infer the encoding from the dtype.
-        encoding, target_dtype, value_max = (
-            ("32FC1", np.float32, 1.0)
-            if np.issubdtype(arr.dtype, np.floating)
-            else ("16UC1", np.uint16, 10000.0)
-        )
-        if not raw_depth_values:
-            # Rescale onto the encoding's display max with respect to the given depth_range.
-            lo, hi = depth_range if depth_range is not None else (float(arr.min()), float(arr.max()))
-            arr = arr.clip(lo, hi).astype(np.float32)
-            arr = (arr - lo) / ((hi - lo) if hi > lo else 1.0) * value_max
-        arr = np.ascontiguousarray(arr, dtype=target_dtype)
-    else:
-        if n_channels == 3 and np.issubdtype(arr.dtype, np.floating):
-            arr = (arr * 255.0).clip(0, 255)
-        arr = np.ascontiguousarray(arr, dtype=np.uint8)
-
-        if compress_images and n_channels == 3:
-            buf_src = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
-            _, buf = cv2.imencode(".jpg", buf_src)
-            channel = channels.get(topic)
-            if channel is None:
-                channel = channels[topic] = CompressedImageChannel(topic=topic)
-            channel.log(
-                CompressedImage(timestamp=timestamp, frame_id=frame_id, data=buf.tobytes(), format="jpeg"),
-                **log_kwargs,
-            )
-            return
-
-        encoding = {1: "mono8", 3: "rgb8", 4: "rgba8"}.get(n_channels)
-        if encoding is None:
-            logging.warning(
-                "Foxglove: skipping image on topic '%s' with unsupported shape %s (%d channels); "
-                "expected 1 (mono8/16UC1/32FC1), 3 (rgb8), or 4 (rgba8) channels.",
-                topic,
-                tuple(arr.shape),
-                n_channels,
-            )
-            return
-
-    channel = channels.get(topic)
-    if channel is None:
-        channel = channels[topic] = RawImageChannel(topic=topic)
-    channel.log(
-        RawImage(
-            timestamp=timestamp,
-            frame_id=frame_id,
-            width=width,
-            height=height,
-            encoding=encoding,
-            step=width * n_channels * arr.itemsize,
-            data=arr.tobytes(),
-        ),
-        **log_kwargs,
-    )
-
-
-def log_foxglove_data(
-    observation: RobotObservation | None = None,
-    action: RobotAction | None = None,
-    compress_images: bool = False,
-) -> None:
-    """
-    Logs observation and action data to a Foxglove WebSocket server for real-time visualization.
-
-    Mirrors ``log_rerun_data`` but emits Foxglove messages over the server started by
-    :func:`init_foxglove`. Data is mapped as follows:
-    - Scalars (and elements of 1D arrays) are accumulated per source and logged on the
-      ``/observation/state`` and ``/action/state`` topics as typed JSON messages using the static
-      ``lerobot.Scalars`` schema: a ``scalars`` array of ``{label, value}`` objects (see
-      :data:`_SCALARS_SCHEMA`). The ``label`` field lets Foxglove name each series automatically, so
-      ``/observation/state.scalars[:].value`` plots every feature at once.
-    - 3D NumPy arrays that resemble images are transposed from CHW to HWC when needed and logged on a
-      per-source topic (e.g. ``/observation/images/front``) as a ``RawImage`` (or a JPEG
-      ``CompressedImage`` when ``compress_images`` is True).
-
-    Args:
-        observation: An optional dictionary containing observation data to log.
-        action: An optional dictionary containing action data to log.
-        compress_images: Whether to JPEG-compress images before logging to save bandwidth in exchange
-            for CPU and quality.
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-
-    if getattr(log_foxglove_data, "server", None) is None:
-        raise RuntimeError("init_foxglove() must be called before log_foxglove_data().")
-
-    now = time.time_ns()
-
-    if observation:
-        obs_scalars: dict[str, float] = {}
-        for k, v in observation.items():
-            if v is None:
-                continue
-            key = k[len(OBS_PREFIX) :] if str(k).startswith(OBS_PREFIX) else str(k)
-            if _is_scalar(v):
-                obs_scalars[key] = float(v)
-            elif isinstance(v, np.ndarray):
-                if v.ndim == 1:
-                    obs_scalars.update(_labeled_scalars(key, v))
-                else:
-                    _log_foxglove_image(
-                        _foxglove_topic(k, is_image=True),
-                        key,
-                        v,
-                        compress_images=compress_images,
-                        log_time=now,
-                    )
-        _log_foxglove_scalars(_foxglove_topic(OBS_STATE), obs_scalars, log_time=now)
-
-    if action:
-        action_scalars: dict[str, float] = {}
-        for k, v in action.items():
-            if v is None:
-                continue
-            key = k[len(ACTION_PREFIX) :] if str(k).startswith(ACTION_PREFIX) else str(k)
-            if _is_scalar(v):
-                action_scalars[key] = float(v)
-            elif isinstance(v, np.ndarray):
-                action_scalars.update(_labeled_scalars(key, v.flatten()))
-        _log_foxglove_scalars(_foxglove_topic(ACTION), action_scalars, log_time=now)
-
-
-# ── Dataset playback over a Foxglove WebSocket server ─────────────────────
-# A LeRobotDataset is random-access on disk, so rather than fire-and-forget a forward stream we
-# advertise a seekable timeline and serve frames on demand for whatever time the user scrubs/plays
-# to in the Foxglove app. This relies on the SDK's PlaybackControl capability.
-
-
-def _feature_dim_names(feature: dict | None) -> list[str] | None:
-    """Best-effort per-dimension series labels for a 1D feature, or ``None`` to fall back to indices.
-
-    LeRobot records a feature's ``names`` inconsistently: a flat list (``["x", "y"]``), a category
-    mapping (``{"motors": ["motor_0", "motor_1"]}``), or a name->index mapping
-    (``{"delta_x": 0, "delta_y": 1}``). Each is handled, but labels are only returned when their count
-    matches the feature's 1D shape, so a malformed/mismatched ``names`` can't silently mislabel series.
-    """
-
-    if not feature:
-        return None
-    shape = feature.get("shape")
-    dim = shape[0] if shape and len(shape) == 1 else None
-    names = feature.get("names")
-    labels: list[str] | None = None
-    if isinstance(names, dict):
-        values = list(names.values())
-        if values and all(isinstance(v, (list, tuple)) for v in values):
-            labels = [str(n) for group in values for n in group]
-        elif values and all(isinstance(v, int) and not isinstance(v, bool) for v in values):
-            labels = [name for name, _ in sorted(names.items(), key=lambda kv: kv[1])]
-    elif isinstance(names, (list, tuple)):
-        labels = [str(n) for n in names]
-    if labels is not None and dim is not None and len(labels) == dim:
-        return labels
-    return None
-
-
-def _frame_to_scalars(sample: dict, key: str, labels: list[str] | None = None) -> dict[str, float]:
-    """Flatten a frame's vector/scalar feature ``key`` into ``{label: value}`` entries.
-
-    ``labels`` provides one name per dimension (from the dataset's feature metadata); when absent or
-    the wrong length, dimensions fall back to ``{name}_{i}`` (the short feature name), matching the
-    live stream so series names agree. A scalar feature becomes a single entry. Missing or ``None``
-    features yield an empty mapping.
-    """
-
-    v = sample.get(key)
-    if v is None:
-        return {}
-    arr = v.numpy() if hasattr(v, "numpy") else np.asarray(v)
-    if key.startswith(OBS_PREFIX):
-        name = key[len(OBS_PREFIX) :]
-    elif key.startswith(ACTION_PREFIX):
-        name = key[len(ACTION_PREFIX) :]
-    else:
-        name = key
-    if arr.ndim == 0:
-        return {name: float(arr)}
-    return _labeled_scalars(name, arr.flatten(), labels)
-
-
-def serve_foxglove_dataset_playback(
-    dataset,
-    episode_index: int,
-    *,
-    host: str = "127.0.0.1",
-    port: int = 8765,
-    compress_images: bool = False,
-    autoplay: bool = True,
-) -> None:
-    """Serve a single dataset episode to Foxglove as a seekable, scrubbable timeline.
-
-    Starts a Foxglove WebSocket server advertising the ``PlaybackControl`` capability over the
-    episode's time range. The Foxglove app drives play/pause/seek/speed; a background thread and a
-    ``ServerListener`` read frames from the on-disk ``dataset`` on demand and log them stamped at
-    their dataset timestamps, so the user can scrub anywhere in the episode. Blocks until interrupted.
-
-    Args:
-        dataset: A ``LeRobotDataset`` loaded for the single episode to visualize.
-        episode_index: Index of the episode being visualized (used only for the session name).
-        host: Host interface to bind the WebSocket server to.
-        port: Port to bind the WebSocket server to.
-        compress_images: Whether to JPEG-compress camera frames before logging.
-        autoplay: If True, start playing automatically as soon as a client connects, instead of
-            waiting for the user to press play in the Foxglove app.
-    """
-
-    require_package("foxglove-sdk", extra="viz", import_name="foxglove")
-    import bisect
-    import threading
-
-    import foxglove
-    from foxglove.websocket import (
-        Capability,
-        PlaybackCommand,
-        PlaybackControlRequest,
-        PlaybackState,
-        PlaybackStatus,
-        ServerListener,
-    )
-
-    # Per-frame timestamps in nanoseconds (read straight from the table, no video decode).
-    times_ns = [int(round(float(t) * 1e9)) for t in dataset.hf_dataset["timestamp"]]
-    n_frames = len(times_ns)
-    if n_frames == 0:
-        raise ValueError("Cannot visualize an empty episode.")
-    first_ns, last_ns = times_ns[0], times_ns[-1]
-    camera_keys = list(dataset.meta.camera_keys)
-    # Dataset-wide q01/q99 depth bounds (fallback min/max) used to normalize depth to [0, 1].
-    depth_ranges: dict[str, tuple[float, float]] = {}
-    for key in dataset.meta.depth_keys:
-        stats = (dataset.meta.stats or {}).get(key)
-        if not stats:
-            continue
-        lo = stats["q01"] if "q01" in stats else stats["min"]
-        hi = stats["q99"] if "q99" in stats else stats["max"]
-        depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
-    # Per-dimension series labels from the dataset metadata (e.g. joint names), computed once.
-    scalar_labels = {
-        OBS_STATE: _feature_dim_names(dataset.meta.features.get(OBS_STATE)),
-        ACTION: _feature_dim_names(dataset.meta.features.get(ACTION)),
-    }
-    # Local channel cache so the playback server is self-contained and doesn't touch the live-stream cache.
-    channels: dict = {}
-
-    def emit_frame(i: int) -> None:
-        """Log every channel for frame ``i`` stamped at its dataset timestamp."""
-        sample = dataset[i]
-        log_time = times_ns[i]
-        for key in camera_keys:
-            arr = sample.get(key)
-            if arr is None:
-                continue
-            arr = arr.numpy() if hasattr(arr, "numpy") else np.asarray(arr)
-            _log_foxglove_image(
-                _foxglove_topic(key, is_image=True),
-                key,
-                arr,
-                compress_images=compress_images,
-                channels=channels,
-                log_time=log_time,
-                depth_range=depth_ranges.get(key),
-                raw_depth_values=True,
-            )
-        _log_foxglove_scalars(
-            _foxglove_topic(OBS_STATE),
-            _frame_to_scalars(sample, OBS_STATE, scalar_labels[OBS_STATE]),
-            channels=channels,
-            log_time=log_time,
-        )
-        _log_foxglove_scalars(
-            _foxglove_topic(ACTION),
-            _frame_to_scalars(sample, ACTION, scalar_labels[ACTION]),
-            channels=channels,
-            log_time=log_time,
-        )
-        episode_scalars = {}
-        for feat, label in (
-            (DONE, "done"),
-            (TRUNCATED, "truncated"),
-            (REWARD, "reward"),
-            (SUCCESS, "success"),
-        ):
-            v = sample.get(feat)
-            if v is not None:
-                episode_scalars[label] = float(v)
-        _log_foxglove_scalars("/episode/state", episode_scalars, channels=channels, log_time=log_time)
-
-    lock = threading.Lock()
-    stop_event = threading.Event()
-    # Shared playback state, guarded by ``lock``. ``seek_idx`` is a one-shot request set by the
-    # listener and serviced by the playback loop, which is the *only* thread that emits frames (so
-    # concurrent random access into the on-disk dataset / video decoder never overlaps).
-    state = {
-        "status": PlaybackStatus.Paused,
-        "cursor": first_ns,
-        "speed": 1.0,
-        "last_idx": -1,
-        "seek_idx": None,
-    }
-
-    def index_at(t_ns: int) -> int:
-        return max(0, min(n_frames - 1, bisect.bisect_right(times_ns, t_ns) - 1))
-
-    # One-shot latch so autoplay fires only on the first client subscription.
-    autoplay_started = threading.Event()
-
-    class _PlaybackListener(ServerListener):
-        def on_subscribe(self, client, channel):
-            # Start playing automatically once a client actually connects (subscribes). Using the
-            # subscribe hook, rather than starting in Playing up front, means the timeline doesn't
-            # advance before anyone is watching. Fires once; the user can still pause/seek after.
-            if not autoplay:
-                return
-            with lock:
-                if autoplay_started.is_set() or state["status"] != PlaybackStatus.Paused:
-                    return
-                autoplay_started.set()
-                state["status"] = PlaybackStatus.Playing
-                cursor, speed = state["cursor"], state["speed"]
-            server.broadcast_playback_state(PlaybackState(PlaybackStatus.Playing, cursor, speed, False, ""))
-
-        def on_playback_control_request(self, req: PlaybackControlRequest):
-            # Only mutate state here; the playback loop performs all frame emission.
-            with lock:
-                did_seek = False
-                if req.seek_time is not None:
-                    cursor = max(first_ns, min(last_ns, req.seek_time))
-                    state["cursor"] = cursor
-                    state["last_idx"] = state["seek_idx"] = index_at(cursor)
-                    did_seek = True
-                if req.playback_speed and req.playback_speed > 0:
-                    state["speed"] = req.playback_speed
-                if req.playback_command == PlaybackCommand.Play:
-                    # Restarting from the end replays from the beginning.
-                    if state["cursor"] >= last_ns:
-                        state["cursor"] = first_ns
-                        state["last_idx"] = state["seek_idx"] = 0
-                        did_seek = True
-                    state["status"] = PlaybackStatus.Playing
-                elif req.playback_command == PlaybackCommand.Pause:
-                    state["status"] = PlaybackStatus.Paused
-                status, cursor, speed = state["status"], state["cursor"], state["speed"]
-                request_id = req.request_id or ""
-            return PlaybackState(status, cursor, speed, did_seek, request_id)
-
-    server = foxglove.start_server(
-        name=f"{dataset.repo_id}/episode_{episode_index}",
-        host=host,
-        port=port,
-        capabilities=[Capability.PlaybackControl, Capability.Time],
-        server_listener=_PlaybackListener(),
-        playback_time_range=(first_ns, last_ns),
-    )
-
-    def playback_loop() -> None:
-        # Cap how far the cursor may advance in a single tick. A slow frame decode (or any stall)
-        # would otherwise make ``dt`` huge and produce one enormous catch-up batch; clamping it makes
-        # playback trail wall-clock under a slow decoder while each tick emits a bounded frame range.
-        max_tick_dt_s = 0.25
-        prev = time.monotonic()
-        while not stop_event.is_set():
-            time.sleep(1.0 / 60.0)
-            ended = False
-            speed = 1.0
-            with lock:
-                now = time.monotonic()
-                dt = min(now - prev, max_tick_dt_s)
-                prev = now
-                # A queued seek is always serviced, even while paused, so scrubbing updates the view.
-                work = []
-                seek_idx = state["seek_idx"]
-                if seek_idx is not None:
-                    state["seek_idx"] = None
-                    work.append(seek_idx)
-                if state["status"] == PlaybackStatus.Playing:
-                    cursor = state["cursor"] + int(dt * 1e9 * state["speed"])
-                    start_idx = state["last_idx"] + 1
-                    if cursor >= last_ns:
-                        cursor, target, ended = last_ns, n_frames - 1, True
-                    else:
-                        target = index_at(cursor)
-                    state["cursor"] = cursor
-                    work.extend(range(start_idx, target + 1))
-                    # cursor only grows while playing (seeks reset last_idx in the listener), so
-                    # target >= last_idx here; a plain assignment is correct and clearer than max().
-                    state["last_idx"] = target
-                    if ended:
-                        state["status"] = PlaybackStatus.Ended
-                if not work:
-                    continue
-                cursor, speed = state["cursor"], state["speed"]
-            # Emit outside the lock; this is the only thread that calls emit_frame. Re-check
-            # stop_event between frames so shutdown stays responsive even mid-batch.
-            for i in work:
-                if stop_event.is_set():
-                    break
-                emit_frame(i)
-            server.broadcast_time(cursor)
-            if ended:
-                server.broadcast_playback_state(PlaybackState(PlaybackStatus.Ended, cursor, speed, False, ""))
-
-    # Emit the first frame so channels are advertised (done before the loop starts, so emission stays
-    # single-threaded). Late-connecting clients re-receive frames once they seek/play.
-    emit_frame(0)
-    with lock:
-        state["last_idx"] = 0
-    server.broadcast_time(first_ns)
-    server.broadcast_playback_state(PlaybackState(PlaybackStatus.Paused, first_ns, 1.0, True, ""))
-
-    thread = threading.Thread(target=playback_loop, name="foxglove-playback", daemon=True)
-    thread.start()
-
-    print(f"Foxglove server running. Connect the Foxglove app to ws://{host}:{port}")
-    print("Use the playback controls in Foxglove to play/pause and scrub the episode. Ctrl-C to exit.")
-    try:
-        while not stop_event.is_set():
-            time.sleep(0.5)
-    except KeyboardInterrupt:
-        print("Ctrl-C received. Exiting.")
-    finally:
-        stop_event.set()
-        thread.join(timeout=2.0)
-        server.stop()
-        channels.clear()

src/lerobot/utils/hub.py

@@ -20,33 +20,9 @@ from typing import Any, TypeVar
 from huggingface_hub import HfApi
 from huggingface_hub.utils import validate_hf_hub_args
 
-from .constants import CHECKPOINTS_DIR
-
 T = TypeVar("T", bound="HubMixin")
 
 
-def find_latest_hub_checkpoint(
-    repo_id: str,
-    *,
-    token: str | bool | None = None,
-    revision: str | None = None,
-) -> str | None:
-    """Repo-relative path of the most recent checkpoint in a training repo.
-
-    Training runs push checkpoints to ``checkpoints/<step>/`` (see
-    ``push_checkpoint_to_hub``). This lists those step dirs and returns
-    ``checkpoints/<highest-step>``, or ``None`` if the repo has no checkpoints.
-    """
-    files = HfApi().list_repo_files(repo_id=repo_id, repo_type="model", revision=revision, token=token)
-    prefix = f"{CHECKPOINTS_DIR}/"
-    steps = {
-        name for f in files if f.startswith(prefix) and (name := f[len(prefix) :].split("/", 1)[0]).isdigit()
-    }
-    if not steps:
-        return None
-    return f"{CHECKPOINTS_DIR}/{max(steps, key=int)}"
-
-
 class HubMixin:
     """
     A Mixin containing the functionality to push an object to the hub.

src/lerobot/utils/rerun_visualization.py

@@ -1,191 +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.
-
-"""Rerun visualization backend.
-
-Live control-loop streaming to the Rerun viewer (:func:`log_rerun_data`). Callers usually select a
-backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
-importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
-"""
-
-import numbers
-import os
-
-import numpy as np
-
-from lerobot.configs import DEPTH_MILLIMETER_UNIT, infer_depth_unit
-from lerobot.types import RobotAction, RobotObservation
-
-from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
-from .import_utils import require_package
-
-
-def _is_scalar(x):
-    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
-        isinstance(x, np.ndarray) and x.ndim == 0
-    )
-
-
-def init_rerun(
-    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
-) -> None:
-    """
-    Initializes the Rerun SDK for visualizing the control loop.
-
-    Args:
-        session_name: Name of the Rerun session.
-        ip: Optional IP for connecting to a Rerun server.
-        port: Optional port for connecting to a Rerun server.
-    """
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    log_rerun_data.blueprint = None  # Reset blueprint cache for new session
-
-    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
-    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
-    rr.init(session_name)
-    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
-    if ip and port:
-        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
-    else:
-        rr.spawn(memory_limit=memory_limit)
-
-
-def shutdown_rerun() -> None:
-    """Shuts down the Rerun SDK gracefully."""
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    rr.rerun_shutdown()
-
-
-def _build_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]):
-    """Build a Rerun blueprint laying out camera images, observation and action scalars in separate views.
-
-    Camera images, observation and action scalars are arranged in a grid.
-    """
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun.blueprint as rrb
-
-    views = [rrb.Spatial2DView(origin=path, name=path) for path in sorted(image_paths)]
-
-    if observation_paths:
-        views.append(rrb.TimeSeriesView(name="observation", contents=sorted(observation_paths)))
-    if action_paths:
-        views.append(rrb.TimeSeriesView(name="action", contents=sorted(action_paths)))
-
-    return rrb.Blueprint(rrb.Grid(*views))
-
-
-def _ensure_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]) -> None:
-    """Build and send the blueprint once, from the first observation and action data."""
-    if getattr(log_rerun_data, "blueprint", None) is not None:
-        return
-
-    if not (observation_paths or action_paths or image_paths):
-        return
-
-    # Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
-    import rerun as rr
-
-    blueprint = _build_blueprint(observation_paths, action_paths, image_paths)
-    log_rerun_data.blueprint = blueprint
-    rr.send_blueprint(blueprint)
-
-
-def log_rerun_data(
-    observation: RobotObservation | None = None,
-    action: RobotAction | None = None,
-    compress_images: bool = False,
-) -> None:
-    """
-    Logs observation and action data to Rerun for real-time visualization.
-
-    This function iterates through the provided observation and action dictionaries and sends their contents
-    to the Rerun viewer. It handles different data types appropriately:
-    - Scalars values (floats, ints) are logged as `rr.Scalars`.
-    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
-      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
-    - 1D NumPy arrays are logged as a single `rr.Scalars` batch under one entity path, so that every
-      dimension shares the same view instead of being split across one view per element.
-    - Multi-dimensional **action** arrays are flattened and logged as a single `rr.Scalars` batch.
-
-    Keys are automatically namespaced with "observation." or "action." if not already present.
-
-    On the first call, a blueprint is built and sent so observation and action scalars get separate
-    time-series views and each image gets its own spatial view.
-
-    Args:
-        observation: An optional dictionary containing observation data to log.
-        action: An optional dictionary containing action data to log.
-        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
-    """
-
-    require_package("rerun-sdk", extra="viz", import_name="rerun")
-    import rerun as rr
-
-    observation_paths: set[str] = set()
-    action_paths: set[str] = set()
-    image_paths: set[str] = set()
-
-    if observation:
-        for k, v in observation.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                observation_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                arr = v
-                # Convert CHW -> HWC when needed
-                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
-                    arr = np.transpose(arr, (1, 2, 0))
-                if arr.ndim == 1:
-                    rr.log(key, rr.Scalars(arr.astype(float)))
-                    observation_paths.add(key)
-                else:
-                    if arr.shape[-1] == 1:
-                        # At record time, the depth unit is inferred from the frame type.
-                        depth_unit = infer_depth_unit(arr.dtype)
-                        img_entity = rr.DepthImage(
-                            arr,
-                            meter=1000.0 if depth_unit == DEPTH_MILLIMETER_UNIT else 1.0,
-                            colormap=rr.components.Colormap.Viridis,
-                        )
-                    else:
-                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
-                    rr.log(key, entity=img_entity, static=True)
-                    image_paths.add(key)
-
-    if action:
-        for k, v in action.items():
-            if v is None:
-                continue
-            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
-
-            if _is_scalar(v):
-                rr.log(key, rr.Scalars(float(v)))
-                action_paths.add(key)
-            elif isinstance(v, np.ndarray):
-                # Flatten any (incl. higher-dimensional) array into a single batched Scalars
-                rr.log(key, rr.Scalars(v.reshape(-1).astype(float)))
-                action_paths.add(key)
-
-    _ensure_blueprint(observation_paths, action_paths, image_paths)

src/lerobot/utils/visualization_utils.py

@@ -12,68 +12,121 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Backend-agnostic visualization dispatch.
+import numbers
+import os
 
-Selects a visualization backend at runtime via a display-mode string (e.g. a ``--display_mode`` CLI
-flag) so callers never branch on the backend. The concrete implementations live in
-:mod:`lerobot.utils.rerun_visualization` and :mod:`lerobot.utils.foxglove_visualization`; importing
-this module does not import ``rerun`` or ``foxglove`` (each backend imports its SDK lazily behind a
-``require_package`` guard).
-"""
+import numpy as np
 
 from lerobot.types import RobotAction, RobotObservation
 
-from .foxglove_visualization import init_foxglove, log_foxglove_data, shutdown_foxglove
-from .rerun_visualization import init_rerun, log_rerun_data, shutdown_rerun
-
-# Visualization backends selectable at runtime via a display-mode string (e.g. a --display_mode flag).
-VISUALIZATION_MODES = ("rerun", "foxglove")
+from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
+from .import_utils import require_package
 
 
-def init_visualization(
-    display_mode: str,
-    *,
-    session_name: str = "lerobot_control_loop",
-    ip: str | None = None,
-    port: int | None = None,
+def init_rerun(
+    session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
 ) -> None:
-    """Initializes the visualization backend selected by ``display_mode``.
+    """
+    Initializes the Rerun SDK for visualizing the control loop.
 
-    For ``"rerun"``, ``ip``/``port`` point at an optional remote Rerun server. For ``"foxglove"``,
-    ``ip`` is the interface to bind the WebSocket server to (``127.0.0.1`` for local only, ``0.0.0.0``
-    for all interfaces) and ``port`` is its port.
+    Args:
+        session_name: Name of the Rerun session.
+        ip: Optional IP for connecting to a Rerun server.
+        port: Optional port for connecting to a Rerun server.
     """
 
-    if display_mode == "rerun":
-        init_rerun(session_name=session_name, ip=ip, port=port)
-    elif display_mode == "foxglove":
-        init_foxglove(host=ip or "127.0.0.1", port=port)
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
+    os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
+    rr.init(session_name)
+    memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
+    if ip and port:
+        rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
     else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+        rr.spawn(memory_limit=memory_limit)
 
 
-def log_visualization_data(
-    display_mode: str,
+def shutdown_rerun() -> None:
+    """Shuts down the Rerun SDK gracefully."""
+
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    rr.rerun_shutdown()
+
+
+def _is_scalar(x):
+    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
+        isinstance(x, np.ndarray) and x.ndim == 0
+    )
+
+
+def log_rerun_data(
     observation: RobotObservation | None = None,
     action: RobotAction | None = None,
     compress_images: bool = False,
 ) -> None:
-    """Logs observation/action data to the backend selected by ``display_mode``."""
+    """
+    Logs observation and action data to Rerun for real-time visualization.
 
-    if display_mode == "rerun":
-        log_rerun_data(observation=observation, action=action, compress_images=compress_images)
-    elif display_mode == "foxglove":
-        log_foxglove_data(observation=observation, action=action, compress_images=compress_images)
-    else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+    This function iterates through the provided observation and action dictionaries and sends their contents
+    to the Rerun viewer. It handles different data types appropriately:
+    - Scalars values (floats, ints) are logged as `rr.Scalars`.
+    - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
+      from CHW to HWC format, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
+    - 1D NumPy arrays are logged as a series of individual scalars, with each element indexed.
+    - Other multi-dimensional arrays are flattened and logged as individual scalars.
 
+    Keys are automatically namespaced with "observation." or "action." if not already present.
 
-def shutdown_visualization(display_mode: str) -> None:
-    """Shuts down the backend selected by ``display_mode``."""
+    Args:
+        observation: An optional dictionary containing observation data to log.
+        action: An optional dictionary containing action data to log.
+        compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
+    """
 
-    if display_mode == "rerun":
-        shutdown_rerun()
-    elif display_mode == "foxglove":
-        shutdown_foxglove()
-    else:
-        raise ValueError(f"Unknown display_mode '{display_mode}'. Expected one of {VISUALIZATION_MODES}.")
+    require_package("rerun-sdk", extra="viz", import_name="rerun")
+    import rerun as rr
+
+    if observation:
+        for k, v in observation.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+            elif isinstance(v, np.ndarray):
+                arr = v
+                # Convert CHW -> HWC when needed
+                if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
+                    arr = np.transpose(arr, (1, 2, 0))
+                if arr.ndim == 1:
+                    for i, vi in enumerate(arr):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                else:
+                    if arr.shape[-1] == 1:
+                        img_entity = rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
+                    else:
+                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
+                    rr.log(key, entity=img_entity, static=True)
+
+    if action:
+        for k, v in action.items():
+            if v is None:
+                continue
+            key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
+
+            if _is_scalar(v):
+                rr.log(key, rr.Scalars(float(v)))
+            elif isinstance(v, np.ndarray):
+                if v.ndim == 1:
+                    for i, vi in enumerate(v):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
+                else:
+                    # Fall back to flattening higher-dimensional arrays
+                    flat = v.flatten()
+                    for i, vi in enumerate(flat):
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))

tests/configs/test_resume_from_hub.py

@@ -1,68 +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.
-
-import pytest
-
-import lerobot.configs.train as tc
-from lerobot.configs.train import TrainPipelineConfig
-
-
-class _FakeHTTPError(tc.HfHubHTTPError):
-    """HfHubHTTPError that can be raised without a real HTTP response object."""
-
-    def __init__(self):
-        pass
-
-
-def test_from_pretrained_falls_back_to_latest_checkpoint_config(tmp_path, monkeypatch):
-    """A Hub repo with no root train_config.json (an interrupted run that only pushed
-    checkpoints/) resolves via the latest checkpoint's config."""
-    # A real train_config.json written by save_pretrained, to be returned by the fallback.
-    parsed = tc.draccus.parse(TrainPipelineConfig, args=["--dataset.repo_id", "u/d"])
-    cfg_file = tmp_path / "train_config.json"
-    parsed._save_pretrained(tmp_path)
-    assert cfg_file.is_file()
-
-    calls = []
-
-    def fake_hf_hub_download(filename=None, **kwargs):
-        calls.append(filename)
-        if filename == "train_config.json":
-            raise _FakeHTTPError()  # no root config
-        if filename == "checkpoints/000010/pretrained_model/train_config.json":
-            return str(cfg_file)
-        raise AssertionError(f"unexpected filename {filename}")
-
-    monkeypatch.setattr(tc, "hf_hub_download", fake_hf_hub_download)
-    monkeypatch.setattr(
-        tc, "find_latest_hub_checkpoint", lambda repo_id, token=None, revision=None: "checkpoints/000010"
-    )
-
-    loaded = TrainPipelineConfig.from_pretrained("user/interrupted-run")
-    assert loaded.dataset.repo_id == "u/d"
-    # Tried the root config first, then fell back to the latest checkpoint's config.
-    assert calls == ["train_config.json", "checkpoints/000010/pretrained_model/train_config.json"]
-
-
-def test_from_pretrained_raises_when_no_root_config_and_no_checkpoints(monkeypatch):
-    """No root config AND no checkpoints → a clear FileNotFoundError, not the raw HTTP error."""
-
-    def fake_hf_hub_download(filename=None, **kwargs):
-        raise _FakeHTTPError()
-
-    monkeypatch.setattr(tc, "hf_hub_download", fake_hf_hub_download)
-    monkeypatch.setattr(tc, "find_latest_hub_checkpoint", lambda repo_id, token=None, revision=None: None)
-
-    with pytest.raises(FileNotFoundError, match="train_config.json not found"):
-        TrainPipelineConfig.from_pretrained("user/empty-repo")

tests/conftest.py

@@ -14,23 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import functools
 import traceback
 
-import draccus.wrappers.docstring as _draccus_docstring
 import pytest
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
 from lerobot.utils.import_utils import is_package_available
 from tests.utils import DEVICE
 
-# On every `draccus.parse()`, draccus rebuilds each dataclass field's help text by
-# re-reading and re-parsing the class source (draccus.wrappers.docstring). For a config
-# as large as TrainPipelineConfig this costs ~2.5s per parse — negligible for the single
-# parse a CLI does, but tests parse configs hundreds of times. The source can't change
-# within a run, so memoize it for the whole test session.
-_draccus_docstring.get_attribute_docstring = functools.cache(_draccus_docstring.get_attribute_docstring)
-
 # Import fixture modules as plugins.
 # Fixtures that depend on optional packages are only registered when those packages are available,
 # so that tests can be collected and run even with a minimal install.