fix(pyproject): adding ceiling bound on mujoco (<3.9.0) (#3751)

* fix(pyproject): adding ceiling bound on mujoco (<3.9.0)

* chore(uv.lock): updating uv.lock

* fix(linux): adding missing linux dependencies

* chore(uv.lock): updating uv.lock

docs/source/_toctree.yml

@@ -9,6 +9,8 @@
 - sections:
   - local: il_robots
     title: Imitation Learning for Robots
+  - local: lelab
+    title: LeLab - Lerobot GUI
   - local: bring_your_own_policies
     title: Adding a Policy
   - local: integrate_hardware

docs/source/il_robots.mdx

@@ -647,5 +647,6 @@ The `--strategy.type` flag selects the execution mode:
 - `sentry`: Continuous recording with auto-upload (useful for large-scale evaluation)
 - `highlight`: Ring buffer recording with keystroke save (useful for capturing interesting events)
 - `dagger`: Human-in-the-loop data collection (see [HIL Data Collection](./hil_data_collection))
+- `episodic`: Episode-oriented policy recording with reset phases between episodes
 
 All strategies support `--inference.type=rtc` for smooth execution with slow VLA models (Pi0, Pi0.5, SmolVLA).

docs/source/inference.mdx

@@ -157,6 +157,44 @@ Foot pedal input is also supported via `--strategy.input_device=pedal`. Configur
 | `--strategy.input_device`            | Input device: `keyboard` or `pedal` (default: keyboard) |
 | `--teleop.type`                      | **Required.** Teleoperator type                         |
 
+### Episodic (`--strategy.type=episodic`)
+
+Episode-oriented recording that mirrors the behavior of `lerobot-record`. The policy drives the robot for each episode; an optional teleoperator can drive the robot during the reset phase between episodes.
+
+```bash
+lerobot-rollout \
+    --strategy.type=episodic \
+    --policy.path=${HF_USER}/my_policy \
+    --robot.type=so100_follower \
+    --robot.port=/dev/ttyACM0 \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/ttyACM1 \
+    --dataset.repo_id=${HF_USER}/my_eval_data \
+    --dataset.num_episodes=20 \
+    --dataset.episode_time_s=30 \
+    --dataset.reset_time_s=10 \
+    --dataset.single_task="Pick up the red cube"
+```
+
+Teleop is optional — if omitted the robot holds its position during the reset phase.
+
+**Keyboard controls:**
+
+| Key         | Action                           |
+| ----------- | -------------------------------- |
+| `→` (right) | End the current episode early    |
+| `←` (left)  | Discard episode and re-record it |
+| `ESC`       | Stop the recording session       |
+
+| Flag                                            | Description                                                                |
+| ----------------------------------------------- | -------------------------------------------------------------------------- |
+| `--dataset.num_episodes`                        | Number of episodes to record                                               |
+| `--dataset.episode_time_s`                      | Duration of each recording episode in seconds                              |
+| `--dataset.reset_time_s`                        | Duration of the reset phase between episodes in seconds                    |
+| `--teleop.type`                                 | Optional. Teleoperator to drive the robot during resets                    |
+| `--strategy.reset_to_initial_position`          | Whether to reset the robot to its initial position between episodes        |
+| `--strategy.smooth_leader_to_follower_handover` | Whether to turn on or off the leader -> follower smooth handover behavior. |
+
 ---
 
 ## Inference Backends

docs/source/lelab.mdx

@@ -0,0 +1,29 @@
+# LeLab - LeRobot Guide
+
+LeLab is a graphical user interface built on top of the LeRobot library, designed to make robotics accessible without needing to memorize CLI commands. From a single app you can configure your robot, teleoperate it, collect datasets, train policies locally or on cloud GPUs via HF Jobs, and deploy trained models back onto your robot. It's the easiest way to go from an unboxed SO-101 to a working policy, and a great companion for anyone learning the LeRobot workflow. Source code and issues live on GitHub: [huggingface/leLab](https://github.com/huggingface/leLab).
+
+> [!TIP]
+> For now LeLab is compatible only with SO-ARM101
+
+<Youtube id="VqyKUuW9V1g" />
+
+### Installation
+
+Requires [`uv`](https://docs.astral.sh/uv/getting-started/installation/). Install and launch in one command:
+
+```
+uv tool install git+https://github.com/huggingface/leLab.git && lelab
+```
+
+After install, run `lelab` from your terminal anytime to start the app.
+
+### Features
+
+- **Add robots** — Select arm type (leader/follower), calibrate each joint from the middle position, and attach cameras.
+- **Teleoperation** — Control the follower arm with the leader and see a live 3D visualization of the arms.
+- **Dataset recording** — Define a task description, number of episodes, and episode/reset durations. Press spacebar to advance between episodes. 30+ episodes recommended.
+- **Local training** — Train a policy directly on your own machine with a selected dataset, policy type, batch size, and step count.
+- **Cloud training with HF Jobs** — Train on powerful GPUs via [HF Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) with transparent pricing. Run `hf auth login` first. See the [Compute HW Guide](hardware_guide) for hardware/batch size tips.
+- **Training visualization** — Watch progress live in the app, with checkpoints saved automatically.
+- **Run trained policies** — Pick any model from your jobs list and run inference on your robot with one click.
+- **Use community datasets** — Provide any Hugging Face dataset ID to train on datasets you didn't record yourself.

docs/source/lerobot-dataset-v3.mdx

@@ -275,7 +275,7 @@ A converter aggregates per‑episode files into larger shards and writes episode
 pip install "https://github.com/huggingface/lerobot/archive/33cad37054c2b594ceba57463e8f11ee374fa93c.zip"
 
 # Convert an existing v2.1 dataset hosted on the Hub:
-python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID>
+python -m lerobot.scripts.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID>
 ```
 
 **What it does**

docs/source/molmoact2.mdx

@@ -238,7 +238,7 @@ your dataset has not been converted with quantile statistics, you can add them
 with:
 
 ```bash
-python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
   --repo-id=your_dataset
 ```
 

docs/source/pi05.mdx

@@ -91,7 +91,7 @@ lerobot-train \
 If your dataset is not converted with `quantiles`, you can convert it with the following command:
 
 ```bash
-python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
     --repo-id=your_dataset \
 ```
 

docs/source/porting_datasets_v3.mdx

@@ -300,7 +300,7 @@ This replaces the old episode-per-file structure with efficient, optimally-sized
 If you have existing datasets in v2.1 format, use the migration tool:
 
 ```bash
-python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
+python src/lerobot/scripts/convert_dataset_v21_to_v30.py \
     --repo-id your_id/existing_dataset
 ```
 

pyproject.toml

@@ -216,7 +216,7 @@ robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot
 topreward = ["lerobot[transformers-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 eo1 = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]"]
-hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
+hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]", "lerobot[mujoco-dep]"]
 vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]
 
 # Features
@@ -231,10 +231,11 @@ video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
 # NOTE: Explicitly listing scipy helps flatten the dependecy tree.
-aloha = ["lerobot[dataset]", "gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
+mujoco-dep = ["mujoco<3.9.0"] # TODO: Fix issues to remove temporary upper bound
+aloha = ["lerobot[dataset]", "gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]", "lerobot[mujoco-dep]"]
 pusht = ["lerobot[dataset]", "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[dataset]", "lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
-metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]"]
+libero = ["lerobot[dataset]", "lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]", "lerobot[mujoco-dep]"]
+metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]", "lerobot[mujoco-dep]"]
 # NOTE: vlabench is NOT exposed as a `lerobot` extra. Its only distribution
 # is the OpenMOSS/VLABench GitHub repo (package name `VLABench`, no PyPI
 # release), so any `vlabench>=X` pip spec is unresolvable. Install it

src/lerobot/common/control_utils.py

@@ -18,6 +18,7 @@ from __future__ import annotations
 # Utilities
 ########################################################################################
 import logging
+import time
 import traceback
 from contextlib import nullcontext
 from copy import copy
@@ -243,3 +244,72 @@ def sanity_check_dataset_robot_compatibility(
         raise ValueError(
             "Dataset metadata compatibility check failed with mismatches:\n" + "\n".join(mismatches)
         )
+
+
+########################################################################################
+# Teleoperator smooth handover helpers
+# NOTE(Maxime): These functions use minimal type hints to maintain compatibility with utils
+# being a root module.
+########################################################################################
+
+
+def teleop_supports_feedback(teleop) -> bool:
+    """Return True when the teleop can receive position feedback (is actuated).
+
+    Actuated teleops (e.g. SO-101, OpenArmMini) have non-empty ``feedback_features``
+    and expose ``enable_torque`` / ``disable_torque`` motor-control methods.
+
+    TODO(Maxime): See if it is possible to unify this interface across teleops instead of duck-typing.
+    """
+    return (
+        bool(teleop.feedback_features)
+        and hasattr(teleop, "disable_torque")
+        and hasattr(teleop, "enable_torque")
+    )
+
+
+def teleop_smooth_move_to(teleop, target_pos: dict, duration_s: float = 2.0, fps: int = 30) -> None:
+    """Smoothly move an actuated teleop to ``target_pos`` via linear interpolation.
+
+    Requires the teleoperator to support feedback (i.e. have non-empty
+    ``feedback_features`` and implement ``disable_torque`` / ``enable_torque``).
+
+    ``target_pos`` is expected to be in the teleop's action/feedback key space.
+    For homogeneous setups (e.g. SO-101 leader + SO-101 follower) this matches
+    the robot action key space directly.
+
+    TODO(Maxime): This blocks up to ``duration_s`` seconds; during this time the
+    follower robot does not receive new actions, which could be an issue on LeKiwi.
+    """
+    teleop.enable_torque()
+    current = teleop.get_action()
+    steps = max(int(duration_s * fps), 1)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {
+            k: current[k] * (1 - t) + target_pos[k] * t if k in target_pos else current[k] for k in current
+        }
+        teleop.send_feedback(interp)
+        time.sleep(1 / fps)
+
+
+def follower_smooth_move_to(
+    robot, current: dict, target: dict, duration_s: float = 1.0, fps: int = 30
+) -> None:
+    """Smoothly move the follower robot from ``current`` to ``target`` action.
+
+    Used when the teleop is non-actuated: instead of driving the leader arm to
+    the follower, the follower is brought to the teleop's current pose so the
+    robot meets the operator's hand rather than jumping to it on the first frame.
+
+    Both ``current`` and ``target`` must be in the robot action key space
+    (i.e. the output of ``robot_action_processor``).
+    """
+    steps = max(int(duration_s * fps), 1)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {k: current[k] * (1 - t) + target[k] * t if k in target else current[k] for k in current}
+        robot.send_action(interp)
+        time.sleep(1 / fps)

src/lerobot/configs/dataset.py

@@ -41,8 +41,8 @@ class DatasetRecordConfig:
     video: bool = True
     # Upload dataset to Hugging Face hub.
     push_to_hub: bool = True
-    # Upload on private repository on the Hugging Face hub.
-    private: bool = False
+    # If True, upload as private; if None, defer to the org default on the Hub (only affects orgs).
+    private: bool | None = None
     # Add tags to your dataset on the hub.
     tags: list[str] | None = None
     # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;

src/lerobot/configs/train.py

@@ -177,6 +177,12 @@ class TrainPipelineConfig(HubMixin):
             )
 
         active_cfg = self.trainable_config
+        if self.rename_map and active_cfg.pretrained_path is None:
+            raise ValueError(
+                "`rename_map` requires a pretrained policy checkpoint. "
+                "Fresh initialization derives feature names from the current dataset, so no rename is applied."
+            )
+
         if not self.job_name:
             if self.env is None:
                 self.job_name = f"{active_cfg.type}"

src/lerobot/datasets/lerobot_dataset.py

@@ -524,7 +524,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         license: str | None = "apache-2.0",
         tag_version: bool = True,
         push_videos: bool = True,
-        private: bool = False,
+        private: bool | None = None,
         allow_patterns: list[str] | str | None = None,
         upload_large_folder: bool = False,
         **card_kwargs,
@@ -543,7 +543,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
             tag_version: If ``True``, create a Git tag for the current codebase
                 version.
             push_videos: If ``False``, skip uploading the ``videos/`` directory.
-            private: If ``True``, create a private repository.
+            private: If ``True``, create a private repository. If ``None``
+                (default), defer to the org default on the Hub (only affects orgs).
             allow_patterns: Glob pattern(s) restricting which files to upload.
             upload_large_folder: If ``True``, use ``upload_large_folder`` instead
                 of ``upload_folder`` for very large datasets.

src/lerobot/processor/pipeline.py

@@ -32,7 +32,6 @@ from __future__ import annotations
 
 import importlib
 import json
-import os
 import re
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Iterable, Sequence
@@ -281,6 +280,11 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 
     before_step_hooks: list[Callable[[int, EnvTransition], None]] = field(default_factory=list, repr=False)
     after_step_hooks: list[Callable[[int, EnvTransition], None]] = field(default_factory=list, repr=False)
+    _serialized_state_filenames: tuple[str | None, ...] | None = field(
+        default=None,
+        init=False,
+        repr=False,
+    )
 
     def __call__(self, data: TInput) -> TOutput:
         """Processes input data through the full pipeline.
@@ -338,30 +342,108 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             transition = processor_step(transition)
             yield transition
 
-    def _save_pretrained(self, save_directory: Path, **kwargs):
-        """Internal method to comply with `HubMixin`'s saving mechanism.
+    def _get_sanitized_name(self) -> str:
+        """Return a filename-safe version of the pipeline name.
 
-        This method does the actual saving work and is called by HubMixin.save_pretrained.
+        Returns:
+            The lower-cased pipeline name with non-alphanumeric characters replaced by underscores.
         """
-        config_filename = kwargs.pop("config_filename", None)
+        return re.sub(r"[^a-zA-Z0-9_]", "_", self.name.lower())
 
-        # Sanitize the pipeline name to create a valid filename prefix.
-        sanitized_name = re.sub(r"[^a-zA-Z0-9_]", "_", self.name.lower())
+    @staticmethod
+    def _get_state_filename(
+        *,
+        step_index: int,
+        registry_name: str | None,
+        sanitized_name: str,
+    ) -> str:
+        """Return the safetensors filename for one stateful processor step.
 
-        if config_filename is None:
-            config_filename = f"{sanitized_name}.json"
+        Args:
+            step_index: The index of the processor step in this pipeline.
+            registry_name: The registered processor step name, if available.
+            sanitized_name: The filename-safe pipeline name.
 
-        config: dict[str, Any] = {
+        Returns:
+            The state filename used by the existing disk serialization format.
+        """
+        if registry_name:
+            return f"{sanitized_name}_step_{step_index}_{registry_name}.safetensors"
+
+        return f"{sanitized_name}_step_{step_index}.safetensors"
+
+    @staticmethod
+    def _get_state_key(state_filename: str) -> str:
+        """Return the in-memory state key for a serialized state filename.
+
+        Args:
+            state_filename: The `.safetensors` filename from the serialized config.
+
+        Returns:
+            The state key used by the in-memory pipeline state dictionary.
+        """
+        return state_filename.removesuffix(".safetensors")
+
+    @staticmethod
+    def _get_state_filenames_from_config(loaded_config: dict[str, Any]) -> tuple[str | None, ...]:
+        """Return serialized state filenames in step order.
+
+        Args:
+            loaded_config: A validated processor pipeline config.
+
+        Returns:
+            A tuple containing each step's serialized state filename, or None for stateless steps.
+        """
+        return tuple(step_entry.get("state_file") for step_entry in loaded_config["steps"])
+
+    def _get_state_filenames_for_loading(self) -> tuple[str | None, ...]:
+        """Return expected state filenames in step order for `load_state_dict()`.
+
+        Returns:
+            The preserved serialized state filenames when available, otherwise filenames derived from
+            current non-empty step state.
+        """
+        if self._serialized_state_filenames is not None and len(self._serialized_state_filenames) == len(
+            self.steps
+        ):
+            return self._serialized_state_filenames
+
+        sanitized_name = self._get_sanitized_name()
+        state_filenames: list[str | None] = []
+
+        for step_index, processor_step in enumerate(self.steps):
+            step_state_dict = processor_step.state_dict()
+            if not step_state_dict:
+                state_filenames.append(None)
+                continue
+
+            registry_name = getattr(processor_step.__class__, "_registry_name", None)
+            state_filenames.append(
+                self._get_state_filename(
+                    step_index=step_index,
+                    registry_name=registry_name,
+                    sanitized_name=sanitized_name,
+                )
+            )
+
+        return tuple(state_filenames)
+
+    def get_config(self) -> dict[str, Any]:
+        """Return the JSON-serializable pipeline configuration.
+
+        Returns:
+            A dictionary with the same content that `save_pretrained()` writes as JSON.
+        """
+        sanitized_name = self._get_sanitized_name()
+        pipeline_config: dict[str, Any] = {
             "name": self.name,
             "steps": [],
         }
 
-        # Iterate through each step to build its configuration entry.
         for step_index, processor_step in enumerate(self.steps):
             registry_name = getattr(processor_step.__class__, "_registry_name", None)
-
             step_entry: dict[str, Any] = {}
-            # Prefer registry name for portability, otherwise fall back to full class path.
+
             if registry_name:
                 step_entry["registry_name"] = registry_name
             else:
@@ -369,31 +451,110 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
                     f"{processor_step.__class__.__module__}.{processor_step.__class__.__name__}"
                 )
 
-            # Save step configuration if `get_config` is implemented.
-            if hasattr(processor_step, "get_config"):
-                step_entry["config"] = processor_step.get_config()
+            step_entry["config"] = processor_step.get_config()
 
-            # Save step state if `state_dict` is implemented and returns a non-empty dict.
-            if hasattr(processor_step, "state_dict"):
-                state = processor_step.state_dict()
-                if state:
-                    # Clone tensors to avoid modifying the original state.
-                    cloned_state = {key: tensor.clone() for key, tensor in state.items()}
+            step_state_dict = processor_step.state_dict()
+            if step_state_dict:
+                step_entry["state_file"] = self._get_state_filename(
+                    step_index=step_index,
+                    registry_name=registry_name,
+                    sanitized_name=sanitized_name,
+                )
 
-                    # Create a unique filename for the state file.
-                    if registry_name:
-                        state_filename = f"{sanitized_name}_step_{step_index}_{registry_name}.safetensors"
-                    else:
-                        state_filename = f"{sanitized_name}_step_{step_index}.safetensors"
+            pipeline_config["steps"].append(step_entry)
 
-                    save_file(cloned_state, os.path.join(str(save_directory), state_filename))
-                    step_entry["state_file"] = state_filename
+        return pipeline_config
 
-            config["steps"].append(step_entry)
+    def state_dict(self) -> dict[str, dict[str, torch.Tensor]]:
+        """Return pipeline state tensors grouped by state key.
 
-        # Write the main configuration JSON file.
-        with open(os.path.join(str(save_directory), config_filename), "w") as file_pointer:
-            json.dump(config, file_pointer, indent=2)
+        Returns:
+            A dictionary mapping suffixless state keys to cloned step state dictionaries.
+        """
+        sanitized_name = self._get_sanitized_name()
+        pipeline_state_dict: dict[str, dict[str, torch.Tensor]] = {}
+
+        for step_index, processor_step in enumerate(self.steps):
+            step_state_dict = processor_step.state_dict()
+            if not step_state_dict:
+                continue
+
+            registry_name = getattr(processor_step.__class__, "_registry_name", None)
+            state_filename = self._get_state_filename(
+                step_index=step_index,
+                registry_name=registry_name,
+                sanitized_name=sanitized_name,
+            )
+            state_key = self._get_state_key(state_filename)
+            pipeline_state_dict[state_key] = {
+                tensor_name: tensor.clone() for tensor_name, tensor in step_state_dict.items()
+            }
+
+        return pipeline_state_dict
+
+    def load_state_dict(
+        self,
+        state_dict: dict[str, dict[str, torch.Tensor]],
+    ) -> None:
+        """Load pipeline state tensors into the existing steps.
+
+        Args:
+            state_dict: A dictionary mapping suffixless state keys to step state dictionaries.
+
+        Raises:
+            KeyError: If loading finds missing expected state or unexpected extra state.
+        """
+        expected_state_filenames = self._get_state_filenames_for_loading()
+        used_state_keys: set[str] = set()
+
+        for step_index, (processor_step, state_filename) in enumerate(
+            zip(self.steps, expected_state_filenames, strict=True)
+        ):
+            if state_filename is None:
+                continue
+
+            state_key = self._get_state_key(state_filename)
+            if state_key not in state_dict:
+                raise KeyError(
+                    f"Missing state key '{state_key}' for processor step {step_index}. "
+                    f"Available state keys: {sorted(state_dict.keys())}"
+                )
+
+            processor_step.load_state_dict(state_dict[state_key])
+            used_state_keys.add(state_key)
+
+        unexpected_state_keys = set(state_dict) - used_state_keys
+        if unexpected_state_keys:
+            expected_state_key_set = {
+                self._get_state_key(state_filename)
+                for state_filename in expected_state_filenames
+                if state_filename is not None
+            }
+            raise KeyError(
+                f"Unexpected processor state keys: {sorted(unexpected_state_keys)}. "
+                f"Expected state keys: {sorted(expected_state_key_set)}"
+            )
+
+    def _save_pretrained(self, save_directory: Path, **kwargs) -> None:
+        """Internal method to comply with `HubMixin`'s saving mechanism.
+
+        This method does the actual saving work and is called by HubMixin.save_pretrained.
+        """
+        config_filename = kwargs.pop("config_filename", None)
+        sanitized_name = self._get_sanitized_name()
+
+        if config_filename is None:
+            config_filename = f"{sanitized_name}.json"
+
+        pipeline_config = self.get_config()
+        pipeline_state_dict = self.state_dict()
+
+        for state_key, step_state_dict in pipeline_state_dict.items():
+            state_filename = f"{state_key}.safetensors"
+            save_file(step_state_dict, save_directory / state_filename)
+
+        with open(save_directory / config_filename, "w") as file_pointer:
+            json.dump(pipeline_config, file_pointer, indent=2)
 
     def save_pretrained(
         self,
@@ -577,12 +738,54 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         cls._validate_overrides_used(validated_overrides, loaded_config)
 
         # 5. Construct and return the final pipeline instance
-        return cls(
+        pipeline = cls(
             steps=steps,
             name=loaded_config.get("name", "DataProcessorPipeline"),
             to_transition=to_transition or cast(Callable[[TInput], EnvTransition], batch_to_transition),
             to_output=to_output or cast(Callable[[EnvTransition], TOutput], transition_to_batch),
         )
+        pipeline._serialized_state_filenames = cls._get_state_filenames_from_config(loaded_config)
+        return pipeline
+
+    @classmethod
+    def from_config(
+        cls,
+        config: dict[str, Any],
+        *,
+        state_dict: dict[str, dict[str, torch.Tensor]] | None = None,
+        overrides: dict[str, Any] | None = None,
+        to_transition: Callable[[TInput], EnvTransition] | None = None,
+        to_output: Callable[[EnvTransition], TOutput] | None = None,
+    ) -> DataProcessorPipeline[TInput, TOutput]:
+        """Build a pipeline from an in-memory config and optional state tensors.
+
+        Args:
+            config: A config dictionary with the same structure as the saved processor JSON.
+            state_dict: Optional in-memory pipeline state grouped by suffixless state key.
+            overrides: Optional constructor overrides keyed by registry name or class name.
+            to_transition: Optional converter from input data to `EnvTransition`.
+            to_output: Optional converter from `EnvTransition` to output data.
+
+        Returns:
+            A processor pipeline built from the config and optional state.
+        """
+        cls._validate_loaded_config("<in-memory config>", config, "<in-memory config>")
+
+        steps, remaining_override_keys = cls._build_steps_from_config(config, overrides or {})
+        cls._validate_overrides_used(remaining_override_keys, config)
+
+        pipeline = cls(
+            steps=steps,
+            name=config.get("name", "DataProcessorPipeline"),
+            to_transition=to_transition or cast(Callable[[TInput], EnvTransition], batch_to_transition),
+            to_output=to_output or cast(Callable[[EnvTransition], TOutput], transition_to_batch),
+        )
+        pipeline._serialized_state_filenames = cls._get_state_filenames_from_config(config)
+
+        if state_dict is not None:
+            pipeline.load_state_dict(state_dict)
+
+        return pipeline
 
     @classmethod
     def _load_config(
@@ -666,9 +869,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
                 ) from e
 
     @classmethod
-    def _validate_loaded_config(
-        cls, model_id: str, loaded_config: dict[str, Any], config_filename: str
-    ) -> None:
+    def _validate_loaded_config(cls, model_id: str, loaded_config: Any, config_filename: str) -> None:
         """Validate that a config was loaded and is a valid processor config.
 
         This method validates processor config format with intelligent migration detection:
@@ -688,7 +889,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 
         Args:
             model_id: The model identifier (used for migration detection)
-            loaded_config: The loaded config dictionary (guaranteed non-None)
+            loaded_config: The loaded config value to validate (may be non-dict)
             config_filename: The config filename that was loaded (for error messages)
 
         Raises:
@@ -702,9 +903,14 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
                     model_id,
                     f"Config file '{config_filename}' is not a valid processor configuration",
                 )
+            loaded_config_description = (
+                list(loaded_config.keys())
+                if isinstance(loaded_config, dict)
+                else type(loaded_config).__name__
+            )
             raise ValueError(
                 f"Config file '{config_filename}' is not a valid processor configuration. "
-                f"Expected a config with 'steps' field, but got: {list(loaded_config.keys())}"
+                f"Expected a config with 'steps' field, but got: {loaded_config_description}"
             )
 
     @classmethod
@@ -766,26 +972,41 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             ImportError: If a step class cannot be imported or found in registry
             ValueError: If a step cannot be instantiated with its configuration
         """
-        steps: list[ProcessorStep] = []
-        override_keys = set(overrides.keys())
+        steps, remaining_override_keys = cls._build_steps_from_config(loaded_config, overrides)
 
-        for step_entry in loaded_config["steps"]:
-            # 1. Get step class and key
-            step_class, step_key = cls._resolve_step_class(step_entry)
-
-            # 2. Instantiate step with overrides
-            step_instance = cls._instantiate_step(step_entry, step_class, step_key, overrides)
-
-            # 3. Load step state if available
+        for step_instance, step_entry in zip(steps, loaded_config["steps"], strict=True):
             cls._load_step_state(step_instance, step_entry, model_id, base_path, hub_download_kwargs)
 
-            # 4. Track used overrides
-            if step_key in override_keys:
-                override_keys.discard(step_key)
+        return steps, remaining_override_keys
 
-            steps.append(step_instance)
+    @classmethod
+    def _build_steps_from_config(
+        cls,
+        loaded_config: dict[str, Any],
+        overrides: dict[str, Any],
+    ) -> tuple[list[ProcessorStep], set[str]]:
+        """Build processor steps from config without loading tensor state.
 
-        return steps, override_keys
+        Args:
+            loaded_config: The loaded processor configuration.
+            overrides: User-provided constructor overrides keyed by step key.
+
+        Returns:
+            A tuple containing instantiated steps and override keys that did not match a step.
+        """
+        processor_steps: list[ProcessorStep] = []
+        remaining_override_keys = set(overrides.keys())
+
+        for step_entry in loaded_config["steps"]:
+            step_class, step_key = cls._resolve_step_class(step_entry)
+            processor_step = cls._instantiate_step(step_entry, step_class, step_key, overrides)
+
+            if step_key in remaining_override_keys:
+                remaining_override_keys.discard(step_key)
+
+            processor_steps.append(processor_step)
+
+        return processor_steps, remaining_override_keys
 
     @classmethod
     def _resolve_step_class(cls, step_entry: dict[str, Any]) -> tuple[type[ProcessorStep], str]:
@@ -1096,7 +1317,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         return True
 
     @classmethod
-    def _is_processor_config(cls, config: dict) -> bool:
+    def _is_processor_config(cls, config: Any) -> bool:
         """Check if config follows DataProcessorPipeline format.
 
         This method validates the processor configuration structure:
@@ -1147,6 +1368,9 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         Returns:
             True if config follows valid DataProcessorPipeline format, False otherwise
         """
+        if not isinstance(config, dict):
+            return False
+
         # Must have a "steps" field with a list of step configurations
         if not isinstance(config.get("steps"), list):
             return False

src/lerobot/processor/relative_action_processor.py

@@ -81,7 +81,7 @@ def to_absolute_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) ->
     return actions
 
 
-@ProcessorStepRegistry.register("delta_actions_processor")
+@ProcessorStepRegistry.register("relative_actions_processor")
 @dataclass
 class RelativeActionsProcessorStep(ProcessorStep):
     """Converts absolute actions to relative actions (action -= state) for masked dimensions.

src/lerobot/rollout/__init__.py

@@ -23,6 +23,7 @@ from .configs import (
     DAggerKeyboardConfig,
     DAggerPedalConfig,
     DAggerStrategyConfig,
+    EpisodicStrategyConfig,
     HighlightStrategyConfig,
     RolloutConfig,
     RolloutStrategyConfig,
@@ -49,6 +50,7 @@ from .inference import (
 from .strategies import (
     BaseStrategy,
     DAggerStrategy,
+    EpisodicStrategy,
     HighlightStrategy,
     RolloutStrategy,
     SentryStrategy,
@@ -66,6 +68,8 @@ __all__ = [
     "HardwareContext",
     "HighlightStrategy",
     "HighlightStrategyConfig",
+    "EpisodicStrategy",
+    "EpisodicStrategyConfig",
     "InferenceEngine",
     "InferenceEngineConfig",
     "PolicyContext",

src/lerobot/rollout/configs.py

@@ -121,6 +121,35 @@ class DAggerPedalConfig:
     upload: str = "KEY_C"
 
 
+@RolloutStrategyConfig.register_subclass("episodic")
+@dataclass
+class EpisodicStrategyConfig(RolloutStrategyConfig):
+    """Episode-oriented recording that mirrors the behavior of ``lerobot-record``.
+
+    Records ``dataset.num_episodes`` episodes of maximum ``dataset.episode_time_s`` each.
+    After each episode, runs ``dataset.reset_time_s`` seconds of reset time.
+
+    Keyboard controls:
+        Right arrow  — end current episode or reset phase early
+        Left arrow   — discard current episode and re-record
+        Escape       — stop recording session
+
+    In between episodes:
+    - if there is no teleop leader, the robot is held at its initial joint positions captured at startup.
+    - else, the robot is moved smoothly to the position of the teleop leader.
+    """
+
+    # This only applies if there are no teleop leaders specified.
+    # When True (default), moves the robot back to the joint positions captured at startup.
+    # Otherwise, leave the robot in its current position.
+    reset_to_initial_position: bool = True
+
+    # Whether to turn on or off the leader -> follower smooth handover behavior.
+    # When False, fallback to follower -> leader handover.
+    # Note that leader -> follower handover is only supported when the leader has `send_feedback` capability.
+    smooth_leader_to_follower_handover: bool = True
+
+
 @RolloutStrategyConfig.register_subclass("dagger")
 @dataclass
 class DAggerStrategyConfig(RolloutStrategyConfig):
@@ -229,7 +258,13 @@ class RolloutConfig:
 
         # TODO(Steven): DAgger shouldn't require a dataset (user may want to just rollout+intervene without recording), but for now we require it to simplify the implementation.
         needs_dataset = isinstance(
-            self.strategy, (SentryStrategyConfig, HighlightStrategyConfig, DAggerStrategyConfig)
+            self.strategy,
+            (
+                SentryStrategyConfig,
+                HighlightStrategyConfig,
+                DAggerStrategyConfig,
+                EpisodicStrategyConfig,
+            ),
         )
         if needs_dataset and (self.dataset is None or not self.dataset.repo_id):
             raise ValueError(f"{self.strategy.type} strategy requires --dataset.repo_id to be set")

src/lerobot/rollout/strategies/__init__.py

@@ -17,6 +17,7 @@
 from .base import BaseStrategy
 from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
 from .dagger import DAggerEvents, DAggerPhase, DAggerStrategy
+from .episodic import EpisodicStrategy
 from .factory import create_strategy
 from .highlight import HighlightStrategy
 from .sentry import SentryStrategy
@@ -27,6 +28,7 @@ __all__ = [
     "DAggerPhase",
     "DAggerStrategy",
     "HighlightStrategy",
+    "EpisodicStrategy",
     "RolloutStrategy",
     "SentryStrategy",
     "create_strategy",

src/lerobot/rollout/strategies/dagger.py

@@ -56,10 +56,14 @@ from typing import Any
 
 import numpy as np
 
-from lerobot.common.control_utils import is_headless
+from lerobot.common.control_utils import (
+    follower_smooth_move_to,
+    is_headless,
+    teleop_smooth_move_to,
+    teleop_supports_feedback,
+)
 from lerobot.datasets import VideoEncodingManager
 from lerobot.datasets.utils import DEFAULT_VIDEO_FILE_SIZE_IN_MB
-from lerobot.teleoperators import Teleoperator
 from lerobot.utils.constants import ACTION, OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.import_utils import _pynput_available
@@ -69,7 +73,6 @@ from lerobot.utils.utils import log_say
 
 from ..configs import DAggerKeyboardConfig, DAggerPedalConfig, DAggerStrategyConfig
 from ..context import RolloutContext
-from ..robot_wrapper import ThreadSafeRobot
 from .core import RolloutStrategy, estimate_max_episode_seconds, safe_push_to_hub, send_next_action
 
 PYNPUT_AVAILABLE = _pynput_available
@@ -171,64 +174,6 @@ class DAggerEvents:
         self.upload_requested.clear()
 
 
-# ---------------------------------------------------------------------------
-# Teleoperator helpers
-# ---------------------------------------------------------------------------
-
-
-def _teleop_supports_feedback(teleop: Teleoperator) -> bool:
-    """Return True when the teleop can receive position feedback (is actuated).
-    TODO(Maxime): See if it is possible to unify this interface across teleops instead of duck-typing.
-    """
-    return (
-        bool(teleop.feedback_features)
-        and hasattr(teleop, "disable_torque")
-        and hasattr(teleop, "enable_torque")
-    )
-
-
-def _teleop_smooth_move_to(
-    teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 30
-) -> None:
-    """Smoothly move an actuated teleop to ``target_pos`` via linear interpolation.
-
-    Requires the teleoperator to support feedback
-    (i.e. have non-empty ``feedback_features`` and implement ``disable_torque`` / ``enable_torque``).
-
-    TODO(Maxime): This blocks up to ``duration_s`` seconds, during this time
-    the follower robot doesn't receive new actions, this could be an issue on LeKiwi.
-    """
-    teleop.enable_torque()
-    current = teleop.get_action()
-    steps = max(int(duration_s * fps), 1)
-
-    for step in range(steps + 1):
-        t = step / steps
-        interp = {
-            k: current[k] * (1 - t) + target_pos[k] * t if k in target_pos else current[k] for k in current
-        }
-        teleop.send_feedback(interp)
-        time.sleep(1 / fps)
-
-
-def _follower_smooth_move_to(
-    robot: ThreadSafeRobot, current: dict, target: dict, duration_s: float = 1.0, fps: int = 30
-) -> None:
-    """Smoothly move the follower robot from ``current`` to ``target`` action.
-
-    Used when the teleop is non-actuated: instead of driving the leader arm
-    to the follower, we bring the follower to the teleop's current pose.
-    Both ``current`` and ``target`` must be in robot-action key space.
-    """
-    steps = max(int(duration_s * fps), 1)
-
-    for step in range(steps + 1):
-        t = step / steps
-        interp = {k: current[k] * (1 - t) + target[k] * t if k in target else current[k] for k in current}
-        robot.send_action(interp)
-        time.sleep(1 / fps)
-
-
 # ---------------------------------------------------------------------------
 # Input device handlers
 # ---------------------------------------------------------------------------
@@ -756,31 +701,31 @@ class DAggerStrategy(RolloutStrategy):
             logger.info("Pausing engine - robot holds position")
             engine.pause()
 
-            if _teleop_supports_feedback(teleop) and prev_action is not None:
+            if teleop_supports_feedback(teleop) and prev_action is not None:
                 # TODO(Maxime): prev_action is in robot action key space (output of robot_action_processor).
                 # send_feedback expects teleop feedback key space. For homogeneous setups (e.g. SO-101
                 # leader + SO-101 follower) the keys are identical so this works. If the processor pipeline
                 # does non-trivial key renaming (e.g. a rename_map on action keys), the interpolation in
-                # _teleop_smooth_move_to silently no-ops and the arm doesn't move.
+                # teleop_smooth_move_to silently no-ops and the arm doesn't move.
                 logger.info("Smooth handover: moving leader arm to follower position")
-                _teleop_smooth_move_to(teleop, prev_action)
+                teleop_smooth_move_to(teleop, prev_action)
 
         elif old_phase == DAggerPhase.PAUSED and new_phase == DAggerPhase.CORRECTING:
             logger.info("Entering correction mode - human teleop control")
-            if not _teleop_supports_feedback(teleop) and prev_action is not None:
+            if not teleop_supports_feedback(teleop) and prev_action is not None:
                 logger.info("Smooth handover: sliding follower to teleop position")
                 obs = robot.get_observation()
                 teleop_action = teleop.get_action()
                 processed = ctx.processors.teleop_action_processor((teleop_action, obs))
                 target = ctx.processors.robot_action_processor((processed, obs))
-                _follower_smooth_move_to(robot, prev_action, target)
+                follower_smooth_move_to(robot, prev_action, target)
 
             # unlock the teleop for human control
-            if _teleop_supports_feedback(teleop):
+            if teleop_supports_feedback(teleop):
                 teleop.disable_torque()
 
         elif old_phase == DAggerPhase.CORRECTING and new_phase == DAggerPhase.PAUSED:
-            if _teleop_supports_feedback(teleop):
+            if teleop_supports_feedback(teleop):
                 teleop.enable_torque()
 
         elif new_phase == DAggerPhase.AUTONOMOUS:
@@ -790,7 +735,7 @@ class DAggerStrategy(RolloutStrategy):
             engine.resume()
 
             # release teleop before resuming the policy
-            if _teleop_supports_feedback(teleop):
+            if teleop_supports_feedback(teleop):
                 teleop.disable_torque()
 
     # ------------------------------------------------------------------

src/lerobot/rollout/strategies/episodic.py

@@ -0,0 +1,335 @@
+# 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.
+
+"""Episodic rollout strategy: mirrors the behavior of ``lerobot-record``.
+
+- Policy drives the robot during each recording episode.
+- An optional teleoperator can drive the robot during reset phases so the
+  operator can bring the environment back to its starting configuration.
+  If no teleop is connected the robot stays in its current position.
+- Keyboard controls:
+
+      Right arrow  — end the current episode or reset phase early
+      Left arrow   — discard the current episode and re-record it
+      Escape       — stop the recording session
+
+Dataset naming follows the rollout convention: repo names must start with ``rollout_``.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import time
+
+from lerobot.common.control_utils import (
+    follower_smooth_move_to,
+    init_keyboard_listener,
+    is_headless,
+    teleop_smooth_move_to,
+    teleop_supports_feedback,
+)
+from lerobot.datasets import VideoEncodingManager
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import log_rerun_data
+
+from ..configs import EpisodicStrategyConfig
+from ..context import RolloutContext
+from .core import RolloutStrategy, safe_push_to_hub, send_next_action
+
+logger = logging.getLogger(__name__)
+
+
+class EpisodicStrategy(RolloutStrategy):
+    """Policy-driven multi-episode recording, mirrors the behavior of ``lerobot-record``.
+
+    Each recording episode runs the policy for maximum ``dataset.episode_time_s``
+    seconds, recording every frame.  A reset phase of ``dataset.reset_time_s``
+    follows every episode (except the last) so the operator can manually
+    reset the environment.  During the reset phase, an optional teleoperator
+    drives the robot; if none is present the robot returns to its initial joint positions captured at startup.
+
+    The policy state (hidden state, RTC queue, interpolator) is reset at
+    the start of each recording episode.
+
+    Keyboard events:
+        right arrow  → end current episode or reset phase early
+        left arrow   → discard & re-record current episode
+        ESC          → stop the session
+    """
+
+    config: EpisodicStrategyConfig
+
+    def __init__(self, config: EpisodicStrategyConfig) -> None:
+        super().__init__(config)
+        self._listener = None
+        self._events: dict | None = None
+
+    def setup(self, ctx: RolloutContext) -> None:
+        """Start the inference engine and attach the keyboard listener."""
+        self._init_engine(ctx)
+        self._listener, self._events = init_keyboard_listener()
+        logger.info("Episodic strategy ready")
+
+    def run(self, ctx: RolloutContext) -> None:
+        """Main multi-episode recording loop."""
+        cfg = ctx.runtime.cfg
+        dataset_cfg = cfg.dataset
+        robot = ctx.hardware.robot_wrapper
+        teleop = ctx.hardware.teleop
+        dataset = ctx.data.dataset
+        events = self._events
+        features = ctx.data.dataset_features
+
+        fps = cfg.fps
+        episode_time_s = dataset_cfg.episode_time_s
+        reset_time_s = dataset_cfg.reset_time_s
+        num_episodes = dataset_cfg.num_episodes
+        single_task = dataset_cfg.single_task or cfg.task
+        play_sounds = cfg.play_sounds
+
+        display_compressed = (
+            True
+            if (cfg.display_data and cfg.display_ip is not None and cfg.display_port is not None)
+            else cfg.display_compressed_images
+        )
+
+        with VideoEncodingManager(dataset):
+            try:
+                recorded_episodes = 0
+                while recorded_episodes < num_episodes and not events["stop_recording"]:
+                    if ctx.runtime.shutdown_event.is_set():
+                        break
+
+                    # Reset policy state at episode start (discard leftover hidden state / queue)
+                    self._engine.reset()
+                    self._interpolator.reset()
+                    self._engine.resume()
+
+                    log_say(f"Recording episode {dataset.num_episodes}", play_sounds)
+                    self._policy_loop(
+                        ctx=ctx,
+                        robot=robot,
+                        events=events,
+                        features=features,
+                        fps=fps,
+                        control_time_s=episode_time_s,
+                        dataset=dataset,
+                        single_task=single_task,
+                    )
+
+                    # Reset phase, skip after the last episode (but run when re-recording)
+                    if not events["stop_recording"] and (
+                        recorded_episodes < num_episodes - 1 or events["rerecord_episode"]
+                    ):
+                        log_say("Reset the environment", play_sounds)
+
+                        if teleop:
+                            # Smooth handover so the transition to teleop control is jerk-free.
+                            # For actuated teleops: drive the leader arm to the follower's current
+                            # position so the operator takes over without fighting the arm.
+                            # For non-actuated teleops: slide the follower to the teleop's current
+                            # pose instead, since the leader cannot be driven.
+                            obs = robot.get_observation()
+                            current_pos = {k: v for k, v in obs.items() if k.endswith(".pos")}
+                            if (
+                                teleop_supports_feedback(teleop)
+                                and self.config.smooth_leader_to_follower_handover
+                            ):
+                                logger.info("Smooth handover: moving leader arm to follower position")
+                                teleop_smooth_move_to(teleop, current_pos, duration_s=2)
+                                teleop.disable_torque()
+                            else:
+                                logger.info("Smooth handover: sliding follower to teleop position")
+                                teleop_action = teleop.get_action()
+                                processed = ctx.processors.teleop_action_processor((teleop_action, obs))
+                                target = ctx.processors.robot_action_processor((processed, obs))
+                                follower_smooth_move_to(robot, current_pos, target, duration_s=1)
+
+                        elif self.config.reset_to_initial_position:
+                            # No teleop: return the robot to its startup position.
+                            self._return_to_initial_position(hw=ctx.hardware, duration_s=1)
+
+                        self._reset_loop(
+                            ctx=ctx,
+                            robot=robot,
+                            teleop=teleop,
+                            events=events,
+                            fps=fps,
+                            control_time_s=reset_time_s,
+                            display_data=cfg.display_data,
+                            display_compressed=display_compressed,
+                        )
+
+                    if events["rerecord_episode"]:
+                        log_say("Re-record episode", play_sounds)
+                        events["rerecord_episode"] = False
+                        events["exit_early"] = False
+                        dataset.clear_episode_buffer()
+
+                        # returns to its initial joint positions captured at startup
+                        if not teleop and self.config.reset_to_initial_position:
+                            self._return_to_initial_position(hw=ctx.hardware, duration_s=1)
+
+                        continue
+
+                    dataset.save_episode()
+                    recorded_episodes += 1
+            finally:
+                # Save any frames buffered in the current episode so an unexpected
+                # exception or KeyboardInterrupt does not silently drop recorded data.
+                # suppress: save_episode raises if the buffer is empty (nothing to lose).
+                logger.info("Episodic control loop ended — saving any in-progress episode")
+                with contextlib.suppress(Exception):
+                    dataset.save_episode()
+
+    def _policy_loop(
+        self,
+        ctx: RolloutContext,
+        robot,
+        events: dict,
+        features: dict,
+        fps: float,
+        control_time_s: float,
+        dataset,
+        single_task: str,
+    ) -> None:
+        """Policy-driven recording loop for a single episode."""
+        interpolator = self._interpolator
+        control_interval = interpolator.get_control_interval(fps)
+
+        timestamp = 0.0
+        start_t = time.perf_counter()
+
+        while timestamp < control_time_s:
+            loop_start = time.perf_counter()
+
+            if events["exit_early"]:
+                events["exit_early"] = False
+                break
+
+            if ctx.runtime.shutdown_event.is_set():
+                break
+
+            obs = robot.get_observation()
+            obs_processed = self._process_observation_and_notify(ctx.processors, obs)
+
+            if self._handle_warmup(ctx.runtime.cfg.use_torch_compile, loop_start, control_interval):
+                continue
+
+            action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
+
+            if action_dict is not None:
+                obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
+                action_frame = build_dataset_frame(features, action_dict, prefix=ACTION)
+                dataset.add_frame({**obs_frame, **action_frame, "task": single_task})
+                self._log_telemetry(obs_processed, action_dict, ctx.runtime)
+
+            dt = time.perf_counter() - loop_start
+            sleep_t = control_interval - dt
+            if sleep_t < 0:
+                logger.warning(
+                    f"Record loop is running slower ({1 / dt:.1f} Hz) than the target FPS ({fps} Hz). "
+                    "Dataset frames might be dropped and robot control might be unstable. "
+                    "Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long "
+                    "3) CPU starvation"
+                )
+            precise_sleep(max(sleep_t, 0.0))
+            timestamp = time.perf_counter() - start_t
+
+    def _reset_loop(
+        self,
+        ctx: RolloutContext,
+        robot,
+        teleop,
+        events: dict,
+        fps: float,
+        control_time_s: float,
+        display_data: bool,
+        display_compressed: bool,
+    ) -> None:
+        """Reset-phase loop: teleop drives the robot if available, no recording."""
+        processors = ctx.processors
+        control_interval = 1.0 / fps
+
+        timestamp = 0.0
+        start_t = time.perf_counter()
+
+        while timestamp < control_time_s:
+            loop_start = time.perf_counter()
+
+            if events["exit_early"]:
+                events["exit_early"] = False
+                break
+
+            if ctx.runtime.shutdown_event.is_set():
+                break
+
+            obs = robot.get_observation()
+
+            if teleop is not None:
+                act = teleop.get_action()
+                act_teleop = processors.teleop_action_processor((act, obs))
+                robot_action = processors.robot_action_processor((act_teleop, obs))
+                robot.send_action(robot_action)
+
+                if display_data:
+                    obs_processed = processors.robot_observation_processor(obs)
+                    log_rerun_data(
+                        observation=obs_processed,
+                        action=act_teleop,
+                        compress_images=display_compressed,
+                    )
+
+            dt = time.perf_counter() - loop_start
+            sleep_t = control_interval - dt
+            precise_sleep(max(sleep_t, 0.0))
+            timestamp = time.perf_counter() - start_t
+
+    def teardown(self, ctx: RolloutContext) -> None:
+        """Finalise dataset, stop listener, push to hub, and disconnect hardware."""
+        cfg = ctx.runtime.cfg
+        play_sounds = cfg.play_sounds
+
+        log_say("Stop recording", play_sounds, blocking=True)
+
+        if not is_headless() and self._listener is not None:
+            self._listener.stop()
+
+        if ctx.data.dataset is not None:
+            logger.info("Finalizing dataset...")
+            ctx.data.dataset.finalize()
+
+        if (
+            cfg.dataset is not None
+            and cfg.dataset.push_to_hub
+            and ctx.data.dataset is not None
+            and safe_push_to_hub(
+                ctx.data.dataset,
+                tags=cfg.dataset.tags,
+                private=cfg.dataset.private,
+            )
+        ):
+            logger.info("Dataset uploaded to hub")
+            log_say("Dataset uploaded to hub", play_sounds)
+
+        self._teardown_hardware(
+            ctx.hardware,
+            return_to_initial_position=cfg.return_to_initial_position,
+        )
+        log_say("Exiting", play_sounds)
+        logger.info("Episodic strategy teardown complete")

src/lerobot/rollout/strategies/factory.py

@@ -21,6 +21,7 @@ from typing import TYPE_CHECKING
 from .base import BaseStrategy
 from .core import RolloutStrategy
 from .dagger import DAggerStrategy
+from .episodic import EpisodicStrategy
 from .highlight import HighlightStrategy
 from .sentry import SentryStrategy
 
@@ -42,4 +43,8 @@ def create_strategy(config: RolloutStrategyConfig) -> RolloutStrategy:
         return HighlightStrategy(config)
     if config.type == "dagger":
         return DAggerStrategy(config)
-    raise ValueError(f"Unknown strategy type '{config.type}'. Available: base, sentry, highlight, dagger")
+    if config.type == "episodic":
+        return EpisodicStrategy(config)
+    raise ValueError(
+        f"Unknown strategy type '{config.type}'. Available: base, sentry, highlight, dagger, episodic"
+    )

src/lerobot/scripts/lerobot_rollout.py

@@ -25,6 +25,7 @@ Strategies
     --strategy.type=sentry     Continuous recording with auto-upload
     --strategy.type=highlight  Ring buffer + keystroke save
     --strategy.type=dagger     Human-in-the-loop (DAgger / RaC)
+    --strategy.type=episodic   Episode-oriented recording with reset phases
 
 Inference backends
 ------------------
@@ -111,6 +112,18 @@ Usage examples
         --display_data=true \\
         --use_torch_compile=true
 
+    # Episodic mode — episode-oriented recording with reset phases
+    lerobot-rollout \\
+        --strategy.type=episodic \\
+        --policy.path=user/my_policy \\
+        --robot.type=so100_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --teleop.type=so100_leader \\
+        --teleop.port=/dev/ttyACM1 \\
+        --dataset.repo_id=user/rollout_episodic_data \\
+        --dataset.num_episodes=20 \\
+        --dataset.single_task="Grab the cube"
+
     # Resume a previous sentry recording session
     lerobot-rollout \\
         --strategy.type=sentry \\

src/lerobot/scripts/lerobot_train.py

@@ -292,19 +292,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
 
     active_cfg = cfg.trainable_config
     processor_pretrained_path = active_cfg.pretrained_path
-    if (
-        getattr(active_cfg, "use_relative_actions", False)
-        and processor_pretrained_path is not None
-        and not cfg.resume
-    ):
-        logging.warning(
-            "use_relative_actions=true with pretrained processors can skip relative transforms if "
-            "the checkpoint processors do not define them. Building processors from current policy config."
-        )
-        processor_pretrained_path = None
 
     processor_kwargs = {}
-    postprocessor_kwargs = {}
     if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
         processor_kwargs["dataset_stats"] = dataset.meta.stats
 
@@ -312,24 +301,31 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         processor_kwargs["dataset_meta"] = dataset.meta
 
     if not cfg.is_reward_model_training and processor_pretrained_path is not None:
-        processor_kwargs["preprocessor_overrides"] = {
+        preprocessor_overrides = {
             "device_processor": {"device": device.type},
             "normalizer_processor": {
                 "stats": dataset.meta.stats,
                 "features": {**policy.config.input_features, **policy.config.output_features},
                 "norm_map": policy.config.normalization_mapping,
             },
+            "rename_observations_processor": {"rename_map": cfg.rename_map},
         }
-        processor_kwargs["preprocessor_overrides"]["rename_observations_processor"] = {
-            "rename_map": cfg.rename_map
-        }
-        postprocessor_kwargs["postprocessor_overrides"] = {
+        postprocessor_overrides = {
             "unnormalizer_processor": {
                 "stats": dataset.meta.stats,
                 "features": policy.config.output_features,
                 "norm_map": policy.config.normalization_mapping,
             },
         }
+        if getattr(active_cfg, "use_relative_actions", False):
+            preprocessor_overrides["relative_actions_processor"] = {
+                "enabled": True,
+                "exclude_joints": getattr(active_cfg, "relative_exclude_joints", []),
+                "action_names": getattr(active_cfg, "action_feature_names", None),
+            }
+            postprocessor_overrides["absolute_actions_processor"] = {"enabled": True}
+        processor_kwargs["preprocessor_overrides"] = preprocessor_overrides
+        processor_kwargs["postprocessor_overrides"] = postprocessor_overrides
 
     if cfg.is_reward_model_training:
         preprocessor, postprocessor = make_reward_pre_post_processors(
@@ -341,7 +337,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             policy_cfg=cfg.policy,
             pretrained_path=processor_pretrained_path,
             **processor_kwargs,
-            **postprocessor_kwargs,
         )
 
     if is_main_process:

tests/processor/test_pipeline.py

@@ -24,6 +24,7 @@ from typing import Any
 import pytest
 import torch
 import torch.nn as nn
+from safetensors.torch import load_file
 
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
@@ -174,6 +175,53 @@ class MockStepWithTensorState(ProcessorStep):
         return features
 
 
+class MockLazyTensorStateStep(ProcessorStep):
+    """Mock step whose tensor state is not present in constructor config."""
+
+    def __init__(
+        self, name: str = "lazy_tensor_step", scale: float = 1.0, initial_value: float | None = None
+    ):
+        self.name = name
+        self.scale = scale
+        self.tensor_state: torch.Tensor | None = None
+
+        if initial_value is not None:
+            self.tensor_state = torch.tensor([initial_value], dtype=torch.float32)
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """Return the transition unchanged."""
+        return transition
+
+    def get_config(self) -> dict[str, Any]:
+        """Return constructor config while intentionally omitting tensor state."""
+        return {
+            "name": self.name,
+            "scale": self.scale,
+        }
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        """Return tensor state only after it has been initialized or loaded."""
+        if self.tensor_state is None:
+            return {}
+
+        return {"tensor_state": self.tensor_state}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        """Load tensor state."""
+        self.tensor_state = state["tensor_state"].clone()
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """Return features unchanged."""
+        return features
+
+
+@ProcessorStepRegistry.register("registered_lazy_tensor_state_step")
+class RegisteredLazyTensorStateStep(MockLazyTensorStateStep):
+    """Registered lazy tensor state step for registry-based serialization tests."""
+
+
 def test_empty_pipeline():
     """Test pipeline with no steps."""
     pipeline = DataProcessorPipeline([], to_transition=identity_transition, to_output=identity_transition)
@@ -620,6 +668,178 @@ def test_mixed_json_and_tensor_state():
         assert torch.allclose(loaded_step.running_mean, step.running_mean)
 
 
+def test_get_config_matches_saved_json():
+    """Test that in-memory config matches the config written by save_pretrained."""
+    stateless_step = MockStep(name="stateless")
+    stateful_step = MockLazyTensorStateStep(name="stateful", initial_value=4.0)
+    pipeline = DataProcessorPipeline([stateless_step, stateful_step], name="Memory Pipeline")
+
+    in_memory_config = pipeline.get_config()
+
+    assert pipeline.get_config() == in_memory_config
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        pipeline.save_pretrained(tmp_dir)
+
+        config_path = Path(tmp_dir) / "memory_pipeline.json"
+        with open(config_path) as file_pointer:
+            saved_config = json.load(file_pointer)
+
+    assert in_memory_config == saved_config
+    assert "state_file" not in in_memory_config["steps"][0]
+    assert in_memory_config["steps"][1]["state_file"] == "memory_pipeline_step_1.safetensors"
+
+
+def test_state_dict_matches_saved_safetensors():
+    """Test that in-memory state matches the safetensors written by save_pretrained."""
+    stateful_step = MockLazyTensorStateStep(initial_value=7.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Stateful Pipeline")
+
+    in_memory_state_dict = pipeline.state_dict()
+    state_filename = "stateful_pipeline_step_0.safetensors"
+    state_key = "stateful_pipeline_step_0"
+
+    assert set(in_memory_state_dict) == {state_key}
+    assert set(in_memory_state_dict[state_key]) == {"tensor_state"}
+
+    in_memory_state_dict[state_key]["tensor_state"].add_(1)
+    assert stateful_step.tensor_state is not None
+    assert torch.equal(stateful_step.tensor_state, torch.tensor([7.0]))
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        pipeline.save_pretrained(tmp_dir)
+        saved_state_dict = load_file(Path(tmp_dir) / state_filename)
+
+    torch.testing.assert_close(saved_state_dict["tensor_state"], torch.tensor([7.0]))
+
+
+def test_save_pretrained_still_writes_expected_serialization_files():
+    """Test that save_pretrained keeps the existing config and state filenames."""
+    stateful_step = MockLazyTensorStateStep(initial_value=3.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Policy Preprocessor")
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        pipeline.save_pretrained(tmp_dir)
+
+        save_path = Path(tmp_dir)
+        assert (save_path / "policy_preprocessor.json").exists()
+        assert (save_path / "policy_preprocessor_step_0.safetensors").exists()
+
+
+def test_from_config_round_trips_stateful_pipeline():
+    """Test that from_config rebuilds a stateful pipeline from in-memory artifacts."""
+    stateful_step = MockLazyTensorStateStep(name="roundtrip", initial_value=11.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Roundtrip Pipeline")
+    config = pipeline.get_config()
+    pipeline_state_dict = pipeline.state_dict()
+
+    loaded_pipeline = DataProcessorPipeline.from_config(config, state_dict=pipeline_state_dict)
+    loaded_step = loaded_pipeline.steps[0]
+
+    assert len(loaded_pipeline) == 1
+    assert isinstance(loaded_step, MockLazyTensorStateStep)
+    torch.testing.assert_close(loaded_step.tensor_state, torch.tensor([11.0]))
+
+
+def test_from_config_round_trips_registered_stateful_pipeline():
+    """Test that from_config resolves registry steps and loads their named tensor state."""
+    stateful_step = RegisteredLazyTensorStateStep(name="registered", initial_value=29.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Registry Pipeline")
+    config = pipeline.get_config()
+    pipeline_state_dict = pipeline.state_dict()
+    state_filename = "registry_pipeline_step_0_registered_lazy_tensor_state_step.safetensors"
+    state_key = "registry_pipeline_step_0_registered_lazy_tensor_state_step"
+
+    assert config["steps"][0]["registry_name"] == "registered_lazy_tensor_state_step"
+    assert config["steps"][0]["state_file"] == state_filename
+    assert set(pipeline_state_dict) == {state_key}
+
+    loaded_pipeline = DataProcessorPipeline.from_config(config, state_dict=pipeline_state_dict)
+    loaded_step = loaded_pipeline.steps[0]
+
+    assert isinstance(loaded_step, RegisteredLazyTensorStateStep)
+    assert loaded_step.tensor_state is not None
+    torch.testing.assert_close(loaded_step.tensor_state, torch.tensor([29.0]))
+
+
+def test_from_config_preserves_state_metadata_for_empty_initial_state():
+    """Test in-memory loading when rebuilt steps start without tensor state."""
+    stateful_step = MockLazyTensorStateStep(name="lazy", initial_value=13.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Lazy Pipeline")
+    config = pipeline.get_config()
+    pipeline_state_dict = pipeline.state_dict()
+
+    loaded_pipeline = DataProcessorPipeline.from_config(config)
+    loaded_step = loaded_pipeline.steps[0]
+
+    assert isinstance(loaded_step, MockLazyTensorStateStep)
+    assert loaded_step.state_dict() == {}
+    assert "state_file" not in loaded_pipeline.get_config()["steps"][0]
+
+    loaded_pipeline.load_state_dict(pipeline_state_dict)
+
+    torch.testing.assert_close(loaded_step.tensor_state, torch.tensor([13.0]))
+
+
+def test_from_config_applies_overrides_before_state_loading():
+    """Test that constructor overrides and tensor state loading are separate operations."""
+    stateful_step = MockLazyTensorStateStep(name="override", scale=1.0, initial_value=17.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Override Pipeline")
+    config = pipeline.get_config()
+    pipeline_state_dict = pipeline.state_dict()
+
+    loaded_pipeline = DataProcessorPipeline.from_config(
+        config,
+        state_dict=pipeline_state_dict,
+        overrides={"MockLazyTensorStateStep": {"scale": 5.0}},
+    )
+    loaded_step = loaded_pipeline.steps[0]
+
+    assert isinstance(loaded_step, MockLazyTensorStateStep)
+    assert loaded_step.scale == 5.0
+    torch.testing.assert_close(loaded_step.tensor_state, torch.tensor([17.0]))
+
+
+def test_load_state_dict_raises_on_missing_expected_state():
+    """Test loading raises when serialized config expects missing state."""
+    stateful_step = MockLazyTensorStateStep(initial_value=19.0)
+    pipeline = DataProcessorPipeline([stateful_step], name="Missing Pipeline")
+    loaded_pipeline = DataProcessorPipeline.from_config(pipeline.get_config())
+
+    with pytest.raises(KeyError, match="missing_pipeline_step_0"):
+        loaded_pipeline.load_state_dict({})
+
+
+def test_load_state_dict_raises_on_unexpected_extra_state():
+    """Test loading raises on unexpected top-level state keys."""
+    pipeline = DataProcessorPipeline([MockStep(name="stateless")], name="Unexpected Pipeline")
+
+    with pytest.raises(KeyError, match="extra"):
+        pipeline.load_state_dict({"extra": {"tensor_state": torch.tensor([1.0])}})
+
+
+def test_stateless_pipeline_in_memory_serialization_returns_empty_state():
+    """Test stateless in-memory serialization and loading."""
+    pipeline = DataProcessorPipeline([MockStep(name="stateless")], name="Stateless Pipeline")
+    config = pipeline.get_config()
+    config_without_name = {"steps": config["steps"]}
+
+    assert pipeline.state_dict() == {}
+    assert all("state_file" not in step_entry for step_entry in config["steps"])
+
+    loaded_pipeline = DataProcessorPipeline.from_config(config_without_name, state_dict={})
+
+    assert loaded_pipeline.name == "DataProcessorPipeline"
+    assert loaded_pipeline.state_dict() == {}
+
+
+@pytest.mark.parametrize("invalid_config", [None, [], "not config"])
+def test_from_config_rejects_non_dict_config(invalid_config):
+    """Test from_config reports invalid top-level config values cleanly."""
+    with pytest.raises(ValueError, match="not a valid processor configuration"):
+        DataProcessorPipeline.from_config(invalid_config)  # type: ignore[arg-type]
+
+
 class MockModuleStep(ProcessorStep, nn.Module):
     """Mock step that inherits from nn.Module to test state_dict handling of module parameters."""
 

tests/test_rollout.py

@@ -59,6 +59,7 @@ def test_strategy_config_types():
     from lerobot.rollout import (
         BaseStrategyConfig,
         DAggerStrategyConfig,
+        EpisodicStrategyConfig,
         HighlightStrategyConfig,
         SentryStrategyConfig,
     )
@@ -67,6 +68,7 @@ def test_strategy_config_types():
     assert SentryStrategyConfig().type == "sentry"
     assert HighlightStrategyConfig().type == "highlight"
     assert DAggerStrategyConfig().type == "dagger"
+    assert EpisodicStrategyConfig().type == "episodic"
 
 
 def test_dagger_config_invalid_input_device():
@@ -203,6 +205,8 @@ def test_create_strategy_dispatches():
         BaseStrategyConfig,
         DAggerStrategy,
         DAggerStrategyConfig,
+        EpisodicStrategy,
+        EpisodicStrategyConfig,
         SentryStrategy,
         SentryStrategyConfig,
         create_strategy,
@@ -211,6 +215,7 @@ def test_create_strategy_dispatches():
     assert isinstance(create_strategy(BaseStrategyConfig()), BaseStrategy)
     assert isinstance(create_strategy(SentryStrategyConfig()), SentryStrategy)
     assert isinstance(create_strategy(DAggerStrategyConfig()), DAggerStrategy)
+    assert isinstance(create_strategy(EpisodicStrategyConfig()), EpisodicStrategy)
 
 
 def test_create_strategy_unknown_raises():

uv.lock

@@ -2712,6 +2712,7 @@ all = [
     { name = "mock-serial", marker = "sys_platform != 'win32'" },
     { name = "motorbridge" },
     { name = "motorbridge-smart-servo" },
+    { name = "mujoco" },
     { name = "mypy" },
     { name = "num2words" },
     { name = "pandas" },
@@ -2749,6 +2750,7 @@ aloha = [
     { name = "datasets" },
     { name = "gym-aloha" },
     { name = "jsonlines" },
+    { name = "mujoco" },
     { name = "pandas" },
     { name = "pyarrow" },
     { name = "scipy" },
@@ -2864,6 +2866,7 @@ hilserl = [
     { name = "grpcio" },
     { name = "gym-hil" },
     { name = "jsonlines" },
+    { name = "mujoco" },
     { name = "pandas" },
     { name = "placo" },
     { name = "protobuf" },
@@ -2895,6 +2898,7 @@ libero = [
     { name = "datasets" },
     { name = "hf-libero", marker = "sys_platform == 'linux'" },
     { name = "jsonlines" },
+    { name = "mujoco" },
     { name = "pandas" },
     { name = "pyarrow" },
     { name = "scipy" },
@@ -2910,6 +2914,7 @@ metaworld = [
     { name = "datasets" },
     { name = "jsonlines" },
     { name = "metaworld" },
+    { name = "mujoco" },
     { name = "pandas" },
     { name = "pyarrow" },
     { name = "scipy" },
@@ -2926,6 +2931,9 @@ motorbridge-dep = [
 motorbridge-smart-servo-dep = [
     { name = "motorbridge-smart-servo" },
 ]
+mujoco-dep = [
+    { name = "mujoco" },
+]
 multi-task-dit = [
     { name = "diffusers" },
     { name = "transformers" },
@@ -3150,6 +3158,10 @@ requires-dist = [
     { name = "lerobot", extras = ["molmoact2"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["motorbridge-dep"], marker = "extra == 'rebot'" },
     { name = "lerobot", extras = ["motorbridge-smart-servo-dep"], marker = "extra == 'rebot'" },
+    { name = "lerobot", extras = ["mujoco-dep"], marker = "extra == 'aloha'" },
+    { name = "lerobot", extras = ["mujoco-dep"], marker = "extra == 'hilserl'" },
+    { name = "lerobot", extras = ["mujoco-dep"], marker = "extra == 'libero'" },
+    { name = "lerobot", extras = ["mujoco-dep"], marker = "extra == 'metaworld'" },
     { name = "lerobot", extras = ["multi-task-dit"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["notebook"], marker = "extra == 'dev'" },
     { name = "lerobot", extras = ["openarms"], marker = "extra == 'all'" },
@@ -3223,6 +3235,7 @@ requires-dist = [
     { name = "mock-serial", marker = "sys_platform != 'win32' and extra == 'test'", specifier = ">=0.0.1,<0.1.0" },
     { name = "motorbridge", marker = "extra == 'motorbridge-dep'", specifier = ">=0.3.2,<0.4.0" },
     { name = "motorbridge-smart-servo", marker = "extra == 'motorbridge-smart-servo-dep'", specifier = ">=0.0.4,<0.1.0" },
+    { name = "mujoco", marker = "extra == 'mujoco-dep'", specifier = "<3.9.0" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.19.1" },
     { name = "ninja", marker = "extra == 'groot'", specifier = ">=1.11.1,<2.0.0" },
     { name = "num2words", marker = "extra == 'smolvla'", specifier = ">=0.5.14,<0.6.0" },
@@ -3276,7 +3289,7 @@ requires-dist = [
     { name = "transformers", marker = "extra == 'transformers-dep'", specifier = ">=5.4.0,<5.6.0" },
     { name = "wandb", marker = "extra == 'training'", specifier = ">=0.24.0,<0.25.0" },
 ]
-provides-extras = ["dataset", "training", "hardware", "viz", "core-scripts", "evaluation", "dataset-viz", "av-dep", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "diffusers-dep", "qwen-vl-utils-dep", "matplotlib-dep", "pyserial-dep", "deepdiff-dep", "pynput-dep", "pyzmq-dep", "motorbridge-dep", "motorbridge-smart-servo-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "rebot", "kinematics", "intelrealsense", "phone", "diffusion", "wallx", "pi", "molmoact2", "smolvla", "multi-task-dit", "groot", "sarm", "robometer", "topreward", "xvla", "eo1", "hilserl", "vla-jepa", "async", "peft", "dev", "notebook", "test", "video-benchmark", "aloha", "pusht", "libero", "metaworld", "all"]
+provides-extras = ["dataset", "training", "hardware", "viz", "core-scripts", "evaluation", "dataset-viz", "av-dep", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "diffusers-dep", "qwen-vl-utils-dep", "matplotlib-dep", "pyserial-dep", "deepdiff-dep", "pynput-dep", "pyzmq-dep", "motorbridge-dep", "motorbridge-smart-servo-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "rebot", "kinematics", "intelrealsense", "phone", "diffusion", "wallx", "pi", "molmoact2", "smolvla", "multi-task-dit", "groot", "sarm", "robometer", "topreward", "xvla", "eo1", "hilserl", "vla-jepa", "async", "peft", "dev", "notebook", "test", "video-benchmark", "mujoco-dep", "aloha", "pusht", "libero", "metaworld", "all"]
 
 [[package]]
 name = "librt"