clean

* filter episodes in load_nested_dataset

* nit

* remove test filtering

* move import to module level

* added missing episode indices to the EpisodeAwareSampler in lerobot_train.py;

.github/workflows/full_tests.yml

@@ -78,7 +78,7 @@ jobs:
           python-version: ${{ env.PYTHON_VERSION }}
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras
+        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv --maxfail=10

.github/workflows/nightly.yml

@@ -189,5 +189,6 @@ jobs:
           python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
 
       - name: Run multi-GPU training tests
-        run: pytest tests/training/test_multi_gpu.py -vv --maxfail=3
+      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
+        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
         timeout-minutes: 10

.github/workflows/release.yml

@@ -82,6 +82,14 @@ jobs:
             exit 1
           fi
 
+      - name: Remove Tags with Git dependencies
+        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        run: |
+          echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
+          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
+          echo "::info:: Git dependencies removed. Proceeding with build."
+
       - name: Install build dependencies
         run: python -m pip install build
 
@@ -103,7 +111,7 @@ jobs:
       - name: Publish to TestPyPI for pre-releases
         # True for tags like 'v0.2.0-rc1'
         if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           repository-url: https://test.pypi.org/legacy/
           verbose: true
@@ -111,7 +119,7 @@ jobs:
 
       - name: Publish to PyPI
         if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@v1.12.4 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
+        uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
         with:
           verbose: true
           print-hash: true
@@ -138,7 +146,7 @@ jobs:
       - name: Setup uv and Python
         uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
         with:
-          enable-cache: true
+          enable-cache: true # zizmor: ignore[cache-poisoning]
           version: ${{ env.UV_VERSION }}
           python-version: ${{ env.PYTHON_VERSION }}
       - name: Create uv virtual environment

.github/workflows/stale.yml

@@ -27,15 +27,17 @@ env:
     This issue was closed because it has been stalled for 14 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   CLOSE_PR_MESSAGE: >
-    This PR was closed because it has been stalled for 14 days with no activity.
+    This PR was closed because it has been stalled for 21 days with no activity.
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   WARN_ISSUE_MESSAGE: >
     This issue has been automatically marked as stale because it has not had
     recent activity (6 months). It will be closed if no further activity occurs.
+    Any change, comment or update to this issue will reset this count.
     Thank you for your contributions.
   WARN_PR_MESSAGE: >
     This PR has been automatically marked as stale because it has not had
-    recent activity (6 months). It will be closed if no further activity occurs.
+    recent activity (1 year). It will be closed if no further activity occurs.
+    Any change, comment or update to this PR will reset this count.
     Thank you for your contributions.
 
 jobs:
@@ -56,10 +58,10 @@ jobs:
           stale-pr-label: stale
           exempt-issue-labels: never-stale
           exempt-pr-labels: never-stale
-          days-before-issue-stale: 180 # TODO(Steven): Will modify this to 90 after initial cleanup
+          days-before-issue-stale: 180
           days-before-issue-close: 14
-          days-before-pr-stale: 180
-          days-before-pr-close: 14
+          days-before-pr-stale: 365
+          days-before-pr-close: 21
           delete-branch: true
           close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }}
           close-pr-message: ${{ env.CLOSE_PR_MESSAGE }}

.github/workflows/unbound_deps_tests.yml

@@ -70,7 +70,7 @@ jobs:
           echo "Dependencies unbound:" && cat pyproject.toml
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras
+        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
 
    ##### General Code Quality & Formatting #####
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
         args: ['--maxkb=1024']
@@ -39,20 +39,20 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.4
+    rev: v0.14.1
     hooks:
       - id: ruff-format
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
 
   - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.34.0
+    rev: v1.38.1
     hooks:
       - id: typos
         args: [--force-exclude]
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.20.0
+    rev: v3.21.0
     hooks:
     -   id: pyupgrade
         args: [--py310-plus]
@@ -68,12 +68,12 @@ repos:
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.27.2
+    rev: v8.28.0
     hooks:
       - id: gitleaks
 
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.11.0
+    rev: v1.15.2
     hooks:
       - id: zizmor
 
@@ -87,7 +87,7 @@ repos:
   # TODO(Steven): Uncomment when ready to use
   ##### Static Analysis & Typing #####
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.16.0
+    rev: v1.18.2
     hooks:
       - id: mypy
         args: [--config-file=pyproject.toml]

CONTRIBUTING.md

@@ -137,7 +137,7 @@ Follow these steps to start contributing:
 4. for development, we advise to use a tool like `poetry` or `uv` instead of just `pip` to easily track our dependencies.
    Follow the instructions to [install poetry](https://python-poetry.org/docs/#installation) (use a version >=2.1.0) or to [install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) if you don't have one of them already.
 
-   Set up a development environment with conda or miniconda:
+   Set up a development environment with conda:
 
    ```bash
    conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev

README.md

@@ -104,14 +104,14 @@ LeRobot works with Python 3.10+ and PyTorch 2.2+.
 
 ### Environment Setup
 
-Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
+Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/):
 
 ```bash
 conda create -y -n lerobot python=3.10
 conda activate lerobot
 ```
 
-When using `miniconda`, install `ffmpeg` in your environment:
+When using `conda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
@@ -185,6 +185,11 @@ _Replace `[...]` with your desired features._
 For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install pi tags, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+>
+> This will be solved in the next patch release
+
 ### Weights & Biases
 
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
@@ -207,13 +212,13 @@ lerobot-dataset-viz \
     --episode-index 0
 ```
 
-or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
+or from a dataset in a local folder with the `root` option and the `--mode local` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
 
 ```bash
 lerobot-dataset-viz \
     --repo-id lerobot/pusht \
     --root ./my_local_data_dir \
-    --local-files-only 1 \
+    --mode local \
     --episode-index 0
 ```
 
@@ -310,7 +315,7 @@ To upload these to the hub, run the following:
 huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
 ```
 
-See [eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/eval.py) for an example of how other people may use your policy.
+See [lerobot_eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_eval.py) for an example of how other people may use your policy.
 
 ### Acknowledgment
 
@@ -337,7 +342,3 @@ If you want, you can cite this work with:
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
-
-```
-
-```

docs/source/_toctree.yml

@@ -37,8 +37,12 @@
     title: π₀ (Pi0)
   - local: pi05
     title: π₀.₅ (Pi05)
+  - local: groot
+    title: NVIDIA GR00T N1.5
   title: "Policies"
 - sections:
+  - local: envhub
+    title: Environments from the Hub
   - local: il_sim
     title: Imitation Learning in Sim
   - local: libero
@@ -55,6 +59,8 @@
     title: Implement your own processor
   - local: processors_robots_teleop
     title: Processors for Robots and Teleoperators
+  - local: env_processor
+    title: Environment Processors
   title: "Robot Processors"
 - sections:
   - local: so101

docs/source/env_processor.mdx

@@ -0,0 +1,418 @@
+# Environment Processors
+
+Environment processors are a critical layer in LeRobot's data processing architecture that handle **environment-specific** transformations, separate from policy-specific processing. This separation of concerns enables cleaner code, better modularity, and easier experimentation with different environments and policies.
+
+## Why Environment Processors?
+
+When working with different robot environments (LIBERO, MetaWorld, Aloha, etc.), each environment often has unique data formats, coordinate systems, and conventions that need standardization **before** policy processing. Without environment processors, these transformations would be:
+
+1. **Hardcoded in environment code** - Making it difficult to experiment with different state representations
+2. **Duplicated across policies** - Each policy would need to handle environment-specific quirks
+3. **Mixed with policy logic** - Violating separation of concerns and making debugging harder
+
+Environment processors solve this by providing a **dedicated processing layer** between raw environment observations and policy inputs.
+
+## The Processing Pipeline
+
+Here's how data flows through the complete processing pipeline during evaluation:
+
+```python
+# In lerobot_eval.py rollout() function:
+
+# 1. Raw environment observation (numpy arrays, various formats)
+raw_observation = env.step(action)
+
+# 2. Convert numpy to torch, normalize images [0,1]
+observation = preprocess_observation(raw_observation)
+
+# 3. Add task metadata (for multi-task environments)
+observation = add_envs_task(env, observation)
+
+# 4. ENVIRONMENT-SPECIFIC preprocessing (NEW!)
+#    - Flatten robot states
+#    - Rotate images to match dataset conventions
+#    - Handle environment-specific coordinate systems
+observation = env_preprocessor(observation)
+
+# 5. POLICY-SPECIFIC preprocessing
+#    - Normalize with dataset statistics
+#    - Add batch dimensions
+#    - Move to GPU
+#    - Tokenize language instructions
+observation = preprocessor(observation)
+
+# 6. Policy inference
+action = policy.select_action(observation)
+
+# 7. POLICY-SPECIFIC postprocessing
+#    - Unnormalize actions
+#    - Remove batch dimensions
+action = postprocessor(action)
+
+# 8. ENVIRONMENT-SPECIFIC postprocessing (NEW!)
+#    - Convert action formats if needed
+#    - Apply environment-specific constraints
+action_transition = {"action": action}
+action_transition = env_postprocessor(action_transition)
+action = action_transition["action"]
+
+# 9. Execute in environment
+env.step(action)
+```
+
+## The Benefits
+
+### 1. **Separation of Concerns**
+
+Environment processors handle transformations specific to the **environment's data format**, while policy processors handle transformations specific to the **model's requirements**.
+
+```python
+# ❌ Before: Mixed concerns
+class LiberoVLAPolicy:
+    def preprocess(self, obs):
+        # Environment-specific: Flatten robot state (shouldn't be in policy!)
+        state = self._flatten_robot_state(obs["robot_state"])
+        # Policy-specific: Normalize with dataset stats
+        state = self.normalizer(state)
+        return state
+
+# ✅ After: Clear separation
+# Environment processor: Handles LIBERO's nested robot state
+env_preprocessor = LiberoProcessorStep()  # Flattens robot_state
+
+# Policy processor: Handles model requirements
+policy_preprocessor = NormalizerProcessorStep(stats=dataset_stats)
+```
+
+### 2. **Flexibility and Reusability**
+
+The same policy can work with different environment processors, and the same environment processor can work with different policies:
+
+```python
+# Use SmolVLA policy with LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
+smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
+
+# Or use ACT policy with the same LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
+act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
+```
+
+### 3. **Easier Experimentation**
+
+Want to try different state representations for LIBERO? Just create a new processor:
+
+```python
+# Original: 8D state (pos + quat→axisangle + gripper)
+@ProcessorStepRegistry.register("libero_processor")
+class LiberoProcessorStep(ObservationProcessorStep):
+    def _process_observation(self, obs):
+        eef_pos = robot_state["eef"]["pos"]          # 3D
+        eef_axisangle = quat2axisangle(quat)         # 3D
+        gripper = robot_state["gripper"]["qpos"]     # 2D
+        state = torch.cat([eef_pos, eef_axisangle, gripper], dim=-1)  # 8D
+        return state
+
+# Experiment: Add velocity for better control
+@ProcessorStepRegistry.register("libero_velocity_processor")
+class LiberoVelocityProcessorStep(ObservationProcessorStep):
+    def _process_observation(self, obs):
+        # Include velocities for 14D state
+        eef_pos = robot_state["eef"]["pos"]          # 3D
+        eef_axisangle = quat2axisangle(quat)         # 3D
+        eef_vel = robot_state["eef"]["vel"]          # 3D  (NEW)
+        gripper_pos = robot_state["gripper"]["qpos"] # 2D
+        gripper_vel = robot_state["gripper"]["qvel"] # 3D  (NEW)
+        state = torch.cat([eef_pos, eef_axisangle, eef_vel,
+                          gripper_pos, gripper_vel], dim=-1)  # 14D
+        return state
+```
+
+### 4. **Cleaner Environment Code**
+
+Environments expose **all available data** without needing to know what downstream models will use:
+
+```python
+# LIBERO environment exposes full robot state
+observation = {
+    "pixels": {"image": img, "image2": img2},
+    "robot_state": {
+        "eef": {"pos": ..., "quat": ..., "vel": ..., "mat": ..., "axisangle": ...},
+        "gripper": {"qpos": ..., "qvel": ...},
+        "joints": {"pos": ..., "vel": ...}
+    }
+}
+
+# Environment processor decides what to use
+# Policy processor handles model-specific transformations
+```
+
+## Using Environment Processors
+
+### Factory Function
+
+The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:
+
+```python
+from lerobot.envs.factory import make_env_pre_post_processors
+from lerobot.envs.configs import LiberoEnv, PushtEnv
+
+# For LIBERO: Returns LiberoProcessorStep in preprocessor
+libero_cfg = LiberoEnv(task="libero_spatial", camera_name=["agentview"])
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg)
+
+# For other environments: Returns identity processors (no-op)
+pusht_cfg = PushtEnv()
+env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg)
+```
+
+### Implementation in `envs/factory.py`
+
+```python
+def make_env_pre_post_processors(
+    env_cfg: EnvConfig,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+]:
+    """
+    Create preprocessor and postprocessor pipelines for environment observations.
+
+    Args:
+        env_cfg: The configuration of the environment.
+
+    Returns:
+        A tuple containing:
+            - preprocessor: Pipeline that processes environment observations
+            - postprocessor: Pipeline that processes environment outputs
+    """
+    # For LIBERO environments, add the LiberoProcessorStep to preprocessor
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
+    else:
+        # For all other environments, return an identity preprocessor
+        preprocessor = PolicyProcessorPipeline(steps=[])
+
+    # Postprocessor is currently identity for all environments
+    # Future: Could add environment-specific action transformations
+    postprocessor = PolicyProcessorPipeline(steps=[])
+
+    return preprocessor, postprocessor
+```
+
+### Integration in Evaluation
+
+In `lerobot_eval.py`, the environment processors are created once and used throughout:
+
+```python
+def eval_main(cfg: EvalPipelineConfig):
+    # Create environment
+    envs = make_env(cfg.env, n_envs=cfg.eval.batch_size)
+
+    # Create policy
+    policy = make_policy(cfg=cfg.policy, env_cfg=cfg.env)
+
+    # Create policy processors
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        pretrained_path=cfg.policy.pretrained_path,
+    )
+
+    # Create environment processors (NEW!)
+    env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
+
+    # Run evaluation with both processor types
+    eval_policy_all(
+        envs=envs,
+        policy=policy,
+        env_preprocessor=env_preprocessor,      # Environment-specific
+        env_postprocessor=env_postprocessor,    # Environment-specific
+        preprocessor=preprocessor,              # Policy-specific
+        postprocessor=postprocessor,            # Policy-specific
+        n_episodes=cfg.eval.n_episodes,
+    )
+```
+
+## Example: LIBERO Environment Processor
+
+The `LiberoProcessorStep` demonstrates a real-world environment processor:
+
+```python
+from lerobot.processor.pipeline import ObservationProcessorStep
+
+@dataclass
+@ProcessorStepRegistry.register(name="libero_processor")
+class LiberoProcessorStep(ObservationProcessorStep):
+    """
+    Processes LIBERO observations into the LeRobot format.
+
+    **State Processing:**
+    - Extracts end-effector position (3D)
+    - Converts quaternion to axis-angle representation (3D)
+    - Extracts gripper joint positions (2D)
+    - Concatenates into 8D state vector
+
+    **Image Processing:**
+    - Rotates images 180° to match HuggingFaceVLA/libero convention
+    """
+
+    def _process_observation(self, observation):
+        processed_obs = observation.copy()
+
+        # Process images: Flip 180° for camera convention
+        for key in list(processed_obs.keys()):
+            if key.startswith("observation.images."):
+                img = processed_obs[key]
+                img = torch.flip(img, dims=[2, 3])  # Flip H and W
+                processed_obs[key] = img
+
+        # Process robot_state: Flatten to 8D vector
+        if "observation.robot_state" in processed_obs:
+            robot_state = processed_obs.pop("observation.robot_state")
+
+            eef_pos = robot_state["eef"]["pos"]           # (B, 3)
+            eef_quat = robot_state["eef"]["quat"]         # (B, 4)
+            gripper_qpos = robot_state["gripper"]["qpos"] # (B, 2)
+
+            # Convert quaternion to axis-angle
+            eef_axisangle = self._quat2axisangle(eef_quat)  # (B, 3)
+
+            # Concatenate into single state vector
+            state = torch.cat((eef_pos, eef_axisangle, gripper_qpos), dim=-1)
+            state = state.float()
+
+            processed_obs["observation.state"] = state
+
+        return processed_obs
+```
+
+### Why These Transformations?
+
+1. **Image Rotation**: The HuggingFaceVLA/libero dataset has images rotated 180° from the raw LIBERO simulator. The processor handles this convention mismatch so policies trained on the dataset work seamlessly.
+
+2. **State Flattening**: The raw LIBERO environment exposes nested dictionaries with all available state information (position, quaternion, velocity, matrix representation, etc.). The processor:
+   - Selects the relevant components (pos, quat, gripper)
+   - Converts quaternion to axis-angle (more suitable for learning)
+   - Flattens to a single 8D vector that policies expect
+
+3. **Flexibility**: The environment still exposes **all** raw data. If you want to try different state representations (e.g., including velocities, using matrix representation instead of axis-angle), you can create a new processor without modifying the environment code.
+
+## Adding Environment Processors for New Environments
+
+To add environment processors for a new environment:
+
+### 1. Create the Processor Step
+
+```python
+# In src/lerobot/processor/env_processor.py
+
+@dataclass
+@ProcessorStepRegistry.register(name="myenv_processor")
+class MyEnvProcessorStep(ObservationProcessorStep):
+    """Process observations from MyEnv."""
+
+    def _process_observation(self, observation):
+        processed = observation.copy()
+
+        # Your environment-specific transformations
+        if "myenv.specific.state" in processed:
+            state = processed.pop("myenv.specific.state")
+            # Transform to standard format
+            processed["observation.state"] = self._transform_state(state)
+
+        return processed
+```
+
+### 2. Update the Factory
+
+```python
+# In src/lerobot/envs/factory.py
+
+def make_env_pre_post_processors(env_cfg: EnvConfig):
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
+    elif isinstance(env_cfg, MyEnvConfig) or "myenv" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[MyEnvProcessorStep()])
+    else:
+        preprocessor = PolicyProcessorPipeline(steps=[])
+
+    postprocessor = PolicyProcessorPipeline(steps=[])
+    return preprocessor, postprocessor
+```
+
+### 3. Use in Evaluation
+
+No changes needed! The evaluation script automatically uses the appropriate processor:
+
+```bash
+lerobot-eval \
+    --policy.path=lerobot/my_policy \
+    --env.type=myenv \  # Automatically uses MyEnvProcessorStep
+    --eval.n_episodes=10
+```
+
+## Future: Environment Postprocessors
+
+Currently, postprocessors are identity (no-op) for all environments. Future use cases include:
+
+### Action Space Transformations
+
+```python
+@dataclass
+class MyEnvActionPostprocessor(ProcessorStep):
+    """Convert policy actions to environment-specific format."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition["action"]
+
+        # Example: Convert from Cartesian to joint space
+        if self.action_space == "joint":
+            action = self.ik_solver(action)
+
+        # Example: Apply environment-specific safety limits
+        action = torch.clamp(action, self.min_action, self.max_action)
+
+        transition["action"] = action
+        return transition
+```
+
+### Coordinate System Conversions
+
+```python
+@dataclass
+class CoordinateTransformPostprocessor(ProcessorStep):
+    """Transform actions between coordinate systems."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition["action"]
+
+        # Example: Policy outputs in world frame, env expects base frame
+        action = self.world_to_base_transform(action)
+
+        transition["action"] = action
+        return transition
+```
+
+## Best Practices
+
+1. **Keep environment processors simple**: They should only handle environment-specific data format issues, not complex learning-related transformations.
+
+2. **Use policy processors for model requirements**: Normalization, batching, device placement, and tokenization belong in policy processors.
+
+3. **Expose all data from environments**: Let processors decide what to use rather than hardcoding choices in the environment.
+
+4. **Document conventions**: Clearly document any coordinate system conventions, camera orientations, or data formats that your processor handles.
+
+5. **Test independently**: Environment processors should be testable without loading full policies or environments.
+
+## Summary
+
+Environment processors provide a **clean separation** between environment-specific data transformations and policy-specific model requirements. This architecture:
+
+- ✅ Enables easy experimentation with different state representations
+- ✅ Allows policies to work seamlessly across different environments
+- ✅ Keeps environment code focused on simulation/hardware interface
+- ✅ Makes processor pipelines more maintainable and debuggable
+- ✅ Follows the single responsibility principle
+
+The key insight: **Environments define data formats, processors standardize them, policies consume standardized data.** Each layer has a clear, focused responsibility.

docs/source/envhub.mdx

@@ -0,0 +1,424 @@
+# Loading Environments from the Hub
+
+The **EnvHub** feature allows you to load simulation environments directly from the Hugging Face Hub with a single line of code. This unlocks a powerful new model for collaboration: instead of environments being locked away inside monolithic libraries, anyone can publish custom environments and share them with the community.
+
+## Overview
+
+With EnvHub, you can:
+
+- Load environments from the Hub instantly
+- Share your custom simulation tasks with the community
+- Version control your environments using Git
+- Distribute complex physics simulations without packaging hassles
+
+## Quick Start
+
+Loading an environment from the Hub is as simple as:
+
+```python
+from lerobot.envs.factory import make_env
+
+# Load a hub environment (requires explicit consent to run remote code)
+env = make_env("lerobot/cartpole-env", trust_remote_code=True)
+```
+
+<Tip warning={true}>
+  **Security Notice**: Loading environments from the Hub executes Python code
+  from third-party repositories. Only use `trust_remote_code=True` with
+  repositories you trust. We strongly recommend pinning to a specific commit
+  hash for reproducibility and security.
+</Tip>
+
+## What is EnvHub?
+
+EnvHub is a framework that allows researchers and developers to:
+
+1. **Publish environments** to the Hugging Face Hub as Git repositories
+2. **Load environments** dynamically without installing them as packages
+3. **Version and track** environment changes using Git semantics
+4. **Discover** new simulation tasks shared by the community
+
+This design means you can go from discovering an interesting environment on the Hub to running experiments in seconds, without worrying about dependency conflicts or complex installation procedures.
+
+## Repository Structure
+
+To make your environment loadable from the Hub, your repository must contain at minimum:
+
+### Required Files
+
+**`env.py`** (or custom Python file)
+
+- Must expose a `make_env(n_envs: int, use_async_envs: bool)` function
+- This function should return one of:
+  - A `gym.vector.VectorEnv` (most common)
+  - A single `gym.Env` (will be automatically wrapped)
+  - A dict mapping `{suite_name: {task_id: VectorEnv}}` (for multi-task benchmarks)
+
+### Optional Files
+
+**`requirements.txt`**
+
+- List any additional dependencies your environment needs
+- Users will need to install these manually before loading your environment
+
+**`README.md`**
+
+- Document your environment: what task it implements, observation/action spaces, rewards, etc.
+- Include usage examples and any special setup instructions
+
+**`.gitignore`**
+
+- Exclude unnecessary files from your repository
+
+### Example Repository Structure
+
+```
+my-environment-repo/
+├── env.py                 # Main environment definition (required)
+├── requirements.txt       # Dependencies (optional)
+├── README.md             # Documentation (recommended)
+├── assets/               # Images, videos, etc. (optional)
+│   └── demo.gif
+└── configs/              # Config files if needed (optional)
+    └── task_config.yaml
+```
+
+## Creating Your Environment Repository
+
+### Step 1: Define Your Environment
+
+Create an `env.py` file with a `make_env` function:
+
+```python
+# env.py
+import gymnasium as gym
+
+def make_env(n_envs: int = 1, use_async_envs: bool = False):
+    """
+    Create vectorized environments for your custom task.
+
+    Args:
+        n_envs: Number of parallel environments
+        use_async_envs: Whether to use AsyncVectorEnv or SyncVectorEnv
+
+    Returns:
+        gym.vector.VectorEnv or dict mapping suite names to vectorized envs
+    """
+    def _make_single_env():
+        # Create your custom environment
+        return gym.make("CartPole-v1")
+
+    # Choose vector environment type
+    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
+
+    # Create vectorized environment
+    vec_env = env_cls([_make_single_env for _ in range(n_envs)])
+
+    return vec_env
+```
+
+### Step 2: Test Locally
+
+Before uploading, test your environment locally:
+
+```python
+from lerobot.envs.utils import _load_module_from_path, _call_make_env, _normalize_hub_result
+
+# Load your module
+module = _load_module_from_path("./env.py")
+
+# Test the make_env function
+result = _call_make_env(module, n_envs=2, use_async_envs=False)
+normalized = _normalize_hub_result(result)
+
+# Verify it works
+suite_name = next(iter(normalized))
+env = normalized[suite_name][0]
+obs, info = env.reset()
+print(f"Observation shape: {obs.shape if hasattr(obs, 'shape') else type(obs)}")
+env.close()
+```
+
+### Step 3: Upload to the Hub
+
+Upload your repository to Hugging Face:
+
+```bash
+# Install huggingface_hub if needed
+pip install huggingface_hub
+
+# Login to Hugging Face
+huggingface-cli login
+
+# Create a new repository
+huggingface-cli repo create my-custom-env --type space --org my-org
+
+# Initialize git and push
+git init
+git add .
+git commit -m "Initial environment implementation"
+git remote add origin https://huggingface.co/my-org/my-custom-env
+git push -u origin main
+```
+
+Alternatively, use the `huggingface_hub` Python API:
+
+```python
+from huggingface_hub import HfApi
+
+api = HfApi()
+
+# Create repository
+api.create_repo("my-custom-env", repo_type="space")
+
+# Upload files
+api.upload_folder(
+    folder_path="./my-env-folder",
+    repo_id="username/my-custom-env",
+    repo_type="space",
+)
+```
+
+## Loading Environments from the Hub
+
+### Basic Usage
+
+```python
+from lerobot.envs.factory import make_env
+
+# Load from the hub
+envs_dict = make_env(
+    "username/my-custom-env",
+    n_envs=4,
+    trust_remote_code=True
+)
+
+# Access the environment
+suite_name = next(iter(envs_dict))
+env = envs_dict[suite_name][0]
+
+# Use it like any gym environment
+obs, info = env.reset()
+action = env.action_space.sample()
+obs, reward, terminated, truncated, info = env.step(action)
+```
+
+### Advanced: Pinning to Specific Versions
+
+For reproducibility and security, pin to a specific Git revision:
+
+```python
+# Pin to a specific branch
+env = make_env("username/my-env@main", trust_remote_code=True)
+
+# Pin to a specific commit (recommended for papers/experiments)
+env = make_env("username/my-env@abc123def456", trust_remote_code=True)
+
+# Pin to a tag
+env = make_env("username/my-env@v1.0.0", trust_remote_code=True)
+```
+
+### Custom File Paths
+
+If your environment definition is not in `env.py`:
+
+```python
+# Load from a custom file
+env = make_env("username/my-env:custom_env.py", trust_remote_code=True)
+
+# Combine with version pinning
+env = make_env("username/my-env@v1.0:envs/task_a.py", trust_remote_code=True)
+```
+
+### Async Environments
+
+For better performance with multiple environments:
+
+```python
+envs_dict = make_env(
+    "username/my-env",
+    n_envs=8,
+    use_async_envs=True,  # Use AsyncVectorEnv for parallel execution
+    trust_remote_code=True
+)
+```
+
+## URL Format Reference
+
+The hub URL format supports several patterns:
+
+| Pattern              | Description                    | Example                                |
+| -------------------- | ------------------------------ | -------------------------------------- |
+| `user/repo`          | Load `env.py` from main branch | `make_env("lerobot/pusht-env")`        |
+| `user/repo@revision` | Load from specific revision    | `make_env("lerobot/pusht-env@main")`   |
+| `user/repo:path`     | Load custom file               | `make_env("lerobot/envs:pusht.py")`    |
+| `user/repo@rev:path` | Revision + custom file         | `make_env("lerobot/envs@v1:pusht.py")` |
+
+## Multi-Task Environments
+
+For benchmarks with multiple tasks (like LIBERO), return a nested dictionary:
+
+```python
+def make_env(n_envs: int = 1, use_async_envs: bool = False):
+    env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
+
+    # Return dict: {suite_name: {task_id: VectorEnv}}
+    return {
+        "suite_1": {
+            0: env_cls([lambda: gym.make("Task1-v0") for _ in range(n_envs)]),
+            1: env_cls([lambda: gym.make("Task2-v0") for _ in range(n_envs)]),
+        },
+        "suite_2": {
+            0: env_cls([lambda: gym.make("Task3-v0") for _ in range(n_envs)]),
+        }
+    }
+```
+
+## Security Considerations
+
+<Tip warning={true}>
+  **Important**: The `trust_remote_code=True` flag is required to execute
+  environment code from the Hub. This is by design for security.
+</Tip>
+
+When loading environments from the Hub:
+
+1. **Review the code first**: Visit the repository and inspect `env.py` before loading
+2. **Pin to commits**: Use specific commit hashes for reproducibility
+3. **Check dependencies**: Review `requirements.txt` for suspicious packages
+4. **Use trusted sources**: Prefer official organizations or well-known researchers
+5. **Sandbox if needed**: Run untrusted code in isolated environments (containers, VMs)
+
+Example of safe usage:
+
+```python
+# ❌ BAD: Loading without inspection
+env = make_env("random-user/untrusted-env", trust_remote_code=True)
+
+# ✅ GOOD: Review code, then pin to specific commit
+# 1. Visit https://huggingface.co/trusted-org/verified-env
+# 2. Review the env.py file
+# 3. Copy the commit hash
+env = make_env("trusted-org/verified-env@a1b2c3d4", trust_remote_code=True)
+```
+
+## Example: CartPole from the Hub
+
+Here's a complete example using the reference CartPole environment:
+
+```python
+from lerobot.envs.factory import make_env
+import numpy as np
+
+# Load the environment
+envs_dict = make_env("lerobot/cartpole-env", n_envs=4, trust_remote_code=True)
+
+# Get the vectorized environment
+suite_name = next(iter(envs_dict))
+env = envs_dict[suite_name][0]
+
+# Run a simple episode
+obs, info = env.reset()
+done = np.zeros(env.num_envs, dtype=bool)
+total_reward = np.zeros(env.num_envs)
+
+while not done.all():
+    # Random policy
+    action = env.action_space.sample()
+    obs, reward, terminated, truncated, info = env.step(action)
+    total_reward += reward
+    done = terminated | truncated
+
+print(f"Average reward: {total_reward.mean():.2f}")
+env.close()
+```
+
+## Benefits of EnvHub
+
+### For Environment Authors
+
+- **Easy distribution**: No PyPI packaging required
+- **Version control**: Use Git for environment versioning
+- **Rapid iteration**: Push updates instantly
+- **Documentation**: Hub README renders beautifully
+- **Community**: Reach LeRobot users directly
+
+### For Researchers
+
+- **Quick experiments**: Load any environment in one line
+- **Reproducibility**: Pin to specific commits
+- **Discovery**: Browse environments on the Hub
+- **No conflicts**: No need to install conflicting packages
+
+### For the Community
+
+- **Growing ecosystem**: More diverse simulation tasks
+- **Standardization**: Common `make_env` API
+- **Collaboration**: Fork and improve existing environments
+- **Accessibility**: Lower barrier to sharing research
+
+## Troubleshooting
+
+### "Refusing to execute remote code"
+
+You must explicitly pass `trust_remote_code=True`:
+
+```python
+env = make_env("user/repo", trust_remote_code=True)
+```
+
+### "Module X not found"
+
+The hub environment has dependencies you need to install:
+
+```bash
+# Check the repo's requirements.txt and install dependencies
+pip install gymnasium numpy
+```
+
+### "make_env not found in module"
+
+Your `env.py` must expose a `make_env` function:
+
+```python
+def make_env(n_envs: int, use_async_envs: bool):
+    # Your implementation
+    pass
+```
+
+### Environment returns wrong type
+
+The `make_env` function must return:
+
+- A `gym.vector.VectorEnv`, or
+- A single `gym.Env`, or
+- A dict `{suite_name: {task_id: VectorEnv}}`
+
+## Best Practices
+
+1. **Document your environment**: Include observation/action space descriptions, reward structure, and termination conditions in your README
+2. **Add requirements.txt**: List all dependencies with versions
+3. **Test thoroughly**: Verify your environment works locally before pushing
+4. **Use semantic versioning**: Tag releases with version numbers
+5. **Add examples**: Include usage examples in your README
+6. **Keep it simple**: Minimize dependencies when possible
+7. **License your work**: Add a LICENSE file to clarify usage terms
+
+## Future Directions
+
+The EnvHub ecosystem enables exciting possibilities:
+
+- **GPU-accelerated physics**: Share Isaac Gym or Brax environments
+- **Photorealistic rendering**: Distribute environments with advanced graphics
+- **Multi-agent scenarios**: Complex interaction tasks
+- **Real-world simulators**: Digital twins of physical setups
+- **Procedural generation**: Infinite task variations
+- **Domain randomization**: Pre-configured DR pipelines
+
+As more researchers and developers contribute, the diversity and quality of available environments will grow, benefiting the entire robotics learning community.
+
+## See Also
+
+- [Hugging Face Hub Documentation](https://huggingface.co/docs/hub/en/index)
+- [Gymnasium Documentation](https://gymnasium.farama.org/index.html)
+- [Example Hub Environment](https://huggingface.co/lerobot/cartpole-env)

docs/source/groot.mdx

@@ -0,0 +1,125 @@
+# GR00T N1.5 Policy
+
+GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
+
+This document outlines the specifics of its integration and usage within the LeRobot framework.
+
+## Model Overview
+
+NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
+
+Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+
+GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
+
+Its strong performance comes from being trained on an expansive and diverse humanoid dataset, which includes:
+
+- Real captured data from robots.
+- Synthetic data generated using NVIDIA Isaac GR00T Blueprint.
+- Internet-scale video data.
+
+This approach allows the model to be highly adaptable through post-training for specific embodiments, tasks, and environments.
+
+## Installation Requirements
+
+As of today, GR00T N1.5 requires flash attention for it's internal working.
+
+We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
+
+1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
+2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
+
+```bash
+# Check https://pytorch.org/get-started/locally/ for your system
+pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
+pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
+pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
+python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
+```
+
+3. Install LeRobot by running:
+
+```bash
+pip install lerobot[groot]
+```
+
+## Usage
+
+To use GR00T in your LeRobot configuration, specify the policy type as:
+
+```python
+policy.type=groot
+```
+
+## Training
+
+### Training Command Example
+
+Here's a complete training command for finetuning the base GR00T model on your own dataset:
+
+```bash
+# Using a multi-GPU setup
+accelerate launch \
+  --multi_gpu \
+  --num_processes=$NUM_GPUS \
+  $(which lerobot-train) \
+  --output_dir=$OUTPUT_DIR \
+  --save_checkpoint=true \
+  --batch_size=$BATCH_SIZE \
+  --steps=$NUM_STEPS \
+  --save_freq=$SAVE_FREQ \
+  --log_freq=$LOG_FREQ \
+  --policy.push_to_hub=true \
+  --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 \
+  --job_name=$JOB_NAME
+```
+
+## Performance Results
+
+### Libero Benchmark Results
+
+> [!NOTE]
+> Follow our instructions for Libero usage: [Libero](./libero)
+
+GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
+
+| Benchmark          | LeRobot Implementation | GR00T Reference |
+| ------------------ | ---------------------- | --------------- |
+| **Libero Spatial** | 82.0%                  | 92.0%           |
+| **Libero Object**  | 99.0%                  | 92.0%           |
+| **Libero Long**    | 82.0%                  | 76.0%           |
+| **Average**        | 87.0%                  | 87.0%           |
+
+These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+
+### Evaluate in your hardware setup
+
+Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Imitation Learning for Robots](./il_robots). For example:
+
+```bash
+lerobot-record \
+  --robot.type=bi_so100_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 \
+  --dataset.repo_id=<user>/eval_groot-bimanual  \
+  --dataset.num_episodes=10 \
+  --dataset.single_task="Grab and handover the red cube to the other arm"
+  --policy.path=<user>/groot-bimanual # your trained model
+  --dataset.episode_time_s=30
+  --dataset.reset_time_s=10
+```
+
+## License
+
+This model follows the **Apache 2.0 License**, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T).

docs/source/il_robots.mdx

@@ -165,7 +165,7 @@ huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 Then store your Hugging Face repository name in a variable:
 
 ```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
+HF_USER=$(hf auth whoami | head -n 1)
 echo $HF_USER
 ```
 

docs/source/installation.mdx

@@ -1,8 +1,15 @@
 # Installation
 
+## Install [`miniforge`](https://conda-forge.org/download/)
+
+```bash
+wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh
+```
+
 ## Environment Setup
 
-Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
+Create a virtual environment with Python 3.10, using conda:
 
 ```bash
 conda create -y -n lerobot python=3.10
@@ -14,7 +21,7 @@ Then activate your conda environment, you have to do this each time you open a s
 conda activate lerobot
 ```
 
-When using `miniconda`, install `ffmpeg` in your environment:
+When using `conda`, install `ffmpeg` in your environment:
 
 ```bash
 conda install ffmpeg -c conda-forge
@@ -74,6 +81,9 @@ _Replace `[...]` with your desired features._
 For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
+
 ### Troubleshooting
 
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.

docs/source/integrate_hardware.mdx

@@ -208,34 +208,36 @@ LeRobot supports saving and loading calibration data automatically. This is usef
 
 <!-- prettier-ignore-start -->
 ```python
-> @property
-> def is_calibrated(self) -> bool:
->    return True
->
-> def calibrate(self) -> None:
->    pass
-> ```
+@property
+def is_calibrated(self) -> bool:
+    return True
+
+def calibrate(self) -> None:
+    pass
+```
+<!-- prettier-ignore-end -->
 
 ### `is_calibrated`
 
 This should reflect whether your robot has the required calibration loaded.
 
-```
-<!-- prettier-ignore-end -->python
+<!-- prettier-ignore-start -->
+```python
 @property
 def is_calibrated(self) -> bool:
     return self.bus.is_calibrated
 ```
+<!-- prettier-ignore-end -->
 
 ### `calibrate()`
 
 The goal of the calibration is twofold:
-    - Know the physical range of motion of each motors in order to only send commands within this range.
-    - Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
+
+- Know the physical range of motion of each motors in order to only send commands within this range.
+- Normalize raw motors positions to sensible continuous values (e.g. percentages, degrees) instead of arbitrary discrete value dependant on the specific motor used that will not replicate elsewhere.
 
 It should implement the logic for calibration (if relevant) and update the `self.calibration` dictionary. If you are using Feetech or Dynamixel motors, our bus interfaces already include methods to help with this.
 
-
 <!-- prettier-ignore-start -->
 ```python
 def calibrate(self) -> None:

docs/source/pi0.mdx

@@ -28,6 +28,11 @@ As described by Physical Intelligence, while AI has achieved remarkable success
    pip install -e ".[pi]"
    ```
 
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
 ## Training Data and Capabilities
 
 π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:

docs/source/pi05.mdx

@@ -36,6 +36,11 @@ This diverse training mixture creates a "curriculum" that enables generalization
    pip install -e ".[pi]"
    ```
 
+   > [!NOTE]
+   > For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+   >
+   > This will be solved in the next patch release
+
 ## Usage
 
 To use π₀.₅ in your LeRobot configuration, specify the policy type as:

docs/source/policy_groot_README.md

@@ -0,0 +1,27 @@
+## Research Paper
+
+Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
+
+## Repository
+
+Code: https://github.com/NVIDIA/Isaac-GR00T
+
+## Citation
+
+```bibtex
+@inproceedings{gr00tn1_2025,
+  archivePrefix = {arxiv},
+  eprint     = {2503.14734},
+  title      = {{GR00T} {N1}: An Open Foundation Model for Generalist Humanoid Robots},
+  author     = {NVIDIA and Johan Bjorck andFernando Castañeda, Nikita Cherniadev and Xingye Da and Runyu Ding and Linxi "Jim" Fan and Yu Fang and Dieter Fox and Fengyuan Hu and Spencer Huang and Joel Jang and Zhenyu Jiang and Jan Kautz and Kaushil Kundalia and Lawrence Lao and Zhiqi Li and Zongyu Lin and Kevin Lin and Guilin Liu and Edith Llontop and Loic Magne and Ajay Mandlekar and Avnish Narayan and Soroush Nasiriany and Scott Reed and You Liang Tan and Guanzhi Wang and Zu Wang and Jing Wang and Qi Wang and Jiannan Xiang and Yuqi Xie and Yinzhen Xu and Zhenjia Xu and Seonghyeon Ye and Zhiding Yu and Ao Zhang and Hao Zhang and Yizhou Zhao and Ruijie Zheng and Yuke Zhu},
+  month      = {March},
+  year       = {2025},
+  booktitle  = {ArXiv Preprint},
+}
+```
+
+## Additional Resources
+
+Blog: https://developer.nvidia.com/isaac/gr00t
+
+Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B

examples/dataset/convert.sh

@@ -0,0 +1,6 @@
+python ./examples/dataset/convert_hdf5_lerobot.py \
+    --src-paths /fsx/jade_choghari/XVLA-Soft-Fold/0808_12am_stage_1_stage2new_new_cam_very_slow_no_sleeve \
+    --output-path /fsx/jade_choghari/new-data \
+    --executor local \
+    --tasks-per-job 3 \
+    --workers 10

examples/dataset/convert_hdf5_lerobot.py

@@ -0,0 +1,437 @@
+import argparse
+import os
+import re
+import shutil
+from pathlib import Path
+
+import pandas as pd
+# import ray
+# from datatrove.executor import LocalPipelineExecutor, RayPipelineExecutor
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+from lerobot.datasets.aggregate import (
+    aggregate_data,
+    aggregate_metadata,
+    aggregate_stats,
+    aggregate_videos,
+    validate_all_metadata,
+)
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import (
+    DEFAULT_CHUNK_SIZE,
+    DEFAULT_DATA_FILE_SIZE_IN_MB,
+    DEFAULT_VIDEO_FILE_SIZE_IN_MB,
+    write_info,
+    write_stats,
+    write_tasks,
+)
+XVLA_SOFT_FOLD_FEATURES = {
+    "observation.images.cam_high": {
+        "dtype": "video",
+        "names": ["height", "width", "channels"],
+        "shape": (480, 640, 3),
+        "names": ["height", "width", "rgb"],
+    },
+    "observation.images.cam_left_wrist": {
+        "dtype": "video",
+        "names": ["height", "width", "channels"],
+        "shape": (480, 640, 3),
+        "names": ["height", "width", "rgb"],
+    },
+    "observation.images.cam_right_wrist": {
+        "dtype": "video",
+        "names": ["height", "width", "channels"],
+        "shape": (480, 640, 3),
+        "names": ["height", "width", "rgb"],
+    },
+
+    "observation.states.eef_euler": {
+        "dtype": "float32",
+        "shape": (14,),   # 14 = 7 joints per arm × 2 arms OR 14-d state representation
+        "names": {"values": [f"eef_euler_{i}" for i in range(14)]},
+    },
+
+    "observation.states.eef_quaternion": {
+        "dtype": "float32",
+        "shape": (16,),   # 16 = 8 quaternion floats per arm × 2 arms
+        "names": {"values": [f"eef_quat_{i}" for i in range(16)]},
+    },
+
+    "observation.states.eef_6d": {
+        "dtype": "float32",
+        "shape": (20,),   # 20 = pos(3) + rot6d(6) + extra dims
+        "names": {"values": [f"eef6d_{i}" for i in range(20)]},
+    },
+
+    "observation.states.eef_left_time": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": {"values": ["eef_left_time"]},
+    },
+
+    "observation.states.eef_right_time": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": {"values": ["eef_right_time"]},
+    },
+
+    "observation.states.qpos": {
+        "dtype": "float32",
+        "shape": (14,),   # 7 per arm × 2 arms
+        "names": {"motors": [f"qpos_{i}" for i in range(14)]},
+    },
+
+    "observation.states.qvel": {
+        "dtype": "float32",
+        "shape": (14,),
+        "names": {"motors": [f"qvel_{i}" for i in range(14)]},
+    },
+
+    "observation.states.effort": {
+        "dtype": "float32",
+        "shape": (14,),
+        "names": {"motors": [f"effort_{i}" for i in range(14)]},
+    },
+
+    "observation.states.qpos_left_time": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": {"values": ["qpos_left_time"]},
+    },
+
+    "observation.states.qpos_right_time": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": {"values": ["qpos_right_time"]},
+    },
+
+    "action": {
+        "dtype": "float32",
+        "shape": (14,),
+        "names": {"motors": [f"joint_action_{i}" for i in range(14)]},
+    },
+
+    "time_stamp": {
+        "dtype": "float32",
+        "shape": (1,),
+        "names": {"values": ["global_timestamp"]},
+    },
+}
+import cv2
+import numpy as np
+
+def decode_image(encoded_array):
+    # HDF5 gives you an array of uint8 → convert to raw bytes
+    data = np.asarray(encoded_array, dtype=np.uint8)
+    img = cv2.imdecode(data, cv2.IMREAD_COLOR)  # returns HWC BGR
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # convert to RGB
+    return img
+
+from pathlib import Path
+
+import numpy as np
+from h5py import File
+
+
+def load_local_episodes(input_h5: Path):
+    """
+    Load one XVLA Soft-Fold episode from a single .hdf5 file.
+    This dataset stores ONE episode per file, NOT a /data/ group.
+    """
+
+    import h5py
+    import numpy as np
+
+    with h5py.File(input_h5, "r") as f:
+
+        # Determine episode length from any observation vector
+        episode_len = f["observations/eef_6d"].shape[0]
+
+        episode = []
+
+        for i in range(episode_len):
+            frame = {
+                # ----------------------
+                # ROOT-LEVEL
+                # ----------------------
+                "task": "fold the cloth",
+                "time_stamp": np.array([f["time_stamp"][i]], dtype=np.float32),
+
+                # ----------------------
+                # OBSERVATIONS
+                # ----------------------
+                "observation": {
+                    "images": {
+                        "cam_high":        f["observations/images/cam_high"][i],
+                        "cam_left_wrist":  f["observations/images/cam_left_wrist"][i],
+                        "cam_right_wrist": f["observations/images/cam_right_wrist"][i],
+                    },
+                    "states": {
+                        "eef_euler":        f["observations/eef"][i],
+                        "eef_quaternion":   f["observations/eef_quaternion"][i],
+                        "eef_6d":           f["observations/eef_6d"][i],
+
+                        "eef_left_time":    np.array([f["observations/eef_left_time"][i]], dtype=np.float32),
+                        "eef_right_time":   np.array([f["observations/eef_right_time"][i]], dtype=np.float32),
+
+                        "qpos":             f["observations/qpos"][i],
+                        "qvel":             f["observations/qvel"][i],
+                        "effort":           f["observations/effort"][i],
+
+                        "qpos_left_time":   np.array([f["observations/qpos_left_time"][i]], dtype=np.float32),
+                        "qpos_right_time":  np.array([f["observations/qpos_right_time"][i]], dtype=np.float32),
+                    },
+                },
+
+                # ----------------------
+                # ACTION (your joint 14-D)
+                # ----------------------
+                "action": f["action"][i].astype(np.float32),
+            }
+
+            episode.append(frame)
+
+        yield episode
+
+# from ray.runtime_env import RuntimeEnv
+from tqdm import tqdm
+
+
+def setup_logger():
+    import sys
+
+    from datatrove.utils.logging import logger
+
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", colorize=True)
+    return logger
+
+
+class SaveLerobotDataset(PipelineStep):
+    name = "Save Temp LerobotDataset"
+    type = "libero2lerobot"
+
+    def __init__(self, tasks: list[tuple[Path, Path, str]]):
+        super().__init__()
+        self.tasks = tasks
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        logger = setup_logger()
+
+        input_h5, output_path, task_instruction = self.tasks[rank]
+
+        if output_path.exists():
+            shutil.rmtree(output_path)
+
+        dataset = LeRobotDataset.create(
+            repo_id=f"{input_h5.parent.name}/{input_h5.name}",
+            root=output_path,
+            fps=20,
+            robot_type="franka",
+            features=XVLA_SOFT_FOLD_FEATURES,
+        )
+
+        logger.info(f"start processing for {input_h5}, saving to {output_path}")
+
+        raw_dataset = load_local_episodes(input_h5)
+        for episode_index, episode_data in enumerate(raw_dataset):
+            with self.track_time("saving episode"):
+
+                for raw_frame in episode_data:
+                    frame_data = {
+                        "task": task_instruction,
+
+                        # ---------------------- IMAGES ----------------------
+                        "observation.images.cam_high":        decode_image(raw_frame["observation"]["images"]["cam_high"]),
+                        "observation.images.cam_left_wrist":  decode_image(raw_frame["observation"]["images"]["cam_left_wrist"]),
+                        "observation.images.cam_right_wrist": decode_image(raw_frame["observation"]["images"]["cam_right_wrist"]),
+
+                        # ---------------------- EEF STATES ----------------------
+                        "observation.states.eef_euler":        raw_frame["observation"]["states"]["eef_euler"],
+                        "observation.states.eef_quaternion":   raw_frame["observation"]["states"]["eef_quaternion"],
+                        "observation.states.eef_6d":           raw_frame["observation"]["states"]["eef_6d"],
+
+                        "observation.states.eef_left_time":    raw_frame["observation"]["states"]["eef_left_time"],
+                        "observation.states.eef_right_time":   raw_frame["observation"]["states"]["eef_right_time"],
+
+                        # ---------------------- JOINT STATES ----------------------
+                        "observation.states.qpos":             raw_frame["observation"]["states"]["qpos"],
+                        "observation.states.qvel":             raw_frame["observation"]["states"]["qvel"],
+                        "observation.states.effort":           raw_frame["observation"]["states"]["effort"],
+
+                        "observation.states.qpos_left_time":   raw_frame["observation"]["states"]["qpos_left_time"],
+                        "observation.states.qpos_right_time":  raw_frame["observation"]["states"]["qpos_right_time"],
+
+                        # ---------------------- ACTION ----------------------
+                        "action": raw_frame["action"],
+
+                        # ---------------------- TIME ----------------------
+                        "time_stamp": raw_frame["time_stamp"],
+                    }
+
+                    dataset.add_frame(frame_data)
+
+                dataset.save_episode()
+                logger.info(f"Processed {dataset.repo_id}, episode {episode_index}, len={len(episode_data)}")
+
+
+def create_aggr_dataset(raw_dirs: list[Path], aggregated_dir: Path):
+    logger = setup_logger()
+
+    all_metadata = [LeRobotDatasetMetadata("", root=raw_dir) for raw_dir in raw_dirs]
+
+    fps, robot_type, features = validate_all_metadata(all_metadata)
+
+    if aggregated_dir.exists():
+        shutil.rmtree(aggregated_dir)
+
+    aggr_meta = LeRobotDatasetMetadata.create(
+        repo_id=f"{aggregated_dir.parent.name}/{aggregated_dir.name}",
+        root=aggregated_dir,
+        fps=fps,
+        robot_type=robot_type,
+        features=features,
+    )
+
+    video_keys = [key for key in features if features[key]["dtype"] == "video"]
+    unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
+    aggr_meta.tasks = pd.DataFrame({"task_index": range(len(unique_tasks))}, index=unique_tasks)
+
+    meta_idx = {"chunk": 0, "file": 0}
+    data_idx = {"chunk": 0, "file": 0}
+    videos_idx = {key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in video_keys}
+
+    aggr_meta.episodes = {}
+
+    for src_meta in tqdm(all_metadata, desc="Copy data and videos"):
+        videos_idx = aggregate_videos(
+            src_meta, aggr_meta, videos_idx, DEFAULT_VIDEO_FILE_SIZE_IN_MB, DEFAULT_CHUNK_SIZE
+        )
+        data_idx = aggregate_data(src_meta, aggr_meta, data_idx, DEFAULT_DATA_FILE_SIZE_IN_MB, DEFAULT_CHUNK_SIZE)
+
+        meta_idx = aggregate_metadata(src_meta, aggr_meta, meta_idx, data_idx, videos_idx)
+
+        aggr_meta.info["total_episodes"] += src_meta.total_episodes
+        aggr_meta.info["total_frames"] += src_meta.total_frames
+
+    logger.info("write tasks")
+    write_tasks(aggr_meta.tasks, aggr_meta.root)
+
+    logger.info("write info")
+    aggr_meta.info.update(
+        {
+            "total_tasks": len(aggr_meta.tasks),
+            "total_episodes": sum(m.total_episodes for m in all_metadata),
+            "total_frames": sum(m.total_frames for m in all_metadata),
+            "splits": {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"},
+        }
+    )
+    write_info(aggr_meta.info, aggr_meta.root)
+
+    logger.info("write stats")
+    aggr_meta.stats = aggregate_stats([m.stats for m in all_metadata])
+    write_stats(aggr_meta.stats, aggr_meta.root)
+
+
+def delete_temp_data(temp_dirs: list[Path]):
+    logger = setup_logger()
+    logger.info("Delete temp data_dir")
+    for temp_dir in temp_dirs:
+        shutil.rmtree(temp_dir)
+
+
+def main(
+    src_paths: list[Path],
+    output_path: Path,
+    executor: str,
+    cpus_per_task: int,
+    tasks_per_job: int,
+    workers: int,
+    resume_dir: Path = None,
+    debug: bool = False,
+    repo_id: str = None,
+    push_to_hub: bool = False,
+):
+    tasks = []
+    for src_path in src_paths:
+        for input_h5 in src_path.glob("*.hdf5"):
+            tasks.append(
+                (
+                    input_h5,
+                    (output_path / (src_path.name + "_temp") / input_h5.stem).resolve(),
+                    "fold the cloth",  # fixed single task
+                )
+            )
+    if len(src_paths) > 1:
+        aggregate_output_path = output_path / ("_".join([src_path.name for src_path in src_paths]) + "_aggregated_lerobot")
+    else:
+        aggregate_output_path = output_path / f"{src_paths[0].name}_lerobot"
+    aggregate_output_path = aggregate_output_path.resolve()
+
+    if debug:
+        executor = "local"
+        workers = 1
+        tasks = tasks[:2]
+        push_to_hub = False
+
+    match executor:
+        case "local":
+            workers = os.cpu_count() // cpus_per_task if workers == -1 else workers
+            executor = LocalPipelineExecutor
+        # case "ray":
+        #     runtime_env = RuntimeEnv(
+        #         env_vars={
+        #             "HDF5_USE_FILE_LOCKING": "FALSE",
+        #             "HF_DATASETS_DISABLE_PROGRESS_BARS": "TRUE",
+        #             "SVT_LOG": "1",
+        #         },
+        #     )
+        #     ray.init(runtime_env=runtime_env)
+        #     executor = RayPipelineExecutor
+        case _:
+            raise ValueError(f"Executor {executor} not supported")
+
+    executor_config = {
+        "tasks": len(tasks),
+        "workers": workers,
+        **({"cpus_per_task": cpus_per_task, "tasks_per_job": tasks_per_job} if False else {}),
+    }
+
+    executor(pipeline=[SaveLerobotDataset(tasks)], **executor_config, logging_dir=resume_dir).run()
+    create_aggr_dataset([task[1] for task in tasks], aggregate_output_path)
+    delete_temp_data([task[1] for task in tasks])
+
+    for task in tasks:
+        shutil.rmtree(task[1].parent, ignore_errors=True)
+
+    if push_to_hub:
+        assert repo_id is not None
+        tags = ["LeRobot", "libero", "franka"]
+        tags.extend([src_path.name for src_path in src_paths])
+        LeRobotDataset(
+            repo_id=repo_id,
+            root=aggregate_output_path,
+        ).push_to_hub(
+            tags=tags,
+            private=False,
+            push_videos=True,
+            license="apache-2.0",
+            upload_large_folder=False,
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--src-paths", type=Path, nargs="+", required=True)
+    parser.add_argument("--output-path", type=Path, required=True)
+    parser.add_argument("--executor", type=str, choices=["local", "ray"], default="local")
+    parser.add_argument("--cpus-per-task", type=int, default=1)
+    parser.add_argument("--tasks-per-job", type=int, default=1, help="number of concurrent tasks per job, only used for ray")
+    parser.add_argument("--workers", type=int, default=-1, help="number of concurrent jobs to run")
+    parser.add_argument("--resume-dir", type=Path, help="logs directory to resume")
+    parser.add_argument("--debug", action="store_true")
+    parser.add_argument("--repo-id", type=str, help="required when push-to-hub is True")
+    parser.add_argument("--push-to-hub", action="store_true", help="upload to hub")
+    args = parser.parse_args()
+
+    main(**vars(args))

examples/dataset/load_lerobot_dataset.py

@@ -132,17 +132,15 @@ print(f"\n{dataset[0][camera_key].shape=}")  # (4, c, h, w)
 print(f"{dataset[0]['observation.state'].shape=}")  # (6, c)
 print(f"{dataset[0]['action'].shape=}\n")  # (64, c)
 
-# Finally, our datasets are fully compatible with PyTorch dataloaders and samplers because they are just
-# PyTorch datasets.
-dataloader = torch.utils.data.DataLoader(
-    dataset,
-    num_workers=4,
-    batch_size=32,
-    shuffle=True,
-)
-
-for batch in dataloader:
-    print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
-    print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
-    print(f"{batch['action'].shape=}")  # (32, 64, c)
-    break
+if __name__ == "__main__":
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=4,
+        batch_size=32,
+        shuffle=True,
+    )
+    for batch in dataloader:
+        print(f"{batch[camera_key].shape=}")  # (32, 4, c, h, w)
+        print(f"{batch['observation.state'].shape=}")  # (32, 6, c)
+        print(f"{batch['action'].shape=}")  # (32, 64, c)
+        break

examples/dataset/utils.py

@@ -0,0 +1,36 @@
+from pathlib import Path
+
+import numpy as np
+from h5py import File
+
+
+def load_local_episodes(input_h5: Path):
+    with File(input_h5, "r") as f:
+        for demo in f["data"].values():
+            demo_len = len(demo["obs/agentview_rgb"])
+            # (-1: open, 1: close) -> (0: close, 1: open)
+            action = np.array(demo["actions"])
+            action = np.concatenate(
+                [
+                    action[:, :6],
+                    (1 - np.clip(action[:, -1], 0, 1))[:, None],
+                ],
+                axis=1,
+            )
+            state = np.concatenate(
+                [
+                    np.array(demo["obs/ee_states"]),
+                    np.array(demo["obs/gripper_states"]),
+                ],
+                axis=1,
+            )
+            episode = {
+                "observation.images.image": np.array(demo["obs/agentview_rgb"]),
+                "observation.images.wrist_image": np.array(demo["obs/eye_in_hand_rgb"]),
+                "observation.state": np.array(state, dtype=np.float32),
+                "observation.states.ee_state": np.array(demo["obs/ee_states"], dtype=np.float32),
+                "observation.states.joint_state": np.array(demo["obs/joint_states"], dtype=np.float32),
+                "observation.states.gripper_state": np.array(demo["obs/gripper_states"], dtype=np.float32),
+                "action": np.array(action, dtype=np.float32),
+            }
+            yield [{**{k: v[i] for k, v in episode.items()}} for i in range(demo_len)]

examples/port_datasets/slurm_aggregate_shards.py

@@ -15,16 +15,12 @@
 # limitations under the License.
 
 import argparse
-import logging
 from pathlib import Path
 
 from datatrove.executor import LocalPipelineExecutor
 from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
-from port_datasets.droid_rlds.port_droid import DROID_SHARDS
-
-from lerobot.datasets.aggregate import aggregate_datasets
-from lerobot.utils.utils import init_logging
+from port_droid import DROID_SHARDS
 
 
 class AggregateDatasets(PipelineStep):
@@ -38,6 +34,11 @@ class AggregateDatasets(PipelineStep):
         self.aggr_repo_id = aggregated_repo_id
 
     def run(self, data=None, rank: int = 0, world_size: int = 1):
+        import logging
+
+        from lerobot.datasets.aggregate import aggregate_datasets
+        from lerobot.utils.utils import init_logging
+
         init_logging()
 
         # Since aggregate_datasets already handles parallel processing internally,

examples/port_datasets/slurm_port_shards.py

@@ -20,7 +20,7 @@ from pathlib import Path
 from datatrove.executor import LocalPipelineExecutor
 from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
-from port_datasets.droid_rlds.port_droid import DROID_SHARDS
+from port_droid import DROID_SHARDS
 
 
 class PortDroidShards(PipelineStep):
@@ -35,7 +35,7 @@ class PortDroidShards(PipelineStep):
 
     def run(self, data=None, rank: int = 0, world_size: int = 1):
         from datasets.utils.tqdm import disable_progress_bars
-        from port_datasets.droid_rlds.port_droid import port_droid, validate_dataset
+        from port_droid import port_droid, validate_dataset
 
         from lerobot.utils.utils import init_logging
 

examples/port_datasets/slurm_upload.py

@@ -24,7 +24,7 @@ from datatrove.executor.slurm import SlurmPipelineExecutor
 from datatrove.pipeline.base import PipelineStep
 from huggingface_hub import HfApi
 from huggingface_hub.constants import REPOCARD_NAME
-from port_datasets.droid_rlds.port_droid import DROID_SHARDS
+from port_droid import DROID_SHARDS
 
 from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata
 from lerobot.datasets.utils import create_lerobot_dataset_card
@@ -185,11 +185,11 @@ class UploadDataset(PipelineStep):
 
 
 def make_upload_executor(
-    repo_id, job_name, logs_dir, workers, partition, cpus_per_task, mem_per_cpu, slurm=True
+    repo_id, job_name, logs_dir, workers, partition, cpus_per_task, mem_per_cpu, private=False, slurm=True
 ):
     kwargs = {
         "pipeline": [
-            UploadDataset(repo_id),
+            UploadDataset(repo_id, private=private),
         ],
         "logging_dir": str(logs_dir / job_name),
     }
@@ -267,6 +267,12 @@ def main():
         default="1950M",
         help="Memory per cpu that each worker will use.",
     )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        default=False,
+        help="Whether to create a private repository.",
+    )
 
     init_logging()
 

examples/tutorial/act/act_training_example.py

@@ -0,0 +1,98 @@
+"""This script demonstrates how to train ACT Policy on a real-world dataset."""
+
+from pathlib import Path
+
+import torch
+
+from lerobot.configs.types import FeatureType
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import dataset_to_policy_features
+from lerobot.policies.act.configuration_act import ACTConfig
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+
+
+def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
+    if delta_indices is None:
+        return [0]
+
+    return [i / fps for i in delta_indices]
+
+
+output_directory = Path("outputs/robot_learning_tutorial/act")
+output_directory.mkdir(parents=True, exist_ok=True)
+
+# Select your device
+device = torch.device("mps")  # or "cuda" or "cpu"
+
+dataset_id = "lerobot/svla_so101_pickplace"
+
+# This specifies the inputs the model will be expecting and the outputs it will produce
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+features = dataset_to_policy_features(dataset_metadata.features)
+
+output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+input_features = {key: ft for key, ft in features.items() if key not in output_features}
+
+cfg = ACTConfig(input_features=input_features, output_features=output_features)
+policy = ACTPolicy(cfg)
+preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
+
+policy.train()
+policy.to(device)
+
+# To perform action chunking, ACT expects a given number of actions as targets
+delta_timestamps = {
+    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
+}
+
+# add image features if they are present
+delta_timestamps |= {
+    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+}
+
+# Instantiate the dataset
+dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
+
+# Create the optimizer and dataloader for offline training
+optimizer = cfg.get_optimizer_preset().build(policy.parameters())
+batch_size = 32
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    pin_memory=device.type != "cpu",
+    drop_last=True,
+)
+
+# Number of training steps and logging frequency
+training_steps = 1
+log_freq = 1
+
+# Run training loop
+step = 0
+done = False
+while not done:
+    for batch in dataloader:
+        batch = preprocessor(batch)
+        loss, _ = policy.forward(batch)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+
+        if step % log_freq == 0:
+            print(f"step: {step} loss: {loss.item():.3f}")
+        step += 1
+        if step >= training_steps:
+            done = True
+            break
+
+# Save the policy checkpoint, alongside the pre/post processors
+policy.save_pretrained(output_directory)
+preprocessor.save_pretrained(output_directory)
+postprocessor.save_pretrained(output_directory)
+
+# Save all assets to the Hub
+policy.push_to_hub("fracapuano/robot_learning_tutorial_act")
+preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")
+postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_act")

examples/tutorial/act/act_using_example.py

@@ -0,0 +1,57 @@
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.policies.act.modeling_act import ACTPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.utils import build_inference_frame, make_robot_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "fracapuano/robot_learning_tutorial_act"
+model = ACTPolicy.from_pretrained(model_id)
+
+dataset_id = "lerobot/svla_so101_pickplace"
+# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+preprocess, postprocess = make_pre_post_processors(model.config, dataset_stats=dataset_metadata.stats)
+
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
+
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
+
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_metadata.features, device=device
+        )
+
+        obs = preprocess(obs_frame)
+
+        action = model.select_action(obs)
+        action = postprocess(action)
+
+        action = make_robot_action(action, dataset_metadata.features)
+
+        robot.send_action(action)
+
+    print("Episode finished! Starting new episode...")

examples/tutorial/async-inf/policy_server.py

@@ -0,0 +1,11 @@
+from lerobot.async_inference.configs import PolicyServerConfig
+from lerobot.async_inference.policy_server import serve
+
+host = ...  # something like "127.0.0.1" if you're exposing to localhost
+port = ...  # something like 8080
+
+config = PolicyServerConfig(
+    host=host,
+    port=port,
+)
+serve(config)

examples/tutorial/async-inf/robot_client.py

@@ -0,0 +1,55 @@
+import threading
+
+from lerobot.async_inference.configs import RobotClientConfig
+from lerobot.async_inference.helpers import visualize_action_queue_size
+from lerobot.async_inference.robot_client import RobotClient
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.robots.so100_follower import SO100FollowerConfig
+
+# these cameras must match the ones expected by the policy - find your cameras with lerobot-find-cameras
+# check the config.json on the Hub for the policy you are using to see the expected camera specs
+camera_cfg = {
+    "up": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "side": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
+
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_cfg)
+
+server_address = ...  # something like "127.0.0.1:8080" if using localhost
+
+# 3. Create client configuration
+client_cfg = RobotClientConfig(
+    robot=robot_cfg,
+    server_address=server_address,
+    policy_device="mps",
+    policy_type="act",
+    pretrained_name_or_path="fracapuano/robot_learning_tutorial_act",
+    chunk_size_threshold=0.5,  # g
+    actions_per_chunk=50,  # make sure this is less than the max actions of the policy
+)
+
+# 4. Create and start client
+client = RobotClient(client_cfg)
+
+# 5. Provide a textual description of the task
+task = ...
+
+if client.start():
+    # Start action receiver thread
+    action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)
+    action_receiver_thread.start()
+
+    try:
+        # Run the control loop
+        client.control_loop(task)
+    except KeyboardInterrupt:
+        client.stop()
+        action_receiver_thread.join()
+        # (Optionally) plot the action queue size
+        visualize_action_queue_size(client.action_queue_size)

examples/tutorial/diffusion/diffusion_training_example.py

@@ -0,0 +1,99 @@
+"""This script demonstrates how to train Diffusion Policy on a real-world dataset."""
+
+from pathlib import Path
+
+import torch
+
+from lerobot.configs.types import FeatureType
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import dataset_to_policy_features
+from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.policies.factory import make_pre_post_processors
+
+
+def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
+    if delta_indices is None:
+        return [0]
+
+    return [i / fps for i in delta_indices]
+
+
+output_directory = Path("outputs/robot_learning_tutorial/diffusion")
+output_directory.mkdir(parents=True, exist_ok=True)
+
+# Select your device
+device = torch.device("mps")  # or "cuda" or "cpu"
+
+dataset_id = "lerobot/svla_so101_pickplace"
+
+# This specifies the inputs the model will be expecting and the outputs it will produce
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+features = dataset_to_policy_features(dataset_metadata.features)
+
+output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+input_features = {key: ft for key, ft in features.items() if key not in output_features}
+
+cfg = DiffusionConfig(input_features=input_features, output_features=output_features)
+policy = DiffusionPolicy(cfg)
+preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
+
+policy.train()
+policy.to(device)
+
+# To perform action chunking, ACT expects a given number of actions as targets
+delta_timestamps = {
+    "observation.state": make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps),
+    "action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
+}
+
+# add image features if they are present
+delta_timestamps |= {
+    k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps) for k in cfg.image_features
+}
+
+# Instantiate the dataset
+dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
+
+# Create the optimizer and dataloader for offline training
+optimizer = cfg.get_optimizer_preset().build(policy.parameters())
+batch_size = 32
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=batch_size,
+    shuffle=True,
+    pin_memory=device.type != "cpu",
+    drop_last=True,
+)
+
+# Number of training steps and logging frequency
+training_steps = 1
+log_freq = 1
+
+# Run training loop
+step = 0
+done = False
+while not done:
+    for batch in dataloader:
+        batch = preprocessor(batch)
+        loss, _ = policy.forward(batch)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+
+        if step % log_freq == 0:
+            print(f"step: {step} loss: {loss.item():.3f}")
+        step += 1
+        if step >= training_steps:
+            done = True
+            break
+
+# Save the policy checkpoint, alongside the pre/post processors
+policy.save_pretrained(output_directory)
+preprocessor.save_pretrained(output_directory)
+postprocessor.save_pretrained(output_directory)
+
+# Save all assets to the Hub
+policy.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+preprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")
+postprocessor.push_to_hub("fracapuano/robot_learning_tutorial_diffusion")

examples/tutorial/diffusion/diffusion_using_example.py

@@ -0,0 +1,60 @@
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.utils import build_inference_frame, make_robot_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "fracapuano/robot_learning_tutorial_diffusion"
+
+model = DiffusionPolicy.from_pretrained(model_id)
+
+dataset_id = "lerobot/svla_so101_pickplace"
+# This only downloads the metadata for the dataset, ~10s of MB even for large-scale datasets
+dataset_metadata = LeRobotDatasetMetadata(dataset_id)
+preprocess, postprocess = make_pre_post_processors(
+    model.config, model_id, dataset_stats=dataset_metadata.stats
+)
+
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+
+# # find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# # the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "side": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "up": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
+
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
+
+
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_metadata.features, device=device
+        )
+
+        obs = preprocess(obs_frame)
+
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_metadata.features)
+        robot.send_action(action)
+
+    print("Episode finished! Starting new episode...")

examples/tutorial/pi0/using_pi0_example.py

@@ -0,0 +1,67 @@
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+from lerobot.policies.utils import build_inference_frame, make_robot_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "lerobot/pi0_base"
+
+model = PI0Policy.from_pretrained(model_id)
+
+preprocess, postprocess = make_pre_post_processors(
+    model.config,
+    model_id,
+    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
+
+# find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "base_0_rgb": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "left_wrist_0_rgb": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+    "right_wrist_0_rgb": OpenCVCameraConfig(index_or_path=2, width=640, height=480, fps=30),
+}
+
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
+
+task = ""  # something like "pick the red block"
+robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+
+# This is used to match the raw observation keys to the keys expected by the policy
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
+
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
+        )
+
+        obs = preprocess(obs_frame)
+
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_features)
+        robot.send_action(action)
+
+    print("Episode finished! Starting new episode...")

examples/tutorial/rl/hilserl_example.py

@@ -0,0 +1,345 @@
+import multiprocessing as mp
+import signal
+from pathlib import Path
+from queue import Empty, Full
+
+import torch
+import torch.optim as optim
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
+from lerobot.policies.sac.configuration_sac import SACConfig
+from lerobot.policies.sac.modeling_sac import SACPolicy
+from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
+from lerobot.rl.buffer import ReplayBuffer
+from lerobot.rl.gym_manipulator import make_robot_env
+from lerobot.robots.so100_follower import SO100FollowerConfig
+from lerobot.teleoperators.so100_leader import SO100LeaderConfig
+from lerobot.teleoperators.utils import TeleopEvents
+
+LOG_EVERY = 10
+SEND_EVERY = 10
+
+
+def run_learner(
+    transitions_queue: mp.Queue,
+    parameters_queue: mp.Queue,
+    shutdown_event: mp.Event,
+    policy_learner: SACPolicy,
+    online_buffer: ReplayBuffer,
+    offline_buffer: ReplayBuffer,
+    lr: float = 3e-4,
+    batch_size: int = 32,
+    device: torch.device = "mps",
+):
+    """The learner process - trains SAC policy on transitions streamed from the actor, updating parameters
+    for the actor to adopt."""
+    policy_learner.train()
+    policy_learner.to(device)
+
+    # Create Adam optimizer from scratch - simple and clean
+    optimizer = optim.Adam(policy_learner.parameters(), lr=lr)
+
+    print(f"[LEARNER] Online buffer capacity: {online_buffer.capacity}")
+    print(f"[LEARNER] Offline buffer capacity: {offline_buffer.capacity}")
+
+    training_step = 0
+
+    while not shutdown_event.is_set():
+        # retrieve incoming transitions from the actor process
+        try:
+            transitions = transitions_queue.get(timeout=0.1)
+            for transition in transitions:
+                # HIL-SERL: Add ALL transitions to online buffer
+                online_buffer.add(**transition)
+
+                # HIL-SERL: Add ONLY human intervention transitions to offline buffer
+                is_intervention = transition.get("complementary_info", {}).get("is_intervention", False)
+                if is_intervention:
+                    offline_buffer.add(**transition)
+                    print(
+                        f"[LEARNER] Human intervention detected! Added to offline buffer (now {len(offline_buffer)} transitions)"
+                    )
+
+        except Empty:
+            pass  # No transitions available, continue
+
+        # Train if we have enough data
+        if len(online_buffer) >= policy_learner.config.online_step_before_learning:
+            # Sample from online buffer (autonomous + human data)
+            online_batch = online_buffer.sample(batch_size // 2)
+
+            # Sample from offline buffer (human demonstrations only, either precollected or at runtime)
+            offline_batch = offline_buffer.sample(batch_size // 2)
+
+            # Combine batches - this is the key HIL-SERL mechanism!
+            batch = {}
+            for key in online_batch:
+                if key in offline_batch:
+                    batch[key] = torch.cat([online_batch[key], offline_batch[key]], dim=0)
+                else:
+                    batch[key] = online_batch[key]
+
+            loss, _ = policy_learner.forward(batch)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            training_step += 1
+
+            if training_step % LOG_EVERY == 0:
+                print(
+                    f"[LEARNER] Training step {training_step}, Loss: {loss.item():.4f}, "
+                    f"Buffers: Online={len(online_buffer)}, Offline={len(offline_buffer)}"
+                )
+
+            # Send updated parameters to actor every 10 training steps
+            if training_step % SEND_EVERY == 0:
+                try:
+                    state_dict = {k: v.cpu() for k, v in policy_learner.state_dict().items()}
+                    parameters_queue.put_nowait(state_dict)
+                    print("[LEARNER] Sent updated parameters to actor")
+                except Full:
+                    # Missing write due to queue not being consumed (should happen rarely)
+                    pass
+
+    print("[LEARNER] Learner process finished")
+
+
+def run_actor(
+    transitions_queue: mp.Queue,
+    parameters_queue: mp.Queue,
+    shutdown_event: mp.Event,
+    policy_actor: SACPolicy,
+    reward_classifier: Classifier,
+    env_cfg: HILSerlRobotEnvConfig,
+    device: torch.device = "mps",
+    output_directory: Path | None = None,
+):
+    """The actor process - interacts with environment and collects data.
+    The policy is frozen and only the parameters are updated, popping the most recent ones from a queue."""
+    policy_actor.eval()
+    policy_actor.to(device)
+
+    reward_classifier.eval()
+    reward_classifier.to(device)
+
+    # Create robot environment inside the actor process
+    env, teleop_device = make_robot_env(env_cfg)
+
+    try:
+        for episode in range(MAX_EPISODES):
+            if shutdown_event.is_set():
+                break
+
+            obs, _info = env.reset()
+            episode_reward = 0.0
+            step = 0
+            episode_transitions = []
+
+            print(f"[ACTOR] Starting episode {episode + 1}")
+
+            while step < MAX_STEPS_PER_EPISODE and not shutdown_event.is_set():
+                try:
+                    new_params = parameters_queue.get_nowait()
+                    policy_actor.load_state_dict(new_params)
+                    print("[ACTOR] Updated policy parameters from learner")
+                except Empty:  # No new updated parameters available from learner, waiting
+                    pass
+
+                # Get action from policy
+                policy_obs = make_policy_obs(obs, device=device)
+                action_tensor = policy_actor.select_action(policy_obs)  # predicts a single action
+                action = action_tensor.squeeze(0).cpu().numpy()
+
+                # Step environment
+                next_obs, _env_reward, terminated, truncated, _info = env.step(action)
+                done = terminated or truncated
+
+                # Predict reward
+                policy_next_obs = make_policy_obs(next_obs, device=device)
+                reward = reward_classifier.predict_reward(policy_next_obs)
+
+                if reward >= 1.0 and not done:  # success detected! halt episode
+                    terminated = True
+                    done = True
+
+                # In HIL-SERL, human interventions come from the teleop device
+                is_intervention = False
+                if hasattr(teleop_device, "get_teleop_events"):
+                    # Real intervention detection from teleop device
+                    teleop_events = teleop_device.get_teleop_events()
+                    is_intervention = teleop_events.get(TeleopEvents.IS_INTERVENTION, False)
+
+                # Store transition with intervention metadata
+                transition = {
+                    "state": policy_obs,
+                    "action": action,
+                    "reward": float(reward) if hasattr(reward, "item") else reward,
+                    "next_state": policy_next_obs,
+                    "done": done,
+                    "truncated": truncated,
+                    "complementary_info": {
+                        "is_intervention": is_intervention,
+                    },
+                }
+
+                episode_transitions.append(transition)
+
+                episode_reward += reward
+                step += 1
+
+                obs = next_obs
+
+                if done:
+                    break
+
+            # Send episode transitions to learner
+            transitions_queue.put_nowait(episode_transitions)
+
+    except KeyboardInterrupt:
+        print("[ACTOR] Interrupted by user")
+    finally:
+        # Clean up
+        if hasattr(env, "robot") and env.robot.is_connected:
+            env.robot.disconnect()
+        if teleop_device and hasattr(teleop_device, "disconnect"):
+            teleop_device.disconnect()
+        if output_directory is not None:
+            policy_actor.save_pretrained(output_directory)
+            print(f"[ACTOR] Latest actor policy saved at: {output_directory}")
+
+        print("[ACTOR] Actor process finished")
+
+
+def make_policy_obs(obs, device: torch.device = "cpu"):
+    return {
+        "observation.state": torch.from_numpy(obs["agent_pos"]).float().unsqueeze(0).to(device),
+        **{
+            f"observation.image.{k}": torch.from_numpy(obs["pixels"][k]).float().unsqueeze(0).to(device)
+            for k in obs["pixels"]
+        },
+    }
+
+
+"""Main function - coordinates actor and learner processes."""
+
+device = "mps"  # or "cuda" or "cpu"
+output_directory = Path("outputs/robot_learning_tutorial/hil_serl")
+output_directory.mkdir(parents=True, exist_ok=True)
+
+# find ports using lerobot-find-port
+follower_port = ...
+leader_port = ...
+
+# the robot ids are used the load the right calibration files
+follower_id = ...
+leader_id = ...
+
+# A pretrained model (to be used in-distribution!)
+reward_classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+reward_classifier = Classifier.from_pretrained(reward_classifier_id)
+
+reward_classifier.to(device)
+reward_classifier.eval()
+
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+# Robot and environment configuration
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id)
+teleop_cfg = SO100LeaderConfig(port=leader_port, id=leader_id)
+processor_cfg = HILSerlProcessorConfig(control_mode="leader")
+
+env_cfg = HILSerlRobotEnvConfig(robot=robot_cfg, teleop=teleop_cfg, processor=processor_cfg)
+
+# Create robot environment
+env, teleop_device = make_robot_env(env_cfg)
+
+obs_features = hw_to_dataset_features(env.robot.observation_features, "observation")
+action_features = hw_to_dataset_features(env.robot.action_features, "action")
+
+# Create SAC policy for action selection
+policy_cfg = SACConfig(
+    device=device,
+    input_features=obs_features,
+    output_features=action_features,
+)
+
+policy_actor = SACPolicy(policy_cfg)
+policy_learner = SACPolicy(policy_cfg)
+
+demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
+offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)
+
+# Online buffer: initialized from scratch
+online_replay_buffer = ReplayBuffer(device=device, state_keys=list(obs_features.keys()))
+# Offline buffer: Created from dataset (pre-populated it with demonstrations)
+offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
+    lerobot_dataset=offline_dataset, device=device, state_keys=list(obs_features.keys())
+)
+
+# Create communication channels between learner and actor processes
+transitions_queue = mp.Queue(maxsize=10)
+parameters_queue = mp.Queue(maxsize=2)
+shutdown_event = mp.Event()
+
+
+# Signal handler for graceful shutdown
+def signal_handler(sig):
+    print(f"\nSignal {sig} received, shutting down...")
+    shutdown_event.set()
+
+
+signal.signal(signal.SIGINT, signal_handler)
+signal.signal(signal.SIGTERM, signal_handler)
+
+# Create processes
+learner_process = mp.Process(
+    target=run_learner,
+    args=(
+        transitions_queue,
+        parameters_queue,
+        shutdown_event,
+        policy_learner,
+        online_replay_buffer,
+        offline_replay_buffer,
+    ),
+    kwargs={"device": device},  # can run on accelerated hardware for training
+)
+
+actor_process = mp.Process(
+    target=run_actor,
+    args=(
+        transitions_queue,
+        parameters_queue,
+        shutdown_event,
+        policy_actor,
+        reward_classifier,
+        env_cfg,
+        output_directory,
+    ),
+    kwargs={"device": "cpu"},  # actor is frozen, can run on CPU or accelerate for inference
+)
+
+learner_process.start()
+actor_process.start()
+
+try:
+    # Wait for actor to finish (it controls the episode loop)
+    actor_process.join()
+    shutdown_event.set()
+    learner_process.join(timeout=10)
+
+except KeyboardInterrupt:
+    print("Main process interrupted")
+    shutdown_event.set()
+    actor_process.join(timeout=5)
+    learner_process.join(timeout=10)
+
+finally:
+    if learner_process.is_alive():
+        learner_process.terminate()
+    if actor_process.is_alive():
+        actor_process.terminate()

examples/tutorial/rl/reward_classifier_example.py

@@ -0,0 +1,62 @@
+import torch
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
+
+# Device to use for training
+device = "mps"  # or "cuda", or "cpu"
+
+# Load the dataset used for training
+repo_id = "lerobot/example_hil_serl_dataset"
+dataset = LeRobotDataset(repo_id)
+
+# Configure the policy to extract features from the image frames
+camera_keys = dataset.meta.camera_keys
+
+config = RewardClassifierConfig(
+    num_cameras=len(camera_keys),
+    device=device,
+    # backbone model to extract features from the image frames
+    model_name="microsoft/resnet-18",
+)
+
+# Make policy, preprocessor, and optimizer
+policy = make_policy(config, ds_meta=dataset.meta)
+optimizer = config.get_optimizer_preset().build(policy.parameters())
+preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
+
+
+classifier_id = "fracapuano/reward_classifier_hil_serl_example"
+
+# Instantiate a dataloader
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
+
+# Training loop
+num_epochs = 5
+for epoch in range(num_epochs):
+    total_loss = 0
+    total_accuracy = 0
+    for batch in dataloader:
+        # Preprocess the batch and move it to the correct device.
+        batch = preprocessor(batch)
+
+        # Forward pass
+        loss, output_dict = policy.forward(batch)
+
+        # Backward pass and optimization
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item()
+        total_accuracy += output_dict["accuracy"]
+
+    avg_loss = total_loss / len(dataloader)
+    avg_accuracy = total_accuracy / len(dataloader)
+    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
+
+print("Training finished!")
+
+# You can now save the trained policy.
+policy.push_to_hub(classifier_id)

examples/tutorial/smolvla/using_smolvla_example.py

@@ -0,0 +1,66 @@
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.datasets.utils import hw_to_dataset_features
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+from lerobot.policies.utils import build_inference_frame, make_robot_action
+from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
+from lerobot.robots.so100_follower.so100_follower import SO100Follower
+
+MAX_EPISODES = 5
+MAX_STEPS_PER_EPISODE = 20
+
+device = torch.device("mps")  # or "cuda" or "cpu"
+model_id = "lerobot/smolvla_base"
+
+model = SmolVLAPolicy.from_pretrained(model_id)
+
+preprocess, postprocess = make_pre_post_processors(
+    model.config,
+    model_id,
+    # This overrides allows to run on MPS, otherwise defaults to CUDA (if available)
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
+
+# find ports using lerobot-find-port
+follower_port = ...  # something like "/dev/tty.usbmodem58760431631"
+
+# the robot ids are used the load the right calibration files
+follower_id = ...  # something like "follower_so100"
+
+# Robot and environment configuration
+# Camera keys must match the name and resolutions of the ones used for training!
+# You can check the camera keys expected by a model in the info.json card on the model card on the Hub
+camera_config = {
+    "camera1": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+    "camera2": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30),
+}
+
+robot_cfg = SO100FollowerConfig(port=follower_port, id=follower_id, cameras=camera_config)
+robot = SO100Follower(robot_cfg)
+robot.connect()
+
+task = ""  # something like "pick the red block"
+robot_type = ""  # something like "so100_follower" for multi-embodiment datasets
+
+# This is used to match the raw observation keys to the keys expected by the policy
+action_features = hw_to_dataset_features(robot.action_features, "action")
+obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+dataset_features = {**action_features, **obs_features}
+
+for _ in range(MAX_EPISODES):
+    for _ in range(MAX_STEPS_PER_EPISODE):
+        obs = robot.get_observation()
+        obs_frame = build_inference_frame(
+            observation=obs, ds_features=dataset_features, device=device, task=task, robot_type=robot_type
+        )
+
+        obs = preprocess(obs_frame)
+
+        action = model.select_action(obs)
+        action = postprocess(action)
+        action = make_robot_action(action, dataset_features)
+        robot.send_action(action)
+
+    print("Episode finished! Starting new episode...")

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.3.4"
+version = "0.4.2"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -74,15 +74,15 @@ dependencies = [
     "packaging>=24.2,<26.0",
     "pynput>=1.7.7,<1.9.0",
     "pyserial>=3.5,<4.0",
-    "wandb>=0.20.0,<0.23.0",
+    "wandb>=0.20.0,<0.22.0", # TODO: Bumb dependency (compatible with protobuf)
 
     "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency
     "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency
     "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency
 
     "draccus==0.10.0", # TODO: Remove ==
-    "gymnasium>=1.0.0",
-    "rerun-sdk>=0.21.0,<0.23.0", # TODO: Bumb dependency
+    "gymnasium>=1.1.1,<2.0.0",
+    "rerun-sdk>=0.24.0,<0.27.0",
 
     # Support dependencies
     "deepdiff>=7.0.1,<9.0.0",
@@ -97,7 +97,7 @@ dependencies = [
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.10.0"]
 transformers-dep = ["transformers>=4.53.0,<5.0.0"]
-grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"]
+grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # TODO: Bumb dependency (compatible with wandb)
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
@@ -113,17 +113,23 @@ intelrealsense = [
     "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
     "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0"]
-# stretch = [
-#     "hello-robot-stretch-body>=0.7.27 ; sys_platform == 'linux'",
-#     "pyrender @ git+https://github.com/mmatl/pyrender.git ; sys_platform == 'linux'",
-#     "pyrealsense2>=2.55.1.6486 ; sys_platform != 'darwin'"
-# ] # TODO: Currently not supported
+phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
 
 # Policies
 pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
-hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.11,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
+groot = [
+    "lerobot[transformers-dep]",
+    "peft>=0.13.0,<1.0.0",
+    "dm-tree>=0.1.8,<1.0.0",
+    "timm>=1.0.0,<1.1.0",
+    "safetensors>=0.4.3,<1.0.0",
+    "Pillow>=10.0.0,<13.0.0",
+    "decord>=0.6.0,<1.0.0; (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
+    "ninja>=1.11.1,<2.0.0",
+    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
+]
+hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
 async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
@@ -136,8 +142,8 @@ video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 # Simulation
 aloha = ["gym-aloha>=0.1.2,<0.2.0"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"]
-metaworld = ["metaworld>=3.0.0"]
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
+metaworld = ["metaworld==3.0.0"]
 
 # All
 all = [
@@ -150,6 +156,7 @@ all = [
     "lerobot[intelrealsense]",
     "lerobot[pi]",
     "lerobot[smolvla]",
+    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[hilserl]",
     "lerobot[async]",
     "lerobot[dev]",
@@ -234,9 +241,6 @@ exclude_dirs = [
     "tests",
     "benchmarks",
     "src/lerobot/datasets/push_dataset_to_hub",
-    "src/lerobot/datasets/v2/convert_dataset_v1_to_v2",
-    "src/lerobot/policies/pi0/conversion_scripts",
-    "src/lerobot/scripts/push_dataset_to_hub.py",
 ]
 skips = ["B101", "B311", "B404", "B603", "B615"]
 
@@ -251,6 +255,8 @@ default.extend-ignore-identifiers-re = [
     "pn",
     "ser",
     "ein",
+    "thw",
+    "inpt",
 ]
 
 # TODO: Uncomment when ready to use
@@ -289,7 +295,6 @@ ignore_errors = true
 
 [[tool.mypy.overrides]]
 module = "lerobot.envs.*"
-# Enable type checking only for the envs module
 ignore_errors = false
 
 
@@ -297,17 +302,22 @@ ignore_errors = false
 # module = "lerobot.utils.*"
 # ignore_errors = false
 
-# [[tool.mypy.overrides]]
-# module = "lerobot.configs.*"
-# ignore_errors = false
+[[tool.mypy.overrides]]
+module = "lerobot.configs.*"
+ignore_errors = false
+
+# extra strictness for configs
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.optim.*"
 # ignore_errors = false
 
-# [[tool.mypy.overrides]]
-# module = "lerobot.model.*"
-# ignore_errors = false
+[[tool.mypy.overrides]]
+module = "lerobot.model.*"
+ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.processor.*"
@@ -317,9 +327,9 @@ ignore_errors = false
 # module = "lerobot.datasets.*"
 # ignore_errors = false
 
-# [[tool.mypy.overrides]]
-# module = "lerobot.cameras.*"
-# ignore_errors = false
+[[tool.mypy.overrides]]
+module = "lerobot.cameras.*"
+ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.motors.*"

requirements-macos.txt

@@ -1,3 +1,4 @@
+#
 # This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
@@ -12,47 +13,62 @@ absl-py==2.3.1
     #   dm-tree
     #   labmaze
     #   mujoco
-accelerate==1.9.0
-    # via lerobot
+    #   tensorboard
+accelerate==1.11.0
+    # via
+    #   lerobot
+    #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.12.15
+aiohttp==3.13.1
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
+antlr4-python3-runtime==4.9.3
+    # via
+    #   hydra-core
+    #   omegaconf
+anyio==4.11.0
+    # via
+    #   starlette
+    #   watchfiles
 asttokens==3.0.0
     # via stack-data
 async-timeout==5.0.1
     # via aiohttp
-attrs==25.3.0
+attrs==25.4.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
+    #   jsonschema
+    #   referencing
     #   rerun-sdk
-av==15.0.0
+av==15.1.0
     # via lerobot
-blinker==1.9.0
-    # via flask
-certifi==2025.7.14
+bddl==1.0.1
+    # via libero
+certifi==2025.10.5
     # via
     #   requests
     #   sentry-sdk
-cffi==1.17.1
+cffi==2.0.0
     # via pymunk
 cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.2
+charset-normalizer==3.4.4
     # via requests
-click==8.2.1
+click==8.3.0
     # via
-    #   flask
+    #   uvicorn
     #   wandb
 cloudpickle==3.1.1
-    # via gymnasium
-cmake==4.0.3
+    # via
+    #   gymnasium
+    #   libero
+cmake==4.1.0
     # via lerobot
 cmeel==0.57.3
     # via
@@ -94,27 +110,27 @@ coal-library==3.0.1
     # via pin
 contourpy==1.3.2
     # via matplotlib
-coverage[toml]==7.10.1
+coverage[toml]==7.11.0
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==3.6.0
+datasets==4.1.1
     # via lerobot
-debugpy==1.8.15
+debugpy==1.8.17
     # via lerobot
 decorator==5.2.1
     # via ipython
-deepdiff==8.5.0
+deepdiff==8.6.1
     # via lerobot
-diffusers==0.34.0
+diffusers==0.35.2
     # via lerobot
-dill==0.3.8
+dill==0.4.0
     # via
     #   datasets
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.14
+dm-control==1.0.34
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -122,29 +138,45 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
+    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
     # via lerobot
-dynamixel-sdk==3.7.31
+dynamixel-sdk==3.8.4
     # via lerobot
+easydict==1.13
+    # via libero
+egl-probe @ git+https://github.com/huggingface/egl_probe.git
+    # via
+    #   libero
+    #   robomimic
 eigenpy==3.10.3
     # via coal-library
 einops==0.8.1
-    # via lerobot
+    # via
+    #   lerobot
+    #   libero
 eiquadprog==1.2.9
     # via placo
+etils[epath,epy]==1.13.0
+    # via mujoco
 exceptiongroup==1.3.0
     # via
+    #   anyio
     #   ipython
     #   pytest
-executing==2.2.0
+executing==2.2.1
     # via stack-data
 farama-notifications==0.0.4
     # via gymnasium
+fastapi==0.119.1
+    # via teleop
+fastjsonschema==2.21.2
+    # via nbformat
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.18.0
+filelock==3.20.0
     # via
     #   datasets
     #   diffusers
@@ -152,24 +184,25 @@ filelock==3.18.0
     #   torch
     #   transformers
     #   virtualenv
-flask==3.1.1
-    # via lerobot
-fonttools==4.59.0
+fonttools==4.60.1
     # via matplotlib
-frozenlist==1.7.0
+frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.3.0
+fsspec[http]==2025.9.0
     # via
     #   datasets
+    #   etils
     #   huggingface-hub
     #   torch
+future==1.0.0
+    # via libero
 gitdb==4.0.12
     # via gitpython
 gitpython==3.1.45
     # via wandb
-glfw==2.9.0
+glfw==2.10.0
     # via
     #   dm-control
     #   mujoco
@@ -177,61 +210,79 @@ 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-aloha==0.1.1
+gym-hil==0.1.13
     # via lerobot
-gym-hil==0.1.10
+gym-pusht==0.1.6
     # via lerobot
-gym-pusht==0.1.5
-    # via lerobot
-gym-xarm==0.1.1
-    # via lerobot
-gymnasium==0.29.1
+gymnasium==1.2.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
-    #   gym-xarm
-    #   gymnasium-robotics
     #   lerobot
-    #   pettingzoo
-gymnasium-robotics==1.2.4
-    # via gym-xarm
+    #   libero
+    #   metaworld
+h11==0.16.0
+    # via uvicorn
+h5py==3.15.1
+    # via robomimic
+hebi-py==2.11.0
+    # via lerobot
 hf-transfer==0.1.9
     # via huggingface-hub
-hf-xet==1.1.5
+hf-xet==1.1.10
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-huggingface-hub[cli,hf-transfer]==0.34.3
+httptools==0.7.1
+    # via uvicorn
+huggingface-hub[cli,hf-transfer]==0.35.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
+    #   peft
+    #   timm
     #   tokenizers
     #   transformers
-identify==2.6.12
+hydra-core==1.3.2
+    # via libero
+identify==2.6.15
     # via pre-commit
-idna==3.10
+idna==3.11
     # via
+    #   anyio
     #   requests
     #   yarl
 imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
-    #   gymnasium-robotics
     #   lerobot
+    #   metaworld
+    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via imageio
+    # via
+    #   imageio
+    #   robomimic
 importlib-metadata==8.7.0
     # via diffusers
-iniconfig==2.1.0
+importlib-resources==6.5.2
+    # via etils
+iniconfig==2.3.0
     # via pytest
 inquirerpy==0.3.4
     # via huggingface-hub
@@ -239,50 +290,71 @@ ipython==8.37.0
     # via meshcat
 ischedule==1.2.7
     # via placo
-itsdangerous==2.2.0
-    # via flask
 jedi==0.19.2
     # via ipython
 jinja2==3.1.6
-    # via
-    #   flask
-    #   gymnasium-robotics
-    #   torch
+    # via torch
 jsonlines==4.0.0
     # via lerobot
-kiwisolver==1.4.8
+jsonschema==4.25.1
+    # via nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-core==5.9.1
+    # via nbformat
+jupytext==1.18.1
+    # via bddl
+kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
 lazy-loader==0.4
     # via scikit-image
-lxml==6.0.0
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+    # via lerobot
+llvmlite==0.45.1
+    # via numba
+lxml==6.0.2
     # via dm-control
-markupsafe==3.0.2
+markdown==3.9
+    # via tensorboard
+markdown-it-py==4.0.0
+    # via
+    #   jupytext
+    #   mdit-py-plugins
+markupsafe==3.0.3
     # via
-    #   flask
     #   jinja2
     #   werkzeug
-matplotlib==3.10.5
-    # via lerobot
-matplotlib-inline==0.1.7
+matplotlib==3.10.7
+    # via
+    #   lerobot
+    #   libero
+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==2.3.7
+mujoco==3.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   gym-xarm
-    #   gymnasium-robotics
-multidict==6.6.3
+    #   libero
+    #   metaworld
+    #   robosuite
+multidict==6.7.0
     # via
     #   aiohttp
     #   yarl
@@ -290,17 +362,25 @@ multiprocess==0.70.16
     # via datasets
 mypy-extensions==1.1.0
     # via typing-inspect
+nbformat==5.10.4
+    # via jupytext
 networkx==3.4.2
     # via
+    #   bddl
     #   scikit-image
     #   torch
+ninja==1.13.0
+    # via lerobot
 nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
+numba==0.62.1
+    # via robosuite
 numpy==2.2.6
     # via
     #   accelerate
+    #   bddl
     #   cmeel-boost
     #   contourpy
     #   datasets
@@ -309,25 +389,43 @@ numpy==2.2.6
     #   dm-env
     #   dm-tree
     #   gymnasium
-    #   gymnasium-robotics
+    #   h5py
+    #   hebi-py
     #   imageio
     #   labmaze
+    #   libero
     #   matplotlib
     #   meshcat
+    #   metaworld
     #   mujoco
+    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
-    #   pettingzoo
+    #   peft
+    #   pyquaternion
+    #   reachy2-sdk
     #   rerun-sdk
+    #   robomimic
+    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
+    #   teleop
+    #   tensorboard
+    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
+    #   transforms3d
+omegaconf==2.3.0
+    # via hydra-core
 opencv-python==4.12.0.88
-    # via gym-pusht
+    # via
+    #   gym-pusht
+    #   libero
+    #   reachy2-sdk
+    #   robosuite
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -337,53 +435,63 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
+    #   hydra-core
+    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
+    #   peft
     #   pytest
+    #   reachy2-sdk
     #   scikit-image
+    #   tensorboard
+    #   tensorboardx
     #   transformers
     #   wandb
-pandas==2.3.1
+pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.4
+parso==0.8.5
     # via jedi
-pettingzoo==1.24.3
-    # via gymnasium-robotics
+peft==0.17.1
+    # via lerobot
 pexpect==4.9.0
     # via ipython
 pfzy==0.3.4
     # via inquirerpy
-pillow==11.3.0
+pillow==12.0.0
     # via
     #   diffusers
     #   imageio
+    #   lerobot
     #   matplotlib
     #   meshcat
     #   rerun-sdk
+    #   robosuite
     #   scikit-image
+    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
 placo==0.9.14
     # via lerobot
-platformdirs==4.3.8
+platformdirs==4.5.0
     # via
+    #   jupyter-core
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.2.0
+pre-commit==4.3.0
     # via lerobot
-prompt-toolkit==3.0.51
+prompt-toolkit==3.0.52
     # via
     #   inquirerpy
     #   ipython
-propcache==0.3.2
+propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
@@ -392,11 +500,17 @@ protobuf==6.31.0
     #   dm-control
     #   grpcio-tools
     #   lerobot
+    #   reachy2-sdk
+    #   reachy2-sdk-api
+    #   tensorboard
+    #   tensorboardx
     #   wandb
-psutil==7.0.0
+psutil==7.1.1
     # via
     #   accelerate
     #   imageio
+    #   peft
+    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
@@ -405,11 +519,13 @@ pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.22
+pycparser==2.23
     # via cffi
-pydantic==2.11.7
-    # via wandb
-pydantic-core==2.33.2
+pydantic==2.12.3
+    # via
+    #   fastapi
+    #   wandb
+pydantic-core==2.41.4
     # via pydantic
 pygame==2.6.1
     # via
@@ -424,40 +540,42 @@ pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.2.12
+pyngrok==7.4.1
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyobjc-core==11.1
+pyobjc-core==12.0
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-cocoa
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==11.1
+pyobjc-framework-applicationservices==12.0
     # via pynput
-pyobjc-framework-cocoa==11.1
+pyobjc-framework-cocoa==12.0
     # via
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
     #   pyobjc-framework-quartz
-pyobjc-framework-coretext==11.1
+pyobjc-framework-coretext==12.0
     # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==11.1
+pyobjc-framework-quartz==12.0
     # via
     #   pynput
     #   pyobjc-framework-applicationservices
     #   pyobjc-framework-coretext
-pyopengl==3.1.9
+pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.3
+pyparsing==3.2.5
     # via
     #   dm-control
     #   matplotlib
+pyquaternion==0.9.9
+    # via reachy2-sdk
 pyrealsense2-macosx==2.54.2
     # via lerobot
 pyserial==3.5
@@ -465,12 +583,14 @@ pyserial==3.5
     #   dynamixel-sdk
     #   feetech-servo-sdk
     #   lerobot
-pytest==8.4.1
+pytest==8.4.2
     # via
+    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
-pytest-cov==6.2.1
+    #   teleop
+pytest-cov==7.0.0
     # via lerobot
 pytest-timeout==2.4.0
     # via lerobot
@@ -478,46 +598,73 @@ python-dateutil==2.9.0.post0
     # via
     #   matplotlib
     #   pandas
+python-dotenv==1.1.1
+    # via uvicorn
 pytz==2025.2
     # via pandas
-pyyaml==6.0.2
+pyyaml==6.0.3
     # via
     #   accelerate
     #   datasets
     #   draccus
+    #   hebi-py
     #   huggingface-hub
+    #   jupytext
+    #   omegaconf
+    #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
+    #   timm
     #   transformers
+    #   uvicorn
     #   wandb
 pyyaml-include==1.4.1
     # via draccus
-pyzmq==27.0.0
+pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-regex==2025.7.34
+reachy2-sdk==1.0.14
+    # via lerobot
+reachy2-sdk-api==1.0.21
+    # via reachy2-sdk
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+regex==2025.10.23
     # via
     #   diffusers
     #   transformers
-requests==2.32.4
+requests==2.32.5
     # via
     #   datasets
     #   diffusers
     #   dm-control
     #   huggingface-hub
+    #   teleop
     #   transformers
     #   wandb
-rerun-sdk==0.22.1
+rerun-sdk==0.26.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-safetensors==0.5.3
+robomimic==0.2.0
+    # via libero
+robosuite==1.4.0
+    # via libero
+rpds-py==0.28.0
+    # via
+    #   jsonschema
+    #   referencing
+safetensors==0.6.2
     # via
     #   accelerate
     #   diffusers
     #   lerobot
+    #   peft
+    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
@@ -526,10 +673,12 @@ scikit-image==0.25.2
 scipy==1.15.3
     # via
     #   dm-control
+    #   metaworld
+    #   robosuite
     #   scikit-image
-sentry-sdk==2.34.1
+sentry-sdk==2.42.1
     # via wandb
-shapely==2.1.1
+shapely==2.1.2
     # via gym-pusht
 six==1.17.0
     # via
@@ -537,64 +686,106 @@ six==1.17.0
     #   python-dateutil
 smmap==5.0.2
     # via gitdb
+sniffio==1.3.1
+    # via anyio
 stack-data==0.6.3
     # via ipython
+starlette==0.48.0
+    # via fastapi
 sympy==1.14.0
     # via torch
-termcolor==3.1.0
+teleop==0.1.2
     # via lerobot
+tensorboard==2.20.0
+    # via robomimic
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.4
+    # via robomimic
+termcolor==3.1.0
+    # via
+    #   lerobot
+    #   robomimic
+thop==0.1.1.post2209072238
+    # via libero
 tifffile==2025.5.10
     # via scikit-image
-tokenizers==0.21.4
+timm==1.0.20
+    # via lerobot
+tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.2.1
+tomli==2.3.0
     # via
     #   cmeel
     #   coverage
+    #   jupytext
     #   pytest
 torch==2.7.1
     # via
     #   accelerate
     #   lerobot
+    #   peft
+    #   robomimic
+    #   thop
+    #   timm
     #   torchvision
 torchcodec==0.5
     # via lerobot
 torchvision==0.22.1
-    # via lerobot
-tornado==6.5.1
+    # via
+    #   lerobot
+    #   robomimic
+    #   timm
+tornado==6.5.2
     # via meshcat
 tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
+    #   peft
+    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
+    #   jupyter-core
     #   matplotlib-inline
-transformers==4.51.3
-    # via lerobot
-typing-extensions==4.14.1
+    #   nbformat
+transformers==4.57.1
+    # via
+    #   lerobot
+    #   libero
+    #   peft
+transforms3d==0.4.2
+    # via teleop
+typing-extensions==4.15.0
     # via
     #   aiosignal
+    #   anyio
+    #   etils
     #   exceptiongroup
+    #   fastapi
     #   gymnasium
     #   huggingface-hub
     #   ipython
     #   multidict
     #   pydantic
     #   pydantic-core
+    #   referencing
     #   rerun-sdk
+    #   starlette
     #   torch
     #   typing-inspect
     #   typing-inspection
+    #   uvicorn
+    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
-typing-inspection==0.4.1
+typing-inspection==0.4.2
     # via pydantic
 tzdata==2025.2
     # via pandas
@@ -604,22 +795,36 @@ urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-virtualenv==20.32.0
+uvicorn[standard]==0.38.0
+    # via teleop
+uvloop==0.22.1
+    # via uvicorn
+virtualenv==20.35.3
     # via pre-commit
-wandb==0.21.0
-    # via lerobot
-wcwidth==0.2.13
+wandb==0.21.4
+    # via
+    #   lerobot
+    #   libero
+watchfiles==1.1.1
+    # via uvicorn
+wcwidth==0.2.14
     # via prompt-toolkit
+websocket-client==1.9.0
+    # via teleop
+websockets==15.0.1
+    # via uvicorn
 werkzeug==3.1.3
-    # via flask
-wrapt==1.17.2
+    # via tensorboard
+wrapt==2.0.0
     # via dm-tree
-xxhash==3.5.0
+xxhash==3.6.0
     # via datasets
-yarl==1.20.1
+yarl==1.22.0
     # via aiohttp
 zipp==3.23.0
-    # via importlib-metadata
+    # via
+    #   etils
+    #   importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools

requirements-ubuntu.txt

@@ -13,47 +13,62 @@ absl-py==2.3.1
     #   dm-tree
     #   labmaze
     #   mujoco
-accelerate==1.9.0
-    # via lerobot
+    #   tensorboard
+accelerate==1.11.0
+    # via
+    #   lerobot
+    #   peft
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.12.15
+aiohttp==3.13.1
     # via fsspec
 aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
+antlr4-python3-runtime==4.9.3
+    # via
+    #   hydra-core
+    #   omegaconf
+anyio==4.11.0
+    # via
+    #   starlette
+    #   watchfiles
 asttokens==3.0.0
     # via stack-data
 async-timeout==5.0.1
     # via aiohttp
-attrs==25.3.0
+attrs==25.4.0
     # via
     #   aiohttp
     #   dm-tree
     #   jsonlines
+    #   jsonschema
+    #   referencing
     #   rerun-sdk
-av==15.0.0
+av==15.1.0
     # via lerobot
-blinker==1.9.0
-    # via flask
-certifi==2025.7.14
+bddl==1.0.1
+    # via libero
+certifi==2025.10.5
     # via
     #   requests
     #   sentry-sdk
-cffi==1.17.1
+cffi==2.0.0
     # via pymunk
 cfgv==3.4.0
     # via pre-commit
-charset-normalizer==3.4.2
+charset-normalizer==3.4.4
     # via requests
-click==8.2.1
+click==8.3.0
     # via
-    #   flask
+    #   uvicorn
     #   wandb
 cloudpickle==3.1.1
-    # via gymnasium
-cmake==4.0.3
+    # via
+    #   gymnasium
+    #   libero
+cmake==4.1.0
     # via lerobot
 cmeel==0.57.3
     # via
@@ -95,27 +110,29 @@ coal-library==3.0.1
     # via pin
 contourpy==1.3.2
     # via matplotlib
-coverage[toml]==7.10.1
+coverage[toml]==7.11.0
     # via pytest-cov
 cycler==0.12.1
     # via matplotlib
-datasets==3.6.0
+datasets==4.1.1
     # via lerobot
-debugpy==1.8.15
+debugpy==1.8.17
     # via lerobot
 decorator==5.2.1
     # via ipython
-deepdiff==8.5.0
+decord==0.6.0
     # via lerobot
-diffusers==0.34.0
+deepdiff==8.6.1
     # via lerobot
-dill==0.3.8
+diffusers==0.35.2
+    # via lerobot
+dill==0.4.0
     # via
     #   datasets
     #   multiprocess
 distlib==0.4.0
     # via virtualenv
-dm-control==1.0.14
+dm-control==1.0.34
     # via gym-aloha
 dm-env==1.6
     # via dm-control
@@ -123,31 +140,48 @@ dm-tree==0.1.9
     # via
     #   dm-control
     #   dm-env
+    #   lerobot
 docopt==0.6.2
     # via num2words
 draccus==0.10.0
     # via lerobot
-dynamixel-sdk==3.7.31
+dynamixel-sdk==3.8.4
     # via lerobot
+easydict==1.13
+    # via libero
+egl-probe @ git+https://github.com/huggingface/egl_probe.git
+    # via
+    #   libero
+    #   robomimic
 eigenpy==3.10.3
     # via coal-library
 einops==0.8.1
-    # via lerobot
+    # via
+    #   flash-attn
+    #   lerobot
+    #   libero
 eiquadprog==1.2.9
     # via placo
+etils[epath,epy]==1.13.0
+    # via mujoco
 evdev==1.9.2
     # via pynput
 exceptiongroup==1.3.0
     # via
+    #   anyio
     #   ipython
     #   pytest
-executing==2.2.0
+executing==2.2.1
     # via stack-data
 farama-notifications==0.0.4
     # via gymnasium
+fastapi==0.119.1
+    # via teleop
+fastjsonschema==2.21.2
+    # via nbformat
 feetech-servo-sdk==1.0.0
     # via lerobot
-filelock==3.18.0
+filelock==3.20.0
     # via
     #   datasets
     #   diffusers
@@ -155,24 +189,27 @@ filelock==3.18.0
     #   torch
     #   transformers
     #   virtualenv
-flask==3.1.1
+flash-attn==2.8.3
     # via lerobot
-fonttools==4.59.0
+fonttools==4.60.1
     # via matplotlib
-frozenlist==1.7.0
+frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2025.3.0
+fsspec[http]==2025.9.0
     # via
     #   datasets
+    #   etils
     #   huggingface-hub
     #   torch
+future==1.0.0
+    # via libero
 gitdb==4.0.12
     # via gitpython
 gitpython==3.1.45
     # via wandb
-glfw==2.9.0
+glfw==2.10.0
     # via
     #   dm-control
     #   mujoco
@@ -180,61 +217,79 @@ 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-aloha==0.1.1
+gym-hil==0.1.13
     # via lerobot
-gym-hil==0.1.10
+gym-pusht==0.1.6
     # via lerobot
-gym-pusht==0.1.5
-    # via lerobot
-gym-xarm==0.1.1
-    # via lerobot
-gymnasium==0.29.1
+gymnasium==1.2.1
     # via
     #   gym-aloha
     #   gym-hil
     #   gym-pusht
-    #   gym-xarm
-    #   gymnasium-robotics
     #   lerobot
-    #   pettingzoo
-gymnasium-robotics==1.2.4
-    # via gym-xarm
+    #   libero
+    #   metaworld
+h11==0.16.0
+    # via uvicorn
+h5py==3.15.1
+    # via robomimic
+hebi-py==2.11.0
+    # via lerobot
 hf-transfer==0.1.9
     # via huggingface-hub
-hf-xet==1.1.5
+hf-xet==1.1.10
     # via huggingface-hub
 hidapi==0.14.0.post4
     # via
     #   gym-hil
     #   lerobot
-huggingface-hub[cli,hf-transfer]==0.34.3
+httptools==0.7.1
+    # via uvicorn
+huggingface-hub[cli,hf-transfer]==0.35.3
     # via
     #   accelerate
     #   datasets
     #   diffusers
     #   lerobot
+    #   peft
+    #   timm
     #   tokenizers
     #   transformers
-identify==2.6.12
+hydra-core==1.3.2
+    # via libero
+identify==2.6.15
     # via pre-commit
-idna==3.10
+idna==3.11
     # via
+    #   anyio
     #   requests
     #   yarl
 imageio[ffmpeg]==2.37.0
     # via
     #   gym-aloha
     #   gym-hil
-    #   gymnasium-robotics
     #   lerobot
+    #   metaworld
+    #   robomimic
     #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via imageio
+    # via
+    #   imageio
+    #   robomimic
 importlib-metadata==8.7.0
     # via diffusers
-iniconfig==2.1.0
+importlib-resources==6.5.2
+    # via etils
+iniconfig==2.3.0
     # via pytest
 inquirerpy==0.3.4
     # via huggingface-hub
@@ -242,50 +297,71 @@ ipython==8.37.0
     # via meshcat
 ischedule==1.2.7
     # via placo
-itsdangerous==2.2.0
-    # via flask
 jedi==0.19.2
     # via ipython
 jinja2==3.1.6
-    # via
-    #   flask
-    #   gymnasium-robotics
-    #   torch
+    # via torch
 jsonlines==4.0.0
     # via lerobot
-kiwisolver==1.4.8
+jsonschema==4.25.1
+    # via nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-core==5.9.1
+    # via nbformat
+jupytext==1.18.1
+    # via bddl
+kiwisolver==1.4.9
     # via matplotlib
 labmaze==1.0.6
     # via dm-control
 lazy-loader==0.4
     # via scikit-image
-lxml==6.0.0
+libero @ git+https://github.com/huggingface/lerobot-libero.git@main
+    # via lerobot
+llvmlite==0.45.1
+    # via numba
+lxml==6.0.2
     # via dm-control
-markupsafe==3.0.2
+markdown==3.9
+    # via tensorboard
+markdown-it-py==4.0.0
+    # via
+    #   jupytext
+    #   mdit-py-plugins
+markupsafe==3.0.3
     # via
-    #   flask
     #   jinja2
     #   werkzeug
-matplotlib==3.10.5
-    # via lerobot
-matplotlib-inline==0.1.7
+matplotlib==3.10.7
+    # via
+    #   lerobot
+    #   libero
+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==2.3.7
+mujoco==3.3.7
     # via
     #   dm-control
     #   gym-aloha
     #   gym-hil
-    #   gym-xarm
-    #   gymnasium-robotics
-multidict==6.6.3
+    #   libero
+    #   metaworld
+    #   robosuite
+multidict==6.7.0
     # via
     #   aiohttp
     #   yarl
@@ -293,42 +369,63 @@ multiprocess==0.70.16
     # via datasets
 mypy-extensions==1.1.0
     # via typing-inspect
+nbformat==5.10.4
+    # via jupytext
 networkx==3.4.2
     # via
+    #   bddl
     #   scikit-image
     #   torch
+ninja==1.13.0
+    # via lerobot
 nodeenv==1.9.1
     # via pre-commit
 num2words==0.5.14
     # via lerobot
+numba==0.62.1
+    # via robosuite
 numpy==2.2.6
     # via
     #   accelerate
+    #   bddl
     #   cmeel-boost
     #   contourpy
     #   datasets
+    #   decord
     #   diffusers
     #   dm-control
     #   dm-env
     #   dm-tree
     #   gymnasium
-    #   gymnasium-robotics
+    #   h5py
+    #   hebi-py
     #   imageio
     #   labmaze
+    #   libero
     #   matplotlib
     #   meshcat
+    #   metaworld
     #   mujoco
+    #   numba
     #   opencv-python
     #   opencv-python-headless
     #   pandas
-    #   pettingzoo
+    #   peft
+    #   pyquaternion
+    #   reachy2-sdk
     #   rerun-sdk
+    #   robomimic
+    #   robosuite
     #   scikit-image
     #   scipy
     #   shapely
+    #   teleop
+    #   tensorboard
+    #   tensorboardx
     #   tifffile
     #   torchvision
     #   transformers
+    #   transforms3d
 nvidia-cublas-cu12==12.6.4.1
     # via
     #   nvidia-cudnn-cu12
@@ -366,8 +463,14 @@ nvidia-nvjitlink-cu12==12.6.85
     #   torch
 nvidia-nvtx-cu12==12.6.77
     # via torch
+omegaconf==2.3.0
+    # via hydra-core
 opencv-python==4.12.0.88
-    # via gym-pusht
+    # via
+    #   gym-pusht
+    #   libero
+    #   reachy2-sdk
+    #   robosuite
 opencv-python-headless==4.12.0.88
     # via lerobot
 orderly-set==5.5.0
@@ -377,53 +480,63 @@ packaging==25.0
     #   accelerate
     #   datasets
     #   huggingface-hub
+    #   hydra-core
+    #   jupytext
     #   lazy-loader
     #   lerobot
     #   matplotlib
+    #   peft
     #   pytest
+    #   reachy2-sdk
     #   scikit-image
+    #   tensorboard
+    #   tensorboardx
     #   transformers
     #   wandb
-pandas==2.3.1
+pandas==2.3.3
     # via
     #   datasets
     #   lerobot
-parso==0.8.4
+parso==0.8.5
     # via jedi
-pettingzoo==1.24.3
-    # via gymnasium-robotics
+peft==0.17.1
+    # via lerobot
 pexpect==4.9.0
     # via ipython
 pfzy==0.3.4
     # via inquirerpy
-pillow==11.3.0
+pillow==12.0.0
     # via
     #   diffusers
     #   imageio
+    #   lerobot
     #   matplotlib
     #   meshcat
     #   rerun-sdk
+    #   robosuite
     #   scikit-image
+    #   tensorboard
     #   torchvision
 pin==3.4.0
     # via placo
 placo==0.9.14
     # via lerobot
-platformdirs==4.3.8
+platformdirs==4.5.0
     # via
+    #   jupyter-core
     #   virtualenv
     #   wandb
 pluggy==1.6.0
     # via
     #   pytest
     #   pytest-cov
-pre-commit==4.2.0
+pre-commit==4.3.0
     # via lerobot
-prompt-toolkit==3.0.51
+prompt-toolkit==3.0.52
     # via
     #   inquirerpy
     #   ipython
-propcache==0.3.2
+propcache==0.4.1
     # via
     #   aiohttp
     #   yarl
@@ -432,11 +545,17 @@ protobuf==6.31.0
     #   dm-control
     #   grpcio-tools
     #   lerobot
+    #   reachy2-sdk
+    #   reachy2-sdk-api
+    #   tensorboard
+    #   tensorboardx
     #   wandb
-psutil==7.0.0
+psutil==7.1.1
     # via
     #   accelerate
     #   imageio
+    #   peft
+    #   robomimic
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.3
@@ -445,11 +564,13 @@ pyarrow==21.0.0
     # via
     #   datasets
     #   rerun-sdk
-pycparser==2.22
+pycparser==2.23
     # via cffi
-pydantic==2.11.7
-    # via wandb
-pydantic-core==2.33.2
+pydantic==2.12.3
+    # via
+    #   fastapi
+    #   wandb
+pydantic-core==2.41.4
     # via pydantic
 pygame==2.6.1
     # via
@@ -464,20 +585,22 @@ pymunk==6.11.1
     # via
     #   gym-pusht
     #   lerobot
-pyngrok==7.2.12
+pyngrok==7.4.1
     # via meshcat
 pynput==1.8.1
     # via
     #   gym-hil
     #   lerobot
-pyopengl==3.1.9
+pyopengl==3.1.10
     # via
     #   dm-control
     #   mujoco
-pyparsing==3.2.3
+pyparsing==3.2.5
     # via
     #   dm-control
     #   matplotlib
+pyquaternion==0.9.9
+    # via reachy2-sdk
 pyrealsense2==2.56.5.9235
     # via lerobot
 pyserial==3.5
@@ -485,12 +608,14 @@ pyserial==3.5
     #   dynamixel-sdk
     #   feetech-servo-sdk
     #   lerobot
-pytest==8.4.1
+pytest==8.4.2
     # via
+    #   bddl
     #   lerobot
     #   pytest-cov
     #   pytest-timeout
-pytest-cov==6.2.1
+    #   teleop
+pytest-cov==7.0.0
     # via lerobot
 pytest-timeout==2.4.0
     # via lerobot
@@ -498,48 +623,75 @@ python-dateutil==2.9.0.post0
     # via
     #   matplotlib
     #   pandas
+python-dotenv==1.1.1
+    # via uvicorn
 python-xlib==0.33
     # via pynput
 pytz==2025.2
     # via pandas
-pyyaml==6.0.2
+pyyaml==6.0.3
     # via
     #   accelerate
     #   datasets
     #   draccus
+    #   hebi-py
     #   huggingface-hub
+    #   jupytext
+    #   omegaconf
+    #   peft
     #   pre-commit
     #   pyngrok
     #   pyyaml-include
+    #   timm
     #   transformers
+    #   uvicorn
     #   wandb
 pyyaml-include==1.4.1
     # via draccus
-pyzmq==27.0.0
+pyzmq==27.1.0
     # via
     #   lerobot
     #   meshcat
-regex==2025.7.34
+reachy2-sdk==1.0.14
+    # via lerobot
+reachy2-sdk-api==1.0.21
+    # via reachy2-sdk
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+regex==2025.10.23
     # via
     #   diffusers
     #   transformers
-requests==2.32.4
+requests==2.32.5
     # via
     #   datasets
     #   diffusers
     #   dm-control
     #   huggingface-hub
+    #   teleop
     #   transformers
     #   wandb
-rerun-sdk==0.22.1
+rerun-sdk==0.26.1
     # via lerobot
 rhoban-cmeel-jsoncpp==1.9.4.9
     # via placo
-safetensors==0.5.3
+robomimic==0.2.0
+    # via libero
+robosuite==1.4.0
+    # via libero
+rpds-py==0.28.0
+    # via
+    #   jsonschema
+    #   referencing
+safetensors==0.6.2
     # via
     #   accelerate
     #   diffusers
     #   lerobot
+    #   peft
+    #   timm
     #   transformers
 scikit-image==0.25.2
     # via
@@ -548,10 +700,12 @@ scikit-image==0.25.2
 scipy==1.15.3
     # via
     #   dm-control
+    #   metaworld
+    #   robosuite
     #   scikit-image
-sentry-sdk==2.34.1
+sentry-sdk==2.42.1
     # via wandb
-shapely==2.1.1
+shapely==2.1.2
     # via gym-pusht
 six==1.17.0
     # via
@@ -560,66 +714,109 @@ six==1.17.0
     #   python-xlib
 smmap==5.0.2
     # via gitdb
+sniffio==1.3.1
+    # via anyio
 stack-data==0.6.3
     # via ipython
+starlette==0.48.0
+    # via fastapi
 sympy==1.14.0
     # via torch
-termcolor==3.1.0
+teleop==0.1.2
     # via lerobot
+tensorboard==2.20.0
+    # via robomimic
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.4
+    # via robomimic
+termcolor==3.1.0
+    # via
+    #   lerobot
+    #   robomimic
+thop==0.1.1.post2209072238
+    # via libero
 tifffile==2025.5.10
     # via scikit-image
-tokenizers==0.21.4
+timm==1.0.20
+    # via lerobot
+tokenizers==0.22.1
     # via transformers
 toml==0.10.2
     # via draccus
-tomli==2.2.1
+tomli==2.3.0
     # via
     #   cmeel
     #   coverage
+    #   jupytext
     #   pytest
 torch==2.7.1
     # via
     #   accelerate
+    #   flash-attn
     #   lerobot
+    #   peft
+    #   robomimic
+    #   thop
+    #   timm
     #   torchvision
 torchcodec==0.5
     # via lerobot
 torchvision==0.22.1
-    # via lerobot
-tornado==6.5.1
+    # via
+    #   lerobot
+    #   robomimic
+    #   timm
+tornado==6.5.2
     # via meshcat
 tqdm==4.67.1
     # via
     #   datasets
     #   dm-control
     #   huggingface-hub
+    #   peft
+    #   robomimic
     #   transformers
 traitlets==5.14.3
     # via
     #   ipython
+    #   jupyter-core
     #   matplotlib-inline
-transformers==4.51.3
-    # via lerobot
+    #   nbformat
+transformers==4.57.1
+    # via
+    #   lerobot
+    #   libero
+    #   peft
+transforms3d==0.4.2
+    # via teleop
 triton==3.3.1
     # via torch
-typing-extensions==4.14.1
+typing-extensions==4.15.0
     # via
     #   aiosignal
+    #   anyio
+    #   etils
     #   exceptiongroup
+    #   fastapi
     #   gymnasium
     #   huggingface-hub
     #   ipython
     #   multidict
     #   pydantic
     #   pydantic-core
+    #   referencing
     #   rerun-sdk
+    #   starlette
     #   torch
     #   typing-inspect
     #   typing-inspection
+    #   uvicorn
+    #   virtualenv
     #   wandb
 typing-inspect==0.9.0
     # via draccus
-typing-inspection==0.4.1
+typing-inspection==0.4.2
     # via pydantic
 tzdata==2025.2
     # via pandas
@@ -629,22 +826,36 @@ urllib3==2.5.0
     # via
     #   requests
     #   sentry-sdk
-virtualenv==20.32.0
+uvicorn[standard]==0.38.0
+    # via teleop
+uvloop==0.22.1
+    # via uvicorn
+virtualenv==20.35.3
     # via pre-commit
-wandb==0.21.0
-    # via lerobot
-wcwidth==0.2.13
+wandb==0.21.4
+    # via
+    #   lerobot
+    #   libero
+watchfiles==1.1.1
+    # via uvicorn
+wcwidth==0.2.14
     # via prompt-toolkit
+websocket-client==1.9.0
+    # via teleop
+websockets==15.0.1
+    # via uvicorn
 werkzeug==3.1.3
-    # via flask
-wrapt==1.17.2
+    # via tensorboard
+wrapt==2.0.0
     # via dm-tree
-xxhash==3.5.0
+xxhash==3.6.0
     # via datasets
-yarl==1.20.1
+yarl==1.22.0
     # via aiohttp
 zipp==3.23.0
-    # via importlib-metadata
+    # via
+    #   etils
+    #   importlib-metadata
 
 # The following packages are considered to be unsafe in a requirements file:
 # setuptools

requirements.in

@@ -1,9 +1,9 @@
 # requirements.in
 
-# requirements-macos.txt was generated on macOS and is platform-specific (macOS 15.5 24F74 arm64).
-# Darwin MacBook-Pro.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132 arm64
+# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.0.1 25A362 arm64).
+# Darwin MacBook-Pro.local 25.0.0 Darwin Kernel Version 25.0.0: Wed Sep 17 21:42:08 PDT 2025; root:xnu-12377.1.9~141/RELEASE_ARM64_T8132 arm64
 
-# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.2 LTS x86_64).
-# Linux mlerobot-linux 6.14.0-27-generic #27~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Jul 22 17:38:49 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
+# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.3 LTS x86_64).
+# Linux mlerobot-linux 6.14.0-33-generic #33~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 19 17:02:30 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
 
 -e .[all]

src/lerobot/async_inference/helpers.py

@@ -16,7 +16,7 @@ import logging
 import logging.handlers
 import os
 import time
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import torch
@@ -268,6 +268,7 @@ class RemotePolicyConfig:
     lerobot_features: dict[str, PolicyFeature]
     actions_per_chunk: int
     device: str = "cpu"
+    rename_map: dict[str, str] = field(default_factory=dict)
 
 
 def _compare_observation_states(obs1_state: torch.Tensor, obs2_state: torch.Tensor, atol: float) -> bool:

src/lerobot/async_inference/policy_server.py

@@ -159,7 +159,10 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         self.preprocessor, self.postprocessor = make_pre_post_processors(
             self.policy.config,
             pretrained_path=policy_specs.pretrained_name_or_path,
-            preprocessor_overrides={"device_processor": device_override},
+            preprocessor_overrides={
+                "device_processor": device_override,
+                "rename_observations_processor": {"rename_map": policy_specs.rename_map},
+            },
             postprocessor_overrides={"device_processor": device_override},
         )
 

src/lerobot/cameras/camera.py

@@ -17,7 +17,7 @@
 import abc
 from typing import Any
 
-import numpy as np
+from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
 
 from .configs import CameraConfig, ColorMode
 
@@ -89,7 +89,7 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """Capture and return a single frame from the camera.
 
         Args:
@@ -102,7 +102,7 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def async_read(self, timeout_ms: float = ...) -> np.ndarray:
+    def async_read(self, timeout_ms: float = ...) -> NDArray[Any]:
         """Asynchronously capture and return a single frame from the camera.
 
         Args:

src/lerobot/cameras/configs.py

@@ -18,7 +18,7 @@ import abc
 from dataclasses import dataclass
 from enum import Enum
 
-import draccus
+import draccus  # type: ignore  # TODO: add type stubs for draccus
 
 
 class ColorMode(str, Enum):
@@ -34,11 +34,11 @@ class Cv2Rotation(int, Enum):
 
 
 @dataclass(kw_only=True)
-class CameraConfig(draccus.ChoiceRegistry, abc.ABC):
+class CameraConfig(draccus.ChoiceRegistry, abc.ABC):  # type: ignore  # TODO: add type stubs for draccus
     fps: int | None = None
     width: int | None = None
     height: int | None = None
 
     @property
     def type(self) -> str:
-        return self.get_choice_name(self.__class__)
+        return str(self.get_choice_name(self.__class__))

src/lerobot/cameras/opencv/__init__.py

@@ -14,3 +14,5 @@
 
 from .camera_opencv import OpenCVCamera
 from .configuration_opencv import OpenCVCameraConfig
+
+__all__ = ["OpenCVCamera", "OpenCVCameraConfig"]

src/lerobot/cameras/opencv/camera_opencv.py

@@ -25,11 +25,12 @@ from pathlib import Path
 from threading import Event, Lock, Thread
 from typing import Any
 
+from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
+
 # Fix MSMF hardware transform compatibility for Windows before importing cv2
 if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS" not in os.environ:
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-import cv2
-import numpy as np
+import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 
 from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
@@ -121,7 +122,7 @@ class OpenCVCamera(Camera):
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
-        self.latest_frame: np.ndarray | None = None
+        self.latest_frame: NDArray[Any] | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -140,7 +141,7 @@ class OpenCVCamera(Camera):
         """Checks if the camera is currently connected and opened."""
         return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
 
-    def connect(self, warmup: bool = True):
+    def connect(self, warmup: bool = True) -> None:
         """
         Connects to the OpenCV camera specified in the configuration.
 
@@ -180,12 +181,14 @@ class OpenCVCamera(Camera):
 
     def _configure_capture_settings(self) -> None:
         """
-        Applies the specified FPS, width, and height settings to the connected camera.
+        Applies the specified FOURCC, FPS, width, and height settings to the connected camera.
 
         This method attempts to set the camera properties via OpenCV. It checks if
         the camera successfully applied the settings and raises an error if not.
+        FOURCC is set first (if specified) as it can affect the available FPS and resolution options.
 
         Args:
+            fourcc: The desired FOURCC code (e.g., "MJPG", "YUYV"). If None, auto-detect.
             fps: The desired frames per second. If None, the setting is skipped.
             width: The desired capture width. If None, the setting is skipped.
             height: The desired capture height. If None, the setting is skipped.
@@ -199,10 +202,11 @@ class OpenCVCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
 
-        if self.fps is None:
-            self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
-        else:
-            self._validate_fps()
+        # Set FOURCC first (if specified) as it can affect available FPS/resolution options
+        if self.config.fourcc is not None:
+            self._validate_fourcc()
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
 
         default_width = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_WIDTH)))
         default_height = int(round(self.videocapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
@@ -216,18 +220,56 @@ class OpenCVCamera(Camera):
         else:
             self._validate_width_and_height()
 
+        if self.fps is None:
+            self.fps = self.videocapture.get(cv2.CAP_PROP_FPS)
+        else:
+            self._validate_fps()
+
     def _validate_fps(self) -> None:
         """Validates and sets the camera's frames per second (FPS)."""
 
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+
+        if self.fps is None:
+            raise ValueError(f"{self} FPS is not set")
+
         success = self.videocapture.set(cv2.CAP_PROP_FPS, float(self.fps))
         actual_fps = self.videocapture.get(cv2.CAP_PROP_FPS)
         # Use math.isclose for robust float comparison
         if not success or not math.isclose(self.fps, actual_fps, rel_tol=1e-3):
             raise RuntimeError(f"{self} failed to set fps={self.fps} ({actual_fps=}).")
 
+    def _validate_fourcc(self) -> None:
+        """Validates and sets the camera's FOURCC code."""
+
+        fourcc_code = cv2.VideoWriter_fourcc(*self.config.fourcc)
+
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+
+        success = self.videocapture.set(cv2.CAP_PROP_FOURCC, fourcc_code)
+        actual_fourcc_code = self.videocapture.get(cv2.CAP_PROP_FOURCC)
+
+        # Convert actual FOURCC code back to string for comparison
+        actual_fourcc_code_int = int(actual_fourcc_code)
+        actual_fourcc = "".join([chr((actual_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
+
+        if not success or actual_fourcc != self.config.fourcc:
+            logger.warning(
+                f"{self} failed to set fourcc={self.config.fourcc} (actual={actual_fourcc}, success={success}). "
+                f"Continuing with default format."
+            )
+
     def _validate_width_and_height(self) -> None:
         """Validates and sets the camera's frame capture width and height."""
 
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+
+        if self.capture_width is None or self.capture_height is None:
+            raise ValueError(f"{self} capture_width or capture_height is not set")
+
         width_success = self.videocapture.set(cv2.CAP_PROP_FRAME_WIDTH, float(self.capture_width))
         height_success = self.videocapture.set(cv2.CAP_PROP_FRAME_HEIGHT, float(self.capture_height))
 
@@ -258,11 +300,12 @@ class OpenCVCamera(Camera):
         """
         found_cameras_info = []
 
+        targets_to_scan: list[str | int]
         if platform.system() == "Linux":
             possible_paths = sorted(Path("/dev").glob("video*"), key=lambda p: p.name)
             targets_to_scan = [str(p) for p in possible_paths]
         else:
-            targets_to_scan = list(range(MAX_OPENCV_INDEX))
+            targets_to_scan = [int(i) for i in range(MAX_OPENCV_INDEX)]
 
         for target in targets_to_scan:
             camera = cv2.VideoCapture(target)
@@ -271,6 +314,12 @@ class OpenCVCamera(Camera):
                 default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
                 default_fps = camera.get(cv2.CAP_PROP_FPS)
                 default_format = camera.get(cv2.CAP_PROP_FORMAT)
+
+                # Get FOURCC code and convert to string
+                default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
+                default_fourcc_code_int = int(default_fourcc_code)
+                default_fourcc = "".join([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
+
                 camera_info = {
                     "name": f"OpenCV Camera @ {target}",
                     "type": "OpenCV",
@@ -278,6 +327,7 @@ class OpenCVCamera(Camera):
                     "backend_api": camera.getBackendName(),
                     "default_stream_profile": {
                         "format": default_format,
+                        "fourcc": default_fourcc,
                         "width": default_width,
                         "height": default_height,
                         "fps": default_fps,
@@ -289,7 +339,7 @@ class OpenCVCamera(Camera):
 
         return found_cameras_info
 
-    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
 
@@ -317,6 +367,9 @@ class OpenCVCamera(Camera):
 
         start_time = time.perf_counter()
 
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+
         ret, frame = self.videocapture.read()
 
         if not ret or frame is None:
@@ -329,7 +382,7 @@ class OpenCVCamera(Camera):
 
         return processed_frame
 
-    def _postprocess_image(self, image: np.ndarray, color_mode: ColorMode | None = None) -> np.ndarray:
+    def _postprocess_image(self, image: NDArray[Any], color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw frame.
 
@@ -372,7 +425,7 @@ class OpenCVCamera(Camera):
 
         return processed_image
 
-    def _read_loop(self):
+    def _read_loop(self) -> None:
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -383,6 +436,9 @@ class OpenCVCamera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
+        if self.stop_event is None:
+            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
+
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
@@ -419,7 +475,7 @@ class OpenCVCamera(Camera):
         self.thread = None
         self.stop_event = None
 
-    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
 
@@ -462,7 +518,7 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    def disconnect(self):
+    def disconnect(self) -> None:
         """
         Disconnects from the camera and cleans up resources.
 

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -17,6 +17,8 @@ from pathlib import Path
 
 from ..configs import CameraConfig, ColorMode, Cv2Rotation
 
+__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
+
 
 @CameraConfig.register_subclass("opencv")
 @dataclass
@@ -33,8 +35,9 @@ class OpenCVCameraConfig(CameraConfig):
     OpenCVCameraConfig(0, 30, 1280, 720)   # 1280x720 @ 30FPS
     OpenCVCameraConfig(/dev/video4, 60, 640, 480)   # 640x480 @ 60FPS
 
-    # Advanced configurations
-    OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)     # With 90° rotation
+    # Advanced configurations with FOURCC format
+    OpenCVCameraConfig(128422271347, 30, 640, 480, rotation=Cv2Rotation.ROTATE_90, fourcc="MJPG")     # With 90° rotation and MJPG format
+    OpenCVCameraConfig(0, 30, 1280, 720, fourcc="YUYV")     # With YUYV format
     ```
 
     Attributes:
@@ -46,17 +49,21 @@ class OpenCVCameraConfig(CameraConfig):
         color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
+        fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
+        - FOURCC codes must be 4-character strings (e.g., "MJPG", "YUYV"). Some common FOUCC codes: https://learn.microsoft.com/en-us/windows/win32/medfound/video-fourccs#fourcc-constants
+        - Setting FOURCC can help achieve higher frame rates on some cameras.
     """
 
     index_or_path: int | Path
     color_mode: ColorMode = ColorMode.RGB
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
+    fourcc: str | None = None
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
                 f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
@@ -71,3 +78,8 @@ class OpenCVCameraConfig(CameraConfig):
             raise ValueError(
                 f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
             )
+
+        if self.fourcc is not None and (not isinstance(self.fourcc, str) or len(self.fourcc) != 4):
+            raise ValueError(
+                f"`fourcc` must be a 4-character string (e.g., 'MJPG', 'YUYV'), but '{self.fourcc}' is provided."
+            )

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -16,6 +16,8 @@ from dataclasses import dataclass
 
 from ..configs import CameraConfig, ColorMode
 
+__all__ = ["CameraConfig", "ColorMode", "Reachy2CameraConfig"]
+
 
 @CameraConfig.register_subclass("reachy2_camera")
 @dataclass
@@ -62,7 +64,7 @@ class Reachy2CameraConfig(CameraConfig):
     port: int = 50065
     # use_depth: bool = False
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         if self.name not in ["teleop", "depth"]:
             raise ValueError(f"`name` is expected to be 'teleop' or 'depth', but {self.name} is provided.")
         if (self.name == "teleop" and self.image_type not in ["left", "right"]) or (

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -23,13 +23,17 @@ import time
 from threading import Event, Lock, Thread
 from typing import Any
 
+from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
+
 # Fix MSMF hardware transform compatibility for Windows before importing cv2
 if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS" not in os.environ:
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-import cv2
-import numpy as np
-from reachy2_sdk.media.camera import CameraView
-from reachy2_sdk.media.camera_manager import CameraManager
+import cv2  # type: ignore  # TODO: add type stubs for OpenCV
+import numpy as np  # type: ignore  # TODO: add type stubs for numpy
+from reachy2_sdk.media.camera import CameraView  # type: ignore  # TODO: add type stubs for reachy2_sdk
+from reachy2_sdk.media.camera_manager import (  # type: ignore  # TODO: add type stubs for reachy2_sdk
+    CameraManager,
+)
 
 from lerobot.utils.errors import DeviceNotConnectedError
 
@@ -73,7 +77,7 @@ class Reachy2Camera(Camera):
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
-        self.latest_frame: np.ndarray | None = None
+        self.latest_frame: NDArray[Any] | None = None
         self.new_frame_event: Event = Event()
 
     def __str__(self) -> str:
@@ -83,13 +87,17 @@ class Reachy2Camera(Camera):
     def is_connected(self) -> bool:
         """Checks if the camera is currently connected and opened."""
         if self.config.name == "teleop":
-            return self.cam_manager._grpc_connected and self.cam_manager.teleop if self.cam_manager else False
+            return bool(
+                self.cam_manager._grpc_connected and self.cam_manager.teleop if self.cam_manager else False
+            )
         elif self.config.name == "depth":
-            return self.cam_manager._grpc_connected and self.cam_manager.depth if self.cam_manager else False
+            return bool(
+                self.cam_manager._grpc_connected and self.cam_manager.depth if self.cam_manager else False
+            )
         else:
             raise ValueError(f"Invalid camera name '{self.config.name}'. Expected 'teleop' or 'depth'.")
 
-    def connect(self, warmup: bool = True):
+    def connect(self, warmup: bool = True) -> None:
         """
         Connects to the Reachy2 CameraManager as specified in the configuration.
         """
@@ -131,7 +139,7 @@ class Reachy2Camera(Camera):
         camera_manager.disconnect()
         return initialized_cameras
 
-    def read(self, color_mode: ColorMode | None = None) -> np.ndarray:
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
 
@@ -152,7 +160,7 @@ class Reachy2Camera(Camera):
 
         start_time = time.perf_counter()
 
-        frame = None
+        frame: NDArray[Any] = np.empty((0, 0, 3), dtype=np.uint8)
 
         if self.cam_manager is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -179,7 +187,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
-    def _read_loop(self):
+    def _read_loop(self) -> None:
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -190,6 +198,9 @@ class Reachy2Camera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
+        if self.stop_event is None:
+            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
+
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
@@ -226,7 +237,7 @@ class Reachy2Camera(Camera):
         self.thread = None
         self.stop_event = None
 
-    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
 
@@ -269,7 +280,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
-    def disconnect(self):
+    def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
 

src/lerobot/cameras/realsense/camera_realsense.py

@@ -21,11 +21,12 @@ import time
 from threading import Event, Lock, Thread
 from typing import Any
 
-import cv2
-import numpy as np
+import cv2  # type: ignore  # TODO: add type stubs for OpenCV
+import numpy as np  # type: ignore  # TODO: add type stubs for numpy
+from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
 
 try:
-    import pyrealsense2 as rs
+    import pyrealsense2 as rs  # type: ignore  # TODO: add type stubs for pyrealsense2
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
@@ -132,7 +133,7 @@ class RealSenseCamera(Camera):
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
-        self.latest_frame: np.ndarray | None = None
+        self.latest_frame: NDArray[Any] | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -150,7 +151,7 @@ class RealSenseCamera(Camera):
         """Checks if the camera pipeline is started and streams are active."""
         return self.rs_pipeline is not None and self.rs_profile is not None
 
-    def connect(self, warmup: bool = True):
+    def connect(self, warmup: bool = True) -> None:
         """
         Connects to the RealSense camera specified in the configuration.
 
@@ -264,7 +265,7 @@ class RealSenseCamera(Camera):
         serial_number = str(found_devices[0]["serial_number"])
         return serial_number
 
-    def _configure_rs_pipeline_config(self, rs_config):
+    def _configure_rs_pipeline_config(self, rs_config: Any) -> None:
         """Creates and configures the RealSense pipeline configuration object."""
         rs.config.enable_device(rs_config, self.serial_number)
 
@@ -293,6 +294,9 @@ class RealSenseCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
 
+        if self.rs_profile is None:
+            raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
+
         stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile()
 
         if self.fps is None:
@@ -308,7 +312,7 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
-    def read_depth(self, timeout_ms: int = 200) -> np.ndarray:
+    def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (depth) synchronously from the camera.
 
@@ -336,6 +340,9 @@ class RealSenseCamera(Camera):
 
         start_time = time.perf_counter()
 
+        if self.rs_pipeline is None:
+            raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
+
         ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
 
         if not ret or frame is None:
@@ -351,7 +358,7 @@ class RealSenseCamera(Camera):
 
         return depth_map_processed
 
-    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> np.ndarray:
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (color) synchronously from the camera.
 
@@ -376,6 +383,9 @@ class RealSenseCamera(Camera):
 
         start_time = time.perf_counter()
 
+        if self.rs_pipeline is None:
+            raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
+
         ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
 
         if not ret or frame is None:
@@ -392,8 +402,8 @@ class RealSenseCamera(Camera):
         return color_image_processed
 
     def _postprocess_image(
-        self, image: np.ndarray, color_mode: ColorMode | None = None, depth_frame: bool = False
-    ) -> np.ndarray:
+        self, image: NDArray[Any], color_mode: ColorMode | None = None, depth_frame: bool = False
+    ) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw color frame.
 
@@ -438,7 +448,7 @@ class RealSenseCamera(Camera):
 
         return processed_image
 
-    def _read_loop(self):
+    def _read_loop(self) -> None:
         """
         Internal loop run by the background thread for asynchronous reading.
 
@@ -449,6 +459,9 @@ class RealSenseCamera(Camera):
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
         """
+        if self.stop_event is None:
+            raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
+
         while not self.stop_event.is_set():
             try:
                 color_image = self.read(timeout_ms=500)
@@ -474,7 +487,7 @@ class RealSenseCamera(Camera):
         self.thread.daemon = True
         self.thread.start()
 
-    def _stop_read_thread(self):
+    def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
         if self.stop_event is not None:
             self.stop_event.set()
@@ -486,7 +499,7 @@ class RealSenseCamera(Camera):
         self.stop_event = None
 
     # NOTE(Steven): Missing implementation for depth for now
-    def async_read(self, timeout_ms: float = 200) -> np.ndarray:
+    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame data (color) asynchronously.
 
@@ -529,7 +542,7 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    def disconnect(self):
+    def disconnect(self) -> None:
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.
 

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -59,7 +59,7 @@ class RealSenseCameraConfig(CameraConfig):
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
                 f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."

src/lerobot/cameras/utils.py

@@ -53,14 +53,14 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[s
 
 
 def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
-    import cv2
+    import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 
     if rotation == Cv2Rotation.ROTATE_90:
-        return cv2.ROTATE_90_CLOCKWISE
+        return int(cv2.ROTATE_90_CLOCKWISE)
     elif rotation == Cv2Rotation.ROTATE_180:
-        return cv2.ROTATE_180
+        return int(cv2.ROTATE_180)
     elif rotation == Cv2Rotation.ROTATE_270:
-        return cv2.ROTATE_90_COUNTERCLOCKWISE
+        return int(cv2.ROTATE_90_COUNTERCLOCKWISE)
     else:
         return None
 
@@ -69,8 +69,8 @@ def get_cv2_backend() -> int:
     import cv2
 
     if platform.system() == "Windows":
-        return cv2.CAP_MSMF  # Use MSMF for Windows instead of AVFOUNDATION
+        return int(cv2.CAP_MSMF)  # Use MSMF for Windows instead of AVFOUNDATION
     # elif platform.system() == "Darwin":  # macOS
     #     return cv2.CAP_AVFOUNDATION
     else:  # Linux and others
-        return cv2.CAP_ANY
+        return int(cv2.CAP_ANY)

src/lerobot/configs/default.py

@@ -57,7 +57,7 @@ class EvalConfig:
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     use_async_envs: bool = False
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         if self.batch_size > self.n_episodes:
             raise ValueError(
                 "The eval batch size is greater than the number of eval episodes "

src/lerobot/configs/eval.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import datetime as dt
-import logging
 from dataclasses import dataclass, field
+from logging import getLogger
 from pathlib import Path
 
 from lerobot import envs, policies  # noqa: F401
@@ -22,6 +22,8 @@ from lerobot.configs import parser
 from lerobot.configs.default import EvalConfig
 from lerobot.configs.policies import PreTrainedConfig
 
+logger = getLogger(__name__)
+
 
 @dataclass
 class EvalPipelineConfig:
@@ -34,25 +36,31 @@ class EvalPipelineConfig:
     output_dir: Path | None = None
     job_name: str | None = None
     seed: int | None = 1000
+    # Rename map for the observation to override the image and state keys
+    rename_map: dict[str, str] = field(default_factory=dict)
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         # HACK: We parse again the cli args here to get the pretrained path if there was one.
         policy_path = parser.get_path_arg("policy")
         if policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = policy_path
+            self.policy.pretrained_path = Path(policy_path)
 
         else:
-            logging.warning(
+            logger.warning(
                 "No pretrained path was provided, evaluated policy will be built from scratch (random weights)."
             )
 
         if not self.job_name:
             if self.env is None:
-                self.job_name = f"{self.policy.type}"
+                self.job_name = f"{self.policy.type if self.policy is not None else 'scratch'}"
             else:
-                self.job_name = f"{self.env.type}_{self.policy.type}"
+                self.job_name = (
+                    f"{self.env.type}_{self.policy.type if self.policy is not None else 'scratch'}"
+                )
+
+            logger.warning(f"No job name provided, using '{self.job_name}' as job name.")
 
         if not self.output_dir:
             now = dt.datetime.now()

src/lerobot/configs/parser.py

@@ -16,14 +16,19 @@ import inspect
 import pkgutil
 import sys
 from argparse import ArgumentError
-from collections.abc import Sequence
+from collections.abc import Callable, Iterable, Sequence
 from functools import wraps
 from pathlib import Path
+from pkgutil import ModuleInfo
+from types import ModuleType
+from typing import Any, TypeVar, cast
 
 import draccus
 
 from lerobot.utils.utils import has_method
 
+F = TypeVar("F", bound=Callable[..., object])
+
 PATH_KEY = "path"
 PLUGIN_DISCOVERY_SUFFIX = "discover_packages_path"
 
@@ -60,7 +65,7 @@ def parse_arg(arg_name: str, args: Sequence[str] | None = None) -> str | None:
     return None
 
 
-def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict:
+def parse_plugin_args(plugin_arg_suffix: str, args: Sequence[str]) -> dict[str, str]:
     """Parse plugin-related arguments from command-line arguments.
 
     This function extracts arguments from command-line arguments that match a specified suffix pattern.
@@ -127,7 +132,7 @@ def load_plugin(plugin_path: str) -> None:
             f"Failed to load plugin '{plugin_path}'. Verify the path and installation: {str(e)}"
         ) from e
 
-    def iter_namespace(ns_pkg):
+    def iter_namespace(ns_pkg: ModuleType) -> Iterable[ModuleInfo]:
         return pkgutil.iter_modules(ns_pkg.__path__, ns_pkg.__name__ + ".")
 
     try:
@@ -148,6 +153,8 @@ def get_type_arg(field_name: str, args: Sequence[str] | None = None) -> str | No
 
 
 def filter_arg(field_to_filter: str, args: Sequence[str] | None = None) -> list[str]:
+    if args is None:
+        return []
     return [arg for arg in args if not arg.startswith(f"--{field_to_filter}=")]
 
 
@@ -171,7 +178,8 @@ def filter_path_args(fields_to_filter: str | list[str], args: Sequence[str] | No
     if isinstance(fields_to_filter, str):
         fields_to_filter = [fields_to_filter]
 
-    filtered_args = args
+    filtered_args = [] if args is None else list(args)
+
     for field in fields_to_filter:
         if get_path_arg(field, args):
             if get_type_arg(field, args):
@@ -184,7 +192,7 @@ def filter_path_args(fields_to_filter: str | list[str], args: Sequence[str] | No
     return filtered_args
 
 
-def wrap(config_path: Path | None = None):
+def wrap(config_path: Path | None = None) -> Callable[[F], F]:
     """
     HACK: Similar to draccus.wrap but does three additional things:
         - Will remove '.path' arguments from CLI in order to process them later on.
@@ -195,9 +203,9 @@ def wrap(config_path: Path | None = None):
             from the CLI '.type' arguments
     """
 
-    def wrapper_outer(fn):
+    def wrapper_outer(fn: F) -> F:
         @wraps(fn)
-        def wrapper_inner(*args, **kwargs):
+        def wrapper_inner(*args: Any, **kwargs: Any) -> Any:
             argspec = inspect.getfullargspec(fn)
             argtype = argspec.annotations[argspec.args[0]]
             if len(args) > 0 and type(args[0]) is argtype:
@@ -225,6 +233,6 @@ def wrap(config_path: Path | None = None):
             response = fn(cfg, *args, **kwargs)
             return response
 
-        return wrapper_inner
+        return cast(F, wrapper_inner)
 
-    return wrapper_outer
+    return cast(Callable[[F], F], wrapper_outer)

src/lerobot/configs/policies.py

@@ -14,12 +14,12 @@
 import abc
 import builtins
 import json
-import logging
 import os
 import tempfile
 from dataclasses import dataclass, field
+from logging import getLogger
 from pathlib import Path
-from typing import TypeVar
+from typing import Any, TypeVar
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -34,10 +34,11 @@ from lerobot.utils.hub import HubMixin
 from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 
 T = TypeVar("T", bound="PreTrainedConfig")
+logger = getLogger(__name__)
 
 
 @dataclass
-class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
+class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: ignore[misc,name-defined] #TODO: draccus issue
     """
     Base configuration class for policy models.
 
@@ -57,12 +58,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     input_features: dict[str, PolicyFeature] = field(default_factory=dict)
     output_features: dict[str, PolicyFeature] = field(default_factory=dict)
 
-    device: str | None = None  # cuda | cpu | mp
+    device: str | None = None  # e.g. "cuda", "cuda:0", "cpu", or "mps"
     # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
     # automatic gradient scaling is used.
     use_amp: bool = False
 
-    push_to_hub: bool = True
+    push_to_hub: bool = True  # type: ignore[assignment] # TODO: use a different name to avoid override
     repo_id: str | None = None
 
     # Upload on private repository on the Hugging Face hub.
@@ -73,38 +74,41 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     license: str | None = None
     # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights
     # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch.
-    pretrained_path: str | None = None
+    pretrained_path: Path | None = None
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         if not self.device or not is_torch_device_available(self.device):
             auto_device = auto_select_torch_device()
-            logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
+            logger.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
             self.device = auto_device.type
 
         # Automatically deactivate AMP if necessary
         if self.use_amp and not is_amp_available(self.device):
-            logging.warning(
+            logger.warning(
                 f"Automatic Mixed Precision (amp) is not available on device '{self.device}'. Deactivating AMP."
             )
             self.use_amp = False
 
     @property
     def type(self) -> str:
-        return self.get_choice_name(self.__class__)
+        choice_name = self.get_choice_name(self.__class__)
+        if not isinstance(choice_name, str):
+            raise TypeError(f"Expected string from get_choice_name, got {type(choice_name)}")
+        return choice_name
 
     @property
     @abc.abstractmethod
-    def observation_delta_indices(self) -> list | None:
+    def observation_delta_indices(self) -> list | None:  # type: ignore[type-arg] #TODO: No implementation
         raise NotImplementedError
 
     @property
     @abc.abstractmethod
-    def action_delta_indices(self) -> list | None:
+    def action_delta_indices(self) -> list | None:  # type: ignore[type-arg]    #TODO: No implementation
         raise NotImplementedError
 
     @property
     @abc.abstractmethod
-    def reward_delta_indices(self) -> list | None:
+    def reward_delta_indices(self) -> list | None:  # type: ignore[type-arg]    #TODO: No implementation
         raise NotImplementedError
 
     @abc.abstractmethod
@@ -154,13 +158,13 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,
-        resume_download: bool = None,
-        proxies: dict | None = None,
+        resume_download: bool | None = None,
+        proxies: dict[Any, Any] | None = None,
         token: str | bool | None = None,
         cache_dir: str | Path | None = None,
         local_files_only: bool = False,
         revision: str | None = None,
-        **policy_kwargs,
+        **policy_kwargs: Any,
     ) -> T:
         model_id = str(pretrained_name_or_path)
         config_file: str | None = None
@@ -168,7 +172,7 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
             if CONFIG_NAME in os.listdir(model_id):
                 config_file = os.path.join(model_id, CONFIG_NAME)
             else:
-                print(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
+                logger.error(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}")
         else:
             try:
                 config_file = hf_hub_download(
@@ -194,6 +198,9 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
         with draccus.config_type("json"):
             orig_config = draccus.parse(cls, config_file, args=[])
 
+        if config_file is None:
+            raise FileNotFoundError(f"{CONFIG_NAME} not found in {model_id}")
+
         with open(config_file) as f:
             config = json.load(f)
 

src/lerobot/configs/train.py

@@ -16,6 +16,7 @@ import datetime as dt
 import os
 from dataclasses import dataclass, field
 from pathlib import Path
+from typing import Any
 
 import draccus
 from huggingface_hub import hf_hub_download
@@ -63,18 +64,18 @@ class TrainPipelineConfig(HubMixin):
     scheduler: LRSchedulerConfig | None = None
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
+    checkpoint_path: Path | None = field(init=False, default=None)
+    # Rename map for the observation to override the image and state keys
+    rename_map: dict[str, str] = field(default_factory=dict)
 
-    def __post_init__(self):
-        self.checkpoint_path = None
-
-    def validate(self):
+    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")
         if policy_path:
             # Only load the policy config
             cli_overrides = parser.get_cli_overrides("policy")
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
-            self.policy.pretrained_path = policy_path
+            self.policy.pretrained_path = Path(policy_path)
         elif self.resume:
             # The entire train config is already loaded, we just need to get the checkpoint dir
             config_path = parser.parse_arg("config_path")
@@ -82,14 +83,22 @@ class TrainPipelineConfig(HubMixin):
                 raise ValueError(
                     f"A config_path is expected when resuming a run. Please specify path to {TRAIN_CONFIG_NAME}"
                 )
+
             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."
                 )
-            policy_path = Path(config_path).parent
-            self.policy.pretrained_path = policy_path
-            self.checkpoint_path = policy_path.parent
+
+            policy_dir = Path(config_path).parent
+            if self.policy is not None:
+                self.policy.pretrained_path = policy_dir
+            self.checkpoint_path = policy_dir.parent
+
+        if self.policy is None:
+            raise ValueError(
+                "Policy is not configured. Please specify a pretrained policy with `--policy.path`."
+            )
 
         if not self.job_name:
             if self.env is None:
@@ -126,8 +135,8 @@ class TrainPipelineConfig(HubMixin):
         """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
         return ["policy"]
 
-    def to_dict(self) -> dict:
-        return draccus.encode(self)
+    def to_dict(self) -> dict[str, Any]:
+        return draccus.encode(self)  # type: ignore[no-any-return]  # because of the third-party library draccus uses Any as the return type
 
     def _save_pretrained(self, save_directory: Path) -> None:
         with open(save_directory / TRAIN_CONFIG_NAME, "w") as f, draccus.config_type("json"):
@@ -139,13 +148,13 @@ class TrainPipelineConfig(HubMixin):
         pretrained_name_or_path: str | Path,
         *,
         force_download: bool = False,
-        resume_download: bool = None,
-        proxies: dict | None = None,
+        resume_download: bool | None = None,
+        proxies: dict[Any, Any] | None = None,
         token: str | bool | None = None,
         cache_dir: str | Path | None = None,
         local_files_only: bool = False,
         revision: str | None = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> "TrainPipelineConfig":
         model_id = str(pretrained_name_or_path)
         config_file: str | None = None
@@ -181,4 +190,6 @@ class TrainPipelineConfig(HubMixin):
 
 @dataclass(kw_only=True)
 class TrainRLServerPipelineConfig(TrainPipelineConfig):
-    dataset: DatasetConfig | None = None  # NOTE: In RL, we don't need an offline dataset
+    # NOTE: In RL, we don't need an offline dataset
+    # TODO: Make `TrainPipelineConfig.dataset` optional
+    dataset: DatasetConfig | None = None  # type: ignore[assignment] # because the parent class has made it's type non-optional

src/lerobot/configs/types.py

@@ -42,4 +42,4 @@ class NormalizationMode(str, Enum):
 @dataclass
 class PolicyFeature:
     type: FeatureType
-    shape: tuple
+    shape: tuple[int, ...]

src/lerobot/datasets/dataset_tools.py

@@ -39,6 +39,7 @@ from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.datasets.utils import (
+    DATA_DIR,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -962,28 +963,23 @@ def _copy_data_with_feature_changes(
     remove_features: list[str] | None = None,
 ) -> None:
     """Copy data while adding or removing features."""
-    if dataset.meta.episodes is None:
-        dataset.meta.episodes = load_episodes(dataset.meta.root)
+    data_dir = dataset.root / DATA_DIR
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
 
-    # Map file paths to episode indices to extract chunk/file indices
-    file_to_episodes: dict[Path, set[int]] = {}
-    for ep_idx in range(dataset.meta.total_episodes):
-        file_path = dataset.meta.get_data_file_path(ep_idx)
-        if file_path not in file_to_episodes:
-            file_to_episodes[file_path] = set()
-        file_to_episodes[file_path].add(ep_idx)
+    if not parquet_files:
+        raise ValueError(f"No parquet files found in {data_dir}")
 
     frame_idx = 0
 
-    for src_path in tqdm(sorted(file_to_episodes.keys()), desc="Processing data files"):
-        df = pd.read_parquet(dataset.root / src_path).reset_index(drop=True)
+    for src_path in tqdm(parquet_files, desc="Processing data files"):
+        df = pd.read_parquet(src_path).reset_index(drop=True)
 
-        # Get chunk_idx and file_idx from the source file's first episode
-        episodes_in_file = file_to_episodes[src_path]
-        first_ep_idx = min(episodes_in_file)
-        src_ep = dataset.meta.episodes[first_ep_idx]
-        chunk_idx = src_ep["data/chunk_index"]
-        file_idx = src_ep["data/file_index"]
+        relative_path = src_path.relative_to(dataset.root)
+        chunk_dir = relative_path.parts[1]
+        file_name = relative_path.parts[2]
+
+        chunk_idx = int(chunk_dir.split("-")[1])
+        file_idx = int(file_name.split("-")[1].split(".")[0])
 
         if remove_features:
             df = df.drop(columns=remove_features, errors="ignore")
@@ -1009,7 +1005,7 @@ def _copy_data_with_feature_changes(
                         df[feature_name] = feature_slice
             frame_idx = end_idx
 
-        # Write using the preserved chunk_idx and file_idx from source
+        # Write using the same chunk/file structure as source
         dst_path = new_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         dst_path.parent.mkdir(parents=True, exist_ok=True)
 

src/lerobot/datasets/lerobot_dataset.py

@@ -430,9 +430,7 @@ class LeRobotDatasetMetadata:
         video_keys = [video_key] if video_key is not None else self.video_keys
         for key in video_keys:
             if not self.features[key].get("info", None):
-                video_path = self.root / self.video_path.format(
-                    video_key=video_key, chunk_index=0, file_index=0
-                )
+                video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
                 self.info["features"][key]["info"] = get_video_info(video_path)
 
     def update_chunk_settings(
@@ -686,6 +684,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.episode_buffer = None
         self.writer = None
         self.latest_episode = None
+        self._current_file_start_frame = None  # Track the starting frame index of the current parquet file
 
         self.root.mkdir(exist_ok=True, parents=True)
 
@@ -708,7 +707,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
             if not self._check_cached_episodes_sufficient():
                 raise FileNotFoundError("Cached dataset doesn't contain all requested episodes")
         except (AssertionError, FileNotFoundError, NotADirectoryError):
-            self.revision = get_safe_version(self.repo_id, self.revision)
+            if is_valid_version(self.revision):
+                self.revision = get_safe_version(self.repo_id, self.revision)
             self.download(download_videos)
             self.hf_dataset = self.load_hf_dataset()
 
@@ -830,31 +830,40 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def load_hf_dataset(self) -> datasets.Dataset:
         """hf_dataset contains all the observations, states, actions, rewards, etc."""
         features = get_hf_features_from_features(self.features)
-        hf_dataset = load_nested_dataset(self.root / "data", features=features)
+        hf_dataset = load_nested_dataset(self.root / "data", features=features, episodes=self.episodes)
         hf_dataset.set_transform(hf_transform_to_torch)
         return hf_dataset
 
     def _check_cached_episodes_sufficient(self) -> bool:
-        """Check if the cached dataset contains all requested episodes."""
+        """Check if the cached dataset contains all requested episodes and their video files."""
         if self.hf_dataset is None or len(self.hf_dataset) == 0:
             return False
 
         # Get available episode indices from cached dataset
         available_episodes = {
             ep_idx.item() if isinstance(ep_idx, torch.Tensor) else ep_idx
-            for ep_idx in self.hf_dataset["episode_index"]
+            for ep_idx in self.hf_dataset.unique("episode_index")
         }
 
         # Determine requested episodes
         if self.episodes is None:
-            # Requesting all episodes - check if we have all episodes from metadata
             requested_episodes = set(range(self.meta.total_episodes))
         else:
-            # Requesting specific episodes
             requested_episodes = set(self.episodes)
 
         # Check if all requested episodes are available in cached data
-        return requested_episodes.issubset(available_episodes)
+        if not requested_episodes.issubset(available_episodes):
+            return False
+
+        # Check if all required video files exist
+        if len(self.meta.video_keys) > 0:
+            for ep_idx in requested_episodes:
+                for vid_key in self.meta.video_keys:
+                    video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
+                    if not video_path.exists():
+                        return False
+
+        return True
 
     def create_hf_dataset(self) -> datasets.Dataset:
         features = get_hf_features_from_features(self.features)
@@ -929,11 +938,26 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        return {
-            key: torch.stack(self.hf_dataset[q_idx][key])
-            for key, q_idx in query_indices.items()
-            if key not in self.meta.video_keys
-        }
+        """
+        Query dataset for indices across keys, skipping video keys.
+
+        Tries column-first [key][indices] for speed, falls back to row-first.
+
+        Args:
+            query_indices: Dict mapping keys to index lists to retrieve
+
+        Returns:
+            Dict with stacked tensors of queried data (video keys excluded)
+        """
+        result: dict = {}
+        for key, q_idx in query_indices.items():
+            if key in self.meta.video_keys:
+                continue
+            try:
+                result[key] = torch.stack(self.hf_dataset[key][q_idx])
+            except (KeyError, TypeError, IndexError):
+                result[key] = torch.stack(self.hf_dataset[q_idx][key])
+        return result
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function
@@ -1231,6 +1255,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             # Initialize indices and frame count for a new dataset made of the first episode data
             chunk_idx, file_idx = 0, 0
             global_frame_index = 0
+            self._current_file_start_frame = 0
             # However, if the episodes already exists
             # It means we are resuming recording, so we need to load the latest episode
             # Update the indices to avoid overwriting the latest episode
@@ -1242,6 +1267,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
                 # When resuming, move to the next file
                 chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
+                self._current_file_start_frame = global_frame_index
         else:
             # Retrieve information from the latest parquet file
             latest_ep = self.latest_episode
@@ -1252,7 +1278,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             latest_path = self.root / self.meta.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
             latest_size_in_mb = get_file_size_in_mb(latest_path)
 
-            frames_in_current_file = global_frame_index - latest_ep["dataset_from_index"]
+            frames_in_current_file = global_frame_index - self._current_file_start_frame
             av_size_per_frame = (
                 latest_size_in_mb / frames_in_current_file if frames_in_current_file > 0 else 0
             )
@@ -1266,6 +1292,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.meta.chunks_size)
                 self._close_writer()
                 self._writer_closed_for_reading = False
+                self._current_file_start_frame = global_frame_index
 
         ep_dict["data/chunk_index"] = chunk_idx
         ep_dict["data/file_index"] = file_idx
@@ -1472,6 +1499,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.video_backend = video_backend if video_backend is not None else get_safe_default_codec()
         obj.writer = None
         obj.latest_episode = None
+        obj._current_file_start_frame = None
         # Initialize tracking for incremental recording
         obj._lazy_loading = False
         obj._recorded_frames = 0

src/lerobot/datasets/transforms.py

@@ -206,6 +206,11 @@ class ImageTransformsConfig:
                 type="SharpnessJitter",
                 kwargs={"sharpness": (0.5, 1.5)},
             ),
+            "affine": ImageTransformConfig(
+                weight=1.0,
+                type="RandomAffine",
+                kwargs={"degrees": (-5.0, 5.0), "translate": (0.05, 0.05)},
+            ),
         }
     )
 
@@ -217,6 +222,8 @@ def make_transform_from_config(cfg: ImageTransformConfig):
         return v2.ColorJitter(**cfg.kwargs)
     elif cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
+    elif cfg.type == "RandomAffine":
+        return v2.RandomAffine(**cfg.kwargs)
     else:
         raise ValueError(f"Transform '{cfg.type}' is not valid.")
 

src/lerobot/datasets/utils.py

@@ -28,6 +28,7 @@ import numpy as np
 import packaging.version
 import pandas
 import pandas as pd
+import pyarrow.dataset as pa_ds
 import pyarrow.parquet as pq
 import torch
 from datasets import Dataset
@@ -103,7 +104,9 @@ def update_chunk_file_indices(chunk_idx: int, file_idx: int, chunks_size: int) -
     return chunk_idx, file_idx
 
 
-def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None) -> Dataset:
+def load_nested_dataset(
+    pq_dir: Path, features: datasets.Features | None = None, episodes: list[int] | None = None
+) -> Dataset:
     """Find parquet files in provided directory {pq_dir}/chunk-xxx/file-xxx.parquet
     Convert parquet files to pyarrow memory mapped in a cache folder for efficient RAM usage
     Concatenate all pyarrow references to return HF Dataset format
@@ -111,15 +114,26 @@ def load_nested_dataset(pq_dir: Path, features: datasets.Features | None = None)
     Args:
         pq_dir: Directory containing parquet files
         features: Optional features schema to ensure consistent loading of complex types like images
+        episodes: Optional list of episode indices to filter. Uses PyArrow predicate pushdown for efficiency.
     """
     paths = sorted(pq_dir.glob("*/*.parquet"))
     if len(paths) == 0:
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
-    # TODO(rcadene): set num_proc to accelerate conversion to pyarrow
     with SuppressProgressBars():
-        datasets = Dataset.from_parquet([str(path) for path in paths], features=features)
-    return datasets
+        # When no filtering needed, Dataset uses memory-mapped loading for efficiency
+        # PyArrow loads the entire dataset into memory
+        if episodes is None:
+            return Dataset.from_parquet([str(path) for path in paths], features=features)
+
+        arrow_dataset = pa_ds.dataset(paths, format="parquet")
+        filter_expr = pa_ds.field("episode_index").isin(episodes)
+        table = arrow_dataset.to_table(filter=filter_expr)
+
+        if features is not None:
+            table = table.cast(features.arrow_schema)
+
+        return Dataset(table)
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -98,7 +98,7 @@ OLD
 videos/chunk-000/CAMERA/episode_000000.mp4
 
 NEW
-videos/chunk-000/file_000.mp4
+videos/CAMERA/chunk-000/file_000.mp4
 -------------------------
 OLD
 episodes.jsonl

src/lerobot/datasets/video_utils.py

@@ -342,8 +342,8 @@ def encode_video_frames(
     # Define video output frame size (assuming all input frames are the same size)
     if len(input_list) == 0:
         raise FileNotFoundError(f"No images found in {imgs_dir}.")
-    dummy_image = Image.open(input_list[0])
-    width, height = dummy_image.size
+    with Image.open(input_list[0]) as dummy_image:
+        width, height = dummy_image.size
 
     # Define video codec options
     video_options = {}
@@ -373,11 +373,12 @@ def encode_video_frames(
 
         # Loop through input frames and encode them
         for input_data in input_list:
-            input_image = Image.open(input_data).convert("RGB")
-            input_frame = av.VideoFrame.from_image(input_image)
-            packet = output_stream.encode(input_frame)
-            if packet:
-                output.mux(packet)
+            with Image.open(input_data) as input_image:
+                input_image = input_image.convert("RGB")
+                input_frame = av.VideoFrame.from_image(input_image)
+                packet = output_stream.encode(input_frame)
+                if packet:
+                    output.mux(packet)
 
         # Flush the encoder
         packet = output_stream.encode()

src/lerobot/envs/configs.py

@@ -37,6 +37,16 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
     def type(self) -> str:
         return self.get_choice_name(self.__class__)
 
+    @property
+    def package_name(self) -> str:
+        """Package name to import if environment not found in gym registry"""
+        return f"gym_{self.type}"
+
+    @property
+    def gym_id(self) -> str:
+        """ID string used in gym.make() to instantiate the environment"""
+        return f"{self.package_name}/{self.task}"
+
     @property
     @abc.abstractmethod
     def gym_kwargs(self) -> dict:
@@ -236,7 +246,14 @@ class LiberoEnv(EnvConfig):
     features_map: dict[str, str] = field(
         default_factory=lambda: {
             ACTION: ACTION,
-            "agent_pos": OBS_STATE,
+            "robot_state/eef/pos": f"{OBS_STATE}.eef_pos",
+            "robot_state/eef/quat": f"{OBS_STATE}.eef_quat",
+            "robot_state/eef/mat": f"{OBS_STATE}.eef_mat",
+            "robot_state/eef/axisangle": f"{OBS_STATE}.eef_axisangle",
+            "robot_state/gripper/qpos": f"{OBS_STATE}.gripper_qpos",
+            "robot_state/gripper/qvel": f"{OBS_STATE}.gripper_qvel",
+            "robot_state/joints/pos": f"{OBS_STATE}.joint_pos",
+            "robot_state/joints/vel": f"{OBS_STATE}.joint_vel",
             "pixels/agentview_image": f"{OBS_IMAGES}.image",
             "pixels/robot0_eye_in_hand_image": f"{OBS_IMAGES}.image2",
         }
@@ -251,13 +268,44 @@ class LiberoEnv(EnvConfig):
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
         elif self.obs_type == "pixels_agent_pos":
-            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(8,))
             self.features["pixels/agentview_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
             self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature(
                 type=FeatureType.VISUAL, shape=(self.observation_height, self.observation_width, 3)
             )
+            self.features["robot_state/eef/pos"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(3,),
+            )
+            self.features["robot_state/eef/quat"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(4,),
+            )
+            self.features["robot_state/eef/mat"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(3, 3),
+            )
+            self.features["robot_state/eef/axisangle"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(3,),
+            )
+            self.features["robot_state/gripper/qpos"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(2,),
+            )
+            self.features["robot_state/gripper/qvel"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(2,),
+            )
+            self.features["robot_state/joints/pos"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(7,),
+            )
+            self.features["robot_state/joints/vel"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(7,),
+            )
         else:
             raise ValueError(f"Unsupported obs_type: {self.obs_type}")
 

src/lerobot/envs/factory.py

@@ -14,10 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import importlib
+from typing import Any
 
 import gymnasium as gym
+from gymnasium.envs.registration import registry as gym_registry
 
 from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv
+from lerobot.envs.utils import _call_make_env, _download_hub_file, _import_hub_module, _normalize_hub_result
+from lerobot.processor.env_processor import LiberoProcessorStep
+from lerobot.processor.pipeline import PolicyProcessorPipeline
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -31,16 +36,59 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         raise ValueError(f"Policy type '{env_type}' is not available.")
 
 
-def make_env(
-    cfg: EnvConfig, n_envs: int = 1, use_async_envs: bool = False
-) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-    """Makes a gym vector environment according to the config.
+def make_env_pre_post_processors(
+    env_cfg: EnvConfig,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+]:
+    """
+    Create preprocessor and postprocessor pipelines for environment observations.
+
+    This function creates processor pipelines that transform raw environment
+    observations and actions. By default, it returns identity processors that do nothing.
+    For specific environments like LIBERO, it adds environment-specific processing steps.
 
     Args:
-        cfg (EnvConfig): the config of the environment to instantiate.
+        env_cfg: The configuration of the environment.
+
+    Returns:
+        A tuple containing:
+            - preprocessor: Pipeline that processes environment observations
+            - postprocessor: Pipeline that processes environment outputs (currently identity)
+    """
+    # For LIBERO environments, add the LiberoProcessorStep to preprocessor
+    if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
+        preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
+    else:
+        # For all other environments, return an identity preprocessor (does nothing)
+        preprocessor = PolicyProcessorPipeline(steps=[])
+
+    # Postprocessor is currently identity for all environments
+    postprocessor = PolicyProcessorPipeline(steps=[])
+
+    return preprocessor, postprocessor
+
+
+def make_env(
+    cfg: EnvConfig | str,
+    n_envs: int = 1,
+    use_async_envs: bool = False,
+    hub_cache_dir: str | None = None,
+    trust_remote_code: bool = False,
+) -> dict[str, dict[int, gym.vector.VectorEnv]]:
+    """Makes a gym vector environment according to the config or Hub reference.
+
+    Args:
+        cfg (EnvConfig | str): Either an `EnvConfig` object describing the environment to build locally,
+            or a Hugging Face Hub repository identifier (e.g. `"username/repo"`). In the latter case,
+            the repo must include a Python file (usually `env.py`).
         n_envs (int, optional): The number of parallelized env to return. Defaults to 1.
         use_async_envs (bool, optional): Whether to return an AsyncVectorEnv or a SyncVectorEnv. Defaults to
             False.
+        hub_cache_dir (str | None): Optional cache path for downloaded hub files.
+        trust_remote_code (bool): **Explicit consent** to execute remote code from the Hub.
+            Default False — must be set to True to import/exec hub `env.py`.
 
     Raises:
         ValueError: if n_envs < 1
@@ -53,6 +101,21 @@ def make_env(
             - For single-task environments: a single suite entry (cfg.type) with task_id=0.
 
     """
+    # if user passed a hub id string (e.g., "username/repo", "username/repo@main:env.py")
+    # simplified: only support hub-provided `make_env`
+    if isinstance(cfg, str):
+        # _download_hub_file will raise the same RuntimeError if trust_remote_code is False
+        repo_id, file_path, local_file, revision = _download_hub_file(cfg, trust_remote_code, hub_cache_dir)
+
+        # import and surface clear import errors
+        module = _import_hub_module(local_file, repo_id)
+
+        # call the hub-provided make_env
+        raw_result = _call_make_env(module, n_envs=n_envs, use_async_envs=use_async_envs)
+
+        # normalize the return into {suite: {task_id: vec_env}}
+        return _normalize_hub_result(raw_result)
+
     if n_envs < 1:
         raise ValueError("`n_envs` must be at least 1")
 
@@ -84,17 +147,24 @@ def make_env(
             gym_kwargs=cfg.gym_kwargs,
             env_cls=env_cls,
         )
-    package_name = f"gym_{cfg.type}"
-    try:
-        importlib.import_module(package_name)
-    except ModuleNotFoundError as e:
-        print(f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.type}]'`")
-        raise e
 
-    gym_handle = f"{package_name}/{cfg.task}"
+    if cfg.gym_id not in gym_registry:
+        print(f"gym id '{cfg.gym_id}' not found, attempting to import '{cfg.package_name}'...")
+        try:
+            importlib.import_module(cfg.package_name)
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError(
+                f"Package '{cfg.package_name}' required for env '{cfg.type}' not found. "
+                f"Please install it or check PYTHONPATH."
+            ) from e
+
+        if cfg.gym_id not in gym_registry:
+            raise gym.error.NameNotFound(
+                f"Environment '{cfg.gym_id}' not registered even after importing '{cfg.package_name}'."
+            )
 
     def _make_one():
-        return gym.make(gym_handle, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {}))
+        return gym.make(cfg.gym_id, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {}))
 
     vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=gym.vector.AutoresetMode.SAME_STEP)
 

src/lerobot/envs/libero.py

@@ -175,11 +175,39 @@ class LiberoEnv(gym.Env):
             self.observation_space = spaces.Dict(
                 {
                     "pixels": spaces.Dict(images),
-                    "agent_pos": spaces.Box(
-                        low=AGENT_POS_LOW,
-                        high=AGENT_POS_HIGH,
-                        shape=(OBS_STATE_DIM,),
-                        dtype=np.float64,
+                    "robot_state": spaces.Dict(
+                        {
+                            "eef": spaces.Dict(
+                                {
+                                    "pos": spaces.Box(low=-np.inf, high=np.inf, shape=(3,), dtype=np.float64),
+                                    "quat": spaces.Box(
+                                        low=-np.inf, high=np.inf, shape=(4,), dtype=np.float64
+                                    ),
+                                    "mat": spaces.Box(
+                                        low=-np.inf, high=np.inf, shape=(3, 3), dtype=np.float64
+                                    ),
+                                    "axisangle": spaces.Box(
+                                        low=-np.inf, high=np.inf, shape=(3,), dtype=np.float64
+                                    ),
+                                }
+                            ),
+                            "gripper": spaces.Dict(
+                                {
+                                    "qpos": spaces.Box(
+                                        low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64
+                                    ),
+                                    "qvel": spaces.Box(
+                                        low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64
+                                    ),
+                                }
+                            ),
+                            "joints": spaces.Dict(
+                                {
+                                    "pos": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
+                                    "vel": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
+                                }
+                            ),
+                        }
                     ),
                 }
             )
@@ -191,6 +219,7 @@ class LiberoEnv(gym.Env):
     def render(self):
         raw_obs = self._env.env._get_observations()
         image = self._format_raw_obs(raw_obs)["pixels"]["image"]
+        image = image[::-1, ::-1]  # flip both H and W for visualization
         return image
 
     def _make_envs_task(self, task_suite: Any, task_id: int = 0):
@@ -212,23 +241,50 @@ class LiberoEnv(gym.Env):
         images = {}
         for camera_name in self.camera_name:
             image = raw_obs[camera_name]
-            image = image[::-1, ::-1]  # rotate 180 degrees
             images[self.camera_name_mapping[camera_name]] = image
-        state = np.concatenate(
-            (
-                raw_obs["robot0_eef_pos"],
-                quat2axisangle(raw_obs["robot0_eef_quat"]),
-                raw_obs["robot0_gripper_qpos"],
-            )
-        )
-        agent_pos = state
+
+        eef_pos = raw_obs.get("robot0_eef_pos")
+        eef_quat = raw_obs.get("robot0_eef_quat")
+
+        # rotation matrix from controller
+        eef_mat = self._env.robots[0].controller.ee_ori_mat if eef_pos is not None else None
+        eef_axisangle = quat2axisangle(eef_quat) if eef_quat is not None else None
+        gripper_qpos = raw_obs.get("robot0_gripper_qpos")
+        gripper_qvel = raw_obs.get("robot0_gripper_qvel")
+        joint_pos = raw_obs.get("robot0_joint_pos")
+        joint_vel = raw_obs.get("robot0_joint_vel")
+        obs = {
+            "pixels": images,
+            "robot_state": {
+                "eef": {
+                    "pos": eef_pos,  # (3,)
+                    "quat": eef_quat,  # (4,)
+                    "mat": eef_mat,  # (3, 3)
+                    "axisangle": eef_axisangle,  # (3)
+                },
+                "gripper": {
+                    "qpos": gripper_qpos,  # (2,)
+                    "qvel": gripper_qvel,  # (2,)
+                },
+                "joints": {
+                    "pos": joint_pos,  # (7,)
+                    "vel": joint_vel,  # (7,)
+                },
+            },
+        }
         if self.obs_type == "pixels":
             return {"pixels": images.copy()}
+
         if self.obs_type == "pixels_agent_pos":
-            return {
-                "pixels": images.copy(),
-                "agent_pos": agent_pos,
-            }
+            # Validate required fields are present
+            if eef_pos is None or eef_quat is None or gripper_qpos is None:
+                raise ValueError(
+                    f"Missing required robot state fields in raw observation. "
+                    f"Got eef_pos={eef_pos is not None}, eef_quat={eef_quat is not None}, "
+                    f"gripper_qpos={gripper_qpos is not None}"
+                )
+            return obs
+
         raise NotImplementedError(
             f"The observation type '{self.obs_type}' is not supported in LiberoEnv. "
             "Please switch to an image-based obs_type (e.g. 'pixels', 'pixels_agent_pos')."
@@ -355,12 +411,10 @@ def create_libero_envs(
         print(f"Restricting to task_ids={task_ids_filter}")
 
     out: dict[str, dict[int, Any]] = defaultdict(dict)
-
     for suite_name in suite_names:
         suite = _get_suite(suite_name)
         total = len(suite.tasks)
         selected = _select_task_ids(total, task_ids_filter)
-
         if not selected:
             raise ValueError(f"No tasks selected for suite '{suite_name}' (available: {total}).")
 

src/lerobot/envs/utils.py

@@ -13,6 +13,8 @@
 # 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 importlib.util
+import os
 import warnings
 from collections.abc import Mapping, Sequence
 from functools import singledispatch
@@ -22,14 +24,27 @@ import einops
 import gymnasium as gym
 import numpy as np
 import torch
+from huggingface_hub import hf_hub_download, snapshot_download
 from torch import Tensor
 
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.envs.configs import EnvConfig
-from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
+from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, OBS_STR
 from lerobot.utils.utils import get_channel_first_image_shape
 
 
+def _convert_nested_dict(d):
+    result = {}
+    for k, v in d.items():
+        if isinstance(v, dict):
+            result[k] = _convert_nested_dict(v)
+        elif isinstance(v, np.ndarray):
+            result[k] = torch.from_numpy(v)
+        else:
+            result[k] = v
+    return result
+
+
 def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Tensor]:
     # TODO(aliberts, rcadene): refactor this to use features from the environment (no hardcoding)
     """Convert environment observation to LeRobot format observation.
@@ -75,12 +90,14 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
 
         return_observations[OBS_ENV_STATE] = env_state
 
-    # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing
-    agent_pos = torch.from_numpy(observations["agent_pos"]).float()
-    if agent_pos.dim() == 1:
-        agent_pos = agent_pos.unsqueeze(0)
-    return_observations[OBS_STATE] = agent_pos
+    if "agent_pos" in observations:
+        agent_pos = torch.from_numpy(observations["agent_pos"]).float()
+        if agent_pos.dim() == 1:
+            agent_pos = agent_pos.unsqueeze(0)
+        return_observations[OBS_STATE] = agent_pos
 
+    if "robot_state" in observations:
+        return_observations[f"{OBS_STR}.robot_state"] = _convert_nested_dict(observations["robot_state"])
     return return_observations
 
 
@@ -195,3 +212,132 @@ def _(envs: Sequence) -> None:
 @close_envs.register
 def _(env: gym.Env) -> None:
     _close_single_env(env)
+
+
+# helper to safely load a python file as a module
+def _load_module_from_path(path: str, module_name: str | None = None):
+    module_name = module_name or f"hub_env_{os.path.basename(path).replace('.', '_')}"
+    spec = importlib.util.spec_from_file_location(module_name, path)
+    if spec is None:
+        raise ImportError(f"Could not load module spec for {module_name} from {path}")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+# helper to parse hub string (supports "user/repo", "user/repo@rev", optional path)
+# examples:
+#   "user/repo" -> will look for env.py at repo root
+#   "user/repo@main:envs/my_env.py" -> explicit revision and path
+def _parse_hub_url(hub_uri: str):
+    # very small parser: [repo_id][@revision][:path]
+    # repo_id is required (user/repo or org/repo)
+    revision = None
+    file_path = "env.py"
+    if "@" in hub_uri:
+        repo_and_rev, *rest = hub_uri.split(":", 1)
+        repo_id, rev = repo_and_rev.split("@", 1)
+        revision = rev
+        if rest:
+            file_path = rest[0]
+    else:
+        repo_id, *rest = hub_uri.split(":", 1)
+        if rest:
+            file_path = rest[0]
+    return repo_id, revision, file_path
+
+
+def _download_hub_file(
+    cfg_str: str,
+    trust_remote_code: bool,
+    hub_cache_dir: str | None,
+) -> tuple[str, str, str, str]:
+    """
+    Parse `cfg_str` (hub URL), enforce `trust_remote_code`, and return
+    (repo_id, file_path, local_file, revision).
+    """
+    if not trust_remote_code:
+        raise RuntimeError(
+            f"Refusing to execute remote code from the Hub for '{cfg_str}'. "
+            "Executing hub env modules runs arbitrary Python code from third-party repositories. "
+            "If you trust this repo and understand the risks, call `make_env(..., trust_remote_code=True)` "
+            "and prefer pinning to a specific revision: 'user/repo@<commit-hash>:env.py'."
+        )
+
+    repo_id, revision, file_path = _parse_hub_url(cfg_str)
+
+    try:
+        local_file = hf_hub_download(
+            repo_id=repo_id, filename=file_path, revision=revision, cache_dir=hub_cache_dir
+        )
+    except Exception as e:
+        # fallback to snapshot download
+        snapshot_dir = snapshot_download(repo_id=repo_id, revision=revision, cache_dir=hub_cache_dir)
+        local_file = os.path.join(snapshot_dir, file_path)
+        if not os.path.exists(local_file):
+            raise FileNotFoundError(
+                f"Could not find {file_path} in repository {repo_id}@{revision or 'main'}"
+            ) from e
+
+    return repo_id, file_path, local_file, revision
+
+
+def _import_hub_module(local_file: str, repo_id: str) -> Any:
+    """
+    Import the downloaded file as a module and surface helpful import error messages.
+    """
+    module_name = f"hub_env_{repo_id.replace('/', '_')}"
+    try:
+        module = _load_module_from_path(local_file, module_name=module_name)
+    except ModuleNotFoundError as e:
+        missing = getattr(e, "name", None) or str(e)
+        raise ModuleNotFoundError(
+            f"Hub env '{repo_id}:{os.path.basename(local_file)}' failed to import because the dependency "
+            f"'{missing}' is not installed locally.\n\n"
+        ) from e
+    except ImportError as e:
+        raise ImportError(
+            f"Failed to load hub env module '{repo_id}:{os.path.basename(local_file)}'. Import error: {e}\n\n"
+        ) from e
+    return module
+
+
+def _call_make_env(module: Any, n_envs: int, use_async_envs: bool) -> Any:
+    """
+    Ensure module exposes make_env and call it.
+    """
+    if not hasattr(module, "make_env"):
+        raise AttributeError(
+            f"The hub module {getattr(module, '__name__', 'hub_module')} must expose `make_env(n_envs=int, use_async_envs=bool)`."
+        )
+    entry_fn = module.make_env
+    return entry_fn(n_envs=n_envs, use_async_envs=use_async_envs)
+
+
+def _normalize_hub_result(result: Any) -> dict[str, dict[int, gym.vector.VectorEnv]]:
+    """
+    Normalize possible return types from hub `make_env` into the mapping:
+      { suite_name: { task_id: vector_env } }
+    Accepts:
+      - dict (assumed already correct)
+      - gym.vector.VectorEnv
+      - gym.Env (will be wrapped into SyncVectorEnv)
+    """
+    if isinstance(result, dict):
+        return result
+
+    # VectorEnv: use its spec.id if available
+    if isinstance(result, gym.vector.VectorEnv):
+        suite_name = getattr(result, "spec", None) and getattr(result.spec, "id", None) or "hub_env"
+        return {suite_name: {0: result}}
+
+    # Single Env: wrap into SyncVectorEnv
+    if isinstance(result, gym.Env):
+        vec = gym.vector.SyncVectorEnv([lambda: result])
+        suite_name = getattr(result, "spec", None) and getattr(result.spec, "id", None) or "hub_env"
+        return {suite_name: {0: vec}}
+
+    raise ValueError(
+        "Hub `make_env` must return either a mapping {suite: {task_id: vec_env}}, "
+        "a gym.vector.VectorEnv, or a single gym.Env."
+    )

src/lerobot/model/kinematics.py

@@ -22,18 +22,18 @@ class RobotKinematics:
         self,
         urdf_path: str,
         target_frame_name: str = "gripper_frame_link",
-        joint_names: list[str] = None,
+        joint_names: list[str] | None = None,
     ):
         """
         Initialize placo-based kinematics solver.
 
         Args:
-            urdf_path: Path to the robot URDF file
-            target_frame_name: Name of the end-effector frame in the URDF
-            joint_names: List of joint names to use for the kinematics solver
+            urdf_path (str): Path to the robot URDF file
+            target_frame_name (str): Name of the end-effector frame in the URDF
+            joint_names (list[str] | None): List of joint names to use for the kinematics solver
         """
         try:
-            import placo
+            import placo  # type: ignore[import-not-found] # C++ library with Python bindings, no type stubs available. TODO: Create stub file or request upstream typing support.
         except ImportError as e:
             raise ImportError(
                 "placo is required for RobotKinematics. "
@@ -52,7 +52,7 @@ class RobotKinematics:
         # Initialize frame task for IK
         self.tip_frame = self.solver.add_frame_task(self.target_frame_name, np.eye(4))
 
-    def forward_kinematics(self, joint_pos_deg):
+    def forward_kinematics(self, joint_pos_deg: np.ndarray) -> np.ndarray:
         """
         Compute forward kinematics for given joint configuration given the target frame name in the constructor.
 
@@ -77,8 +77,12 @@ class RobotKinematics:
         return self.robot.get_T_world_frame(self.target_frame_name)
 
     def inverse_kinematics(
-        self, current_joint_pos, desired_ee_pose, position_weight=1.0, orientation_weight=0.01
-    ):
+        self,
+        current_joint_pos: np.ndarray,
+        desired_ee_pose: np.ndarray,
+        position_weight: float = 1.0,
+        orientation_weight: float = 0.01,
+    ) -> np.ndarray:
         """
         Compute inverse kinematics using placo solver.
 

src/lerobot/motors/dynamixel/dynamixel.py

@@ -60,7 +60,7 @@ class OperatingMode(Enum):
 
     # This mode controls position. This mode is identical to the Multi-turn Position Control from existing
     # DYNAMIXEL. 512 turns are supported(-256[rev] ~ 256[rev]). This mode is ideal for multi-turn wrists or
-    # conveyer systems or a system that requires an additional reduction gear. Note that Max Position
+    # conveyor systems or a system that requires an additional reduction gear. Note that Max Position
     # Limit(48), Min Position Limit(52) are not used on Extended Position Control Mode.
     EXTENDED_POSITION = 4
 

src/lerobot/motors/feetech/tables.py

@@ -206,8 +206,12 @@ MODEL_BAUDRATE_TABLE = {
 # Sign-Magnitude encoding bits
 STS_SMS_SERIES_ENCODINGS_TABLE = {
     "Homing_Offset": 11,
+    "Goal_Position": 15,
     "Goal_Velocity": 15,
+    "Goal_Speed": 15,
+    "Present_Position": 15,
     "Present_Velocity": 15,
+    "Present_Speed": 15,
 }
 
 MODEL_ENCODING_TABLE = {

src/lerobot/policies/__init__.py

@@ -14,6 +14,7 @@
 
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
+from .groot.configuration_groot import GrootConfig as GrootConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi05.configuration_pi05 import PI05Config as PI05Config
 from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
@@ -29,4 +30,5 @@ __all__ = [
     "SmolVLAConfig",
     "TDMPCConfig",
     "VQBeTConfig",
+    "GrootConfig",
 ]

src/lerobot/policies/act/modeling_act.py

@@ -626,8 +626,8 @@ class ACTDecoderLayer(nn.Module):
             x: (Decoder Sequence, Batch, Channel) tensor of input tokens.
             encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are
                 cross-attending with.
-            decoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder).
-            encoder_pos_embed: (DS, 1, C) Positional_embedding for the queries (from the decoder).
+            encoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder).
+            decoder_pos_embed: (DS, 1, C) positional embedding for the queries (from the decoder).
         Returns:
             (DS, B, C) tensor of decoder output features.
         """

src/lerobot/policies/factory.py

@@ -30,6 +30,7 @@ from lerobot.envs.configs import EnvConfig
 from lerobot.envs.utils import env_to_policy_features
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.groot.configuration_groot import GrootConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy
@@ -37,6 +38,7 @@ from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
+from lerobot.policies.utils import validate_visual_features_consistency
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline
 from lerobot.processor.converters import (
@@ -101,6 +103,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
+    elif name == "groot":
+        from lerobot.policies.groot.modeling_groot import GrootPolicy
+
+        return GrootPolicy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -142,6 +148,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return SmolVLAConfig(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
+    elif policy_type == "groot":
+        return GrootConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 
@@ -199,6 +207,27 @@ def make_pre_post_processors(
             policy configuration type.
     """
     if pretrained_path:
+        # TODO(Steven): Temporary patch, implement correctly the processors for Gr00t
+        if isinstance(policy_cfg, GrootConfig):
+            # GROOT handles normalization in groot_pack_inputs_v3 step
+            # Need to override both stats AND normalize_min_max since saved config might be empty
+            preprocessor_overrides = {}
+            postprocessor_overrides = {}
+            preprocessor_overrides["groot_pack_inputs_v3"] = {
+                "stats": kwargs.get("dataset_stats"),
+                "normalize_min_max": True,
+            }
+
+            # Also ensure postprocessing slices to env action dim and unnormalizes with dataset stats
+            env_action_dim = policy_cfg.output_features["action"].shape[0]
+            postprocessor_overrides["groot_action_unpack_unnormalize_v1"] = {
+                "stats": kwargs.get("dataset_stats"),
+                "normalize_min_max": True,
+                "env_action_dim": env_action_dim,
+            }
+            kwargs["preprocessor_overrides"] = preprocessor_overrides
+            kwargs["postprocessor_overrides"] = postprocessor_overrides
+
         return (
             PolicyProcessorPipeline.from_pretrained(
                 pretrained_model_name_or_path=pretrained_path,
@@ -293,6 +322,14 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
+    elif isinstance(policy_cfg, GrootConfig):
+        from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
+
+        processors = make_groot_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
+
     else:
         raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")
 
@@ -303,6 +340,7 @@ def make_policy(
     cfg: PreTrainedConfig,
     ds_meta: LeRobotDatasetMetadata | None = None,
     env_cfg: EnvConfig | None = None,
+    rename_map: dict[str, str] | None = None,
 ) -> PreTrainedPolicy:
     """
     Instantiate a policy model.
@@ -319,6 +357,8 @@ def make_policy(
                  statistics for normalization layers.
         env_cfg: Environment configuration used to infer feature shapes and types.
                  One of `ds_meta` or `env_cfg` must be provided.
+        rename_map: Optional mapping of dataset or environment feature keys to match
+                 expected policy feature names (e.g., `"left"` → `"camera1"`).
 
     Returns:
         An instantiated and device-placed policy model.
@@ -360,8 +400,10 @@ def make_policy(
             raise ValueError("env_cfg cannot be None when ds_meta is not provided")
         features = env_to_policy_features(env_cfg)
 
-    cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
-    cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
+    if not cfg.output_features:
+        cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+    if not cfg.input_features:
+        cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
     kwargs["config"] = cfg
 
     if cfg.pretrained_path:
@@ -378,4 +420,8 @@ def make_policy(
 
     # policy = torch.compile(policy, mode="reduce-overhead")
 
+    if not rename_map:
+        validate_visual_features_consistency(cfg, features)
+        # TODO: (jadechoghari) - add a check_state(cfg, features) and check_action(cfg, features)
+
     return policy

src/lerobot/policies/groot/README.md

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

src/lerobot/policies/groot/__init__.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2025 Nvidia and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,5 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .configuration_stretch3 import Stretch3RobotConfig
-from .robot_stretch3 import Stretch3Robot
+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"]

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

@@ -1,18 +1,14 @@
-#!/usr/bin/env python
-
-# Copyright 2025 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,
 # 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 .config_viperx import ViperXConfig
-from .viperx import ViperX

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

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

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

@@ -0,0 +1,370 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from diffusers import ConfigMixin, ModelMixin
+from diffusers.configuration_utils import register_to_config
+from diffusers.models.attention import Attention, FeedForward
+from diffusers.models.embeddings import (
+    SinusoidalPositionalEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from torch import nn
+
+
+class TimestepEncoder(nn.Module):
+    def __init__(self, embedding_dim, compute_dtype=torch.float32):
+        super().__init__()
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timesteps):
+        dtype = next(self.parameters()).dtype
+        timesteps_proj = self.time_proj(timesteps).to(dtype)
+        timesteps_emb = self.timestep_embedder(timesteps_proj)  # (N, D)
+        return timesteps_emb
+
+
+class AdaLayerNorm(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-5,
+        chunk_dim: int = 0,
+    ):
+        super().__init__()
+        self.chunk_dim = chunk_dim
+        output_dim = embedding_dim * 2
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        temb: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        temb = self.linear(self.silu(temb))
+        scale, shift = temb.chunk(2, dim=1)
+        x = self.norm(x) * (1 + scale[:, None]) + shift[:, None]
+        return x
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: int | None = None,
+        activation_fn: str = "geglu",
+        attention_bias: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        attention_type: str = "default",
+        positional_embeddings: str | None = None,
+        num_positional_embeddings: int | None = None,
+        ff_inner_dim: int | None = None,
+        ff_bias: bool = True,
+        attention_out_bias: bool = True,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        self.dropout = dropout
+        self.cross_attention_dim = cross_attention_dim
+        self.activation_fn = activation_fn
+        self.attention_bias = attention_bias
+        self.norm_elementwise_affine = norm_elementwise_affine
+        self.positional_embeddings = positional_embeddings
+        self.num_positional_embeddings = num_positional_embeddings
+        self.norm_type = norm_type
+
+        if positional_embeddings and (num_positional_embeddings is None):
+            raise ValueError(
+                "If `positional_embeddings` type is defined, `num_positional_embeddings` must also be defined."
+            )
+
+        if positional_embeddings == "sinusoidal":
+            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
+        else:
+            self.pos_embed = None
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if norm_type == "ada_norm":
+            self.norm1 = AdaLayerNorm(dim)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim,
+            upcast_attention=upcast_attention,
+            out_bias=attention_out_bias,
+        )
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+            inner_dim=ff_inner_dim,
+            bias=ff_bias,
+        )
+        if final_dropout:
+            self.final_dropout = nn.Dropout(dropout)
+        else:
+            self.final_dropout = None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        encoder_hidden_states: torch.Tensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        temb: torch.LongTensor | None = None,
+    ) -> torch.Tensor:
+        # 0. Self-Attention
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, temb)
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            # encoder_attention_mask=encoder_attention_mask,
+        )
+        if self.final_dropout:
+            attn_output = self.final_dropout(attn_output)
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 4. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+        return hidden_states
+
+
+class DiT(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 8,
+        attention_head_dim: int = 64,
+        output_dim: int = 26,
+        num_layers: int = 12,
+        dropout: float = 0.1,
+        attention_bias: bool = True,
+        activation_fn: str = "gelu-approximate",
+        num_embeds_ada_norm: int | None = 1000,
+        upcast_attention: bool = False,
+        norm_type: str = "ada_norm",
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-5,
+        max_num_positional_embeddings: int = 512,
+        compute_dtype=torch.float32,
+        final_dropout: bool = True,
+        positional_embeddings: str | None = "sinusoidal",
+        interleave_self_attention=False,
+        cross_attention_dim: int | None = None,
+    ):
+        super().__init__()
+
+        self.attention_head_dim = attention_head_dim
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+        self.gradient_checkpointing = False
+
+        # Timestep encoder
+        self.timestep_encoder = TimestepEncoder(
+            embedding_dim=self.inner_dim, compute_dtype=self.config.compute_dtype
+        )
+
+        all_blocks = []
+        for idx in range(self.config.num_layers):
+            use_self_attn = idx % 2 == 1 and interleave_self_attention
+            curr_cross_attention_dim = cross_attention_dim if not use_self_attn else None
+
+            all_blocks += [
+                BasicTransformerBlock(
+                    self.inner_dim,
+                    self.config.num_attention_heads,
+                    self.config.attention_head_dim,
+                    dropout=self.config.dropout,
+                    activation_fn=self.config.activation_fn,
+                    attention_bias=self.config.attention_bias,
+                    upcast_attention=self.config.upcast_attention,
+                    norm_type=norm_type,
+                    norm_elementwise_affine=self.config.norm_elementwise_affine,
+                    norm_eps=self.config.norm_eps,
+                    positional_embeddings=positional_embeddings,
+                    num_positional_embeddings=self.config.max_num_positional_embeddings,
+                    final_dropout=final_dropout,
+                    cross_attention_dim=curr_cross_attention_dim,
+                )
+            ]
+        self.transformer_blocks = nn.ModuleList(all_blocks)
+
+        # Output blocks
+        self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
+        self.proj_out_2 = nn.Linear(self.inner_dim, self.config.output_dim)
+        print(
+            "Total number of DiT parameters: ",
+            sum(p.numel() for p in self.parameters() if p.requires_grad),
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # Shape: (B, T, D)
+        encoder_hidden_states: torch.Tensor,  # Shape: (B, S, D)
+        timestep: torch.LongTensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        return_all_hidden_states: bool = False,
+    ):
+        # Encode timesteps
+        temb = self.timestep_encoder(timestep)
+
+        # Process through transformer blocks - single pass through the blocks
+        hidden_states = hidden_states.contiguous()
+        encoder_hidden_states = encoder_hidden_states.contiguous()
+
+        all_hidden_states = [hidden_states]
+
+        # Process through transformer blocks
+        for idx, block in enumerate(self.transformer_blocks):
+            if idx % 2 == 1 and self.config.interleave_self_attention:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=None,
+                    encoder_attention_mask=None,
+                    temb=temb,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=None,
+                    temb=temb,
+                )
+            all_hidden_states.append(hidden_states)
+
+        # Output processing
+        conditioning = temb
+        shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
+        if return_all_hidden_states:
+            return self.proj_out_2(hidden_states), all_hidden_states
+        else:
+            return self.proj_out_2(hidden_states)
+
+
+class SelfAttentionTransformer(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 8,
+        attention_head_dim: int = 64,
+        output_dim: int = 26,
+        num_layers: int = 12,
+        dropout: float = 0.1,
+        attention_bias: bool = True,
+        activation_fn: str = "gelu-approximate",
+        num_embeds_ada_norm: int | None = 1000,
+        upcast_attention: bool = False,
+        max_num_positional_embeddings: int = 512,
+        compute_dtype=torch.float32,
+        final_dropout: bool = True,
+        positional_embeddings: str | None = "sinusoidal",
+        interleave_self_attention=False,
+    ):
+        super().__init__()
+
+        self.attention_head_dim = attention_head_dim
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+        self.gradient_checkpointing = False
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    self.inner_dim,
+                    self.config.num_attention_heads,
+                    self.config.attention_head_dim,
+                    dropout=self.config.dropout,
+                    activation_fn=self.config.activation_fn,
+                    attention_bias=self.config.attention_bias,
+                    upcast_attention=self.config.upcast_attention,
+                    positional_embeddings=positional_embeddings,
+                    num_positional_embeddings=self.config.max_num_positional_embeddings,
+                    final_dropout=final_dropout,
+                )
+                for _ in range(self.config.num_layers)
+            ]
+        )
+        print(
+            "Total number of SelfAttentionTransformer parameters: ",
+            sum(p.numel() for p in self.parameters() if p.requires_grad),
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,  # Shape: (B, T, D)
+        return_all_hidden_states: bool = False,
+    ):
+        # Process through transformer blocks - single pass through the blocks
+        hidden_states = hidden_states.contiguous()
+        all_hidden_states = [hidden_states]
+
+        # Process through transformer blocks
+        for _idx, block in enumerate(self.transformer_blocks):
+            hidden_states = block(hidden_states)
+            all_hidden_states.append(hidden_states)
+
+        if return_all_hidden_states:
+            return hidden_states, all_hidden_states
+        else:
+            return hidden_states

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

@@ -0,0 +1,406 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from torch import nn
+from torch.distributions import Beta
+
+from lerobot.utils.import_utils import _transformers_available
+
+# Conditional import for type checking and lazy loading
+if TYPE_CHECKING or _transformers_available:
+    from transformers import PretrainedConfig
+    from transformers.feature_extraction_utils import BatchFeature
+else:
+    PretrainedConfig = object
+    BatchFeature = None
+
+from lerobot.policies.groot.action_head.action_encoder import (
+    SinusoidalPositionalEncoding,
+    swish,
+)
+
+from .cross_attention_dit import DiT, SelfAttentionTransformer
+
+
+class CategorySpecificLinear(nn.Module):
+    def __init__(self, num_categories, input_dim, hidden_dim):
+        super().__init__()
+        self.num_categories = num_categories
+        # For each category, we have separate weights and biases.
+        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
+        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
+
+    def forward(self, x, cat_ids):
+        selected_w = self.W[cat_ids]
+        selected_b = self.b[cat_ids]
+        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
+
+
+class CategorySpecificMLP(nn.Module):
+    def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
+        super().__init__()
+        self.num_categories = num_categories
+        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
+        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
+
+    def forward(self, x, cat_ids):
+        hidden = F.relu(self.layer1(x, cat_ids))
+        return self.layer2(hidden, cat_ids)
+
+
+class MultiEmbodimentActionEncoder(nn.Module):
+    def __init__(self, action_dim, hidden_size, num_embodiments):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_embodiments = num_embodiments
+
+        # W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
+        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)  # (d -> w)
+        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)  # (2w -> w)
+        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)  # (w -> w)
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
+
+    def forward(self, actions, timesteps, cat_ids):
+        """
+        actions:   shape (B, T, action_dim)
+        timesteps: shape (B,)  -- a single scalar per batch item
+        cat_ids:   shape (B,)
+        returns:   shape (B, T, hidden_size)
+        """
+        b, t, _ = actions.shape
+
+        # 1) Expand each batch's single scalar time 'tau' across all T steps
+        #    so that shape => (B, T)
+        #    e.g. if timesteps is (B,), replicate across T
+        if timesteps.dim() == 1 and timesteps.shape[0] == b:
+            # shape (B,) => (B,T)
+            timesteps = timesteps.unsqueeze(1).expand(-1, t)
+        else:
+            raise ValueError("Expected `timesteps` to have shape (B,) so we can replicate across T.")
+
+        # 2) Standard action MLP step for shape => (B, T, w)
+        a_emb = self.W1(actions, cat_ids)
+
+        # 3) Get the sinusoidal encoding (B, T, w)
+        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)
+
+        # 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
+        x = torch.cat([a_emb, tau_emb], dim=-1)
+        x = swish(self.W2(x, cat_ids))
+
+        # 5) Finally W3 => (B, T, w)
+        x = self.W3(x, cat_ids)
+        return x
+
+
+@dataclass
+class FlowmatchingActionHeadConfig(PretrainedConfig):
+    """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""
+
+    add_pos_embed: bool = field(default=True, metadata={"help": "Whether to add positional embedding"})
+    model_dtype: str = field(default="float32", metadata={"help": "Model data type."})
+    diffusion_model_cfg: dict = field(default=None, metadata={"help": "Diffusion model configuration."})
+    input_embedding_dim: int = field(default=1536, metadata={"help": "Input embedding channel dimension."})
+    backbone_embedding_dim: int = field(
+        default=1536, metadata={"help": "Backbone embedding channel dimension."}
+    )
+
+    hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
+    max_seq_len: int = field(default=1024, metadata={"help": "Maximum Sequence Length"})
+    action_dim: int = field(default=None, metadata={"help": "Action dimension."})
+    action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
+    noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
+    noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
+    noise_s: float = field(default=0.999, metadata={"help": "Flow matching noise Beta distribution s."})
+    num_timestep_buckets: int = field(
+        default=1000, metadata={"help": "Number of timestep discretization buckets."}
+    )
+    num_inference_timesteps: int = field(
+        default=None,
+        metadata={"help": "Number of inference steps for noise diffusion."},
+    )
+    max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
+    tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
+    tune_diffusion_model: bool = field(
+        default=True, metadata={"help": "Whether to tune the diffusion model."}
+    )
+    load_pretrained_det_decode_layer_path: str = field(
+        default=None, metadata={"help": "Path to pretrained detection model."}
+    )
+    detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})
+
+    freeze_decode_layer: bool = field(default=False)
+    expand_batch: int = field(default=None)
+    use_vlln: bool = field(default=True)
+
+    vl_self_attention_cfg: dict = field(default=None)
+    num_target_vision_tokens: int = field(default=32, metadata={"help": "Number of target vision tokens."})
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class FlowmatchingActionHead(nn.Module):
+    config_class = FlowmatchingActionHeadConfig
+    supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: FlowmatchingActionHeadConfig,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.input_embedding_dim = config.input_embedding_dim
+
+        self.model = DiT(**config.diffusion_model_cfg)
+        self.action_dim = config.action_dim
+        self.action_horizon = config.action_horizon
+        self.num_inference_timesteps = config.num_inference_timesteps
+
+        self.state_encoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=config.max_state_dim,
+            hidden_dim=self.hidden_size,
+            output_dim=self.input_embedding_dim,
+        )
+        self.action_encoder = MultiEmbodimentActionEncoder(
+            action_dim=config.action_dim,
+            hidden_size=self.input_embedding_dim,
+            num_embodiments=config.max_num_embodiments,
+        )
+        self.action_decoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=self.hidden_size,
+            hidden_dim=self.hidden_size,
+            output_dim=self.action_dim,
+        )
+        self.future_tokens = nn.Embedding(config.num_target_vision_tokens, self.input_embedding_dim)
+        nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)
+
+        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
+        self.vl_self_attention = (
+            SelfAttentionTransformer(**config.vl_self_attention_cfg) if config.use_vlln else nn.Identity()
+        )
+
+        if config.add_pos_embed:
+            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
+            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
+
+        self.beta_dist = Beta(config.noise_beta_alpha, config.noise_beta_beta)
+        self.num_timestep_buckets = config.num_timestep_buckets
+        self.config = config
+        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model)
+
+    def set_trainable_parameters(self, tune_projector: bool, tune_diffusion_model: bool):
+        self.tune_projector = tune_projector
+        self.tune_diffusion_model = tune_diffusion_model
+        for p in self.parameters():
+            p.requires_grad = True
+        if not tune_projector:
+            self.state_encoder.requires_grad_(False)
+            self.action_encoder.requires_grad_(False)
+            self.action_decoder.requires_grad_(False)
+            if self.config.add_pos_embed:
+                self.position_embedding.requires_grad_(False)
+        if not tune_diffusion_model:
+            self.model.requires_grad_(False)
+        print(f"Tune action head projector: {self.tune_projector}")
+        print(f"Tune action head diffusion model: {self.tune_diffusion_model}")
+        # Check if any parameters are still trainable. If not, print a warning.
+        if not tune_projector and not tune_diffusion_model:
+            for name, p in self.named_parameters():
+                if p.requires_grad:
+                    print(f"Action head trainable parameter: {name}")
+        if not any(p.requires_grad for p in self.parameters()):
+            print("Warning: No action head trainable parameters found.")
+
+    def set_frozen_modules_to_eval_mode(self):
+        """
+        Huggingface will call model.train() at each training_step. To ensure
+        the expected behaviors for modules like dropout, batchnorm, etc., we
+        need to call model.eval() for the frozen modules.
+        """
+        if self.training:
+            if not self.tune_projector:
+                self.state_encoder.eval()
+                self.action_encoder.eval()
+                self.action_decoder.eval()
+                if self.config.add_pos_embed:
+                    self.position_embedding.eval()
+            if not self.tune_diffusion_model:
+                self.model.eval()
+
+    def sample_time(self, batch_size, device, dtype):
+        sample = self.beta_dist.sample([batch_size]).to(device, dtype=dtype)
+        return (self.config.noise_s - sample) / self.config.noise_s
+
+    def prepare_input(self, batch: dict) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
+        backbone_features = backbone_output["backbone_features"]
+        backbone_features = self.vlln(backbone_features)
+        backbone_features = self.vl_self_attention(backbone_features)
+        backbone_output["backbone_features"] = backbone_features
+        return backbone_output
+
+    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        # Set frozen modules to eval
+        self.set_frozen_modules_to_eval_mode()
+
+        backbone_output = self.process_backbone_output(backbone_output)
+
+        if self.config.expand_batch is not None:
+            for k, v in backbone_output.items():
+                ndim = len(v.shape)
+                factors = [self.config.expand_batch]
+                while len(factors) < ndim:
+                    factors.append(1)
+                factors = tuple(factors)
+                expanded = v.repeat(*factors)
+                backbone_output[k] = expanded
+
+            for k, v in action_input.items():
+                ndim = len(v.shape)
+                factors = [self.config.expand_batch]
+                while len(factors) < ndim:
+                    factors.append(1)
+                factors = tuple(factors)
+                expanded = v.repeat(*factors)
+                action_input[k] = expanded
+
+        # Get vision and language embeddings.
+        vl_embs = backbone_output.backbone_features
+        device = vl_embs.device
+
+        # Get embodiment ID.
+        embodiment_id = action_input.embodiment_id
+
+        # Embed state.
+        state_features = self.state_encoder(action_input.state, embodiment_id)
+
+        # Embed noised action trajectory.
+        actions = action_input.action
+        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
+        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
+        t = t[:, None, None]  # shape (B,1,1) for broadcast
+
+        noisy_trajectory = (1 - t) * noise + t * actions
+        velocity = actions - noise
+
+        # Convert (continuous) t -> discrete if needed
+        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
+        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
+
+        # Maybe add position embedding.
+        if self.config.add_pos_embed:
+            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+            pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
+            action_features = action_features + pos_embs
+
+        # Join vision, language, state and action embedding along sequence dimension.
+        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
+        sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
+
+        vl_attn_mask = backbone_output.backbone_attention_mask
+
+        model_output = self.model(
+            hidden_states=sa_embs,
+            encoder_hidden_states=vl_embs,
+            encoder_attention_mask=vl_attn_mask,
+            timestep=t_discretized,
+            return_all_hidden_states=False,  # NOTE (YL): not using flare now
+        )
+        pred = self.action_decoder(model_output, embodiment_id)
+        pred_actions = pred[:, -actions.shape[1] :]
+
+        # Slice out only the action portion of pred and target.
+        action_mask = action_input.action_mask
+        loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
+        loss = loss.sum() / action_mask.sum()
+        output_dict = {
+            "loss": loss,
+        }
+        return BatchFeature(data=output_dict)
+
+    @torch.no_grad()
+    def get_action(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        backbone_output = self.process_backbone_output(backbone_output)
+
+        # Get vision and language embeddings.
+        vl_embs = backbone_output.backbone_features
+        embodiment_id = action_input.embodiment_id
+
+        # Embed state.
+        state_features = self.state_encoder(action_input.state, embodiment_id)
+
+        # Set initial actions as the sampled noise.
+        batch_size = vl_embs.shape[0]
+        device = vl_embs.device
+        actions = torch.randn(
+            size=(batch_size, self.config.action_horizon, self.config.action_dim),
+            dtype=vl_embs.dtype,
+            device=device,
+        )
+
+        num_steps = self.num_inference_timesteps
+        dt = 1.0 / num_steps
+
+        # Run denoising steps.
+        for t in range(num_steps):
+            t_cont = t / float(num_steps)  # e.g. goes 0, 1/N, 2/N, ...
+            t_discretized = int(t_cont * self.num_timestep_buckets)
+
+            # Embed noised action trajectory.
+            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
+            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
+            # Maybe add position embedding.
+            if self.config.add_pos_embed:
+                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+                pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
+                action_features = action_features + pos_embs
+
+            # Join vision, language, state and action embedding along sequence dimension.
+            future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
+            sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
+
+            # Run model forward.
+            model_output = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embs,
+                timestep=timesteps_tensor,
+            )
+            pred = self.action_decoder(model_output, embodiment_id)
+
+            pred_velocity = pred[:, -self.action_horizon :]
+
+            # Update actions using euler integration.
+            actions = actions + dt * pred_velocity
+        return BatchFeature(data={"action_pred": actions})
+
+    @property
+    def device(self):
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self):
+        return next(iter(self.parameters())).dtype

src/lerobot/policies/groot/configuration_groot.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python
+
+# Copyright 2024 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from 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 CosineDecayWithWarmupSchedulerConfig
+
+
+@PreTrainedConfig.register_subclass("groot")
+@dataclass
+class GrootConfig(PreTrainedConfig):
+    """Configuration for Groot policy wrapper."""
+
+    # Basic policy settings
+    n_obs_steps: int = 1
+    chunk_size: int = 50
+    n_action_steps: int = 50
+
+    # Dimension settings (must match pretrained GR00T model expectations)
+    # Maximum state dimension. Shorter states will be zero-padded.
+    max_state_dim: int = 64
+
+    # Maximum action dimension. Shorter actions will be zero-padded.
+    max_action_dim: int = 32
+
+    # Normalization (start with identity, adjust as needed)
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MEAN_STD,
+        }
+    )
+
+    # Image preprocessing (adjust to match Groot's expected input)
+    image_size: tuple[int, int] = (224, 224)
+
+    # Groot-specific model parameters (from groot_finetune_script.py)
+
+    # Path or HuggingFace model ID for the base Groot model
+    base_model_path: str = "nvidia/GR00T-N1.5-3B"
+
+    # HF repo ID (or local path) that hosts vocab.json and merges.txt for Eagle tokenizer.
+    tokenizer_assets_repo: str = "lerobot/eagle2hg-processor-groot-n1p5"
+
+    # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
+    embodiment_tag: str = "new_embodiment"
+
+    # Fine-tuning control arguments
+
+    # Whether to fine-tune the llm backbone
+    tune_llm: bool = False
+
+    # Whether to fine-tune the vision tower
+    tune_visual: bool = False
+
+    # Whether to fine-tune the projector
+    tune_projector: bool = True
+
+    # Whether to fine-tune the diffusion model
+    tune_diffusion_model: bool = True
+
+    # LoRA parameters (from groot_finetune_script.py)
+    # Rank for the LORA model. If 0, no LORA will be used.
+    lora_rank: int = 0
+
+    # Alpha value for the LORA model
+    lora_alpha: int = 16
+
+    # Dropout rate for the LORA model
+    lora_dropout: float = 0.1
+
+    # Whether to use the full model for LORA
+    lora_full_model: bool = False
+
+    # Training parameters (matching groot_finetune_script.py)
+    optimizer_lr: float = 1e-4
+    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
+
+    # Dataset parameters
+    # Video backend to use for training ('decord' or 'torchvision_av')
+    video_backend: str = "decord"
+
+    # Whether to balance dataset weights in mixture datasets
+    balance_dataset_weights: bool = True
+
+    # Whether to sample trajectories weighted by their length
+    balance_trajectory_weights: bool = True
+
+    # Optional dataset paths for delegating training to Isaac-GR00T runner
+    dataset_paths: list[str] | None = None
+    output_dir: str = "./tmp/gr00t"
+    save_steps: int = 1000
+    max_steps: int = 10000
+    batch_size: int = 32
+    dataloader_num_workers: int = 8
+    report_to: str = "wandb"
+    resume: bool = False
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"n_action_steps ({self.n_action_steps}) cannot exceed chunk_size ({self.chunk_size})"
+            )
+
+        # groot_repo_path is now optional since we ported the components
+        # No validation needed
+
+    def validate_features(self) -> None:
+        """Validate and set up input/output features for Groot."""
+        image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
+        if not image_features:
+            raise ValueError(
+                "Groot policy requires at least one visual input feature. "
+                "No features of type FeatureType.VISUAL found in input_features."
+            )
+
+        if "observation.state" not in self.input_features:
+            state_feature = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(self.max_state_dim,),
+            )
+            self.input_features["observation.state"] = state_feature
+        else:
+            state_shape = self.input_features["observation.state"].shape
+            state_dim = state_shape[0] if state_shape else 0
+            if state_dim > self.max_state_dim:
+                raise ValueError(
+                    f"State dimension {state_dim} exceeds max_state_dim {self.max_state_dim}. "
+                    f"Either reduce state dimension or increase max_state_dim in config."
+                )
+
+        if "action" not in self.output_features:
+            action_feature = PolicyFeature(
+                type=FeatureType.ACTION,
+                shape=(self.max_action_dim,),
+            )
+            self.output_features["action"] = action_feature
+        else:
+            action_shape = self.output_features["action"].shape
+            action_dim = action_shape[0] if action_shape else 0
+            if action_dim > self.max_action_dim:
+                raise ValueError(
+                    f"Action dimension {action_dim} exceeds max_action_dim {self.max_action_dim}. "
+                    f"Either reduce action dimension or increase max_action_dim in config."
+                )
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        """Return optimizer configuration."""
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+        )
+
+    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
+    def observation_delta_indices(self) -> None:
+        """Return indices for delta observations (None for Groot)."""
+        return None
+
+    @property
+    def action_delta_indices(self) -> list[int]:
+        """Return indices for delta actions."""
+        return list(range(min(self.chunk_size, 16)))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        """Return indices for delta rewards (None for Groot)."""
+        return None

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

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

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

@@ -0,0 +1,504 @@
+# --------------------------------------------------------
+# NVIDIA
+# Copyright (c) 2025 NVIDIA
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+
+# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
+from typing import Optional
+
+from transformers.image_processing_utils import (
+    BatchFeature,
+    get_patch_output_size,
+)
+from transformers.image_processing_utils_fast import (
+    BaseImageProcessorFast,
+    DefaultFastImageProcessorKwargs,
+    group_images_by_shape,
+    reorder_images,
+)
+from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,  # 0.5, 0.5, 0.5
+    IMAGENET_STANDARD_STD,  # 0.5, 0.5, 0.5
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+    make_flat_list_of_images,
+    validate_kwargs,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    TensorType,
+    add_start_docstrings,
+    is_torch_available,
+    is_torchvision_v2_available,
+)
+from transformers.video_utils import VideoInput
+
+if is_torch_available():
+    import torch
+if is_torchvision_v2_available():
+    from torchvision.transforms.v2 import functional as F  # noqa: N812
+    from transformers.image_utils import pil_torch_interpolation_mapping
+else:
+    from torchvision.transforms import functional as F  # noqa: N812
+
+
+def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> torch.Tensor:
+    """Crop the given numpy array.
+
+    Args:
+        img (torch.Tensor): Image to be cropped. Format should be (C, H, W).
+        left (int): The left coordinate of the crop box.
+        top (int): The top coordinate of the crop box.
+        right (int): The right coordinate of the crop box.
+        bottom (int): The bottom coordinate of the crop box.
+
+    Returns:
+        torch.Tensor: Cropped image.
+    """
+    if not isinstance(img, torch.Tensor):
+        raise TypeError(f"img should be torch.Tensor. Got {type(img)}")
+
+    if img.ndim not in [2, 3]:
+        raise ValueError(f"Image should have 2 or 3 dimensions. Got {img.ndim}")
+
+    img_height = img.shape[1]
+    img_width = img.shape[2]
+    if top < 0 or left < 0 or bottom > img_height or right > img_width:
+        raise ValueError("Crop coordinates out of bounds")
+
+    if top >= bottom or left >= right:
+        raise ValueError("Invalid crop coordinates")
+
+    return img[:, top:bottom, left:right]
+
+
+class Eagle25VLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+    max_dynamic_tiles: int | None
+    min_dynamic_tiles: int | None
+    use_thumbnail: bool | None
+    pad_during_tiling: bool | None
+    do_pad: bool | None
+
+
+@add_start_docstrings(
+    "Constructs a fast ConvNeXT image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.",
+    # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, TODO: this was depreciated from transformers remove!
+    """
+        image_grid_pinpoints (`List[List[int]]`, *optional*):
+            A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+            based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+            method. Not used for processing videos.
+        do_pad (`bool`, *optional*):
+            Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+            number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+    """,
+)
+class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 448, "width": 448}
+    default_to_square = False
+    crop_size = None
+    do_resize = True
+    do_center_crop = None
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    do_pad = True
+    max_dynamic_tiles = 12
+    min_dynamic_tiles = 1
+    use_thumbnail = True
+    pad_during_tiling = False
+    valid_kwargs = Eagle25VLFastImageProcessorKwargs
+    model_input_names = ["pixel_values_videos"]
+
+    def __init__(self, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    @add_start_docstrings(
+        # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, TODO: this was depreciated from transformers remove!
+        """
+            max_dynamic_tiles (`int`, *optional*):
+                The maximum number of dynamic tiles to use for processing high resolution images.
+            min_dynamic_tiles (`int`, *optional*):
+                The minimum number of dynamic tiles to use for processing high resolution images.
+            use_thumbnail (`bool`, *optional*):
+                Whether to use a thumbnail for processing high resolution images.
+            pad_during_tiling (`bool`, *optional*):
+                Whether to pad the image during tiling.
+            do_pad (`bool`, *optional*):
+                    Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+                    number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+        """,
+    )
+
+    # NOTE(YL): we will overload the preprocess method to add the image_flags
+    # def preprocess(
+    #     self, images: ImageInput, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]
+    # ) -> BatchFeature:
+    #     return super().preprocess(images, **kwargs)
+
+    def _prepare_images_structure(
+        self,
+        images: ImageInput,
+        expected_ndims: int = 3,
+    ) -> ImageInput:
+        """
+        Prepare the images structure for processing.
+
+        Args:
+            images (`ImageInput`):
+                The input images to process.
+            expected_ndims (`int`, *optional*, defaults to 3):
+                Expected number of dimensions for the images (added for transformers >=4.53.0 compatibility).
+
+        Returns:
+            `ImageInput`: The images with a valid nesting.
+        """
+        return make_flat_list_of_images(images)
+
+    def _resize_for_patching(
+        self,
+        image: "torch.Tensor",
+        target_resolution: tuple,
+        interpolation: "F.InterpolationMode",
+        input_data_format: ChannelDimension,
+    ) -> "torch.Tensor":
+        """
+        Resizes an image to a target resolution while maintaining aspect ratio.
+
+        Args:
+            image ("torch.Tensor"):
+                The input image.
+            target_resolution (tuple):
+                The target resolution (height, width) of the image.
+            interpolation (`InterpolationMode`):
+                Resampling filter to use if resizing the image.
+            input_data_format (`ChannelDimension` or `str`):
+                The channel dimension format of the input image.
+
+        Returns:
+            "torch.Tensor": The resized and padded image.
+        """
+        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+        # Resize the image
+        resized_image = F.resize(image, (new_height, new_width), interpolation=interpolation)
+
+        return resized_image
+
+    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
+        """
+        previous version mainly focus on ratio.
+        We also consider area ratio here.
+        """
+        best_factor = float("-inf")
+        best_ratio = (1, 1)
+        area = width * height
+        for ratio in target_ratios:
+            target_aspect_ratio = ratio[0] / ratio[1]
+            # ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+            # area_ratio = (ratio[0] * ratio[1] * image_size * image_size) / area
+            """
+            new area > 60% of original image area is enough.
+            """
+            factor_based_on_area_n_ratio = min(
+                (ratio[0] * ratio[1] * image_size * image_size) / area, 0.6
+            ) * min(target_aspect_ratio / aspect_ratio, aspect_ratio / target_aspect_ratio)
+
+            if factor_based_on_area_n_ratio > best_factor:
+                best_factor = factor_based_on_area_n_ratio
+                best_ratio = ratio
+
+        return best_ratio
+
+    def _pad_for_patching(
+        self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
+    ) -> "torch.Tensor":
+        """
+        Pad an image to a target resolution while maintaining aspect ratio.
+        """
+        target_height, target_width = target_resolution
+        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+        paste_x = (target_width - new_width) // 2
+        paste_y = (target_height - new_height) // 2
+
+        padded_image = F.pad(image, padding=[paste_x, paste_y, paste_x, paste_y])
+
+        return padded_image
+
+    def _get_image_patches(
+        self,
+        image: "torch.Tensor",
+        min_num: int,
+        max_num: int,
+        size: tuple,
+        tile_size: int,
+        use_thumbnail: bool,
+        interpolation: "F.InterpolationMode",
+        pad_during_tiling: bool,
+    ) -> list["torch.Tensor"]:
+        image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
+        orig_height, orig_width = image_size
+        aspect_ratio = orig_width / orig_height
+
+        # calculate the existing image aspect ratio
+        target_ratios = {
+            (i, j)
+            for n in range(min_num, max_num + 1)
+            for i in range(1, n + 1)
+            for j in range(1, n + 1)
+            if i * j <= max_num and i * j >= min_num
+        }
+        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+        # find the closest aspect ratio to the target
+        target_aspect_ratio = self.find_closest_aspect_ratio(
+            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
+        )
+
+        # calculate the target width and height
+        target_width = tile_size * target_aspect_ratio[0]
+        target_height = tile_size * target_aspect_ratio[1]
+        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+        if pad_during_tiling:
+            resized_image = self._resize_for_patching(
+                image,
+                (target_height, target_width),
+                interpolation=interpolation,
+                input_data_format=ChannelDimension.FIRST,
+            )
+            padded_image = self._pad_for_patching(
+                resized_image,
+                (target_height, target_width),
+                input_data_format=ChannelDimension.FIRST,
+            )
+            image_used_to_split = padded_image
+        else:
+            image_used_to_split = F.resize(image, (target_height, target_width), interpolation=interpolation)
+
+        processed_tiles = []
+        for i in range(blocks):
+            box = (
+                (i % (target_width // tile_size)) * tile_size,
+                (i // (target_width // tile_size)) * tile_size,
+                ((i % (target_width // tile_size)) + 1) * tile_size,
+                ((i // (target_width // tile_size)) + 1) * tile_size,
+            )
+            # split the image
+            split_img = crop(image_used_to_split, box[0], box[1], box[2], box[3])
+            processed_tiles.append(split_img)
+        assert len(processed_tiles) == blocks
+
+        if use_thumbnail and len(processed_tiles) != 1:
+            thumbnail_img = F.resize(image, (tile_size, tile_size), interpolation=interpolation)
+            processed_tiles.append(thumbnail_img)
+
+        return processed_tiles
+
+    def _pad_for_batching(
+        self,
+        pixel_values: list["torch.Tensor"],
+    ) -> list["torch.Tensor"]:
+        """
+        Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
+
+        Args:
+            pixel_values (`List[torch.Tensor]`):
+                An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
+
+        Returns:
+            List[`torch.Tensor`]: The padded images.
+        """
+        max_patch = max(len(x) for x in pixel_values)
+        pixel_values = [
+            torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
+            for image in pixel_values
+        ]
+
+        return pixel_values
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        max_dynamic_tiles: int,
+        min_dynamic_tiles: int,
+        use_thumbnail: bool,
+        pad_during_tiling: bool,
+        interpolation: Optional["F.InterpolationMode"],
+        do_center_crop: bool,
+        crop_size: SizeDict,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        do_pad: bool,
+        return_tensors: str | TensorType | None,
+        pad_size: SizeDict | None = None,  # Added for transformers >=4.53.0 compatibility
+        disable_grouping: bool | None = None,  # Added for transformers >=4.53.0 compatibility
+    ) -> BatchFeature:
+        processed_images = []
+        image_sizes = []
+        # Determine the size tuple
+        if size and size.height and size.width:
+            size_tuple = (size.height, size.width)
+        else:
+            size_tuple = (size.shortest_edge, size.shortest_edge)
+
+        # Determine the patch size
+        if crop_size and crop_size.height:
+            tile_size = crop_size.height
+        elif size and size.height:
+            tile_size = size.height
+        else:
+            tile_size = size.shortest_edge
+
+        for image in images:
+            image_patches = self._get_image_patches(
+                image,
+                min_num=min_dynamic_tiles,
+                max_num=max_dynamic_tiles,
+                size=size_tuple,
+                tile_size=tile_size,
+                use_thumbnail=use_thumbnail,
+                interpolation=interpolation,
+                pad_during_tiling=pad_during_tiling,
+            )
+
+            # Group images by size for batched processing
+            processed_image_patches_grouped = {}
+            # Added for transformers >=4.53.0 compatibility
+            grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
+                image_patches,
+                disable_grouping=disable_grouping,
+            )
+
+            for shape, stacked_image_patches in grouped_image_patches.items():
+                if do_resize:
+                    stacked_image_patches = self.resize(
+                        image=stacked_image_patches,
+                        size=size,
+                        interpolation=interpolation,
+                    )
+                if do_center_crop:
+                    stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
+                # Fused rescale and normalize
+                stacked_image_patches = self.rescale_and_normalize(
+                    stacked_image_patches,
+                    do_rescale,
+                    rescale_factor,
+                    do_normalize,
+                    image_mean,
+                    image_std,
+                )
+                processed_image_patches_grouped[shape] = stacked_image_patches
+            processed_image_patches = reorder_images(
+                processed_image_patches_grouped, grouped_image_patches_index
+            )
+            processed_image_patches = (
+                torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
+            )
+            processed_images.append(processed_image_patches)
+            image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
+
+        if do_pad:
+            processed_images = self._pad_for_batching(processed_images)
+
+        # processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+        processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
+        return BatchFeature(
+            data={"pixel_values": processed_images, "image_sizes": image_sizes},
+            tensor_type=return_tensors,
+        )
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        videos: VideoInput = None,
+        **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs],
+    ) -> BatchFeature:
+        validate_kwargs(
+            captured_kwargs=kwargs.keys(),
+            valid_processor_keys=self.valid_kwargs.__annotations__.keys(),
+        )
+        # Set default kwargs from self. This ensures that if a kwarg is not provided
+        # by the user, it gets its default value from the instance, or is set to None.
+        for kwarg_name in self.valid_kwargs.__annotations__:
+            kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
+
+        # Extract parameters that are only used for preparing the input images
+        do_convert_rgb = kwargs.pop("do_convert_rgb")
+        input_data_format = kwargs.pop("input_data_format")
+        device = kwargs.pop("device")
+        # Prepare input images
+        # transformers >= 4.53.0: uses _prepare_image_like_inputs instead of _prepare_input_images
+        if images is not None:
+            images = self._prepare_image_like_inputs(
+                images=images,
+                do_convert_rgb=do_convert_rgb,
+                input_data_format=input_data_format,
+                device=device,
+            )
+
+        if videos is not None:
+            videos = self._prepare_image_like_inputs(
+                images=videos,
+                do_convert_rgb=do_convert_rgb,
+                input_data_format=input_data_format,
+                device=device,
+            )
+
+        # Update kwargs that need further processing before being validated
+        kwargs = self._further_process_kwargs(**kwargs)
+
+        # Validate kwargs
+        self._validate_preprocess_kwargs(**kwargs)
+
+        # torch resize uses interpolation instead of resample
+        # Added for transformers >=4.53.0 compatibility
+        resample = kwargs.pop("resample", self.resample)
+        kwargs["interpolation"] = (
+            pil_torch_interpolation_mapping[resample]
+            if isinstance(resample, PILImageResampling | int)
+            else resample
+        )
+
+        # Filter kwargs to only include those accepted by _preprocess
+        valid_preprocess_kwargs = {
+            "do_resize",
+            "size",
+            "max_dynamic_tiles",
+            "min_dynamic_tiles",
+            "use_thumbnail",
+            "pad_during_tiling",
+            "interpolation",
+            "do_center_crop",
+            "crop_size",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "do_pad",
+            "return_tensors",
+            "pad_size",
+            "disable_grouping",
+        }
+        filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_preprocess_kwargs}
+        if images is not None:
+            return self._preprocess(images, **filtered_kwargs)
+        elif videos is not None:
+            return self._preprocess(videos, **filtered_kwargs)
+
+
+__all__ = ["Eagle25VLImageProcessorFast"]

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

@@ -0,0 +1,395 @@
+# --------------------------------------------------------
+# NVIDIA
+# Copyright (c) 2025 NVIDIA
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import inspect
+
+import torch
+import torch.utils.checkpoint as cp
+from peft import LoraConfig, get_peft_model
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import GenerationConfig
+from transformers.generation import GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.llama.modeling_llama import LlamaForCausalLM
+from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
+from transformers.models.qwen3.modeling_qwen3 import Qwen3ForCausalLM
+from transformers.models.siglip.modeling_siglip import SiglipVisionModel
+from transformers.utils import add_start_docstrings, logging
+
+from .configuration_eagle2_5_vl import Eagle25VLConfig
+
+logger = logging.get_logger(__name__)
+
+
+# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/modeling_llava_onevision.py#L241C1-L280C1
+EAGLE2_5_VL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`Eagle25VLConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Eagle2_5_VL Model outputting raw hidden-states without any specific head on top.",
+    EAGLE2_5_VL_START_DOCSTRING,
+)
+class Eagle25VLPreTrainedModel(PreTrainedModel):
+    config_class = Eagle25VLConfig
+    base_model_prefix = "model"
+    main_input_name = "input_ids"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "Qwen2DecoderLayer",
+        "LlamaDecoderLayer",
+        "Siglip2EncoderLayer",
+        "SiglipEncoderLayer",
+    ]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+    _supports_static_cache = True
+    _supports_quantized_cache = True
+    _supports_sdpa = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear | nn.Conv2d):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class Eagle25VLForConditionalGeneration(Eagle25VLPreTrainedModel, GenerationMixin):
+    config_class = Eagle25VLConfig
+
+    def __init__(self, config: Eagle25VLConfig, vision_model=None, language_model=None):
+        super().__init__(config)
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        if config.use_pixel_shuffle:
+            self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio**2))
+        else:
+            self.num_image_token = int((image_size // patch_size) ** 2)
+
+        self.select_layer = config.select_layer
+        self.downsample_ratio = config.downsample_ratio
+        self.loss_version = config.loss_version
+        self.mlp_checkpoint = config.mlp_checkpoint
+        self.use_pixel_shuffle = config.use_pixel_shuffle
+        self.mlp_connector_layers = config.mlp_connector_layers
+        logger.info(f"num_image_token: {self.num_image_token}")
+        logger.info(f"mlp_checkpoint: {self.mlp_checkpoint}")
+        if vision_model is not None:
+            self.vision_model = vision_model
+        else:
+            if config.vision_config.model_type == "siglip_vision_model":
+                config.vision_config._attn_implementation = "flash_attention_2"
+                self.vision_model = SiglipVisionModel(config.vision_config)
+            else:
+                raise NotImplementedError(f"{config.vision_config.model_type} is not implemented.")
+
+        if language_model is not None:
+            self.language_model = language_model
+        else:
+            if config.text_config.architectures[0] == "LlamaForCausalLM":
+                self.language_model = LlamaForCausalLM(config.text_config)
+            elif config.text_config.architectures[0] == "Phi3ForCausalLM":
+                raise NotImplementedError("Phi3 is not implemented.")
+                # self.language_model = Phi3ForCausalLM(config.text_config)
+            elif config.text_config.architectures[0] == "Qwen2ForCausalLM":
+                assert config.text_config._attn_implementation == "flash_attention_2", (
+                    f"Qwen2 must use flash_attention_2 but got {config.text_config._attn_implementation}"
+                )
+                self.language_model = Qwen2ForCausalLM(config.text_config)
+            elif config.text_config.architectures[0] == "Qwen3ForCausalLM":
+                self.language_model = Qwen3ForCausalLM(config.text_config)
+            else:
+                raise NotImplementedError(f"{config.text_config.architectures[0]} is not implemented.")
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        if config.mlp_connector_layers == 2:
+            self.mlp1 = nn.Sequential(
+                nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+                nn.GELU(),
+                nn.Linear(llm_hidden_size, llm_hidden_size),
+            )
+        elif config.mlp_connector_layers == 1 and config.use_pixel_shuffle:
+            self.mlp1 = nn.Sequential(
+                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+            )
+        elif config.mlp_connector_layers == 1 and not config.use_pixel_shuffle:
+            self.mlp1 = nn.Sequential(
+                nn.Linear(vit_hidden_size, llm_hidden_size),
+            )
+        else:
+            raise NotImplementedError(f"{config.mlp_connector_layers} is not implemented.")
+
+        self.image_token_index = config.image_token_index
+        self.neftune_alpha = None
+
+        if config.use_backbone_lora:
+            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
+
+        self.use_llm_lora = config.use_llm_lora
+        if config.use_llm_lora:
+            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
+
+        self.check_forward_kwargs()
+
+    def check_forward_kwargs(self):
+        # We intentionally avoid using **kwargs in forward because Hugging Face Transformers
+        # has special handling for functions with **kwargs parameters that would affect
+        # how our model is processed during training and inference.
+        forward_params = inspect.signature(self.forward).parameters
+        assert not any(k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values())
+
+    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=[
+                "self_attn.q_proj",
+                "self_attn.k_proj",
+                "self_attn.v_proj",
+                "self_attn.out_proj",
+                "mlp.fc1",
+                "mlp.fc2",
+            ],
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+        )
+        self.vision_model = get_peft_model(self.vision_model, lora_config)
+        self.vision_model.print_trainable_parameters()
+
+    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=[
+                "self_attn.q_proj",
+                "self_attn.k_proj",
+                "self_attn.v_proj",
+                "self_attn.o_proj",
+                "mlp.gate_proj",
+                "mlp.down_proj",
+                "mlp.up_proj",
+            ],
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            task_type="CAUSAL_LM",
+        )
+        self.language_model = get_peft_model(self.language_model, lora_config)
+        self.language_model.enable_input_require_grads()
+        self.language_model.print_trainable_parameters()
+        self.use_llm_lora = True
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        image_flags: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        num_tiles_list: list[torch.Tensor] | None = None,
+    ) -> tuple | CausalLMOutputWithPast:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        vit_embeds = self.extract_feature(pixel_values)
+
+        if image_flags is not None:
+            image_flags = image_flags.view(-1)
+            vit_embeds = vit_embeds[image_flags == 1]
+
+        b, n, c = input_embeds.shape
+        input_embeds = input_embeds.reshape(b * n, c)
+
+        input_ids = input_ids.reshape(b * n)
+        selected = input_ids == self.image_token_index
+        try:
+            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, c)
+        except Exception as e:
+            vit_embeds = vit_embeds.reshape(-1, c)
+            print(
+                f"warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, "
+                f"vit_embeds.shape={vit_embeds.shape}"
+            )
+            n_token = selected.sum()
+            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
+
+        input_embeds = input_embeds.reshape(b, n, c)
+
+        outputs = self.language_model(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        logits = outputs.logits
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor)))
+
+        x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
+            )
+            if hasattr(vit_embeds, "last_hidden_state"):
+                vit_embeds = vit_embeds.last_hidden_state
+
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
+            ).hidden_states[self.select_layer]
+
+        if self.use_pixel_shuffle:
+            h = w = int(vit_embeds.shape[1] ** 0.5)
+            vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+            vit_embeds = self.pixel_shuffle(
+                vit_embeds, scale_factor=self.downsample_ratio
+            )  # torch.Size([B, 1024, 1024]) -> torch.Size([B, 16, 16, 4096])
+            vit_embeds = vit_embeds.reshape(
+                vit_embeds.shape[0], -1, vit_embeds.shape[-1]
+            )  # torch.Size([B, 16, 16, 4096]) -> torch.Size([B, 256, 4096])
+
+        if self.mlp_checkpoint and vit_embeds.requires_grad:
+            vit_embeds = cp.checkpoint(self.mlp1, vit_embeds)
+        else:
+            vit_embeds = self.mlp1(vit_embeds)
+
+        return vit_embeds
+
+    @torch.no_grad()
+    def generate(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        input_ids: torch.FloatTensor | None = None,
+        attention_mask: torch.LongTensor | None = None,
+        visual_features: torch.FloatTensor | None = None,
+        generation_config: GenerationConfig | None = None,
+        output_hidden_states: bool | None = None,
+        image_sizes: list[tuple[int, int]] | None = None,
+        **generate_kwargs,
+    ) -> torch.LongTensor:
+        if pixel_values is not None:
+            if visual_features is not None:
+                vit_embeds = visual_features
+            else:
+                vit_embeds = self.extract_feature(pixel_values)
+
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+            b, n, c = input_embeds.shape
+            input_embeds = input_embeds.reshape(b * n, c)
+
+            input_ids = input_ids.reshape(b * n)
+            selected = input_ids == self.config.image_token_index
+            assert selected.sum() != 0
+            input_embeds[selected] = vit_embeds.reshape(-1, c).to(input_embeds.device)
+
+            input_embeds = input_embeds.reshape(b, n, c)
+        else:
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        if "use_cache" not in generate_kwargs:
+            generate_kwargs["use_cache"] = True
+
+        outputs = self.language_model.generate(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            **generate_kwargs,
+        )
+
+        return outputs
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_output_embeddings
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder
+    def get_decoder(self):
+        return self.language_model.get_decoder()

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

@@ -0,0 +1,518 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for Eagle25VL.
+copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/processing_llava_onevision.py
+"""
+
+import base64
+import os
+import re
+from io import BytesIO
+
+import requests
+import torch
+from PIL import Image
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import logging
+from transformers.video_utils import VideoInput
+
+logger = logging.get_logger(__name__)
+
+
+FRAME_FACTOR = 2
+FPS = 2.0
+FPS_MIN_FRAMES = 4
+FPS_MAX_FRAMES = 256
+
+
+def to_rgb(pil_image: Image.Image) -> Image.Image:
+    if pil_image.mode == "RGBA":
+        white_background = Image.new("RGB", pil_image.size, (255, 255, 255))
+        white_background.paste(pil_image, mask=pil_image.split()[3])  # Use alpha channel as mask
+        return white_background
+    else:
+        return pil_image.convert("RGB")
+
+
+def fetch_image(ele: dict[str, str | Image.Image]) -> Image.Image:
+    image = ele["image"] if "image" in ele else ele["image_url"]
+    image_obj = None
+    if isinstance(image, Image.Image):
+        image_obj = image
+    elif image.startswith("http://") or image.startswith("https://"):
+        response = requests.get(image, stream=True, timeout=10)
+        image_obj = Image.open(BytesIO(response.content))
+    elif image.startswith("file://"):
+        image_obj = Image.open(image[7:])
+    elif image.startswith("data:image"):
+        if "base64," in image:
+            _, base64_data = image.split("base64,", 1)
+            data = base64.b64decode(base64_data)
+            image_obj = Image.open(BytesIO(data))
+    else:
+        image_obj = Image.open(image)
+    if image_obj is None:
+        raise ValueError(
+            f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}"
+        )
+    image = to_rgb(image_obj)
+    if "scale_factor" in ele:
+        scale_factor = ele["scale_factor"]
+        image = image.resize((image.width * scale_factor, image.height * scale_factor), Image.BILINEAR)
+    return image
+
+
+class Eagle25VLProcessorKwargs(ProcessingKwargs, total=False):
+    # see processing_utils.ProcessingKwargs documentation for usage.
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {},
+        "videos_kwargs": {"max_dynamic_tiles": 1},
+    }
+
+
+class Eagle25VLProcessor(ProcessorMixin):
+    r"""
+    Constructs a Eagle25VL processor which wraps a Eagle25VL video processor, Eagle25VL image processor and a Eagle25VL tokenizer into a single processor.
+
+    [`Eagle25VLProcessor`] offers all the functionalities of [`Eagle25VLVideoProcessor`], [`Eagle25VLImageProcessor`] and [`Eagle25VLTokenizer`]. See the
+    [`~Eagle25VLVideoProcessor.__call__`], [`~Eagle25VLProcessor.__call__`] and [`~Eagle25VLProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        num_image_tokens (`int`, *optional*):
+            Number of image tokens for one imagethat will be returned by vision tower.
+        vision_feature_select_strategy (`str`, *optional*):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Should be same as in model's config
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+        image_token (`str`, *optional*, defaults to `"<image>"`):
+            Special token used to denote image location.
+        video_token (`str`, *optional*, defaults to `"<video>"`):
+            Special token used to denote video location.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = [
+        "chat_template",
+        "num_image_tokens",
+        "vision_feature_select_strategy",
+        "image_token",
+        "video_token",
+        "images_kwargs",
+        "videos_kwargs",
+        "text_kwargs",
+    ]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        vision_feature_select_strategy=None,
+        chat_template=None,
+        image_token="<IMG_CONTEXT>",  # nosec: B107
+        video_token="<IMG_CONTEXT>",  # nosec: B107
+        tokens_per_tile=256,
+        image_placeholder="image",
+        video_placeholder="video",
+        image_start_token="<img>",
+        image_end_token="</img>",
+        **kwargs,
+    ):
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+        self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
+        )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.video_token)
+        )
+        self.image_placeholder = image_placeholder
+        self.video_placeholder = video_placeholder
+        self.tokens_per_tile = tokens_per_tile
+        self.image_start_token = image_start_token
+        self.image_end_token = image_end_token
+        if "auto_map" in kwargs:
+            self.auto_map = kwargs["auto_map"]
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def replace_media_placeholder(
+        self, text, image_list, video_list, timestamps_list, fps_list, **output_kwargs
+    ):
+        num_of_images_in_this_sample = 0
+        num_of_videos_in_this_sample = 0
+        # Regular expression pattern to match formats like <image-1> or <video-2>
+        pattern = re.compile(rf"<({self.image_placeholder}|{self.video_placeholder})-(\d+)>")
+        unified_frame_list = []
+
+        # image_min_dynamic_tiles = output_kwargs["images_kwargs"].get(
+        #     "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
+        # )
+        # image_max_dynamic_tiles = output_kwargs["images_kwargs"].get(
+        #     "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
+        # )
+        # image_use_thumbnail = output_kwargs["images_kwargs"].get(
+        #     "use_thumbnail", self.image_processor.use_thumbnail
+        # )
+        video_min_dynamic_tiles = output_kwargs["videos_kwargs"].get(
+            "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
+        )
+        video_max_dynamic_tiles = output_kwargs["videos_kwargs"].get(
+            "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
+        )
+        video_use_thumbnail = output_kwargs["videos_kwargs"].get(
+            "use_thumbnail", self.image_processor.use_thumbnail
+        )
+
+        tile_size = self.image_processor.size.get("height", 448)
+
+        # Function to replace tags in a single text
+        def replace_in_text(text):
+            # repl callback function for each match replacement operation
+            def repl(match):
+                nonlocal unified_frame_list
+                nonlocal num_of_images_in_this_sample
+                nonlocal num_of_videos_in_this_sample
+                media_type = match.group(1)  # 'image' or 'video'
+                idx_in_list = int(match.group(2)) - 1  # Convert to list index (0-based)
+                # Select the corresponding path based on media type
+                idx_mapper = {
+                    0: "first",
+                    1: "second",
+                    2: "third",
+                    3: "fourth",
+                    4: "fifth",
+                    5: "sixth",
+                    6: "seventh",
+                    7: "eighth",
+                    8: "ninth",
+                    9: "tenth",
+                }
+                if media_type == "image":
+                    image_inputs = self.image_processor(
+                        images=[image_list[idx_in_list]],
+                        videos=None,
+                        **output_kwargs["images_kwargs"],
+                    )
+                    num_all_tiles = image_inputs["pixel_values"].shape[0]
+                    special_placeholder = f"<image {idx_in_list + 1}>{self.image_start_token}{self.image_token * num_all_tiles * self.tokens_per_tile}{self.image_end_token}"
+                    unified_frame_list.append(image_inputs)
+                    num_of_images_in_this_sample += 1
+
+                elif media_type == "video":
+                    video_inputs = self.image_processor(
+                        images=None,
+                        videos=[video_list[idx_in_list]],
+                        **output_kwargs["videos_kwargs"],
+                    )
+                    num_all_tiles = video_inputs["pixel_values"].shape[0]
+                    image_sizes = video_inputs["image_sizes"]
+                    if timestamps_list is not None and -1 not in timestamps_list:
+                        frame_timestamps = timestamps_list[idx_in_list]
+                    else:
+                        frame_timestamps = None
+                    sampled_fps = fps_list[idx_in_list] if fps_list is not None else None
+
+                    num_of_tiles_each_frame = [
+                        self.get_number_tiles_based_on_image_size(
+                            image_size,
+                            video_min_dynamic_tiles,
+                            video_max_dynamic_tiles,
+                            video_use_thumbnail,
+                            tile_size,
+                        )
+                        for image_size in image_sizes
+                    ]
+                    assert sum(num_of_tiles_each_frame) == num_all_tiles, (
+                        f"The number of tiles in each frame is not equal to the total number of tiles: {sum(num_of_tiles_each_frame)} != {num_all_tiles}"
+                    )
+
+                    if frame_timestamps is not None:
+                        assert len(frame_timestamps) == len(num_of_tiles_each_frame), (
+                            f"The number of timestamps is not equal to the number of frames: {len(frame_timestamps)} != {len(num_of_tiles_each_frame)}"
+                        )
+                        special_placeholder = [
+                            f"Frame {i + 1} sample at {frame_timestamps[i]:.2f}s: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
+                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
+                        ]
+                    else:
+                        special_placeholder = [
+                            f"Frame {i + 1}: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
+                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
+                        ]
+
+                    if sampled_fps is not None:
+                        special_placeholder = (
+                            f"The {idx_mapper[idx_in_list]} video sampled with {sampled_fps:.2f} fps: "
+                            + "".join(special_placeholder)
+                        )
+                    else:
+                        special_placeholder = f"The {idx_mapper[idx_in_list]} video: " + "".join(
+                            special_placeholder
+                        )
+                    unified_frame_list.append(video_inputs)
+                    num_of_videos_in_this_sample += 1
+                else:
+                    raise ValueError(f"Unknown media type: {media_type}")
+                return special_placeholder
+
+            return pattern.sub(repl, text)
+
+        text = replace_in_text(text)
+        if len(unified_frame_list) > 0:
+            pixel_values = torch.cat([frame["pixel_values"] for frame in unified_frame_list])
+            image_sizes = torch.cat([frame["image_sizes"] for frame in unified_frame_list])
+        else:
+            pixel_values = None
+            image_sizes = None
+        return (
+            text,
+            pixel_values,
+            image_sizes,
+            num_of_images_in_this_sample,
+            num_of_videos_in_this_sample,
+        )
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        audio=None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[Eagle25VLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+        of the above two methods for more information.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
+            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
+        """
+
+        output_kwargs = self._merge_kwargs(
+            Eagle25VLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if isinstance(text, str):
+            text_list = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+        elif isinstance(text, list) and isinstance(text[0], str):
+            text_list = text
+
+        if images is None:
+            images = []
+        if videos is None:
+            videos = []
+
+        pixel_values_list = []
+        image_sizes_list = []
+        new_sample_list = []
+        image_start_idx = 0
+        video_start_idx = 0
+        timestamps_batch = output_kwargs["videos_kwargs"].pop("timestamps", None)
+        fps_batch = output_kwargs["videos_kwargs"].pop("fps", None)
+        for sample in text_list:
+            timestamps_list = timestamps_batch[video_start_idx:] if timestamps_batch is not None else None
+            fps_list = fps_batch[video_start_idx:] if fps_batch is not None else None
+            (
+                sample,
+                pixel_values,
+                image_sizes,
+                num_of_images_in_this_sample,
+                num_of_videos_in_this_sample,
+            ) = self.replace_media_placeholder(
+                sample,
+                images[image_start_idx:],
+                videos[video_start_idx:],
+                timestamps_list,
+                fps_list,
+                **output_kwargs,
+            )
+            new_sample_list.append(sample)
+            if pixel_values is not None:
+                pixel_values_list.append(pixel_values)
+                image_sizes_list.append(image_sizes)
+            image_start_idx += num_of_images_in_this_sample
+            video_start_idx += num_of_videos_in_this_sample
+
+        if len(pixel_values_list) > 0:
+            image_inputs = {
+                "pixel_values": torch.cat(pixel_values_list),
+                "image_sizes": torch.cat(image_sizes_list),
+            }
+        else:
+            image_inputs = {}
+        video_inputs = {}
+        text_inputs = self.tokenizer(new_sample_list, **output_kwargs["text_kwargs"])
+        return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs})
+
+    def get_number_tiles_based_on_image_size(
+        self, image_size: tuple, min_num: int, max_num: int, use_thumbnail: bool, tile_size: int
+    ) -> int:
+        """
+        Get the number of tiles based on the image size.
+        """
+        orig_height, orig_width = image_size
+        aspect_ratio = orig_width / orig_height
+        # calculate the existing image aspect ratio
+        target_ratios = {
+            (i, j)
+            for n in range(min_num, max_num + 1)
+            for i in range(1, n + 1)
+            for j in range(1, n + 1)
+            if i * j <= max_num and i * j >= min_num
+        }
+        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+        # find the closest aspect ratio to the target
+        target_aspect_ratio = self.image_processor.find_closest_aspect_ratio(
+            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
+        )
+        tiles_num = target_aspect_ratio[0] * target_aspect_ratio[1]
+        if use_thumbnail and tiles_num > 1:
+            tiles_num += 1
+        return tiles_num
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+    # override to save video-config in a separate config file
+    def save_pretrained(self, save_directory, **kwargs):
+        if os.path.isfile(save_directory):
+            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
+        os.makedirs(save_directory, exist_ok=True)
+
+        outputs = super().save_pretrained(save_directory, **kwargs)
+        return outputs
+
+    # override to load video-config from a separate config file
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
+        if isinstance(processor, tuple):
+            processor = processor[0]
+        return processor
+
+    # Copy from https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
+    def process_vision_info(
+        self,
+        conversations: list[dict] | list[list[dict]],
+        return_video_kwargs: bool = False,
+    ) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None, dict | None]:
+        vision_infos = self.extract_vision_info(conversations)
+        ## Read images or videos
+        image_inputs = []
+        video_inputs = []
+        video_sample_fps_list = []
+        video_timestamps_list = []
+        for vision_info in vision_infos:
+            if "image" in vision_info or "image_url" in vision_info:
+                image_inputs.append(fetch_image(vision_info))
+            else:
+                raise ValueError("image, image_url or video should in content.")
+        if len(image_inputs) == 0:
+            image_inputs = None
+        if len(video_inputs) == 0:
+            video_inputs = None
+        if return_video_kwargs:
+            return (
+                image_inputs,
+                video_inputs,
+                {"fps": video_sample_fps_list, "timestamps": video_timestamps_list},
+            )
+        return image_inputs, video_inputs
+
+    def extract_vision_info(self, conversations: list[dict] | list[list[dict]]) -> list[dict]:
+        vision_infos = []
+        if isinstance(conversations[0], dict):
+            conversations = [conversations]
+        for conversation in conversations:
+            for message in conversation:
+                if isinstance(message["content"], list):
+                    for ele in message["content"]:
+                        if (
+                            "image" in ele
+                            or "image_url" in ele
+                            or "video" in ele
+                            or ele["type"] in ("image", "image_url", "video")
+                        ):
+                            vision_infos.append(ele)
+        return vision_infos
+
+
+__all__ = ["Eagle25VLProcessor"]

src/lerobot/policies/groot/groot_n1.py

@@ -0,0 +1,376 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
+
+from lerobot.utils.import_utils import _transformers_available
+
+# Conditional import for type checking and lazy loading
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
+    from transformers.feature_extraction_utils import BatchFeature
+else:
+    AutoConfig = None
+    AutoModel = None
+    PretrainedConfig = object
+    PreTrainedModel = object
+    BatchFeature = None
+
+try:
+    import tree
+except ImportError:
+    tree = None
+
+from lerobot.policies.groot.action_head.flow_matching_action_head import (
+    FlowmatchingActionHead,
+    FlowmatchingActionHeadConfig,
+)
+from lerobot.policies.groot.utils import ensure_eagle_cache_ready
+from lerobot.utils.constants import HF_LEROBOT_HOME
+
+DEFAULT_VENDOR_EAGLE_PATH = str((Path(__file__).resolve().parent / "eagle2_hg_model").resolve())
+DEFAULT_TOKENIZER_ASSETS_REPO = "lerobot/eagle2hg-processor-groot-n1p5"
+
+
+class EagleBackbone(nn.Module):
+    def __init__(
+        self,
+        tune_llm: bool = False,
+        tune_visual: bool = False,
+        select_layer: int = -1,
+        reproject_vision: bool = False,
+        use_flash_attention: bool = False,
+        load_bf16: bool = False,
+        eagle_path: str = DEFAULT_VENDOR_EAGLE_PATH,
+        tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO,
+        project_to_dim: int = 1536,
+    ):
+        """
+        Args:
+            tune_llm: whether to tune the LLM model (default: True)
+            tune_visual: whether to tune the visual model (default: False)
+        """
+        super().__init__()
+        assert not reproject_vision, "Reproject vision is not implemented here, set to False"
+
+        # Prefer loading Eagle model config from the cache directory where vendor files were copied.
+        vendor_dir = DEFAULT_VENDOR_EAGLE_PATH
+        cache_dir = HF_LEROBOT_HOME / tokenizer_assets_repo
+        try:
+            ensure_eagle_cache_ready(vendor_dir, cache_dir, tokenizer_assets_repo)
+        except Exception as exc:  # nosec: B110
+            print(f"[GROOT] Warning: failed to prepare Eagle cache for backbone: {exc}")
+
+        config = AutoConfig.from_pretrained(str(cache_dir), trust_remote_code=True)
+        self.eagle_model = AutoModel.from_config(config, trust_remote_code=True)
+
+        if project_to_dim is not None:
+            self.eagle_linear = torch.nn.Linear(2048, project_to_dim)
+        else:
+            self.eagle_linear = torch.nn.Identity()
+
+        # needed since we don't use these layers. Also saves compute
+        while len(self.eagle_model.language_model.model.layers) > select_layer:
+            self.eagle_model.language_model.model.layers.pop(-1)
+
+        self.select_layer = select_layer
+        self.set_trainable_parameters(tune_llm, tune_visual)
+
+    def set_trainable_parameters(self, tune_llm: bool, tune_visual: bool):
+        self.tune_llm = tune_llm
+        self.tune_visual = tune_visual
+        for p in self.parameters():
+            p.requires_grad = True
+        if not tune_llm:
+            self.eagle_model.language_model.requires_grad_(False)
+        if not tune_visual:
+            self.eagle_model.vision_model.requires_grad_(False)
+            self.eagle_model.mlp1.requires_grad_(False)
+        print(f"Tune backbone llm: {self.tune_llm}")
+        print(f"Tune backbone visual: {self.tune_visual}")
+        # Check if any parameters are still trainable. If not, print a warning.
+        if not tune_llm and not tune_visual:
+            for name, p in self.named_parameters():
+                if p.requires_grad:
+                    print(f"Backbone trainable parameter: {name}")
+        if not any(p.requires_grad for p in self.parameters()):
+            print("Warning: No backbone trainable parameters found.")
+
+    def set_frozen_modules_to_eval_mode(self):
+        """
+        Huggingface will call model.train() at each training_step. To ensure
+        the expected behaviors for modules like dropout, batchnorm, etc., we
+        need to call model.eval() for the frozen modules.
+        """
+        if self.training:
+            if self.eagle_model.language_model and not self.tune_llm:
+                self.eagle_model.language_model.eval()
+            if self.eagle_model.vision_model and not self.tune_visual:
+                self.eagle_model.vision_model.eval()
+
+    def prepare_input(self, batch: dict) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+    def forward_eagle(self, vl_input: BatchFeature) -> BatchFeature:
+        eagle_prefix = "eagle_"
+        eagle_input = {
+            k.removeprefix(eagle_prefix): v for k, v in vl_input.items() if k.startswith(eagle_prefix)
+        }
+        del eagle_input["image_sizes"]
+
+        eagle_output = self.eagle_model(**eagle_input, output_hidden_states=True, return_dict=True)
+        eagle_features = eagle_output.hidden_states[self.select_layer]
+
+        eagle_features = self.eagle_linear(eagle_features)
+        return eagle_features, eagle_input["attention_mask"]
+
+    def forward(self, vl_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+
+        eagle_embeds, eagle_mask = self.forward_eagle(vl_input)
+
+        # YL (TODO HACK): to resolve DDP issue when tune_visual=True
+        # Ensure all trainable parameters in vision_model are used in the forward pass for DDP compatibility
+        if self.training and self.tune_visual:
+            dummy_term = torch.tensor(
+                0.0, device=eagle_embeds.device, dtype=eagle_embeds.dtype, requires_grad=True
+            )
+            for param in self.eagle_model.vision_model.parameters():
+                if param.requires_grad:
+                    dummy_term = dummy_term + 0.0 * param.sum()
+            eagle_embeds = eagle_embeds + dummy_term
+
+        return BatchFeature(
+            data={"backbone_features": eagle_embeds, "backbone_attention_mask": eagle_mask}
+        )  # [B, T2, hidden_size]
+
+
+BACKBONE_FEATURE_KEY = "backbone_features"
+ACTION_KEY = "action_pred"
+LOSS_KEY = "loss"
+ERROR_MSG = "Error: unexpected input/output"
+N_COLOR_CHANNELS = 3
+
+
+# config
+@dataclass
+class GR00TN15Config(PretrainedConfig):
+    model_type = "gr00t_n1_5"
+    backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
+
+    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
+
+    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
+
+    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
+    compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+# real model
+class GR00TN15(PreTrainedModel):
+    supports_gradient_checkpointing = True
+    config_class = GR00TN15Config
+    """
+    we expect the backbone output to have a key 'backbone_features' with shape (batch_size, n, hidden_size)
+    here n is variable and can be e.g. time, 1 or user specified
+    we expect the action head output to have a key 'action_pred' with shape (batch_size, time, action_dim) during inference time
+    we expect these to have type BatchFeature, and they can of course have many other user specified keys too
+    """
+
+    def __init__(
+        self,
+        config: GR00TN15Config,
+        local_model_path: str,
+    ):
+        assert isinstance(config.backbone_cfg, dict)
+        assert isinstance(config.action_head_cfg, dict)
+
+        super().__init__(config)
+        self.local_model_path = local_model_path
+
+        self.backbone = EagleBackbone(**config.backbone_cfg)
+        action_head_cfg = FlowmatchingActionHeadConfig(**config.action_head_cfg)
+        self.action_head = FlowmatchingActionHead(action_head_cfg)
+
+        self.action_horizon = config.action_horizon
+        self.action_dim = config.action_dim
+        self.compute_dtype = config.compute_dtype
+
+    def validate_inputs(self, inputs):
+        # NOTE -- this should be handled internally by the model
+        # however, doing that will likely be breaking changes -- so we'll need to do it after the deadline
+
+        detected_error = False
+        error_msg = ERROR_MSG
+        if "action" in inputs:
+            action = inputs["action"]
+            # In inference, action may be omitted or None; validate only when it's a tensor.
+            if action is None:
+                pass  # allow None during inference
+            elif isinstance(action, torch.Tensor):
+                shape_ok = (
+                    len(action.shape) == 3
+                    and action.shape[1] == self.action_horizon
+                    and action.shape[2] == self.action_dim
+                )
+                if not shape_ok:
+                    error_msg += f"\n{action.shape=}"
+                    detected_error = True
+            else:
+                # Unexpected non-tensor type provided for action
+                error_msg += f"\nInvalid type for action: {type(action)}"
+                detected_error = True
+
+        if "video" in inputs:
+            video = inputs["video"]
+            type_ok = isinstance(video, np.ndarray)
+            dtype_ok = video.dtype == np.uint8
+            shape_ok = len(video.shape) == 6 and video.shape[3] == N_COLOR_CHANNELS
+            if not type_ok:
+                error_msg += f"\n{type(video)=}"
+                detected_error = True
+            if not dtype_ok:
+                error_msg += f"\n{video.dtype=}"
+                detected_error = True
+            if not shape_ok:
+                error_msg += f"\n{video.shape=}"
+                detected_error = True
+
+        if detected_error:
+            raise ValueError(error_msg)
+
+    def validate_data(self, action_head_outputs, backbone_outputs, is_training):
+        fail_backbone = (
+            not isinstance(backbone_outputs, BatchFeature) or BACKBONE_FEATURE_KEY not in backbone_outputs
+        )
+
+        if fail_backbone:
+            error_msg = ERROR_MSG
+            error_msg += f"\n{isinstance(backbone_outputs, BatchFeature)=}"
+            error_msg += f"\n{BACKBONE_FEATURE_KEY in backbone_outputs=}"
+            error_msg += f"\n{backbone_outputs[BACKBONE_FEATURE_KEY].shape=}"
+            raise ValueError(error_msg)
+
+        fail_action_head = (not isinstance(action_head_outputs, BatchFeature)) or not (
+            (
+                LOSS_KEY in action_head_outputs and is_training
+            )  # there might not be an action prediction during training
+            or (
+                ACTION_KEY in action_head_outputs
+                and action_head_outputs[ACTION_KEY].shape[1] == self.action_horizon
+                and action_head_outputs[ACTION_KEY].shape[2] == self.action_dim
+            )
+        )
+
+        if fail_action_head:
+            error_msg = ERROR_MSG
+            error_msg += f"\n{isinstance(action_head_outputs, BatchFeature)=}"
+            error_msg += f"\n{LOSS_KEY in action_head_outputs=}"
+            error_msg += f"\n{action_head_outputs[ACTION_KEY].shape=}"
+            error_msg += f"\n{self.action_horizon=}"
+            error_msg += f"\n{self.action_dim=}"
+            raise ValueError(error_msg)
+
+    def forward(
+        self,
+        inputs: dict,
+    ) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        action_head_outputs = self.action_head(backbone_outputs, action_inputs)
+        self.validate_data(action_head_outputs, backbone_outputs, is_training=True)
+        return action_head_outputs
+
+    def get_action(
+        self,
+        inputs: dict,
+    ) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        # Because the behavior of backbones remains the same for training and inference, we can use `forward` for backbones.
+        backbone_outputs = self.backbone(backbone_inputs)
+        action_head_outputs = self.action_head.get_action(backbone_outputs, action_inputs)
+        self.validate_data(action_head_outputs, backbone_outputs, is_training=False)
+        return action_head_outputs
+
+    def prepare_input(self, inputs) -> tuple[BatchFeature, BatchFeature]:
+        self.validate_inputs(inputs)
+        backbone_inputs = self.backbone.prepare_input(inputs)
+        action_inputs = self.action_head.prepare_input(inputs)
+
+        def to_device_with_maybe_dtype(x):
+            # Cast floating tensors to a memory-efficient compute dtype when requested.
+            # Rationale: Upcasting backbone activations to fp32 significantly increases VRAM.
+            # When compute_dtype is bfloat16, prefer bf16 for activations to match AMP behavior.
+            if not isinstance(x, torch.Tensor):
+                return x
+            if torch.is_floating_point(x):
+                if getattr(self, "compute_dtype", None) == "bfloat16":
+                    return x.to(self.device, dtype=torch.bfloat16)
+                # Fallback: preserve previous behavior if not using bf16 compute
+                return x.to(self.device, dtype=self.action_head.dtype)
+            # Non-floating tensors: move device only
+            return x.to(self.device)
+
+        backbone_inputs = tree.map_structure(to_device_with_maybe_dtype, backbone_inputs)
+        action_inputs = tree.map_structure(to_device_with_maybe_dtype, action_inputs)
+        return backbone_inputs, action_inputs
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        tune_visual = kwargs.pop("tune_visual", True)
+        tune_llm = kwargs.pop("tune_llm", False)
+        tune_projector = kwargs.pop("tune_projector", True)
+        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
+
+        print(f"Loading pretrained dual brain from {pretrained_model_name_or_path}")
+        print(f"Tune backbone vision tower: {tune_visual}")
+        print(f"Tune backbone LLM: {tune_llm}")
+        print(f"Tune action head projector: {tune_projector}")
+        print(f"Tune action head DiT: {tune_diffusion_model}")
+
+        # get the current model path being downloaded
+        try:
+            # NOTE(YL) This downloads the model to the local cache and returns the local path to the model
+            # saved in ~/.cache/huggingface/hub/
+            local_model_path = snapshot_download(pretrained_model_name_or_path, repo_type="model")
+            # HFValidationError, RepositoryNotFoundError
+        except (HFValidationError, RepositoryNotFoundError):
+            print(
+                f"Model not found or avail in the huggingface hub. Loading from local path: {pretrained_model_name_or_path}"
+            )
+            local_model_path = pretrained_model_name_or_path
+
+        pretrained_model = super().from_pretrained(
+            local_model_path, local_model_path=local_model_path, **kwargs
+        )
+
+        pretrained_model.backbone.set_trainable_parameters(tune_visual=tune_visual, tune_llm=tune_llm)
+        pretrained_model.action_head.set_trainable_parameters(
+            tune_projector=tune_projector, tune_diffusion_model=tune_diffusion_model
+        )
+        return pretrained_model

src/lerobot/policies/groot/modeling_groot.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python
+
+# Copyright 2024 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Groot Policy Wrapper for LeRobot Integration
+
+Minimal integration that delegates to Isaac-GR00T components where possible
+without porting their code. The intent is to:
+
+- Download and load the pretrained GR00T model via GR00TN15.from_pretrained
+- Optionally align action horizon similar to gr00t_finetune.py
+- Expose predict_action via GR00T model.get_action
+- Provide a training forward that can call the GR00T model forward if batch
+  structure matches.
+
+Notes:
+- Dataset loading and full training orchestration is handled by Isaac-GR00T
+  TrainRunner in their codebase. If you want to invoke that flow end-to-end
+  from LeRobot, see `GrootPolicy.finetune_with_groot_runner` below.
+"""
+
+import os
+from collections import deque
+
+import torch
+from torch import Tensor
+
+from lerobot.policies.groot.configuration_groot import GrootConfig
+from lerobot.policies.groot.groot_n1 import GR00TN15
+from lerobot.policies.pretrained import PreTrainedPolicy
+
+
+class GrootPolicy(PreTrainedPolicy):
+    """Wrapper around external Groot model for LeRobot integration."""
+
+    name = "groot"
+    config_class = GrootConfig
+
+    def __init__(self, config: GrootConfig):
+        """Initialize Groot policy wrapper."""
+        super().__init__(config)
+        config.validate_features()
+        self.config = config
+
+        # Initialize GR00T model using ported components
+        self._groot_model = self._create_groot_model()
+
+        self.reset()
+
+    def _create_groot_model(self):
+        """Create and initialize the GR00T model using Isaac-GR00T API.
+
+        This is only called when creating a NEW policy (not when loading from checkpoint).
+
+        Steps (delegating to Isaac-GR00T):
+        1) Download and load pretrained model via GR00TN15.from_pretrained
+        2) Align action horizon with data_config if provided
+        """
+        # Handle Flash Attention compatibility issues
+        self._handle_flash_attention_compatibility()
+
+        model = GR00TN15.from_pretrained(
+            pretrained_model_name_or_path=self.config.base_model_path,
+            tune_llm=self.config.tune_llm,
+            tune_visual=self.config.tune_visual,
+            tune_projector=self.config.tune_projector,
+            tune_diffusion_model=self.config.tune_diffusion_model,
+        )
+
+        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
+        model.config.compute_dtype = model.compute_dtype
+
+        return model
+
+    def reset(self):
+        """Reset policy state when environment resets."""
+        self._action_queue = deque([], maxlen=self.config.n_action_steps)
+
+    def get_optim_params(self) -> dict:
+        return self.parameters()
+
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
+        """Training forward pass.
+
+        Delegates to Isaac-GR00T model.forward when inputs are compatible.
+        """
+        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
+        allowed_base = {"state", "state_mask", "action", "action_mask", "embodiment_id"}
+        groot_inputs = {
+            k: v
+            for k, v in batch.items()
+            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
+        }
+
+        # Get device from model parameters
+        device = next(self.parameters()).device
+
+        # Run GR00T forward under bf16 autocast when enabled to reduce activation memory
+        # Rationale: Matches original GR00T finetuning (bf16 compute, fp32 params) and avoids fp32 upcasts.
+        with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
+            outputs = self._groot_model.forward(groot_inputs)
+
+        # Isaac-GR00T returns a BatchFeature; loss key is typically 'loss'
+        loss = outputs.get("loss")
+
+        loss_dict = {"loss": loss.item()}
+
+        return loss, loss_dict
+
+    @torch.no_grad()
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """Predict a chunk of actions for inference by delegating to Isaac-GR00T.
+
+        Returns a tensor of shape (B, n_action_steps, action_dim).
+        """
+        self.eval()
+
+        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
+        # Preprocessing is handled by the processor pipeline, so we just filter the batch
+        # NOTE: During inference, we should NOT pass action/action_mask (that's what we're predicting)
+        allowed_base = {"state", "state_mask", "embodiment_id"}
+        groot_inputs = {
+            k: v
+            for k, v in batch.items()
+            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
+        }
+
+        # Get device from model parameters
+        device = next(self.parameters()).device
+
+        # Use bf16 autocast for inference to keep memory low and match backbone dtype
+        with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
+            outputs = self._groot_model.get_action(groot_inputs)
+
+        actions = outputs.get("action_pred")
+
+        original_action_dim = self.config.output_features["action"].shape[0]
+        actions = actions[:, :, :original_action_dim]
+
+        return actions
+
+    @torch.no_grad()
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
+        """Select single action from action queue."""
+        self.eval()
+
+        if len(self._action_queue) == 0:
+            actions = self.predict_action_chunk(batch)
+            self._action_queue.extend(actions.transpose(0, 1))
+        return self._action_queue.popleft()
+
+    # -------------------------
+    # Internal helpers
+    # -------------------------
+    def _handle_flash_attention_compatibility(self) -> None:
+        """Handle Flash Attention compatibility issues by setting environment variables.
+
+        This addresses the common 'undefined symbol' error that occurs when Flash Attention
+        is compiled against a different PyTorch version than what's currently installed.
+        """
+
+        # Set environment variables to handle Flash Attention compatibility
+        # These help with symbol resolution issues
+        os.environ.setdefault("FLASH_ATTENTION_FORCE_BUILD", "0")
+        os.environ.setdefault("FLASH_ATTENTION_SKIP_CUDA_BUILD", "0")
+
+        # Try to import flash_attn and handle failures gracefully
+        try:
+            import flash_attn
+
+            print(f"[GROOT] Flash Attention version: {flash_attn.__version__}")
+        except ImportError as e:
+            print(f"[GROOT] Flash Attention not available: {e}")
+            print("[GROOT] Will use fallback attention mechanism")
+        except Exception as e:
+            if "undefined symbol" in str(e):
+                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
+                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
+                print("[GROOT] Consider reinstalling Flash Attention with compatible version:")
+                print("  pip uninstall flash-attn")
+                print("  pip install --no-build-isolation flash-attn==2.6.3")
+                print("[GROOT] Continuing with fallback attention mechanism")
+            else:
+                print(f"[GROOT] Flash Attention error: {e}")
+                print("[GROOT] Continuing with fallback attention mechanism")

src/lerobot/policies/groot/processor_groot.py

@@ -0,0 +1,664 @@
+#!/usr/bin/env python
+
+# Copyright 2024 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import torch
+from einops import rearrange
+from PIL import Image
+
+from lerobot.utils.import_utils import _transformers_available
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoProcessor, ProcessorMixin
+else:
+    AutoProcessor = None
+    ProcessorMixin = object
+
+from lerobot.configs.types import (
+    FeatureType,
+    NormalizationMode,
+    PolicyFeature,
+)
+from lerobot.policies.groot.configuration_groot import GrootConfig
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    RenameObservationsProcessorStep,
+)
+from lerobot.processor.converters import (
+    policy_action_to_transition,
+    transition_to_policy_action,
+)
+from lerobot.processor.core import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    HF_LEROBOT_HOME,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+)
+
+# Defaults for Eagle processor locations
+DEFAULT_TOKENIZER_ASSETS_REPO = "lerobot/eagle2hg-processor-groot-n1p5"
+
+
+def make_groot_pre_post_processors(
+    config: GrootConfig, dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """Create preprocessor and postprocessor for Groot policy.
+
+    This creates a processing pipeline that transforms LeRobot data format into
+    the format expected by Isaac-GR00T models:
+
+    Preprocessing steps:
+    1. Optional key renaming (dataset-specific key mapping)
+    2. Add batch dimension to unbatched data
+    3. Pack video/state/action/language/embodiment and apply optional min-max normalization before padding
+    4. Encode video+language with Eagle VLM into intermediate eagle_content
+    5. Collate eagle_content into batched eagle_* tensors
+    6. Move tensors to device (GPU)
+
+    NOTE: We optionally apply min-max normalization to STATE and ACTION using
+    dataset-provided statistics prior to padding, mapping values to [-1, 1].
+    This mirrors SO100-style preprocessing and keeps scales consistent with GR00T.
+
+    Args:
+        config: Groot configuration containing data_config, embodiment_tag, etc.
+        dataset_stats: Optional per-key min/max statistics for normalization before padding.
+
+    Returns:
+        Tuple of (preprocessor, postprocessor) pipelines
+    """
+
+    # Get horizon/dimension parameters from config
+    # These should match the config used for the pretrained model
+    # Default values match most GR00T configs (state_horizon=1, action_horizon=16)
+    state_horizon = 1
+    # CRITICAL: Pretrained GR00T models use action_horizon=16 max!
+    # The model architecture hardcodes this limit
+    action_horizon = min(config.chunk_size, 16)
+    max_state_dim = config.max_state_dim
+    max_action_dim = config.max_action_dim
+
+    # Pass raw dataset_stats; normalization will occur inside pack step before padding
+    padded_stats = dataset_stats or {}
+
+    # Define feature specs for optional normalization steps
+    _features: dict[str, PolicyFeature] = {
+        # Observation features (only add those we may normalize)
+        "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(state_horizon, max_state_dim)),
+        # Action feature
+        "action": PolicyFeature(type=FeatureType.ACTION, shape=(action_horizon, max_action_dim)),
+    }
+
+    # Normalize STATE and ACTION with min_max (SO100-like default)
+    _norm_map = {
+        FeatureType.ACTION: NormalizationMode.MIN_MAX,
+        FeatureType.STATE: NormalizationMode.MIN_MAX,
+    }
+
+    # Determine env action dimension from config (simple, object-like PolicyFeature)
+    try:
+        env_action_dim = int(config.output_features["action"].shape[0])
+    except Exception:
+        env_action_dim = 0
+
+    input_steps: list[ProcessorStep] = [
+        # 1. Rename keys if needed (e.g., dataset-specific camera names)
+        # Leave empty for now - add mappings if your dataset uses different key names
+        RenameObservationsProcessorStep(rename_map={}),
+        # 2. Add batch dimension for single samples
+        AddBatchDimensionProcessorStep(),
+        # 3. Pack video/state/action/language/embodiment; apply optional min-max normalization before padding
+        GrootPackInputsStep(
+            state_horizon=state_horizon,
+            action_horizon=action_horizon,
+            max_state_dim=max_state_dim,
+            max_action_dim=max_action_dim,
+            language_key="task",
+            formalize_language=False,
+            embodiment_tag=config.embodiment_tag,
+            normalize_min_max=True,
+            stats=padded_stats,
+        ),
+        # 4. Eagle encode (creates eagle_content)
+        GrootEagleEncodeStep(
+            tokenizer_assets_repo=config.tokenizer_assets_repo,
+        ),
+        # 5. Collate eagle_content -> eagle_* tensors
+        GrootEagleCollateStep(
+            tokenizer_assets_repo=config.tokenizer_assets_repo,
+        ),
+        # 6. Move to device
+        DeviceProcessorStep(device=config.device),
+    ]
+
+    # Postprocessing: slice to env action dim and unnormalize to env scale, then move to CPU
+    output_steps: list[ProcessorStep] = [
+        GrootActionUnpackUnnormalizeStep(
+            env_action_dim=env_action_dim,
+            stats=padded_stats,
+            normalize_min_max=True,
+        ),
+        # Finally, move to CPU for env interaction
+        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,
+        ),
+    )
+
+
+# GR00T specific processor steps
+
+
+def _to_uint8_np_bhwc(img_t: torch.Tensor) -> np.ndarray:
+    # img_t: (B, C, H, W) float in [0,1] or uint8
+    if img_t.dtype.is_floating_point:
+        img_t = (img_t.clamp(0, 1) * 255.0).to(torch.uint8)
+    return rearrange(img_t.cpu().numpy(), "b c h w -> b h w c")
+
+
+def _build_eagle_processor(tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO) -> ProcessorMixin:
+    # Validate that the cache directory is ready. If not, instruct the user.
+    cache_dir = HF_LEROBOT_HOME / tokenizer_assets_repo
+    required = [
+        cache_dir / "processor_config.json",
+        cache_dir / "preprocessor_config.json",
+        cache_dir / "image_processing_eagle2_5_vl_fast.py",
+    ]
+    if not all(p.exists() for p in required):
+        raise FileNotFoundError(
+            f"[GROOT] Eagle processor cache at '{cache_dir}' is not populated. "
+            "Vendor files are copied during model creation. Create the policy/model first, "
+            "or call ensure_eagle_cache_ready() before building processors."
+        )
+    proc = AutoProcessor.from_pretrained(str(cache_dir), trust_remote_code=True, use_fast=True)
+    proc.tokenizer.padding_side = "left"
+    return proc
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="groot_pack_inputs_v3")
+class GrootPackInputsStep(ProcessorStep):
+    state_horizon: int = 1
+    action_horizon: int = 16
+    max_state_dim: int = 64
+    max_action_dim: int = 32
+    language_key: str = "task"
+    formalize_language: bool = False
+    embodiment_tag: str = "new_embodiment"
+    embodiment_mapping: dict[str, int] = field(
+        default_factory=lambda: {
+            "new_embodiment": 31,  # Match original GR00T EMBODIMENT_TAG_MAPPING
+            "oxe_droid": 17,
+            "agibot_genie1": 26,
+            "gr1": 24,
+            "so100": 2,
+            "unitree_g1": 3,
+        }
+    )
+    # Min-max normalization (SO100-like) applied BEFORE padding
+    normalize_min_max: bool = True
+    stats: dict[str, dict[str, Any]] | None = None
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        obs = transition.get(TransitionKey.OBSERVATION, {}) or {}
+        comp = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) or {}
+
+        def _align_vec(vec: Any, target_dim: int, *, default: float) -> torch.Tensor:
+            t = torch.as_tensor(vec)
+            t = t.flatten().to(
+                dtype=torch.float32,
+                device=next(
+                    (v.device for v in obs.values() if isinstance(v, torch.Tensor)), torch.device("cpu")
+                ),
+            )
+            d = int(t.shape[-1]) if t.numel() > 0 else 0
+            if d == target_dim:
+                return t
+            if d < target_dim:
+                pad = torch.full((target_dim - d,), default, dtype=t.dtype, device=t.device)
+                return torch.cat([t, pad], dim=0)
+            return t[:target_dim]
+
+        def _min_max_norm(x: torch.Tensor, key: str) -> torch.Tensor:
+            if not self.normalize_min_max:
+                return x
+            if self.stats is None or key not in self.stats:
+                return x
+            stats_k = self.stats[key]
+            last_dim = x.shape[-1]
+            min_v = _align_vec(stats_k.get("min", torch.zeros(last_dim)), last_dim, default=0.0)
+            max_v = _align_vec(stats_k.get("max", torch.ones(last_dim)), last_dim, default=1.0)
+            denom = max_v - min_v
+            mask = denom != 0
+            safe_denom = torch.where(mask, denom, torch.ones_like(denom))
+            mapped = 2 * (x - min_v) / safe_denom - 1
+            return torch.where(mask, mapped, torch.zeros_like(mapped))
+
+        # 1) Video (B, T=1, V, H, W, C) uint8
+        img_keys = sorted([k for k in obs if k.startswith("observation.images.")])
+        if not img_keys and "observation.image" in obs:
+            img_keys = ["observation.image"]
+        if img_keys:
+            cams = [_to_uint8_np_bhwc(obs[k]) for k in img_keys]
+            video = np.stack(cams, axis=1)  # (B, V, H, W, C)
+            video = np.expand_dims(video, axis=1)  # (B, 1, V, H, W, C)
+            # GR00T validates that video.shape[3] == 3 (channels), so reorder to (B, T, V, C, H, W)
+            video = np.transpose(video, (0, 1, 2, 5, 3, 4))  # (B, 1, V, C, H, W)
+            obs["video"] = video
+            # Drop raw images to avoid confusion downstream
+            for k in img_keys:
+                obs.pop(k, None)
+
+        # 2) Language (string)
+        lang = comp.get(self.language_key)
+        if isinstance(lang, list):
+            lang = lang[0] if len(lang) > 0 else None
+        if not lang:
+            lang = "Perform the task."
+        if self.formalize_language:
+            lang = (lang or "").lower()
+            lang = "".join(ch for ch in lang if ch.isalnum() or ch.isspace())
+        comp["language"] = lang
+
+        # 3) State/state_mask -> (B, 1, max_state_dim)
+        if "observation.state" in obs:
+            state = obs["observation.state"]  # (B, D)
+            if state.dim() != 2:
+                raise ValueError(f"state must be (B, D), got {tuple(state.shape)}")
+            bsz, d = state.shape
+            # Normalize BEFORE padding
+            if self.normalize_min_max:
+                state = _min_max_norm(state, "observation.state")
+            state = state.unsqueeze(1)  # (B, 1, D)
+            if d > self.max_state_dim:
+                state = state[:, :, : self.max_state_dim]
+                d = self.max_state_dim
+            elif d < self.max_state_dim:
+                pad = torch.zeros(bsz, 1, self.max_state_dim - d, dtype=state.dtype, device=state.device)
+                state = torch.cat([state, pad], dim=2)
+            state_mask = torch.zeros(bsz, 1, self.max_state_dim, dtype=torch.bool, device=state.device)
+            state_mask[:, :, :d] = True
+            obs["state"] = state
+            obs["state_mask"] = state_mask
+
+        # 4) Action/action_mask -> (B, action_horizon, max_action_dim)
+        action = transition.get(TransitionKey.ACTION)
+        if isinstance(action, torch.Tensor):
+            # Normalize BEFORE temporal expansion/padding
+            if self.normalize_min_max:
+                if action.dim() == 2:
+                    action = _min_max_norm(action, "action")
+                elif action.dim() == 3:
+                    b, t, d = action.shape
+                    flat = action.reshape(b * t, d)
+                    flat = _min_max_norm(flat, "action")
+                    action = flat.view(b, t, d)
+            if action.dim() == 2:
+                action = action.unsqueeze(1).repeat(1, self.action_horizon, 1)
+            elif action.dim() == 3:
+                b, t, d = action.shape
+                if t < self.action_horizon:
+                    last = action[:, -1:, :]
+                    pad = last.repeat(1, self.action_horizon - t, 1)
+                    action = torch.cat([action, pad], dim=1)
+                elif t > self.action_horizon:
+                    action = action[:, : self.action_horizon, :]
+            else:
+                raise ValueError(f"action must be (B, D) or (B, T, D), got {tuple(action.shape)}")
+
+            b, t, d = action.shape
+            if d > self.max_action_dim:
+                action = action[:, :, : self.max_action_dim]
+                d = self.max_action_dim
+            elif d < self.max_action_dim:
+                pad = torch.zeros(b, t, self.max_action_dim - d, dtype=action.dtype, device=action.device)
+                action = torch.cat([action, pad], dim=2)
+            action_mask = torch.zeros(b, t, self.max_action_dim, dtype=torch.bool, device=action.device)
+            action_mask[:, :, :d] = True
+            transition[TransitionKey.ACTION] = action
+            comp["action_mask"] = action_mask
+
+        # 5) Embodiment id as LongTensor (B,)
+        emb_id = self.embodiment_mapping.get(self.embodiment_tag, 0)
+        # Infer batch size/device from any tensor in obs or action
+        bsz = None
+        device = torch.device("cpu")
+        for v in list(obs.values()) + [transition.get(TransitionKey.ACTION)]:
+            if isinstance(v, torch.Tensor):
+                bsz = v.shape[0]
+                device = v.device
+                break
+        if bsz is None and "video" in obs and isinstance(obs["video"], np.ndarray):
+            bsz = obs["video"].shape[0]
+        if bsz is None:
+            bsz = 1
+        comp["embodiment_id"] = torch.full((bsz,), emb_id, dtype=torch.long, device=device)
+
+        transition[TransitionKey.OBSERVATION] = obs
+        transition[TransitionKey.COMPLEMENTARY_DATA] = comp
+        return transition
+
+    # Pipeline API requirement: declare how features change (we keep it simple)
+    def transform_features(self, features):
+        return features
+
+    def get_config(self) -> dict[str, Any]:
+        """
+        Returns a serializable dictionary of the processor's configuration.
+
+        Excludes 'stats' since they are saved separately via state_dict().
+        """
+        return {
+            "state_horizon": self.state_horizon,
+            "action_horizon": self.action_horizon,
+            "max_state_dim": self.max_state_dim,
+            "max_action_dim": self.max_action_dim,
+            "language_key": self.language_key,
+            "formalize_language": self.formalize_language,
+            "embodiment_tag": self.embodiment_tag,
+            "embodiment_mapping": self.embodiment_mapping,
+            "normalize_min_max": self.normalize_min_max,
+        }
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        """
+        Returns normalization statistics as a flat state dictionary.
+
+        This enables saving stats to safetensors files, similar to normalizer_processor.
+        """
+        if not self.stats:
+            return {}
+
+        flat: dict[str, torch.Tensor] = {}
+        for key, sub in self.stats.items():
+            for stat_name, value in sub.items():
+                tensor = torch.as_tensor(value).cpu()
+                flat[f"{key}.{stat_name}"] = tensor
+        return flat
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        """
+        Loads normalization statistics from a flat state dictionary.
+
+        This enables loading stats from safetensors files during from_pretrained.
+        """
+        if not state:
+            return
+
+        reconstructed: dict[str, dict[str, Any]] = {}
+        for flat_key, tensor in state.items():
+            if "." in flat_key:
+                key, stat_name = flat_key.rsplit(".", 1)
+                if key not in reconstructed:
+                    reconstructed[key] = {}
+                reconstructed[key][stat_name] = tensor
+
+        if reconstructed:
+            self.stats = reconstructed
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="groot_eagle_encode_v3")
+class GrootEagleEncodeStep(ProcessorStep):
+    tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO
+    _proc: ProcessorMixin | None = field(default=None, init=False, repr=False)
+
+    @property
+    def proc(self) -> ProcessorMixin:
+        if self._proc is None:
+            self._proc = _build_eagle_processor(self.tokenizer_assets_repo)
+        return self._proc
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        obs = transition.get(TransitionKey.OBSERVATION, {}) or {}
+        comp = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) or {}
+
+        if "video" not in obs:
+            return transition
+
+        video = obs["video"]  # (B, T, V, H, W, C) uint8
+        lang = comp.get("language", "Perform the task.")
+        if isinstance(lang, list):
+            lang = lang[0] if len(lang) > 0 else "Perform the task."
+
+        bsz = video.shape[0]
+        eagle_contents: list[dict[str, Any]] = []
+        for b in range(bsz):
+            vt = video[b]  # (T, V, C, H, W) after reorder
+            if vt.ndim != 5:
+                # Fallback: assume (T, V, H, W, C)
+                t, v, h, w, c = vt.shape
+                flat = rearrange(vt, "t v h w c -> (t v) h w c")
+            else:
+                t, v, c, h, w = vt.shape
+                flat = rearrange(vt, "t v c h w -> (t v) h w c")
+            images = [Image.fromarray(flat[i]) for i in range(t * v)]
+            # Format language as string list representation to match Original GROOT
+            lang_formatted = str([lang])
+            text_content = [{"type": "text", "text": lang_formatted}]
+            image_content = [{"type": "image", "image": img} for img in images]
+            conv = [{"role": "user", "content": image_content + text_content}]
+            text_list = [self.proc.apply_chat_template(conv, tokenize=False, add_generation_prompt=True)]
+            img_inputs, vid_inputs = self.proc.process_vision_info(conv)
+            eagle_contents.append(
+                {
+                    "text_list": text_list,
+                    "image_inputs": img_inputs,
+                    "video_inputs": vid_inputs,
+                }
+            )
+
+        comp["eagle_content"] = eagle_contents
+        transition[TransitionKey.OBSERVATION] = obs
+        transition[TransitionKey.COMPLEMENTARY_DATA] = comp
+        return transition
+
+    # Pipeline API requirement: declare how features change (no schema change here)
+    def transform_features(self, features):
+        return features
+
+
+# Original GR00T-style collate: converts eagle_content -> eagle_* tensors
+def collate(features: list[dict[str, Any]], eagle_processor: ProcessorMixin) -> dict[str, Any]:
+    batch: dict[str, Any] = {}
+    keys = features[0].keys()
+
+    for key in keys:
+        values = [elem[key] for elem in features]
+
+        if key == "eagle_content":
+            text_list: list[str] = []
+            image_inputs: list[Any] = []
+            for v in values:
+                curr_text_list = v["text_list"]
+                curr_image_inputs = v["image_inputs"]
+                text_list += curr_text_list
+                image_inputs += curr_image_inputs
+            eagle_inputs = eagle_processor(
+                text=text_list,
+                images=image_inputs,
+                images_kwargs={"min_dynamic_tiles": 1, "max_dynamic_tiles": 1, "use_thumbnail": False},
+                return_tensors="pt",
+                padding=True,
+            )
+            for k, v in eagle_inputs.items():
+                k = "eagle_" + k
+                batch[k] = v
+        elif key in ("pixel_values", "image_grid_thw", "attention_mask", "input_ids"):
+            # Concat in existing batch dimension.
+            batch[key] = torch.cat(values)
+        else:
+            # state, state_mask, action and action_mask.
+            # Stack to form the batch dimension.
+            batch[key] = torch.from_numpy(np.stack(values))
+    return batch
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="groot_eagle_collate_v3")
+class GrootEagleCollateStep(ProcessorStep):
+    tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO
+    _proc: ProcessorMixin | None = field(default=None, init=False, repr=False)
+
+    @property
+    def proc(self) -> ProcessorMixin:
+        if self._proc is None:
+            self._proc = _build_eagle_processor(self.tokenizer_assets_repo)
+        return self._proc
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        obs = transition.get(TransitionKey.OBSERVATION, {}) or {}
+        comp = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) or {}
+        contents = comp.get("eagle_content")
+        if not contents:
+            return transition
+
+        # Build features list as original API expects: one dict per batch item
+        features = [{"eagle_content": content} for content in contents]
+        batched = collate(features, self.proc)
+
+        # Inject eagle_* tensors and remove the temporary content and raw video to free memory
+        for k, v in batched.items():
+            comp[k] = v
+        comp.pop("eagle_content", None)
+        obs.pop(
+            "video", None
+        )  # The video has been fully encoded into eagle_* tensors, so we don't need the raw video anymore
+        transition[TransitionKey.OBSERVATION] = obs
+        transition[TransitionKey.COMPLEMENTARY_DATA] = comp
+        return transition
+
+    def transform_features(self, features):
+        return features
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="groot_action_unpack_unnormalize_v1")
+class GrootActionUnpackUnnormalizeStep(ProcessorStep):
+    env_action_dim: int = 0
+    # Apply inverse of min-max normalization if it was used in preprocessor
+    normalize_min_max: bool = True
+    stats: dict[str, dict[str, Any]] | None = None
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        # Expect model outputs to be in TransitionKey.ACTION as (B, T, D_model)
+        action = transition.get(TransitionKey.ACTION)
+        if not isinstance(action, torch.Tensor):
+            return transition
+
+        # Select last timestep and slice to env dimension
+        if action.dim() == 3:
+            action = action[:, -1, :]
+        # Now action is (B, D_model)
+        if self.env_action_dim and action.shape[-1] >= self.env_action_dim:
+            action = action[..., : self.env_action_dim]
+
+        # Inverse min-max normalization mirroring _min_max_norm:
+        # forward: y = 2 * (x - min) / denom - 1, with y=0 when denom==0
+        # inverse: x = (y+1)/2 * denom + min, and when denom==0 -> x = min
+        if self.normalize_min_max and self.stats is not None:
+            stats_k = self.stats.get("action", {})
+            d = action.shape[-1]
+            min_v = torch.as_tensor(
+                stats_k.get("min", torch.zeros(d)), dtype=action.dtype, device=action.device
+            )
+            max_v = torch.as_tensor(
+                stats_k.get("max", torch.ones(d)), dtype=action.dtype, device=action.device
+            )
+            if min_v.numel() != d:
+                min_v = torch.nn.functional.pad(min_v.flatten()[:d], (0, max(0, d - min_v.numel())))
+                min_v = min_v.to(action.device, dtype=action.dtype)
+            if max_v.numel() != d:
+                max_v = torch.nn.functional.pad(max_v.flatten()[:d], (0, max(0, d - max_v.numel())))
+                max_v = max_v.to(action.device, dtype=action.dtype)
+            denom = max_v - min_v
+            mask = denom != 0
+            safe_denom = torch.where(mask, denom, torch.ones_like(denom))
+            inv = (action + 1.0) * 0.5 * safe_denom + min_v
+            action = torch.where(mask, inv, min_v)
+
+        transition[TransitionKey.ACTION] = action
+        return transition
+
+    def transform_features(self, features):
+        return features
+
+    def get_config(self) -> dict[str, Any]:
+        """
+        Returns a serializable dictionary of the processor's configuration.
+
+        Excludes 'stats' since they are saved separately via state_dict().
+        """
+        return {
+            "env_action_dim": self.env_action_dim,
+            "normalize_min_max": self.normalize_min_max,
+        }
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        """
+        Returns normalization statistics as a flat state dictionary.
+
+        This enables saving stats to safetensors files, similar to normalizer_processor.
+        """
+        if not self.stats:
+            return {}
+
+        flat: dict[str, torch.Tensor] = {}
+        for key, sub in self.stats.items():
+            for stat_name, value in sub.items():
+                tensor = torch.as_tensor(value).cpu()
+                flat[f"{key}.{stat_name}"] = tensor
+        return flat
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        """
+        Loads normalization statistics from a flat state dictionary.
+
+        This enables loading stats from safetensors files during from_pretrained.
+        """
+        if not state:
+            return
+
+        reconstructed: dict[str, dict[str, Any]] = {}
+        for flat_key, tensor in state.items():
+            if "." in flat_key:
+                key, stat_name = flat_key.rsplit(".", 1)
+                if key not in reconstructed:
+                    reconstructed[key] = {}
+                reconstructed[key][stat_name] = tensor
+
+        if reconstructed:
+            self.stats = reconstructed

src/lerobot/policies/groot/utils.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+from shutil import copytree
+
+from huggingface_hub import hf_hub_download
+
+
+def ensure_eagle_cache_ready(vendor_dir: Path, cache_dir: Path, assets_repo: str) -> None:
+    """Populate the Eagle processor directory in cache and ensure tokenizer assets exist.
+
+    - Copies the vendored Eagle files into cache_dir (overwriting when needed).
+    - Downloads vocab.json and merges.txt into the same cache_dir if missing.
+    """
+    cache_dir = Path(cache_dir)
+    vendor_dir = Path(vendor_dir)
+
+    try:
+        # Populate/refresh cache with vendor files to ensure a complete processor directory
+        print(f"[GROOT] Copying vendor Eagle files to cache: {vendor_dir} -> {cache_dir}")
+        copytree(vendor_dir, cache_dir, dirs_exist_ok=True)
+    except Exception as exc:  # nosec: B110
+        print(f"[GROOT] Warning: Failed to copy vendor Eagle files to cache: {exc}")
+
+    required_assets = [
+        "vocab.json",
+        "merges.txt",
+        "added_tokens.json",
+        "chat_template.json",
+        "special_tokens_map.json",
+        "config.json",
+        "generation_config.json",
+        "preprocessor_config.json",
+        "processor_config.json",
+        "tokenizer_config.json",
+    ]
+
+    print(f"[GROOT] Assets repo: {assets_repo} \n Cache dir: {cache_dir}")
+
+    for fname in required_assets:
+        dst = cache_dir / fname
+        if not dst.exists():
+            print(f"[GROOT] Fetching {fname}")
+            hf_hub_download(
+                repo_id=assets_repo,
+                filename=fname,
+                repo_type="model",
+                local_dir=str(cache_dir),
+            )

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -485,6 +485,7 @@ class VLAFlowMatching(nn.Module):
             num_vlm_layers=self.config.num_vlm_layers,
             self_attn_every_n_layers=self.config.self_attn_every_n_layers,
             expert_width_multiplier=self.config.expert_width_multiplier,
+            device=self.config.device,
         )
         self.state_proj = nn.Linear(
             self.config.max_state_dim, self.vlm_with_expert.config.text_config.hidden_size

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -70,13 +70,14 @@ class SmolVLMWithExpertModel(nn.Module):
         num_vlm_layers: int = -1,
         self_attn_every_n_layers: int = -1,
         expert_width_multiplier: float = 0.5,
+        device: str = "auto",
     ):
         super().__init__()
         if load_vlm_weights:
             print(f"Loading  {model_id} weights ...")
             self.vlm = AutoModelForImageTextToText.from_pretrained(
                 model_id,
-                device_map="auto",
+                device_map=device,
                 torch_dtype="bfloat16",
                 low_cpu_mem_usage=True,
             )

src/lerobot/policies/utils.py

@@ -22,6 +22,8 @@ import numpy as np
 import torch
 from torch import nn
 
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.utils import build_dataset_frame
 from lerobot.processor import PolicyAction, RobotAction, RobotObservation
 from lerobot.utils.constants import ACTION, OBS_STR
@@ -198,3 +200,42 @@ def make_robot_action(action_tensor: PolicyAction, ds_features: dict[str, dict])
         f"{name}": float(action_tensor[i]) for i, name in enumerate(action_names)
     }
     return act_processed_policy
+
+
+def raise_feature_mismatch_error(
+    provided_features: set[str],
+    expected_features: set[str],
+) -> None:
+    """
+    Raises a standardized ValueError for feature mismatches between dataset/environment and policy config.
+    """
+    missing = expected_features - provided_features
+    extra = provided_features - expected_features
+    # TODO (jadechoghari): provide a dynamic rename map suggestion to the user.
+    raise ValueError(
+        f"Feature mismatch between dataset/environment and policy config.\n"
+        f"- Missing features: {sorted(missing) if missing else 'None'}\n"
+        f"- Extra features: {sorted(extra) if extra else 'None'}\n\n"
+        f"Please ensure your dataset and policy use consistent feature names.\n"
+        f"If your dataset uses different observation keys (e.g., cameras named differently), "
+        f"use the `--rename_map` argument, for example:\n"
+        f'  --rename_map=\'{{"observation.images.left": "observation.images.camera1", '
+        f'"observation.images.top": "observation.images.camera2"}}\''
+    )
+
+
+def validate_visual_features_consistency(
+    cfg: PreTrainedConfig,
+    features: dict[str, PolicyFeature],
+) -> None:
+    """
+    Validates visual feature consistency between a policy config and provided dataset/environment features.
+
+    Args:
+        cfg (PreTrainedConfig): The model or policy configuration containing input_features and type.
+        features (Dict[str, PolicyFeature]): A mapping of feature names to PolicyFeature objects.
+    """
+    expected_visuals = {k for k, v in cfg.input_features.items() if v.type == FeatureType.VISUAL}
+    provided_visuals = {k for k, v in features.items() if v.type == FeatureType.VISUAL}
+    if not provided_visuals.issubset(expected_visuals):
+        raise_feature_mismatch_error(provided_visuals, expected_visuals)

src/lerobot/processor/env_processor.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python
+
+# 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 dataclasses import dataclass
+
+import torch
+
+from lerobot.configs.types import PipelineFeatureType, PolicyFeature
+from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
+
+from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="libero_processor")
+class LiberoProcessorStep(ObservationProcessorStep):
+    """
+    Processes LIBERO observations into the LeRobot format.
+
+    This step handles the specific observation structure from LIBERO environments,
+    which includes nested robot_state dictionaries and image observations.
+
+    **State Processing:**
+    -   Processes the `robot_state` dictionary which contains nested end-effector,
+        gripper, and joint information.
+    -   Extracts and concatenates:
+        - End-effector position (3D)
+        - End-effector quaternion converted to axis-angle (3D)
+        - Gripper joint positions (2D)
+    -   Maps the concatenated state to `"observation.state"`.
+
+    **Image Processing:**
+    -   Rotates images by 180 degrees by flipping both height and width dimensions.
+    -   This accounts for the HuggingFaceVLA/libero camera orientation convention.
+    """
+
+    def _process_observation(self, observation):
+        """
+        Processes both image and robot_state observations from LIBERO.
+        """
+        processed_obs = observation.copy()
+        for key in list(processed_obs.keys()):
+            if key.startswith(f"{OBS_IMAGES}."):
+                img = processed_obs[key]
+
+                # Flip both H and W
+                img = torch.flip(img, dims=[2, 3])
+
+                processed_obs[key] = img
+        # Process robot_state into a flat state vector
+        if "observation.robot_state" in processed_obs:
+            robot_state = processed_obs.pop("observation.robot_state")
+
+            # Extract components
+            eef_pos = robot_state["eef"]["pos"]  # (B, 3,)
+            eef_quat = robot_state["eef"]["quat"]  # (B, 4,)
+            gripper_qpos = robot_state["gripper"]["qpos"]  # (B, 2,)
+
+            # Convert quaternion to axis-angle
+            eef_axisangle = self._quat2axisangle(eef_quat)  # (B, 3)
+            # Concatenate into a single state vector
+            state = torch.cat((eef_pos, eef_axisangle, gripper_qpos), dim=-1)
+
+            # ensure float32
+            state = state.float()
+            if state.dim() == 1:
+                state = state.unsqueeze(0)
+
+            processed_obs[OBS_STATE] = state
+        return processed_obs
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """
+        Transforms feature keys from the LIBERO format to the LeRobot standard.
+        """
+        new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {}
+
+        # copy over non-STATE features
+        for ft, feats in features.items():
+            if ft != PipelineFeatureType.STATE:
+                new_features[ft] = feats.copy()
+
+        # rebuild STATE features
+        state_feats = {}
+
+        # add our new flattened state
+        state_feats["observation.state"] = PolicyFeature(
+            key="observation.state",
+            shape=(8,),  # [eef_pos(3), axis_angle(3), gripper(2)]
+            dtype="float32",
+            description=("Concatenated end-effector position (3), axis-angle (3), and gripper qpos (2)."),
+        )
+
+        new_features[PipelineFeatureType.STATE] = state_feats
+
+        return new_features
+
+    def observation(self, observation):
+        return self._process_observation(observation)
+
+    def _quat2axisangle(self, quat: torch.Tensor) -> torch.Tensor:
+        """
+        Convert batched quaternions to axis-angle format.
+        Only accepts torch tensors of shape (B, 4).
+
+        Args:
+            quat (Tensor): (B, 4) tensor of quaternions in (x, y, z, w) format
+
+        Returns:
+            Tensor: (B, 3) axis-angle vectors
+
+        Raises:
+            TypeError: if input is not a torch tensor
+            ValueError: if shape is not (B, 4)
+        """
+
+        if not isinstance(quat, torch.Tensor):
+            raise TypeError(f"_quat2axisangle expected a torch.Tensor, got {type(quat)}")
+
+        if quat.ndim != 2 or quat.shape[1] != 4:
+            raise ValueError(f"_quat2axisangle expected shape (B, 4), got {tuple(quat.shape)}")
+
+        quat = quat.to(dtype=torch.float32)
+        device = quat.device
+        batch_size = quat.shape[0]
+
+        w = quat[:, 3].clamp(-1.0, 1.0)
+
+        den = torch.sqrt(torch.clamp(1.0 - w * w, min=0.0))
+
+        result = torch.zeros((batch_size, 3), device=device)
+
+        mask = den > 1e-10
+
+        if mask.any():
+            angle = 2.0 * torch.acos(w[mask])  # (M,)
+            axis = quat[mask, :3] / den[mask].unsqueeze(1)
+            result[mask] = axis * angle.unsqueeze(1)
+
+        return result

src/lerobot/rl/eval_policy.py

@@ -65,7 +65,7 @@ def main(cfg: TrainRLServerPipelineConfig):
         # env_cfg=cfg.env,
         ds_meta=dataset_meta,
     )
-    policy.from_pretrained(env_cfg.pretrained_policy_name_or_path)
+    policy = policy.from_pretrained(env_cfg.pretrained_policy_name_or_path)
     policy.eval()
 
     eval_policy(env, policy=policy, n_episodes=10)

src/lerobot/robots/lekiwi/lekiwi.py

@@ -153,7 +153,7 @@ class LeKiwi(Robot):
         homing_offsets.update(dict.fromkeys(self.base_motors, 0))
 
         full_turn_motor = [
-            motor for motor in motors if any(keyword in motor for keyword in ["wheel", "wrist"])
+            motor for motor in motors if any(keyword in motor for keyword in ["wheel", "wrist_roll"])
         ]
         unknown_range_motors = [motor for motor in motors if motor not in full_turn_motor]
 

src/lerobot/robots/lekiwi/lekiwi_host.py

@@ -18,14 +18,24 @@ import base64
 import json
 import logging
 import time
+from dataclasses import dataclass, field
 
 import cv2
+import draccus
 import zmq
 
 from .config_lekiwi import LeKiwiConfig, LeKiwiHostConfig
 from .lekiwi import LeKiwi
 
 
+@dataclass
+class LeKiwiServerConfig:
+    """Configuration for the LeKiwi host script."""
+
+    robot: LeKiwiConfig = field(default_factory=LeKiwiConfig)
+    host: LeKiwiHostConfig = field(default_factory=LeKiwiHostConfig)
+
+
 class LeKiwiHost:
     def __init__(self, config: LeKiwiHostConfig):
         self.zmq_context = zmq.Context()
@@ -47,17 +57,16 @@ class LeKiwiHost:
         self.zmq_context.term()
 
 
-def main():
+@draccus.wrap()
+def main(cfg: LeKiwiServerConfig):
     logging.info("Configuring LeKiwi")
-    robot_config = LeKiwiConfig()
-    robot = LeKiwi(robot_config)
+    robot = LeKiwi(cfg.robot)
 
     logging.info("Connecting LeKiwi")
     robot.connect()
 
     logging.info("Starting HostAgent")
-    host_config = LeKiwiHostConfig()
-    host = LeKiwiHost(host_config)
+    host = LeKiwiHost(cfg.host)
 
     last_cmd_time = time.time()
     watchdog_active = False

src/lerobot/robots/stretch3/README.md

@@ -1,173 +0,0 @@
-This tutorial explains how to use [Stretch 3](https://hello-robot.com/stretch-3-product) with LeRobot.
-
-## Setup
-
-Familiarize yourself with Stretch by following its [tutorials](https://docs.hello-robot.com/0.3/getting_started/hello_robot/) (recommended).
-
-To use LeRobot on Stretch, 3 options are available:
-
-- [tethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup)
-- [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup)
-- ssh directly into Stretch (you will first need to install and configure openssh-server on stretch using one of the two above setups)
-
-## Install LeRobot
-
-On Stretch's CLI, follow these steps:
-
-1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
-
-```bash
-mkdir -p ~/miniconda3
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
-bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-rm ~/miniconda3/miniconda.sh
-~/miniconda3/bin/conda init bash
-```
-
-2. Comment out these lines in `~/.profile` (this can mess up paths used by conda and ~/.local/bin should already be in your PATH)
-
-```
-# set PATH so it includes user's private bin if it exists
-if [ -d "$HOME/.local/bin" ] ; then
-    PATH="$HOME/.local/bin:$PATH"
-fi
-```
-
-3. Restart shell or `source ~/.bashrc`
-
-4. Create and activate a fresh conda environment for lerobot
-
-```bash
-conda create -y -n lerobot python=3.10 && conda activate lerobot
-```
-
-5. Clone LeRobot:
-
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-6. When using `miniconda`, install `ffmpeg` in your environment:
-
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-7. Install LeRobot with stretch dependencies:
-
-```bash
-cd ~/lerobot && pip install -e ".[stretch]"
-```
-
-> **Note:** If you get this message, you can ignore it: `ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.`
-
-8. Run a [system check](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#system-check) to make sure your robot is ready:
-
-```bash
-stretch_system_check.py
-```
-
-> **Note:** You may need to free the "robot process" after booting Stretch by running `stretch_free_robot_process.py`. For more info this Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#turning-off-gamepad-teleoperation).
-
-You should get something like this:
-
-```bash
-For use with S T R E T C H (R) from Hello Robot Inc.
----------------------------------------------------------------------
-
-Model = Stretch 3
-Tool = DexWrist 3 w/ Gripper
-Serial Number = stretch-se3-3054
-
----- Checking Hardware ----
-[Pass] Comms are ready
-[Pass] Actuators are ready
-[Warn] Sensors not ready (IMU AZ = -10.19 out of range -10.1 to -9.5)
-[Pass] Battery voltage is 13.6 V
-
----- Checking Software ----
-[Pass] Ubuntu 22.04 is ready
-[Pass] All APT pkgs are setup correctly
-[Pass] Firmware is up-to-date
-[Pass] Python pkgs are up-to-date
-[Pass] ROS2 Humble is ready
-```
-
-## Teleoperate, record a dataset and run a policy
-
-**Calibrate (Optional)**
-Before operating Stretch, you need to [home](https://docs.hello-robot.com/0.3/getting_started/stretch_hardware_overview/#homing) it first. Be mindful about giving Stretch some space as this procedure will move the robot's arm and gripper. Now run this command:
-
-```bash
-python lerobot/scripts/control_robot.py \
-    --robot.type=stretch \
-    --control.type=calibrate
-```
-
-This is equivalent to running `stretch_robot_home.py`
-
-> **Note:** If you run any of the LeRobot scripts below and Stretch is not properly homed, it will automatically home/calibrate first.
-
-**Teleoperate**
-Before trying teleoperation, you need to activate the gamepad controller by pressing the middle button. For more info, see Stretch's [doc](https://docs.hello-robot.com/0.3/getting_started/hello_robot/#gamepad-teleoperation).
-
-Now try out teleoperation (see above documentation to learn about the gamepad controls):
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-    --robot.type=stretch \
-    --control.type=teleoperate
-```
-
-This is essentially the same as running `stretch_gamepad_teleop.py`
-
-**Record a dataset**
-Once you're familiar with the gamepad controls and after a bit of practice, you can try to record your first dataset with Stretch.
-
-If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Record one episode:
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=stretch \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/stretch_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-> **Note:** If you're using ssh to connect to Stretch and run this script, you won't be able to visualize its cameras feed (though they will still be recording). To see the cameras stream, use [tethered](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#tethered-setup) or [untethered setup](https://docs.hello-robot.com/0.3/getting_started/connecting_to_stretch/#untethered-setup).
-
-**Replay an episode**
-Now try to replay this episode (make sure the robot's initial position is the same):
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=stretch \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/stretch_test \
-  --control.episode=0
-```
-
-If you need help, please reach out on Discord in the channel `#stretch3-mobile-arm`.

src/lerobot/robots/stretch3/configuration_stretch3.py

@@ -1,51 +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 dataclasses import dataclass, field
-
-from lerobot.cameras import CameraConfig
-from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.cameras.realsense import RealSenseCameraConfig
-
-from ..config import RobotConfig
-
-
-@RobotConfig.register_subclass("stretch3")
-@dataclass
-class Stretch3RobotConfig(RobotConfig):
-    # cameras
-    cameras: dict[str, CameraConfig] = field(
-        default_factory=lambda: {
-            "navigation": OpenCVCameraConfig(
-                index_or_path="/dev/hello-nav-head-camera",
-                fps=10,
-                width=1280,
-                height=720,
-                rotation=-90,
-            ),
-            "head": RealSenseCameraConfig(
-                name="Intel RealSense D435I",
-                fps=30,
-                width=640,
-                height=480,
-                rotation=90,
-            ),
-            "wrist": RealSenseCameraConfig(
-                name="Intel RealSense D405",
-                fps=30,
-                width=640,
-                height=480,
-            ),
-        }
-    )

src/lerobot/robots/stretch3/robot_stretch3.py

@@ -1,180 +0,0 @@
-#!/usr/bin/env python
-
-# 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 time
-
-import numpy as np
-from stretch_body.gamepad_teleop import GamePadTeleop
-from stretch_body.robot import Robot as StretchAPI
-from stretch_body.robot_params import RobotParams
-
-from lerobot.cameras.utils import make_cameras_from_configs
-from lerobot.datasets.utils import get_nested_item
-from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
-
-from ..robot import Robot
-from .configuration_stretch3 import Stretch3RobotConfig
-
-# {lerobot_keys: stretch.api.keys}
-STRETCH_MOTORS = {
-    "head_pan.pos": "head.head_pan.pos",
-    "head_tilt.pos": "head.head_tilt.pos",
-    "lift.pos": "lift.pos",
-    "arm.pos": "arm.pos",
-    "wrist_pitch.pos": "end_of_arm.wrist_pitch.pos",
-    "wrist_roll.pos": "end_of_arm.wrist_roll.pos",
-    "wrist_yaw.pos": "end_of_arm.wrist_yaw.pos",
-    "gripper.pos": "end_of_arm.stretch_gripper.pos",
-    "base_x.vel": "base.x_vel",
-    "base_y.vel": "base.y_vel",
-    "base_theta.vel": "base.theta_vel",
-}
-
-
-class Stretch3Robot(Robot):
-    """[Stretch 3](https://hello-robot.com/stretch-3-product), by Hello Robot."""
-
-    config_class = Stretch3RobotConfig
-    name = "stretch3"
-
-    def __init__(self, config: Stretch3RobotConfig):
-        raise NotImplementedError
-        super().__init__(config)
-
-        self.config = config
-        self.robot_type = self.config.type
-
-        self.api = StretchAPI()
-        self.cameras = make_cameras_from_configs(config.cameras)
-
-        self.is_connected = False
-        self.logs = {}
-
-        self.teleop = None  # TODO remove
-
-        # TODO(aliberts): test this
-        RobotParams.set_logging_level("WARNING")
-        RobotParams.set_logging_formatter("brief_console_formatter")
-
-        self.state_keys = None
-        self.action_keys = None
-
-    @property
-    def observation_features(self) -> dict:
-        return {
-            "dtype": "float32",
-            "shape": (len(STRETCH_MOTORS),),
-            "names": {"motors": list(STRETCH_MOTORS)},
-        }
-
-    @property
-    def action_features(self) -> dict:
-        return self.observation_features
-
-    @property
-    def camera_features(self) -> dict[str, dict]:
-        cam_ft = {}
-        for cam_key, cam in self.cameras.items():
-            cam_ft[cam_key] = {
-                "shape": (cam.height, cam.width, cam.channels),
-                "names": ["height", "width", "channels"],
-                "info": None,
-            }
-        return cam_ft
-
-    def connect(self) -> None:
-        self.is_connected = self.api.startup()
-        if not self.is_connected:
-            print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
-            raise ConnectionError()
-
-        for cam in self.cameras.values():
-            cam.connect()
-            self.is_connected = self.is_connected and cam.is_connected
-
-        if not self.is_connected:
-            print("Could not connect to the cameras, check that all cameras are plugged-in.")
-            raise ConnectionError()
-
-        self.calibrate()
-
-    def calibrate(self) -> None:
-        if not self.api.is_homed():
-            self.api.home()
-
-    def _get_state(self) -> dict:
-        status = self.api.get_status()
-        return {k: get_nested_item(status, v, sep=".") for k, v in STRETCH_MOTORS.items()}
-
-    def get_observation(self) -> dict[str, np.ndarray]:
-        obs_dict = {}
-
-        # Read Stretch state
-        before_read_t = time.perf_counter()
-        state = self._get_state()
-        self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
-
-        if self.state_keys is None:
-            self.state_keys = list(state)
-
-        state = np.asarray(list(state.values()))
-        obs_dict[OBS_STATE] = state
-
-        # Capture images from cameras
-        for cam_key, cam in self.cameras.items():
-            before_camread_t = time.perf_counter()
-            obs_dict[f"{OBS_IMAGES}.{cam_key}"] = cam.async_read()
-            self.logs[f"read_camera_{cam_key}_dt_s"] = cam.logs["delta_timestamp_s"]
-            self.logs[f"async_read_camera_{cam_key}_dt_s"] = time.perf_counter() - before_camread_t
-
-        return obs_dict
-
-    def send_action(self, action: np.ndarray) -> np.ndarray:
-        if not self.is_connected:
-            raise ConnectionError()
-
-        if self.teleop is None:
-            self.teleop = GamePadTeleop(robot_instance=False)
-            self.teleop.startup(robot=self)
-
-        if self.action_keys is None:
-            dummy_action = self.teleop.gamepad_controller.get_state()
-            self.action_keys = list(dummy_action.keys())
-
-        action_dict = dict(zip(self.action_keys, action.tolist(), strict=True))
-
-        before_write_t = time.perf_counter()
-        self.teleop.do_motion(state=action_dict, robot=self)
-        self.push_command()
-        self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
-
-        # TODO(aliberts): return action_sent when motion is limited
-        return action
-
-    def teleop_safety_stop(self) -> None:
-        if self.teleop is not None:
-            self.teleop._safety_stop(robot=self)
-
-    def disconnect(self) -> None:
-        self.api.stop()
-        if self.teleop is not None:
-            self.teleop.gamepad_controller.stop()
-            self.teleop.stop()
-
-        for cam in self.cameras.values():
-            cam.disconnect()
-
-        self.is_connected = False

src/lerobot/robots/utils.py

@@ -40,14 +40,6 @@ def make_robot_from_config(config: RobotConfig) -> Robot:
         from .lekiwi import LeKiwi
 
         return LeKiwi(config)
-    elif config.type == "stretch3":
-        from .stretch3 import Stretch3Robot
-
-        return Stretch3Robot(config)
-    elif config.type == "viperx":
-        from .viperx import ViperX
-
-        return ViperX(config)
     elif config.type == "hope_jr_hand":
         from .hope_jr import HopeJrHand
 

src/lerobot/robots/viperx/README.md

@@ -1,196 +0,0 @@
-This tutorial explains how to use [Aloha and Aloha 2 stationary](https://www.trossenrobotics.com/aloha-stationary) with LeRobot.
-
-## Setup
-
-Follow the [documentation from Trossen Robotics](https://docs.trossenrobotics.com/aloha_docs/2.0/getting_started/stationary/hardware_setup.html) for setting up the hardware and plugging the 4 arms and 4 cameras to your computer.
-
-## Install LeRobot
-
-On your computer:
-
-1. [Install Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install):
-
-```bash
-mkdir -p ~/miniconda3
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
-bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-rm ~/miniconda3/miniconda.sh
-~/miniconda3/bin/conda init bash
-```
-
-2. Restart shell or `source ~/.bashrc`
-
-3. Create and activate a fresh conda environment for lerobot
-
-```bash
-conda create -y -n lerobot python=3.10 && conda activate lerobot
-```
-
-4. Clone LeRobot:
-
-```bash
-git clone https://github.com/huggingface/lerobot.git ~/lerobot
-```
-
-5. When using `miniconda`, install `ffmpeg` in your environment:
-
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-6. Install LeRobot with dependencies for the Aloha motors (dynamixel) and cameras (intelrealsense):
-
-```bash
-cd ~/lerobot && pip install -e ".[dynamixel, intelrealsense]"
-```
-
-## Teleoperate
-
-\*\*/!\ FOR SAFETY, READ THIS /!\*\*
-Teleoperation consists in manually operating the leader arms to move the follower arms. Importantly:
-
-1. Make sure your leader arms are in the same position as the follower arms, so that the follower arms don't move too fast to match the leader arms,
-2. Our code assumes that your robot has been assembled following Trossen Robotics instructions. This allows us to skip calibration, as we use the pre-defined calibration files in `.cache/calibration/aloha_default`. If you replace a motor, make sure you follow the exact instructions from Trossen Robotics.
-
-By running the following code, you can start your first **SAFE** teleoperation:
-
-> **NOTE:** To visualize the data, enable `--control.display_data=true`. This streams the data using `rerun`.
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=aloha \
-  --robot.max_relative_target=5 \
-  --control.type=teleoperate
-```
-
-By adding `--robot.max_relative_target=5`, we override the default value for `max_relative_target` defined in [`ViperXConfig`](./config_viperx.py). It is expected to be `5` to limit the magnitude of the movement for more safety, but the teleoperation won't be smooth. When you feel confident, you can disable this limit by adding `--robot.max_relative_target=null` to the command line:
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=aloha \
-  --robot.max_relative_target=null \
-  --control.type=teleoperate
-```
-
-## Record a dataset
-
-Once you're familiar with teleoperation, you can record your first dataset with Aloha.
-
-If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens):
-
-```bash
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-
-```bash
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-
-Record 2 episodes and upload your dataset to the hub:
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=aloha \
-  --robot.max_relative_target=null \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/aloha_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=2 \
-  --control.push_to_hub=true
-```
-
-## Visualize a dataset
-
-If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by:
-
-```bash
-echo ${HF_USER}/aloha_test
-```
-
-If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with [Rerun](https://github.com/rerun-io/rerun):
-
-```bash
-lerobot-dataset-viz \
-  --repo-id ${HF_USER}/aloha_test --episode 0
-```
-
-## Replay an episode
-
-\*\*/!\ FOR SAFETY, READ THIS /!\*\*
-Replay consists in automatically replaying the sequence of actions (i.e. goal positions for your motors) recorded in a given dataset episode. Make sure the current initial position of your robot is similar to the one in your episode, so that your follower arms don't move too fast to go to the first goal positions. For safety, you might want to add `--robot.max_relative_target=5` to your command line as explained above.
-
-Now try to replay the first episode on your robot:
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=aloha \
-  --robot.max_relative_target=null \
-  --control.type=replay \
-  --control.fps=30 \
-  --control.repo_id=${HF_USER}/aloha_test \
-  --control.episode=0
-```
-
-## Train a policy
-
-To train a policy to control your robot, use the [`lerobot-train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
-
-```bash
-lerobot-train \
-  --dataset.repo_id=${HF_USER}/aloha_test \
-  --policy.type=act \
-  --output_dir=outputs/train/act_aloha_test \
-  --job_name=act_aloha_test \
-  --policy.device=cuda \
-  --wandb.enable=true
-```
-
-Let's explain it:
-
-1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/aloha_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
-3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
-4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
-
-For more information on the `train` script see the previous tutorial: [`examples/4_train_policy_with_script.md`](../examples/4_train_policy_with_script.md)
-
-Training should take several hours. You will find checkpoints in `outputs/train/act_aloha_test/checkpoints`.
-
-## Evaluate your policy
-
-You can use the `record` function from [`lerobot/scripts/control_robot.py`](../src/lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:
-
-```bash
-python lerobot/scripts/control_robot.py \
-  --robot.type=aloha \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_act_aloha_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=outputs/train/act_aloha_test/checkpoints/last/pretrained_model \
-  --control.num_image_writer_processes=1
-```
-
-As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:
-
-1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_aloha_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_aloha_test`).
-2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_aloha_test`).
-3. We use `--control.num_image_writer_processes=1` instead of the default value (`0`). On our computer, using a dedicated process to write images from the 4 cameras on disk allows to reach constant 30 fps during inference. Feel free to explore different values for `--control.num_image_writer_processes`.
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-## More
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-If you have any question or need help, please reach out on Discord in the channel `#aloha-arm`.