fix

- Use normalization processor as default example
- Add section on transform features
- Add section on overrides.

.github/PULL_REQUEST_TEMPLATE.md

@@ -30,7 +30,7 @@ pytest -sx tests/test_stuff.py::test_something
 ```
 
 ```bash
-lerobot-train --some.option=true
+python -m lerobot.scripts.train --some.option=true
 ```
 
 ## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR

.github/workflows/nightly.yml

@@ -29,8 +29,8 @@ on:
 env:
   UV_VERSION: "0.8.0"
   PYTHON_VERSION: "3.10"
-  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
-  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
+  DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-gpu:latest
+  DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-cpu:latest
 
 # Ensures that only the latest commit is built, canceling older runs.
 concurrency:

Makefile

@@ -44,7 +44,7 @@ test-end-to-end:
 	${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
 
 test-act-ete-train:
-	lerobot-train \
+	python -m lerobot.scripts.train \
 		--policy.type=act \
 		--policy.dim_model=64 \
 		--policy.n_action_steps=20 \
@@ -68,12 +68,12 @@ test-act-ete-train:
 		--output_dir=tests/outputs/act/
 
 test-act-ete-train-resume:
-	lerobot-train \
+	python -m lerobot.scripts.train \
 		--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
 		--resume=true
 
 test-act-ete-eval:
-	lerobot-eval \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \
@@ -82,7 +82,7 @@ test-act-ete-eval:
 		--eval.batch_size=1
 
 test-diffusion-ete-train:
-	lerobot-train \
+	python -m lerobot.scripts.train \
 		--policy.type=diffusion \
 		--policy.down_dims='[64,128,256]' \
 		--policy.diffusion_step_embed_dim=32 \
@@ -106,7 +106,7 @@ test-diffusion-ete-train:
 		--output_dir=tests/outputs/diffusion/
 
 test-diffusion-ete-eval:
-	lerobot-eval \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=pusht \
@@ -115,7 +115,7 @@ test-diffusion-ete-eval:
 		--eval.batch_size=1
 
 test-tdmpc-ete-train:
-	lerobot-train \
+	python -m lerobot.scripts.train \
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
@@ -137,7 +137,7 @@ test-tdmpc-ete-train:
 		--output_dir=tests/outputs/tdmpc/
 
 test-tdmpc-ete-eval:
-	lerobot-eval \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=xarm \
@@ -148,7 +148,7 @@ test-tdmpc-ete-eval:
 
 
 test-smolvla-ete-train:
-	lerobot-train \
+	python -m lerobot.scripts.train \
 		--policy.type=smolvla \
 		--policy.n_action_steps=20 \
 		--policy.chunk_size=20 \
@@ -171,7 +171,7 @@ test-smolvla-ete-train:
 		--output_dir=tests/outputs/smolvla/
 
 test-smolvla-ete-eval:
-	lerobot-eval \
+	python -m lerobot.scripts.eval \
 		--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
 		--policy.device=$(DEVICE) \
 		--env.type=aloha \

README.md

@@ -6,7 +6,7 @@
 
 <div align="center">
 
-[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain)
+[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nighty.yml?query=branch%3Amain)
 [![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
@@ -276,7 +276,7 @@ Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/
 We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
 
 ```bash
-lerobot-eval \
+python -m lerobot.scripts.eval \
     --policy.path=lerobot/diffusion_pusht \
     --env.type=pusht \
     --eval.batch_size=10 \
@@ -288,10 +288,10 @@ lerobot-eval \
 Note: After training your own policy, you can re-evaluate the checkpoints with:
 
 ```bash
-lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
+python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
 ```
 
-See `lerobot-eval --help` for more instructions.
+See `python -m lerobot.scripts.eval --help` for more instructions.
 
 ### Train your own policy
 
@@ -303,7 +303,7 @@ A link to the wandb logs for the run will also show up in yellow in your termina
 
 \<img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/wandb.png" alt="WandB logs example"\>
 
-Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions.
+Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
 
 #### Reproduce state-of-the-art (SOTA)
 
@@ -311,7 +311,7 @@ We provide some pretrained policies on our [hub page](https://huggingface.co/ler
 You can reproduce their training by loading the config from their run. Simply running:
 
 ```bash
-lerobot-train --config_path=lerobot/diffusion_pusht
+python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
 ```
 
 reproduces SOTA results for Diffusion Policy on the PushT task.

docs/source/_toctree.yml

@@ -24,9 +24,16 @@
   - local: smolvla
     title: Finetune SmolVLA
   title: "Policies"
+
+- sections:
+  - local: introduction_processors
+    title: Introduction to Robot Processors
+  - local: implement_your_own_processor
+    title: Implement your own processor
+  - local: processors_robots_teleop
+    title: Processors for Robots and Teleoperators
+  title: "Robot Processors"
 - sections:
-  - local: hope_jr
-    title: Hope Jr
   - local: so101
     title: SO-101
   - local: so100
@@ -35,7 +42,13 @@
     title: Koch v1.1
   - local: lekiwi
     title: LeKiwi
+  - local: hope_jr
+    title: Hope Jr
   title: "Robots"
+- sections:
+  - local: phone_teleop
+    title: Phone
+  title: "Teleoperators"
 - sections:
   - local: notebooks
     title: Notebooks

docs/source/cameras.mdx

@@ -9,7 +9,7 @@ To instantiate a camera, you need a camera identifier. This identifier might cha
 To find the camera indices of the cameras plugged into your system, run the following script:
 
 ```bash
-lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
+python -m lerobot.find_cameras opencv # or realsense for Intel Realsense cameras
 ```
 
 The output will look something like this if you have two cameras connected:

docs/source/hilserl.mdx

@@ -4,13 +4,7 @@ In this tutorial you will go through the full Human-in-the-Loop Sample-Efficient
 
 HIL-SERL is a sample-efficient reinforcement learning algorithm that combines human demonstrations with online learning and human interventions. The approach starts from a small set of human demonstrations, uses them to train a reward classifier, and then employs an actor-learner architecture where humans can intervene during policy execution to guide exploration and correct unsafe behaviors. In this tutorial, you'll use a gamepad to provide interventions and control the robot during the learning process.
 
-It combines three key ingredients:
-
-1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point.
-
-2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour.
-
-3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
+It combines three key ingredients: 1. **Offline demonstrations & reward classifier:** a handful of human-teleop episodes plus a vision-based success detector give the policy a shaped starting point. 2. **On-robot actor / learner loop with human interventions:** a distributed Soft Actor Critic (SAC) learner updates the policy while an actor explores on the physical robot; the human can jump in at any time to correct dangerous or unproductive behaviour. 3. **Safety & efficiency tools:** joint/end-effector (EE) bounds, crop region of interest (ROI) preprocessing and WandB monitoring keep the data useful and the hardware safe.
 
 Together these elements let HIL-SERL reach near-perfect task success and faster cycle times than imitation-only baselines.
 
@@ -62,243 +56,30 @@ pip install -e ".[hilserl]"
 
 ### Understanding Configuration
 
-The training process begins with proper configuration for the HILSerl environment. The main configuration class is `GymManipulatorConfig` in `lerobot/scripts/rl/gym_manipulator.py`, which contains nested `HILSerlRobotEnvConfig` and `DatasetConfig`. The configuration is organized into focused, nested sub-configs:
+The training process begins with proper configuration for the HILSerl environment. The configuration class of interest is `HILSerlRobotEnvConfig` in `lerobot/envs/configs.py`. Which is defined as:
 
 <!-- prettier-ignore-start -->
 ```python
-class GymManipulatorConfig:
-    env: HILSerlRobotEnvConfig    # Environment configuration (nested)
-    dataset: DatasetConfig    # Dataset recording/replay configuration (nested)
-    mode: str | None = None    # "record", "replay", or None (for training)
-    device: str = "cpu"    # Compute device
-
 class HILSerlRobotEnvConfig(EnvConfig):
     robot: RobotConfig | None = None    # Main robot agent (defined in `lerobot/robots`)
-    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm
-    processor: HILSerlProcessorConfig    # Processing pipeline configuration (nested)
-    name: str = "real_robot"    # Environment name
-    task: str | None = None    # Task identifier
+    teleop: TeleoperatorConfig | None = None    # Teleoperator agent, e.g., gamepad or leader arm, (defined in `lerobot/teleoperators`)
+    wrapper: EnvTransformConfig | None = None    # Environment wrapper settings; check `lerobot/scripts/server/gym_manipulator.py`
     fps: int = 10    # Control frequency
-
-# Nested processor configuration
-class HILSerlProcessorConfig:
-    control_mode: str = "gamepad"    # Control mode
-    observation: ObservationConfig | None = None    # Observation processing settings
-    image_preprocessing: ImagePreprocessingConfig | None = None    # Image crop/resize settings
-    gripper: GripperConfig | None = None    # Gripper control and penalty settings
-    reset: ResetConfig | None = None    # Environment reset and timing settings
-    inverse_kinematics: InverseKinematicsConfig | None = None    # IK processing settings
-    reward_classifier: RewardClassifierConfig | None = None    # Reward classifier settings
-    max_gripper_pos: float | None = 100.0    # Maximum gripper position
-
-# Sub-configuration classes
-class ObservationConfig:
-    add_joint_velocity_to_observation: bool = False    # Add joint velocities to state
-    add_current_to_observation: bool = False    # Add motor currents to state
-    add_ee_pose_to_observation: bool = False    # Add end-effector pose to state
-    display_cameras: bool = False    # Display camera feeds during execution
-
-class ImagePreprocessingConfig:
-    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None    # Image cropping parameters
-    resize_size: tuple[int, int] | None = None    # Target image size
-
-class GripperConfig:
-    use_gripper: bool = True    # Enable gripper control
-    gripper_penalty: float = 0.0    # Penalty for inappropriate gripper usage
-    gripper_penalty_in_reward: bool = False    # Include gripper penalty in reward
-
-class ResetConfig:
-    fixed_reset_joint_positions: Any | None = None    # Joint positions for reset
-    reset_time_s: float = 5.0    # Time to wait during reset
-    control_time_s: float = 20.0    # Maximum episode duration
-    terminate_on_success: bool = True    # Whether to terminate episodes on success detection
-
-class InverseKinematicsConfig:
-    urdf_path: str | None = None    # Path to robot URDF file
-    target_frame_name: str | None = None    # End-effector frame name
-    end_effector_bounds: dict[str, list[float]] | None = None    # EE workspace bounds
-    end_effector_step_sizes: dict[str, float] | None = None    # EE step sizes per axis
-
-class RewardClassifierConfig:
-    pretrained_path: str | None = None    # Path to pretrained reward classifier
-    success_threshold: float = 0.5    # Success detection threshold
-    success_reward: float = 1.0    # Reward value for successful episodes
-
-# Dataset configuration
-class DatasetConfig:
-    repo_id: str    # LeRobot dataset repository ID
-    dataset_root: str    # Local dataset root directory
-    task: str    # Task identifier
-    num_episodes: int    # Number of episodes for recording
-    episode: int    # Episode index for replay
-    push_to_hub: bool    # Whether to push datasets to Hub
+    name: str = "real_robot"    # Environment name
+    mode: str = None    # "record", "replay", or None (for training)
+    repo_id: str | None = None    # LeRobot dataset repository ID
+    dataset_root: str | None = None    # Local dataset root (optional)
+    task: str = ""    # Task identifier
+    num_episodes: int = 10    # Number of episodes for recording
+    episode: int = 0    # episode index for replay
+    device: str = "cuda"    # Compute device
+    push_to_hub: bool = True    # Whether to push the recorded datasets to Hub
+    pretrained_policy_name_or_path: str | None = None    # For policy loading
+    reward_classifier_pretrained_path: str | None = None    # For reward model
+    number_of_steps_after_success: int = 0    # For reward classifier, collect more positive examples after a success to train a classifier
 ```
 <!-- prettier-ignore-end -->
 
-### Processor Pipeline Architecture
-
-HIL-SERL uses a modular processor pipeline architecture that processes robot observations and actions through a series of composable steps. The pipeline is divided into two main components:
-
-#### Environment Processor Pipeline
-
-The environment processor (`env_processor`) handles incoming observations and environment state:
-
-1. **VanillaObservationProcessor**: Converts raw robot observations into standardized format
-2. **JointVelocityProcessor** (optional): Adds joint velocity information to observations
-3. **MotorCurrentProcessor** (optional): Adds motor current readings to observations
-4. **ForwardKinematicsJointsToEE** (optional): Computes end-effector pose from joint positions
-5. **ImageCropResizeProcessor** (optional): Crops and resizes camera images
-6. **TimeLimitProcessor** (optional): Enforces episode time limits
-7. **GripperPenaltyProcessor** (optional): Applies penalties for inappropriate gripper usage
-8. **RewardClassifierProcessor** (optional): Automated reward detection using vision models
-9. **ToBatchProcessor**: Converts data to batch format for neural network processing
-10. **DeviceProcessor**: Moves data to the specified compute device (CPU/GPU)
-
-#### Action Processor Pipeline
-
-The action processor (`action_processor`) handles outgoing actions and human interventions:
-
-1. **AddTeleopActionAsComplimentaryData**: Captures teleoperator actions for logging
-2. **AddTeleopEventsAsInfo**: Records intervention events and episode control signals
-3. **AddRobotObservationAsComplimentaryData**: Stores raw robot state for processing
-4. **InterventionActionProcessor**: Handles human interventions and episode termination
-5. **Inverse Kinematics Pipeline** (when enabled):
-   - **MapDeltaActionToRobotAction**: Converts delta actions to robot action format
-   - **EEReferenceAndDelta**: Computes end-effector reference and delta movements
-   - **EEBoundsAndSafety**: Enforces workspace safety bounds
-   - **InverseKinematicsEEToJoints**: Converts end-effector actions to joint targets
-   - **GripperVelocityToJoint**: Handles gripper control commands
-
-#### Configuration Examples
-
-**Basic Observation Processing**:
-
-```json
-{
-  "env": {
-    "processor": {
-      "observation": {
-        "add_joint_velocity_to_observation": true,
-        "add_current_to_observation": false,
-        "display_cameras": false
-      }
-    }
-  }
-}
-```
-
-**Image Processing**:
-
-```json
-{
-  "env": {
-    "processor": {
-      "image_preprocessing": {
-        "crop_params_dict": {
-          "observation.images.front": [180, 250, 120, 150],
-          "observation.images.side": [180, 207, 180, 200]
-        },
-        "resize_size": [128, 128]
-      }
-    }
-  }
-}
-```
-
-**Inverse Kinematics Setup**:
-
-```json
-{
-  "env": {
-    "processor": {
-      "inverse_kinematics": {
-        "urdf_path": "path/to/robot.urdf",
-        "target_frame_name": "end_effector",
-        "end_effector_bounds": {
-          "min": [0.16, -0.08, 0.03],
-          "max": [0.24, 0.2, 0.1]
-        },
-        "end_effector_step_sizes": {
-          "x": 0.02,
-          "y": 0.02,
-          "z": 0.02
-        }
-      }
-    }
-  }
-}
-```
-
-### Advanced Observation Processing
-
-The HIL-SERL framework supports additional observation processing features that can improve policy learning:
-
-#### Joint Velocity Processing
-
-Enable joint velocity estimation to provide the policy with motion information:
-
-```json
-{
-  "env": {
-    "processor": {
-      "observation": {
-        "add_joint_velocity_to_observation": true
-      }
-    }
-  }
-}
-```
-
-This processor:
-
-- Estimates joint velocities using finite differences between consecutive joint position readings
-- Adds velocity information to the observation state vector
-- Useful for policies that need motion awareness for dynamic tasks
-
-#### Motor Current Processing
-
-Monitor motor currents to detect contact forces and load conditions:
-
-```json
-{
-  "env": {
-    "processor": {
-      "observation": {
-        "add_current_to_observation": true
-      }
-    }
-  }
-}
-```
-
-This processor:
-
-- Reads motor current values from the robot's control system
-- Adds current measurements to the observation state vector
-- Helps detect contact events, object weights, and mechanical resistance
-- Useful for contact-rich manipulation tasks
-
-#### Combined Observation Processing
-
-You can enable multiple observation processing features simultaneously:
-
-```json
-{
-  "env": {
-    "processor": {
-      "observation": {
-        "add_joint_velocity_to_observation": true,
-        "add_current_to_observation": true,
-        "add_ee_pose_to_observation": false,
-        "display_cameras": false
-      }
-    }
-  }
-}
-```
-
-**Note**: Enabling additional observation features increases the state space dimensionality, which may require adjusting your policy network architecture and potentially collecting more training data.
-
 ### Finding Robot Workspace Bounds
 
 Before collecting demonstrations, you need to determine the appropriate operational bounds for your robot.
@@ -349,56 +130,22 @@ With the bounds defined, you can safely collect demonstrations for training. Tra
 
 Create a configuration file for recording demonstrations (or edit an existing one like [env_config_so100.json](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_so100.json)):
 
-1. Set `mode` to `"record"` at the root level
-2. Specify a unique `repo_id` for your dataset in the `dataset` section (e.g., "username/task_name")
-3. Set `num_episodes` in the `dataset` section to the number of demonstrations you want to collect
-4. Set `env.processor.image_preprocessing.crop_params_dict` to `{}` initially (we'll determine crops later)
-5. Configure `env.robot`, `env.teleop`, and other hardware settings in the `env` section
+1. Set `mode` to `"record"`
+2. Specify a unique `repo_id` for your dataset (e.g., "username/task_name")
+3. Set `num_episodes` to the number of demonstrations you want to collect
+4. Set `crop_params_dict` to `null` initially (we'll determine crops later)
+5. Configure `robot`, `cameras`, and other hardware settings
 
 Example configuration section:
 
 ```json
-{
-  "env": {
-    "type": "gym_manipulator",
-    "name": "real_robot",
-    "fps": 10,
-    "processor": {
-      "control_mode": "gamepad",
-      "observation": {
-        "display_cameras": false
-      },
-      "image_preprocessing": {
-        "crop_params_dict": {},
-        "resize_size": [128, 128]
-      },
-      "gripper": {
-        "use_gripper": true,
-        "gripper_penalty": 0.0
-      },
-      "reset": {
-        "reset_time_s": 5.0,
-        "control_time_s": 20.0
-      }
-    },
-    "robot": {
-      // ... robot configuration ...
-    },
-    "teleop": {
-      // ... teleoperator configuration ...
-    }
-  },
-  "dataset": {
-    "repo_id": "username/pick_lift_cube",
-    "dataset_root": null,
-    "task": "pick_and_lift",
-    "num_episodes": 15,
-    "episode": 0,
-    "push_to_hub": true
-  },
-  "mode": "record",
-  "device": "cpu"
-}
+"mode": "record",
+"repo_id": "username/pick_lift_cube",
+"dataset_root": null,
+"task": "pick_and_lift",
+"num_episodes": 15,
+"episode": 0,
+"push_to_hub": true
 ```
 
 ### Using a Teleoperation Device
@@ -444,20 +191,10 @@ The gamepad provides a very convenient way to control the robot and the episode
 To setup the gamepad, you need to set the `control_mode` to `"gamepad"` and define the `teleop` section in the configuration file.
 
 ```json
-{
-  "env": {
     "teleop": {
-      "type": "gamepad",
-      "use_gripper": true
-    },
-    "processor": {
-      "control_mode": "gamepad",
-      "gripper": {
+        "type": "gamepad",
         "use_gripper": true
-      }
-    }
-  }
-}
+    },
 ```
 
 <p align="center">
@@ -479,21 +216,11 @@ The SO101 leader arm has reduced gears that allows it to move and track the foll
 To setup the SO101 leader, you need to set the `control_mode` to `"leader"` and define the `teleop` section in the configuration file.
 
 ```json
-{
-  "env": {
     "teleop": {
-      "type": "so101_leader",
-      "port": "/dev/tty.usbmodem585A0077921",
-      "use_degrees": true
+        "type": "so101_leader",
+        "port": "/dev/tty.usbmodem585A0077921", # check your port number
+        "use_degrees": true
     },
-    "processor": {
-      "control_mode": "leader",
-      "gripper": {
-        "use_gripper": true
-      }
-    }
-  }
-}
 ```
 
 In order to annotate the success/failure of the episode, **you will need** to use a keyboard to press `s` for success, `esc` for failure.
@@ -524,7 +251,7 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/e
 
 During recording:
 
-1. The robot will reset to the initial position defined in the configuration file `env.processor.reset.fixed_reset_joint_positions`
+1. The robot will reset to the initial position defined in the configuration file `fixed_reset_joint_positions`
 2. Complete the task successfully
 3. The episode ends with a reward of 1 when you press the "success" button
 4. If the time limit is reached, or the fail button is pressed, the episode ends with a reward of 0
@@ -583,19 +310,11 @@ observation.images.front: [180, 250, 120, 150]
 Add these crop parameters to your training configuration:
 
 ```json
-{
-  "env": {
-    "processor": {
-      "image_preprocessing": {
-        "crop_params_dict": {
-          "observation.images.side": [180, 207, 180, 200],
-          "observation.images.front": [180, 250, 120, 150]
-        },
-        "resize_size": [128, 128]
-      }
-    }
-  }
-}
+"crop_params_dict": {
+    "observation.images.side": [180, 207, 180, 200],
+    "observation.images.front": [180, 250, 120, 150]
+},
+"resize_size": [128, 128]
 ```
 
 **Recommended image resolution**
@@ -624,52 +343,26 @@ python -m lerobot.scripts.rl.gym_manipulator --config_path src/lerobot/configs/r
 
 **Key Parameters for Data Collection**
 
-- **mode**: set it to `"record"` to collect a dataset (at root level)
-- **dataset.repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
-- **dataset.num_episodes**: Number of episodes to record
-- **env.processor.reset.terminate_on_success**: Whether to automatically terminate episodes when success is detected (default: `true`)
-- **env.fps**: Number of frames per second to record
-- **dataset.push_to_hub**: Whether to push the dataset to the hub
+- **mode**: set it to `"record"` to collect a dataset
+- **repo_id**: `"hf_username/dataset_name"`, name of the dataset and repo on the hub
+- **num_episodes**: Number of episodes to record
+- **number_of_steps_after_success**: Number of additional frames to record after a success (reward=1) is detected
+- **fps**: Number of frames per second to record
+- **push_to_hub**: Whether to push the dataset to the hub
 
-The `env.processor.reset.terminate_on_success` parameter allows you to control episode termination behavior. When set to `false`, episodes will continue even after success is detected, allowing you to collect more positive examples with the reward=1 label. This is crucial for training reward classifiers as it provides more success state examples in your dataset. When set to `true` (default), episodes terminate immediately upon success detection.
-
-**Important**: For reward classifier training, set `terminate_on_success: false` to collect sufficient positive examples. For regular HIL-SERL training, keep it as `true` to enable automatic episode termination when the task is completed successfully.
+The `number_of_steps_after_success` parameter is crucial as it allows you to collect more positive examples. When a success is detected, the system will continue recording for the specified number of steps while maintaining the reward=1 label. Otherwise, there won't be enough states in the dataset labeled to 1 to train a good classifier.
 
 Example configuration section for data collection:
 
 ```json
 {
-  "env": {
-    "type": "gym_manipulator",
-    "name": "real_robot",
-    "fps": 10,
-    "processor": {
-      "reset": {
-        "reset_time_s": 5.0,
-        "control_time_s": 20.0,
-        "terminate_on_success": false
-      },
-      "gripper": {
-        "use_gripper": true
-      }
-    },
-    "robot": {
-      // ... robot configuration ...
-    },
-    "teleop": {
-      // ... teleoperator configuration ...
-    }
-  },
-  "dataset": {
-    "repo_id": "hf_username/dataset_name",
-    "dataset_root": "data/your_dataset",
-    "task": "reward_classifier_task",
-    "num_episodes": 20,
-    "episode": 0,
-    "push_to_hub": true
-  },
   "mode": "record",
-  "device": "cpu"
+  "repo_id": "hf_username/dataset_name",
+  "dataset_root": "data/your_dataset",
+  "num_episodes": 20,
+  "push_to_hub": true,
+  "fps": 10,
+  "number_of_steps_after_success": 15
 }
 ```
 
@@ -719,7 +412,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
 To train the classifier, use the `train.py` script with your configuration:
 
 ```bash
-lerobot-train --config_path path/to/reward_classifier_train_config.json
+python -m lerobot.scripts.train --config_path path/to/reward_classifier_train_config.json
 ```
 
 **Deploying and Testing the Model**
@@ -728,17 +421,9 @@ To use your trained reward classifier, configure the `HILSerlRobotEnvConfig` to
 
 <!-- prettier-ignore-start -->
 ```python
-config = GymManipulatorConfig(
-    env=HILSerlRobotEnvConfig(
-        processor=HILSerlProcessorConfig(
-            reward_classifier=RewardClassifierConfig(
-                pretrained_path="path_to_your_pretrained_trained_model"
-            )
-        ),
-        # Other environment parameters
-    ),
-    dataset=DatasetConfig(...),
-    mode=None  # For training
+env_config = HILSerlRobotEnvConfig(
+    reward_classifier_pretrained_path="path_to_your_pretrained_trained_model",
+    # Other environment parameters
 )
 ```
 <!-- prettier-ignore-end -->
@@ -747,18 +432,7 @@ or set the argument in the json config file.
 
 ```json
 {
-  "env": {
-    "processor": {
-      "reward_classifier": {
-        "pretrained_path": "path_to_your_pretrained_model",
-        "success_threshold": 0.7,
-        "success_reward": 1.0
-      },
-      "reset": {
-        "terminate_on_success": true
-      }
-    }
-  }
+  "reward_classifier_pretrained_path": "path_to_your_pretrained_model"
 }
 ```
 
@@ -784,7 +458,7 @@ The reward classifier will automatically provide rewards based on the visual inp
 3. **Train the classifier**:
 
    ```bash
-   lerobot-train --config_path src/lerobot/configs/reward_classifier_train_config.json
+   python -m lerobot.scripts.train --config_path src/lerobot/configs/reward_classifier_train_config.json
    ```
 
 4. **Test the classifier**:

docs/source/hilserl_sim.mdx

@@ -32,12 +32,9 @@ To use `gym_hil` with LeRobot, you need to create a configuration file. An examp
 
 ```json
 {
-  "env": {
-    "type": "gym_manipulator",
-    "name": "gym_hil",
-    "task": "PandaPickCubeGamepad-v0",
-    "fps": 10
-  },
+  "type": "hil",
+  "name": "franka_sim",
+  "task": "PandaPickCubeGamepad-v0",
   "device": "cuda"
 }
 ```
@@ -48,40 +45,28 @@ Available tasks:
 - `PandaPickCubeGamepad-v0`: With gamepad control
 - `PandaPickCubeKeyboard-v0`: With keyboard control
 
-### Processor Configuration
+### Gym Wrappers Configuration
 
 ```json
-{
-  "env": {
-    "processor": {
-      "control_mode": "gamepad",
-      "gripper": {
-        "use_gripper": true,
-        "gripper_penalty": -0.02
-      },
-      "reset": {
-        "control_time_s": 15.0,
-        "fixed_reset_joint_positions": [
-          0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785
-        ]
-      },
-      "inverse_kinematics": {
-        "end_effector_step_sizes": {
-          "x": 0.025,
-          "y": 0.025,
-          "z": 0.025
-        }
-      }
+"wrapper": {
+    "gripper_penalty": -0.02,
+    "control_time_s": 15.0,
+    "use_gripper": true,
+    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
+    "end_effector_step_sizes": {
+        "x": 0.025,
+        "y": 0.025,
+        "z": 0.025
+    },
+    "control_mode": "gamepad"
     }
-  }
-}
 ```
 
 Important parameters:
 
-- `gripper.gripper_penalty`: Penalty for excessive gripper movement
-- `gripper.use_gripper`: Whether to enable gripper control
-- `inverse_kinematics.end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
+- `gripper_penalty`: Penalty for excessive gripper movement
+- `use_gripper`: Whether to enable gripper control
+- `end_effector_step_sizes`: Size of the steps in the x,y,z axes of the end-effector
 - `control_mode`: Set to `"gamepad"` to use a gamepad controller
 
 ## Running with HIL RL of LeRobot
@@ -90,50 +75,39 @@ Important parameters:
 
 To run the environment, set mode to null:
 
-```bash
+<!-- prettier-ignore-start -->
+```python
 python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
 ```
+<!-- prettier-ignore-end -->
 
 ### Recording a Dataset
 
 To collect a dataset, set the mode to `record` whilst defining the repo_id and number of episodes to record:
 
-```json
-{
-  "env": {
-    "type": "gym_manipulator",
-    "name": "gym_hil",
-    "task": "PandaPickCubeGamepad-v0"
-  },
-  "dataset": {
-    "repo_id": "username/sim_dataset",
-    "dataset_root": null,
-    "task": "pick_cube",
-    "num_episodes": 10,
-    "episode": 0,
-    "push_to_hub": true
-  },
-  "mode": "record"
-}
-```
-
-```bash
+<!-- prettier-ignore-start -->
+```python
 python -m lerobot.scripts.rl.gym_manipulator --config_path path/to/gym_hil_env.json
 ```
+<!-- prettier-ignore-end -->
 
 ### Training a Policy
 
 To train a policy, checkout the configuration example available [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/train_gym_hil_env.json) and run the actor and learner servers:
 
-```bash
+<!-- prettier-ignore-start -->
+```python
 python -m lerobot.scripts.rl.actor --config_path path/to/train_gym_hil_env.json
 ```
+<!-- prettier-ignore-end -->
 
 In a different terminal, run the learner server:
 
-```bash
+<!-- prettier-ignore-start -->
+```python
 python -m lerobot.scripts.rl.learner --config_path path/to/train_gym_hil_env.json
 ```
+<!-- prettier-ignore-end -->
 
 The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.
 

docs/source/hope_jr.mdx

@@ -19,7 +19,7 @@ pip install -e ".[hopejr]"
 Before starting calibration and operation, you need to identify the USB ports for each HopeJR component. Run this script to find the USB ports for the arm, hand, glove, and exoskeleton:
 
 ```bash
-lerobot-find-port
+python -m lerobot.find_port
 ```
 
 This will display the available USB ports and their associated devices. Make note of the port paths (e.g., `/dev/tty.usbmodem58760433331`, `/dev/tty.usbmodem11301`) as you'll need to specify them in the `--robot.port` and `--teleop.port` parameters when recording data, replaying episodes, or running teleoperation scripts.
@@ -31,7 +31,7 @@ Before performing teleoperation, HopeJR's limbs need to be calibrated. Calibrati
 ### 1.1 Calibrate Robot Hand
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=hope_jr_hand \
     --robot.port=/dev/tty.usbmodem58760432281 \
     --robot.id=blue \
@@ -81,7 +81,7 @@ Once you have set the appropriate boundaries for all joints, click "Save" to sav
 ### 1.2 Calibrate Teleoperator Glove
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=homunculus_glove \
     --teleop.port=/dev/tty.usbmodem11201 \
     --teleop.id=red \
@@ -120,7 +120,7 @@ Once calibration is complete, the system will save the calibration to `/Users/yo
 ### 1.3 Calibrate Robot Arm
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=hope_jr_arm \
     --robot.port=/dev/tty.usbserial-1110 \
     --robot.id=white
@@ -146,7 +146,7 @@ Use the calibration interface to set the range boundaries for each joint. Move e
 ### 1.4 Calibrate Teleoperator Exoskeleton
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=homunculus_arm \
     --teleop.port=/dev/tty.usbmodem11201 \
     --teleop.id=black
@@ -178,7 +178,7 @@ Due to global variable conflicts in the Feetech middleware, teleoperation for ar
 ### Hand
 
 ```bash
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
     --robot.type=hope_jr_hand \
     --robot.port=/dev/tty.usbmodem58760432281 \
     --robot.id=blue \
@@ -194,7 +194,7 @@ lerobot-teleoperate \
 ### Arm
 
 ```bash
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
     --robot.type=hope_jr_arm \
     --robot.port=/dev/tty.usbserial-1110 \
     --robot.id=white \
@@ -214,7 +214,7 @@ Record, Replay and Train with Hope-JR is still experimental.
 This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).
 
 ```bash
-lerobot-record \
+python -m lerobot.record \
     --robot.type=hope_jr_hand \
     --robot.port=/dev/tty.usbmodem58760432281 \
     --robot.id=right \
@@ -236,7 +236,7 @@ lerobot-record \
 ### Replay
 
 ```bash
-lerobot-replay \
+python -m lerobot.replay \
     --robot.type=hope_jr_hand \
     --robot.port=/dev/tty.usbmodem58760432281 \
     --robot.id=right \
@@ -248,7 +248,7 @@ lerobot-replay \
 ### Train
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --dataset.repo_id=nepyope/hand_record_test_with_video_data \
   --policy.type=act \
   --output_dir=outputs/train/hopejr_hand \
@@ -263,7 +263,7 @@ lerobot-train \
 This training run can be viewed as an example [here](https://wandb.ai/tino/lerobot/runs/rp0k8zvw?nw=nwusertino).
 
 ```bash
-lerobot-record \
+python -m lerobot.record \
   --robot.type=hope_jr_hand \
   --robot.port=/dev/tty.usbmodem58760432281 \
   --robot.id=right \

docs/source/il_robots.mdx

@@ -45,7 +45,7 @@ Note that the `id` associated with a robot is used to store the calibration file
 <hfoptions id="teleoperate_so101">
 <hfoption id="Command">
 ```bash
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -101,7 +101,7 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 <hfoptions id="teleoperate_koch_camera">
 <hfoption id="Command">
 ```bash
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -174,7 +174,7 @@ Now you can record a dataset. To record 5 episodes and upload your dataset to th
 <hfoptions id="record">
 <hfoption id="Command">
 ```bash
-lerobot-record \
+python -m lerobot.record \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem585A0076841 \
     --robot.id=my_awesome_follower_arm \
@@ -376,7 +376,7 @@ You can replay the first episode on your robot with either the command below or
 <hfoptions id="replay">
 <hfoption id="Command">
 ```bash
-lerobot-replay \
+python -m lerobot.replay \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=my_awesome_follower_arm \
@@ -428,10 +428,10 @@ Your robot should replicate movements similar to those you recorded. For example
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --dataset.repo_id=${HF_USER}/so101_test \
   --policy.type=act \
   --output_dir=outputs/train/act_so101_test \
@@ -453,7 +453,7 @@ Training should take several hours. You will find checkpoints in `outputs/train/
 To resume training from a checkpoint, below is an example command to resume from `last` checkpoint of the `act_so101_test` policy:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --config_path=outputs/train/act_so101_test/checkpoints/last/pretrained_model/train_config.json \
   --resume=true
 ```
@@ -490,7 +490,7 @@ You can use the `record` script from [`lerobot/record.py`](https://github.com/hu
 <hfoptions id="eval">
 <hfoption id="Command">
 ```bash
-lerobot-record  \
+python -m lerobot.record  \
   --robot.type=so100_follower \
   --robot.port=/dev/ttyACM1 \
   --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \

docs/source/il_sim.mdx

@@ -24,36 +24,11 @@ pip install -e ".[hilserl]"
 
 To use `gym_hil` with LeRobot, you need to use a configuration file. An example config file can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/env_config_gym_hil_il.json).
 
-To teleoperate and collect a dataset, we need to modify this config file. Here's an example configuration for imitation learning data collection:
+To teleoperate and collect a dataset, we need to modify this config file and you should add your `repo_id` here: `"repo_id": "il_gym",` and `"num_episodes": 30,` and make sure you set `mode` to `record`, "mode": "record".
 
-```json
-{
-  "env": {
-    "type": "gym_manipulator",
-    "name": "gym_hil",
-    "task": "PandaPickCubeGamepad-v0",
-    "fps": 10
-  },
-  "dataset": {
-    "repo_id": "your_username/il_gym",
-    "dataset_root": null,
-    "task": "pick_cube",
-    "num_episodes": 30,
-    "episode": 0,
-    "push_to_hub": true
-  },
-  "mode": "record",
-  "device": "cuda"
-}
-```
+If you do not have a Nvidia GPU also change `"device": "cuda"` parameter in the config file (for example to `mps` for MacOS).
 
-Key configuration points:
-
-- Set your `repo_id` in the `dataset` section: `"repo_id": "your_username/il_gym"`
-- Set `num_episodes: 30` to collect 30 demonstration episodes
-- Ensure `mode` is set to `"record"`
-- If you don't have an NVIDIA GPU, change `"device": "cuda"` to `"mps"` for macOS or `"cpu"`
-- To use keyboard instead of gamepad, change `"task"` to `"PandaPickCubeKeyboard-v0"`
+By default the config file assumes you use a controller. To use your keyboard please change the envoirment specified at `"task"` in the config file and set it to `"PandaPickCubeKeyboard-v0"`.
 
 Then we can run this command to start:
 
@@ -121,10 +96,10 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`lerobot-train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --dataset.repo_id=${HF_USER}/il_gym \
   --policy.type=act \
   --output_dir=outputs/train/il_sim_test \
@@ -165,32 +140,9 @@ huggingface-cli upload ${HF_USER}/il_sim_test${CKPT} \
 
 ## Evaluate your policy in Sim
 
-To evaluate your policy we have to use a configuration file. An example can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
+To evaluate your policy we have to use the config file that can be found [here](https://huggingface.co/datasets/aractingi/lerobot-example-config-files/blob/main/eval_config_gym_hil.json).
 
-Here's an example evaluation configuration:
-
-```json
-{
-  "env": {
-    "type": "gym_manipulator",
-    "name": "gym_hil",
-    "task": "PandaPickCubeGamepad-v0",
-    "fps": 10
-  },
-  "dataset": {
-    "repo_id": "your_username/il_sim_dataset",
-    "dataset_root": null,
-    "task": "pick_cube"
-  },
-  "pretrained_policy_name_or_path": "your_username/il_sim_model",
-  "device": "cuda"
-}
-```
-
-Make sure to replace:
-
-- `repo_id` with the dataset you trained on (e.g., `your_username/il_sim_dataset`)
-- `pretrained_policy_name_or_path` with your model ID (e.g., `your_username/il_sim_model`)
+Make sure to replace the `repo_id` with the dataset you trained on, for example `pepijn223/il_sim_dataset` and replace the `pretrained_policy_name_or_path` with your model id, for example `pepijn223/il_sim_model`
 
 Then you can run this command to visualize your trained policy
 

docs/source/implement_your_own_processor.mdx

@@ -0,0 +1,323 @@
+# Implement your own Robot Processor
+
+In this tutorial, you'll learn how to implement your own Robot Processor.
+It begins by exploring the need for a custom processor, then uses the Normalization processors as the running example to explain how to implement, configure, and serialize a processor. Finally, it lists all helper processors that ship with LeRobot.
+
+## Why would you need a custom processor?
+
+In most cases, when reading raw data from a sensor like the camera and robot motor encoders,
+you will need to process this data to transform it into a format that is compatible to use with the policies in LeRobot.
+For example, raw images are encoded with `uint8` and the values are in the range `[0, 255]`.
+To use these images with the policies, you will need to cast them to `float32` and normalize them to the range `[0, 1]`.
+
+For example, in LeRobot's `VanillaObservationProcessor`, raw images come from the environment as numpy arrays with `uint8` values in range `[0, 255]` and in channel-last format `(H, W, C)`. The processor transforms them into PyTorch tensors with `float32` values in range `[0, 1]` and channel-first format `(C, H, W)`:
+
+```python
+# Input: numpy array with shape (480, 640, 3) and dtype uint8
+raw_image = env_observation["pixels"]  # Values in [0, 255]
+
+# After processing: torch tensor with shape (1, 3, 480, 640) and dtype float32
+processed_image = processor(transition)["observation"]["observation.image"]  # Values in [0, 1]
+```
+
+On the other hand, when a model returns a certain action to be executed on the robot, it is often that one has to post-process this action to make it compatible to run on the robot.
+For example, the model might return joint positions values that range from `[-1, 1]` and one would need to scale them to the ranges of the minimum and maximum joint angle positions of the robot.
+
+In LeRobot, this normalization workflow is handled by the `NormalizerProcessor` (for inputs) and the `UnnormalizerProcessor` (for outputs). These processors are heavily used by policies (e.g., Pi0, SmolVLA) and integrate tightly with the `RobotProcessor`'s `get_config`, `state_dict`, and `load_state_dict` APIs.
+
+For instance, `UnnormalizerProcessor` converts model outputs in `[-1, 1]` back to actual robot joint ranges:
+
+```python
+# Input: model action with normalized values in [-1, 1]
+normalized_action = torch.tensor([-0.5, 0.8, -1.0, 0.2])  # Model output
+
+# After post-processing: real joint positions in robot's native ranges
+# Example: joints range from [-180.0, 180.0]
+real_action = unnormalizer(transition)["action"]
+# real action after post-processing: [ -90.,  144., -180.,   36.]
+```
+
+The unnormalizer uses the dataset statistics to convert back:
+
+```python
+# For MIN_MAX normalization: action = (normalized + 1) * (max - min) / 2 + min
+real_action = (normalized_action + 1) * (max_val - min_val) / 2 + min_val
+```
+
+All these situations point us towards the need for a mechanism to preprocess the data before being passed to the policies and then post-process the action that are returned to be executed on the robot.
+
+To that end, LeRobot provides a pipeline mechanism to implement a sequence of processing steps for the input data and the output action.
+
+## How to implement your own processor?
+
+We'll use the `NormalizerProcessor` as a concrete running example because it is central to most policies and demonstrates configuration and state serialization cleanly.
+
+Prepare the sequence of processing steps necessary for your problem. A processor step is a class that implements the following methods:
+
+- `__call__`: implements the processing step for the input transition.
+- `get_config`: gets the configuration of the processor step.
+- `state_dict`: gets the state of the processor step.
+- `load_state_dict`: loads the state of the processor step.
+- `reset`: resets the state of the processor step.
+- `feature_contract`: displays the modification to the feature space during the processor step.
+
+### Implement the `__call__` method
+
+The `__call__` method is the core of your processor step. It takes an `EnvTransition` and returns a modified `EnvTransition`. Here's how the `NormalizerProcessor` conceptually works (simplified):
+
+```python
+from dataclasses import dataclass
+import torch
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.processor.pipeline import EnvTransition, TransitionKey
+
+@dataclass
+class NormalizerProcessor:
+    features: dict[str, PolicyFeature]
+    norm_map: dict[FeatureType, NormalizationMode]
+    stats: dict[str, dict[str, torch.Tensor]]
+    eps: float = 1e-8
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        normalized_info = {}
+
+        obs = transition.get(TransitionKey.OBSERVATION)
+        act = transition.get(TransitionKey.ACTION)
+
+        new_obs = self._normalize_observation(obs, normalized_info)
+        new_act = self._normalize_action(act, normalized_info)
+
+        new_transition = transition.copy()
+        new_transition[TransitionKey.OBSERVATION] = new_obs
+        new_transition[TransitionKey.ACTION] = new_act
+
+        # Record what was normalized into complementary_data
+        if normalized_info:
+            comp = new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
+            comp = dict(comp)
+            comp["normalized_keys"] = normalized_info
+            new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp
+
+        return new_transition
+```
+
+See the full implementation in `src/lerobot/processor/normalize_processor.py` for details on mean/std and min/max modes and key selection.
+
+**Key principles:**
+
+- Always check if required data exists before processing
+- Return unchanged transition if no processing is needed
+- Use `transition.copy()` to avoid side effects
+- Only modify the specific keys your processor handles
+
+**Tip**: For observation-only processors, you can inherit from `ObservationProcessor` to avoid writing `__call__` boilerplate. The normalizer is mixed (observations and actions), so it implements `__call__` directly.
+
+### Configuration and State Management
+
+Processors support serialization through three methods that separate configuration from tensor state. This is especially important for normalization processors, which carry dataset statistics (tensors) in their state, and hyperparameters in their config:
+
+```python
+from dataclasses import dataclass, field
+from typing import Any
+import torch
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+
+@dataclass
+class NormalizerProcessor:
+    features: dict[str, PolicyFeature]
+    norm_map: dict[FeatureType, NormalizationMode]
+    eps: float = 1e-8
+    _tensor_stats: dict[str, dict[str, torch.Tensor]] = field(default_factory=dict, init=False, repr=False)
+
+    def get_config(self) -> dict[str, Any]:
+        """JSON-serializable configuration (no tensors)."""
+        return {
+            "eps": self.eps,
+            "features": {k: {"type": v.type.value, "shape": v.shape} for k, v in self.features.items()},
+            "norm_map": {ft.value: nm.value for ft, nm in self.norm_map.items()},
+        }
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        """Tensor state only (e.g., dataset statistics)."""
+        flat: dict[str, torch.Tensor] = {}
+        for key, sub in self._tensor_stats.items():
+            for stat_name, tensor in sub.items():
+                flat[f"{key}.{stat_name}"] = tensor
+        return flat
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        """Restore tensor state at runtime."""
+        self._tensor_stats.clear()
+        for flat_key, tensor in state.items():
+            key, stat_name = flat_key.rsplit(".", 1)
+            self._tensor_stats.setdefault(key, {})[stat_name] = tensor
+```
+
+**Usage:**
+
+```python
+# Save (e.g., inside a policy)
+config = processor.get_config()
+tensors = processor.state_dict()
+
+# Restore (e.g., loading a pretrained policy)
+new_processor = NormalizerProcessor(**config)
+new_processor.load_state_dict(tensors)
+```
+
+### Transform features
+
+The `transform_features` method defines how your processor transforms feature names and shapes. This is crucial for policy configuration and debugging.
+
+Normalization typically preserves the feature keys and shapes, so `NormalizerProcessor.transform_features` returns the input features unchanged. When your processor renames or reshapes, implement this method to reflect the mapping for downstream components. For example, a simple rename processor:
+
+```python
+def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+    # Simple renaming
+    if "pixels" in features:
+        features["observation.image"] = features.pop("pixels")
+
+    # Pattern-based renaming
+    for key in list(features.keys()):
+        if key.startswith("env_state."):
+            suffix = key[len("env_state."):]
+            features[f"observation.{suffix}"] = features.pop(key)
+
+    return features
+```
+
+**Key principles:**
+
+- Use `features.pop(old_key)` to remove and get the old feature
+- Use `features[new_key] = old_feature` to add the renamed feature
+- Always return the modified features dictionary
+- Document transformations clearly in the docstring
+
+### Example of usage from the codebase
+
+`transform_features` is used by `RobotProcessor` to derive the dataset/policy feature contract from an initial feature set by applying each step's transformation. You can see concrete examples in the codebase:
+
+- Phone teleoperation record pipeline (`examples/phone_so100_record.py`): processors like `ForwardKinematicsJointsToEE`, `GripperVelocityToJoint`, and `EEBoundsAndSafety` implement `transform_features` to declare which action/observation keys should be materialized in the dataset.
+- SO100 follower kinematics (`src/lerobot/robots/so100_follower/robot_kinematic_processor.py`): each processor's `transform_features` method adds or refines feature keys such as `observation.state.ee.{x,y,z,wx,wy,wz}` or `action.gripper.pos`.
+- Rename and tokenizer processors (`src/lerobot/processor/rename_processor.py`, `src/lerobot/processor/tokenizer_processor.py`): demonstrate key renaming and adding language token features to the contract.
+
+In practice, you will often aggregate features by running `RobotProcessor.transform_features(...)` with your initial features to compute the final contract before recording or training.
+
+## Helper Classes
+
+LeRobot provides pre-built processor classes for common transformations. Below is a comprehensive list of registered processors in the codebase.
+
+### Core processors (observations, actions, normalization)
+
+- **`VanillaObservationProcessor`** (`observation_processor`): Images and state processing to LeRobot format.
+- **`NormalizerProcessor`** (`normalizer_processor`): Normalize observations/actions (mean/std or min/max to [-1, 1]).
+- **`UnnormalizerProcessor`** (`unnormalizer_processor`): Inverse of the normalizer for model outputs.
+- **`DeviceProcessor`** (`device_processor`): Move tensors to a specific device (CPU/GPU) and optional float dtype.
+- **`ToBatchProcessor`** (`to_batch_processor`): Add batch dimension to observations/actions when missing.
+- **`RenameProcessor`** (`rename_processor`): Rename observation keys using a mapping dictionary.
+- **`TokenizerProcessor`** (`tokenizer_processor`): Tokenize language tasks into `observation.language.*` tensors.
+
+### Teleoperation mapping processors
+
+- **`MapDeltaActionToRobotAction`** (`map_delta_action_to_robot_action`): Map teleop deltas (e.g., gamepad) to `action.target_*` fields.
+- **`MapPhoneActionToRobotAction`** (`map_phone_action_to_robot_action`): Map calibrated phone pose/buttons to `action.target_*` and gripper.
+
+### Robot kinematics processors (SO100 follower example)
+
+- **`EEReferenceAndDelta`** (`ee_reference_and_delta`): Compute desired EE pose from target deltas and current pose.
+- **`EEBoundsAndSafety`** (`ee_bounds_and_safety`): Clip EE pose to bounds and check for jumps.
+- **`InverseKinematicsEEToJoints`** (`inverse_kinematics_ee_to_joints`): Convert EE pose to joint targets via IK.
+- **`GripperVelocityToJoint`** (`gripper_velocity_to_joint`): Convert gripper velocity input to joint position command.
+- **`ForwardKinematicsJointsToEE`** (`forward_kinematics_joints_to_ee`): Compute EE pose features from joint positions via FK.
+- **`AddRobotObservationAsComplimentaryData`** (`add_robot_observation`): Read robot observation and insert `raw_joint_positions` into complementary data.
+
+### Policy-specific utility processors
+
+- **`Pi0NewLineProcessor`** (`pi0_new_line_processor`): Ensure text tasks end with a newline (Pi0 tokenizer compatibility).
+- **`SmolVLANewLineProcessor`** (`smolvla_new_line_processor`): Ensure text tasks end with a newline (SmolVLA tokenizer compatibility).
+
+### Usage Example
+
+```python
+from lerobot.processor import NormalizerProcessor, DeviceProcessor, RobotProcessor, ToBatchProcessor
+
+# Create a processing pipeline (typical policy preprocessor)
+steps = [
+    NormalizerProcessor(features=features, norm_map=norm_map, stats=stats),
+    ToBatchProcessor(),
+    DeviceProcessor(device="cuda"),
+]
+
+# Use in RobotProcessor
+processor = RobotProcessor(steps=steps)
+processed_transition = processor(raw_transition)
+```
+
+### Using overrides
+
+You can override step parameters at load-time using `overrides`. This is handy for non-serializable objects or site-specific settings. It works both in policy factories and with `RobotProcessor.from_pretrained(...)`.
+
+Example: during policy evaluation on the robot, override the device and rename map.
+Use this to run a policy trained on CUDA on a CPU-only robot, or to remap camera keys when the robot uses different names than the dataset.
+
+```437:445:src/lerobot/record.py
+preprocessor, postprocessor = make_processor(
+    policy_cfg=cfg.policy,
+    pretrained_path=cfg.policy.pretrained_path,
+    dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
+    preprocessor_overrides={
+        "device_processor": {"device": cfg.policy.device},
+        "rename_processor": {"rename_map": cfg.dataset.rename_map},
+    },
+)
+```
+
+Direct usage with `from_pretrained`:
+
+```python
+from lerobot.processor import RobotProcessor
+
+processor = RobotProcessor.from_pretrained(
+    "username/my-processor",
+    overrides={
+        "device_processor": {"device": "cuda:0"},  # registry name for registered steps
+        "CustomStep": {"param": 42},               # class name for non-registered steps
+    },
+)
+```
+
+## Best Practices
+
+- **Keep processors atomic** - One transformation per processor for reusability and debugging
+- **Use dataclasses** - Clean initialization with `@dataclass`
+- **Always register processors** - Use `@ProcessorStepRegistry.register("name")` for discoverability
+- **Check for None** - Always validate required data exists before processing
+- **Use copy() for safety** - Avoid side effects with `transition.copy()`
+- **Separate config and state** - JSON-serializable config vs tensor state_dict
+- **Use base classes** - Inherit from `ObservationProcessor` for observation-only processing
+
+```python
+@ProcessorStepRegistry.register("my_processor")
+@dataclass
+class MyProcessor(ObservationProcessor):
+    threshold: float = 0.5
+
+    def observation(self, observation):
+        if observation is None:
+            return observation
+        # Your processing logic here
+        return processed_observation
+```
+
+## Conclusion
+
+You now have all the tools to implement custom processors in LeRobot! The key steps are:
+
+1. **Define your processor** as a dataclass with the required methods (`__call__`, `get_config`, `state_dict`, `load_state_dict`, `reset`, `feature_contract`)
+2. **Register it** using `@ProcessorStepRegistry.register("name")` for discoverability
+3. **Integrate it** into a `RobotProcessor` pipeline with other processing steps
+4. **Use base classes** like `ObservationProcessor` when possible to reduce boilerplate
+
+The processor system is designed to be modular and composable, allowing you to build complex data processing pipelines from simple, focused components. Whether you're preprocessing sensor data for training or post-processing model outputs for robot execution, custom processors give you the flexibility to handle any data transformation your robotics application requires. Policies like Pi0 and SmolVLA use the same normalization processors described above, so your understanding here will transfer directly when wiring policy preprocessors and postprocessors.
+
+Start simple, test thoroughly, and leverage the existing helper classes to build robust data processing pipelines for your robot learning workflows.

docs/source/introduction_processors.mdx

@@ -0,0 +1,991 @@
+# Introduction to Processors
+
+In robotics, there's a fundamental mismatch between the data that robots and humans produce and what machine learning models expect. This creates several translation challenges:
+
+**Raw Robot Data → Model Input:**
+
+- Robots output raw sensor data (camera images, joint positions, force readings) that need normalization, batching, and device placement before models can process them
+- Language instructions from humans ("pick up the red cube") must be tokenized into numerical representations
+- Different robots use different coordinate systems and units that need standardization
+
+**Model Output → Robot Commands:**
+
+- Models might output end-effector positions, but robots need joint-space commands
+- Teleoperators (like gamepads) produce relative movements (delta positions), but robots expect absolute commands
+- Model predictions are often normalized and need to be converted back to real-world scales
+
+**Cross-Domain Translation:**
+
+- Training data from one robot setup needs adaptation for deployment on different hardware
+- Models trained with specific camera configurations must work with new camera arrangements
+- Datasets with different naming conventions need harmonization
+
+**That's where processors come in.** They serve as the universal translators that bridge these gaps, ensuring seamless data flow from sensors to models to actuators.
+
+Processors are the data transformation backbone of LeRobot. They handle all the preprocessing and postprocessing steps needed to convert raw environment data into model-ready inputs and vice versa. This guide will walk you through everything you need to know about processors - from basic concepts to advanced usage patterns.
+
+## What are Processors?
+
+In robotics, data comes in many forms - images from cameras, joint positions from sensors, text instructions from users, and more. Each type of data requires specific transformations before a model can use it effectively. Models need this data to be:
+
+- **Normalized**: Scaled to appropriate ranges for neural network processing
+- **Batched**: Organized with proper dimensions for batch processing
+- **Tokenized**: Text converted to numerical representations
+- **Device-placed**: Moved to the right hardware (CPU/GPU)
+- **Type-converted**: Cast to appropriate data types
+
+Processors handle these transformations through composable, reusable steps that can be chained together into pipelines. Think of them as a modular assembly line where each station performs a specific transformation on your data.
+
+## Core Concepts
+
+### EnvTransition: The Universal Data Container
+
+The `EnvTransition` is the fundamental data structure that flows through all processors. It's a typed dictionary that represents a complete robot-environment interaction:
+
+```python
+from lerobot.processor.pipeline import TransitionKey, EnvTransition
+
+# Example transition from a robot collecting data
+transition: EnvTransition = {
+    TransitionKey.OBSERVATION: {
+        "observation.images.camera0": camera0_image_tensor,  # Shape: (H, W, C)
+        "observation.images.camera1": camera1_image_tensor,  # Shape: (H, W, C)
+        "observation.state": joint_positions_tensor,         # Shape: (7,) for 7-DOF arm
+        "observation.environment_state": env_state_tensor    # Shape: (3,) for object position
+    },
+    TransitionKey.ACTION: action_tensor,                    # Shape: (7,) for joint velocities
+    TransitionKey.REWARD: 0.0,                             # Scalar reward signal
+    TransitionKey.DONE: False,                             # Episode termination flag
+    TransitionKey.TRUNCATED: False,                        # Episode truncation flag
+    TransitionKey.INFO: {"success": False},                # Additional metadata
+    TransitionKey.COMPLEMENTARY_DATA: {
+        "task": "pick up the red cube",                    # Language instruction
+        "task_index": 0,                                   # Task identifier
+        "index": 42                                        # Frame index
+    }
+}
+```
+
+Each key in the transition has a specific purpose:
+
+- **OBSERVATION**: All sensor data (images, states, proprioception)
+- **ACTION**: The action to execute or that was executed
+- **REWARD**: Reinforcement learning signal
+- **DONE/TRUNCATED**: Episode boundary indicators
+- **INFO**: Arbitrary metadata
+- **COMPLEMENTARY_DATA**: Task descriptions, indices, padding flags, inter-step data (e.g., you need to compute the velocities and then use this velocity to clip the action)
+
+### ProcessorStep: The Building Block Interface
+
+A `ProcessorStep` is a single transformation unit that processes transitions. It's a protocol (interface) that any processor step must implement:
+
+```python
+from lerobot.processor.pipeline import ProcessorStep, EnvTransition
+from lerobot.configs.types import PolicyFeature
+from typing import Any
+import torch
+
+class MyProcessorStep:
+    """Example processor step interface - all methods must be implemented."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """Transform the transition - this is the main processing logic."""
+        raise NotImplementedError
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        """Declare how this step transforms feature shapes/types."""
+        raise NotImplementedError
+
+    def get_config(self) -> dict[str, Any]:
+        """Return JSON-serializable configuration for saving/loading."""
+        raise NotImplementedError
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        """Return any learnable parameters (tensors only)."""
+        raise NotImplementedError
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        """Load learnable parameters from saved state."""
+        raise NotImplementedError
+
+    def reset(self) -> None:
+        """Reset any internal state between episodes."""
+        raise NotImplementedError
+```
+
+### RobotProcessor: The Pipeline Orchestrator
+
+The `RobotProcessor` chains multiple `ProcessorStep` instances together, executing them sequentially. It provides automatic format conversion to handle both batch dictionaries (from datasets) and EnvTransition dictionaries:
+
+```python
+from lerobot.processor.pipeline import RobotProcessor, _default_batch_to_transition, _default_transition_to_batch
+
+# Create a processing pipeline
+processor = RobotProcessor(
+    steps=[
+        step1,  # First transformation
+        step2,  # Second transformation
+        step3   # Third transformation
+    ],
+    name="my_preprocessing_pipeline",
+
+    # Optional: Custom converters for input/output formats
+    to_transition=_default_batch_to_transition,  # How to convert batch dict → EnvTransition
+    to_output=_default_transition_to_batch       # How to convert EnvTransition → output format
+)
+
+# The processor automatically handles different input formats:
+# 1. If input is a batch dict (from dataset), converts to EnvTransition
+# 2. Passes through each step sequentially
+# 3. Converts back to original format (or custom output format)
+
+# Example with batch dict input (common in training)
+batch_dict = {"observation.state": tensor, "action": tensor}
+output = processor(batch_dict)  # Automatically converted to/from EnvTransition
+
+# Example with EnvTransition input (common in inference)
+transition = {TransitionKey.OBSERVATION: {...}, TransitionKey.ACTION: ...}
+output = processor(transition)  # Stays as EnvTransition throughout
+```
+
+The `to_transition` and `to_output` converters enable seamless integration with existing codebases.
+By default, they handle the standard LeRobot batch format, but you can customize them for different data structures.
+
+### Additional Converter Functions
+
+LeRobot provides several specialized converter functions for common robotics scenarios:
+
+```python
+from lerobot.processor.converters import (
+    to_transition_teleop_action,
+    to_transition_robot_observation,
+    to_output_robot_action,
+    to_dataset_frame
+)
+```
+
+**`to_transition_teleop_action`** - Converts teleoperation device actions to EnvTransitions:
+
+```python
+# Use case: Phone, gamepad, or other teleop device control
+phone_action = {"x": 0.1, "y": -0.2, "gripper": 0.8}
+transition = to_transition_teleop_action(phone_action)
+# Creates: {ACTION: {"action.x": 0.1, "action.y": -0.2, "action.gripper": 0.8}, ...}
+```
+
+**`to_transition_robot_observation`** - Converts robot sensor data to EnvTransitions:
+
+```python
+# Use case: Live robot observation during inference
+robot_obs = {
+    "joint_1": 0.5, "joint_2": -0.3,  # joint positions
+    "camera_0": image_array            # camera images
+}
+transition = to_transition_robot_observation(robot_obs)
+# Creates: {OBSERVATION: {"observation.state.joint_1": 0.5, "observation.images.camera_0": image, ...}}
+```
+
+**`to_output_robot_action`** - Extracts robot-executable actions from EnvTransitions:
+
+```python
+# Use case: Converting model outputs back to robot commands
+model_transition = {ACTION: {"action.joint_1": 0.2, "action.joint_2": 0.1}}
+robot_action = to_output_robot_action(model_transition)
+# Returns: {"joint_1": 0.2, "joint_2": 0.1} - ready for robot.send_action()
+```
+
+**`to_dataset_frame`** - Converts transitions to dataset-compatible format:
+
+```python
+# Use case: Saving processed data or creating training batches
+features = {
+    "action": {"names": ["joint_1", "joint_2"]},
+    "observation.state": {"names": ["joint_1", "joint_2"]},
+    "observation.images.camera0": {...}
+}
+batch = to_dataset_frame(transition, features)
+# Returns: {"action": [0.2, 0.1], "observation.state": [0.5, -0.3], ...}
+```
+
+These converters are particularly useful when integrating with real robots, as shown in the examples:
+
+```python
+# Example from phone_so100_teleop.py - Real robot teleoperation
+phone_to_robot_ee_pose = RobotProcessor(
+    steps=[...],
+    to_transition=to_transition_teleop_action,  # Phone → EnvTransition
+    to_output=lambda tr: tr                     # Keep as EnvTransition
+)
+
+# Example from phone_so100_eval.py - Robot action execution
+robot_ee_to_joints = RobotProcessor(
+    steps=[...],
+    to_transition=lambda tr: tr,                # Already EnvTransition
+    to_output=to_output_robot_action           # EnvTransition → Robot action
+)
+
+# Example from phone_so100_record.py - Dataset recording
+robot_joints_to_ee_pose = RobotProcessor(
+    steps=[...],
+    to_transition=to_transition_robot_observation,  # Robot obs → EnvTransition
+    to_output=lambda tr: tr                         # Keep as EnvTransition for dataset
+)
+```
+
+### Data Format Conversion
+
+Different data sources have different formats, but processors need a unified `EnvTransition` structure internally.
+The default converters handle LeRobot datasets, but you can customize them:
+
+```python
+# Default: LeRobot batch format
+lerobot_batch = {
+    "observation.state": torch.tensor(...),
+    "action": torch.tensor(...),
+    "next.reward": torch.tensor(...),
+    "task": ["pick cube", ...]
+}
+# → Converts to EnvTransition → Processes → Converts back
+
+# Custom: Live robot data
+robot_data = {
+    "cameras": {"wrist_cam": np.array(...)},
+    "joint_positions": np.array(...),
+    "gripper_state": 0.5
+}
+
+def robot_to_transition(data: dict) -> EnvTransition:
+    return {
+        TransitionKey.OBSERVATION: {
+            "observation.images.wrist": torch.from_numpy(data["cameras"]["wrist_cam"]),
+            "observation.state": torch.from_numpy(data["joint_positions"])
+        },
+        TransitionKey.ACTION: None,
+        # ... other fields with defaults
+    }
+
+# Use custom converter
+processor = RobotProcessor(
+    steps=[...],
+    to_transition=robot_to_transition,
+    to_output=lambda transition: transition  # Keep as EnvTransition
+)
+```
+
+**When to customize:** Live robot data, Gymnasium environments, legacy datasets, or any non-LeRobot format.
+
+## Common Processor Steps
+
+LeRobot provides a rich set of pre-built processor steps for common transformations.
+Let's explore each in detail:
+
+### Data Normalization
+
+Normalization is crucial for neural network training and inference.
+The `NormalizerProcessor` handles both mean-std normalization and min-max scaling:
+
+```python
+from lerobot.processor.normalize_processor import NormalizerProcessor, UnnormalizerProcessor
+from lerobot.configs.types import PolicyFeature, FeatureType, NormalizationMode
+
+# Define what features exist in your data
+features = {
+    "observation.images.camera0": PolicyFeature(
+        type=FeatureType.IMAGE,
+        shape=(224, 224, 3)
+    ),
+    "observation.state": PolicyFeature(
+        type=FeatureType.STATE,
+        shape=(7,)
+    ),
+    "action": PolicyFeature(
+        type=FeatureType.ACTION,
+        shape=(7,)
+    )
+}
+
+# Define normalization strategy per feature type
+norm_map = {
+    FeatureType.IMAGE: NormalizationMode.MEAN_STD,    # Images: (x - mean) / std
+    FeatureType.STATE: NormalizationMode.MIN_MAX,     # States: scale to [-1, 1]
+    FeatureType.ACTION: NormalizationMode.MIN_MAX     # Actions: scale to [-1, 1]
+}
+
+# Create normalizer with dataset statistics
+normalizer = NormalizerProcessor(
+    features=features,
+    norm_map=norm_map,
+    stats=dataset.meta.stats,  # Contains mean, std, min, max per feature
+    normalize_keys={"observation.state", "action"}  # Optional: only normalize specific keys
+)
+
+# For postprocessing: inverse transformation
+unnormalizer = UnnormalizerProcessor(
+    features=features,
+    norm_map=norm_map,
+    stats=dataset.meta.stats
+)
+
+# The normalizer automatically:
+# - Detects which normalization to apply based on feature type
+# - Handles device placement of statistics tensors
+# - Skips keys not in stats or not in normalize_keys
+# - Adds metadata about what was normalized
+```
+
+### Device Management
+
+The `DeviceProcessor` ensures tensors are on the right device with the right dtype:
+
+```python
+from lerobot.processor.device_processor import DeviceProcessor
+
+# Basic GPU placement
+gpu_processor = DeviceProcessor(device="cuda:0")
+
+# Advanced: GPU with half-precision for inference
+efficient_processor = DeviceProcessor(
+    device="cuda:0",
+    float_dtype="float16"  # Convert float32 -> float16 for memory efficiency
+)
+
+# The processor:
+# - Moves all tensors to specified device
+# - Preserves non-tensor data unchanged
+# - Optionally converts float dtypes while preserving int/bool types
+# - Uses non_blocking transfers for CUDA devices
+# - Handles nested structures (observations, complementary_data)
+
+# Supported float dtypes:
+# "float16" / "half": 16-bit floating point
+# "float32" / "float": 32-bit floating point (default)
+# "float64" / "double": 64-bit floating point
+# "bfloat16": Brain floating point (better for training)
+```
+
+### Batch Processing
+
+Models expect batched inputs, but robot interactions often produce unbatched data:
+
+```python
+from lerobot.processor.batch_processor import ToBatchProcessor
+
+batch_processor = ToBatchProcessor()
+
+# Automatically adds batch dimensions where needed:
+# State: (7,) -> (1, 7)
+# Image: (224, 224, 3) -> (1, 224, 224, 3)
+# Action: (4,) -> (1, 4)
+# Task: "pick_cube" -> ["pick_cube"]
+# Already batched: (1, 7) -> (1, 7) [unchanged]
+
+# The processor intelligently:
+# - Detects tensor dimensionality
+# - Adds batch dim to 1D states/actions
+# - Adds batch dim to 3D images
+# - Wraps string tasks in lists
+# - Preserves already-batched data
+
+# Example usage in inference:
+single_observation = robot.get_observation()  # Unbatched
+batched_input = batch_processor({"observation": single_observation})
+model_output = model(batched_input)  # Model expects batch dim
+```
+
+### Text Tokenization
+
+For language-conditioned policies, text instructions must be tokenized:
+
+```python
+from lerobot.processor.tokenizer_processor import TokenizerProcessor
+from transformers import AutoTokenizer
+
+# Option 1: Auto-load tokenizer by name
+tokenizer_proc = TokenizerProcessor(
+    tokenizer_name="google/paligemma-3b-pt-224",
+    max_length=128,
+    task_key="task",           # Where to find text in complementary_data
+    padding="max_length",       # Pad to max_length
+    padding_side="right",
+    truncation=True            # Truncate if longer than max_length
+)
+
+# Option 2: Provide custom tokenizer
+custom_tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+custom_proc = TokenizerProcessor(
+    tokenizer=custom_tokenizer,
+    max_length=256,
+    padding_side="left"        # For autoregressive models
+)
+
+# The processor:
+# - Extracts task text from complementary_data
+# - Tokenizes using HuggingFace tokenizer
+# - Adds tokens and attention_mask to observations
+# - Handles both single strings and lists of strings
+# - Preserves original task in complementary_data
+
+# Output structure:
+# observation["observation.language.tokens"] = tensor([101, 2032, ...])
+# observation["observation.language.attention_mask"] = tensor([1, 1, 0, ...])
+```
+
+### Key Renaming
+
+Different datasets and models may use different naming conventions.
+The `RenameProcessor` solves this mismatch:
+
+**Why is this useful?**
+
+- When loading a model trained on a different dataset with different key names
+- When using foundation models that expect specific key naming conventions
+- When standardizing datasets from different sources
+- When adapting legacy code to new naming standards
+
+```python
+from lerobot.processor.rename_processor import RenameProcessor
+
+# Example 1: Dataset uses "top"/"wrist", model expects "camera0"/"camera1"
+rename_proc = RenameProcessor(
+    rename_map={
+        "observation.images.top": "observation.images.camera0",
+        "observation.images.wrist": "observation.images.camera1",
+    }
+)
+
+# Example 2: Foundation model compatibility
+# Your dataset: "observation.state", Foundation model: "proprio"
+foundation_rename = RenameProcessor(
+    rename_map={
+        "observation.state": "proprio",
+        "observation.images.main": "rgb",
+    }
+)
+
+# Example 3: Standardizing multiple datasets
+standardize_rename = RenameProcessor(
+    rename_map={
+        # Different robots might use different names
+        "observation.joint_positions": "observation.state",
+        "observation.gripper_state": "observation.end_effector",
+        "observation.arm_camera": "observation.images.wrist",
+    }
+)
+```
+
+## Building Complete Pipelines
+
+Let's build a real-world preprocessing and postprocessing pipeline for a vision-based
+manipulation policy:
+
+```python
+# Consolidated imports
+from lerobot.processor import (
+    RobotProcessor,
+    NormalizerProcessor,
+    UnnormalizerProcessor,
+    DeviceProcessor,
+    ToBatchProcessor,
+    TokenizerProcessor,
+    RenameProcessor
+)
+
+# Step 1: Define the preprocessing pipeline
+preprocessor = RobotProcessor(
+    steps=[
+        # 1. Standardize naming from dataset
+        RenameProcessor(
+            rename_map={
+                "observation.images.top": "observation.images.camera0",
+                "observation.images.wrist": "observation.images.camera1"
+            }
+        ),
+
+        # 2. Add batch dimensions for model
+        ToBatchProcessor(),
+
+        # 3. Tokenize language instructions if present
+        TokenizerProcessor(
+            tokenizer_name="google/paligemma-3b-pt-224",
+            max_length=64,
+            task_key="task"
+        ),
+
+        # 4. Normalize numerical data
+        NormalizerProcessor(
+            features=policy_features,
+            norm_map={
+                FeatureType.IMAGE: NormalizationMode.MEAN_STD,
+                FeatureType.STATE: NormalizationMode.MIN_MAX,
+                FeatureType.ACTION: NormalizationMode.MIN_MAX
+            },
+            stats=dataset.meta.stats
+        ),
+
+        # 5. Move to GPU and convert to half precision
+        DeviceProcessor(
+            device="cuda:0",
+            float_dtype="float16"
+        )
+    ],
+    name="robot_preprocessor"
+)
+
+# Step 2: Define the postprocessing pipeline
+postprocessor = RobotProcessor(
+    steps=[
+        # 1. Move back to CPU for robot hardware
+        DeviceProcessor(device="cpu"),
+
+        # 2. Denormalize actions to original scale
+        UnnormalizerProcessor(
+            features=policy_features,
+            norm_map={
+                FeatureType.ACTION: NormalizationMode.MIN_MAX
+            },
+            stats=dataset.meta.stats
+        )
+    ],
+    name="robot_postprocessor"
+)
+```
+
+## Using Processors in Practice
+
+### Training Loop Integration
+
+Here's how processors integrate into a training loop using the policy's forward method:
+
+```python
+from torch.utils.data import DataLoader
+
+# Create dataset and dataloader
+dataset = LeRobotDataset(repo_id="your_dataset")
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+# Initialize model and processors
+model = YourPolicy.from_pretrained("your_model")
+preprocessor = RobotProcessor.from_pretrained(
+    "your_model",
+    config_filename="robot_preprocessor.json"
+)
+
+# Training loop
+for epoch in range(num_epochs):
+    for batch in dataloader:
+        # Preprocess batch
+        processed_batch = preprocessor(batch)
+
+        # Forward pass - returns loss and optional metrics
+        loss, metrics = model.forward(processed_batch)
+
+        # Backward pass
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # Log metrics if available
+        if metrics:
+            wandb.log(metrics)
+```
+
+### Inference Pipeline
+
+For deployment, processors ensure consistent data handling with real robots:
+
+```python
+# Load model and processors
+policy = YourPolicy.from_pretrained("path/to/model")
+preprocessor = RobotProcessor.from_pretrained(
+    "path/to/model",
+    config_filename="robot_preprocessor.json"
+)
+postprocessor = RobotProcessor.from_pretrained(
+    "path/to/model",
+    config_filename="robot_postprocessor.json"
+)
+
+# Connect to robot
+robot = make_robot_from_config(robot_config)
+robot.connect()
+
+# Inference loop
+policy.eval()
+# Reset the policy and processors
+policy.reset()
+preprocessor.reset()
+postprocessor.reset()
+
+with torch.no_grad():
+    while not done:
+        # Get observation from robot
+        observation = robot.get_observation()
+
+        # Build dataset-compatible frame
+        observation_frame = build_dataset_frame(
+            dataset.features,
+            observation,
+            prefix="observation"
+        )
+
+        # Add task instruction to complementary data
+        observation_frame["task"] = "pick up the red cube"
+
+        # Preprocess for model
+        model_input = preprocessor(observation_frame)
+
+        # Run policy
+        raw_action = policy.select_action(model_input)
+
+        # Postprocess action
+        action_transition = {TransitionKey.ACTION: raw_action}
+        processed = postprocessor(action_transition)
+        action = processed[TransitionKey.ACTION]
+
+        # Convert to robot action format
+        robot_action = {
+            key: action[i].item()
+            for i, key in enumerate(robot.action_features)
+        }
+
+        # Execute on robot
+        robot.send_action(robot_action)
+```
+
+## Saving and Loading Processors
+
+Processors can be persisted and shared just like models, making them portable across different
+environments and ensuring reproducibility:
+
+### Local Save/Load
+
+```python
+# Save processor configuration and state
+preprocessor.save_pretrained(
+    "./my_robot_processor",
+    config_filename="preprocessor.json"  # Optional custom name
+)
+
+# The save creates:
+# my_robot_processor/
+# ├── preprocessor.json                    # Configuration
+# ├── preprocessor_step_0_normalizer.safetensors  # Step 0 state (stats)
+# └── preprocessor_step_1_device.safetensors     # Step 1 state (if any)
+
+# Load processor
+loaded = RobotProcessor.from_pretrained(
+    "./my_robot_processor",
+    config_filename="preprocessor.json"
+)
+```
+
+### HuggingFace Hub Integration
+
+The HuggingFace Hub provides a centralized place to share and version your processors.
+This is particularly useful for sharing preprocessing configurations with models,
+ensuring that anyone who downloads your model can reproduce your exact preprocessing pipeline.
+It also enables versioning and collaboration on preprocessing strategies.
+
+```python
+# Save to HuggingFace Hub
+preprocessor.save_pretrained("username/my-robot-policy")
+
+# Load from Hub with automatic download
+hub_processor = RobotProcessor.from_pretrained(
+    "username/my-robot-policy",
+    config_filename="robot_preprocessor.json",
+    revision="main",  # Optional: specific revision
+    cache_dir="./cache"  # Optional: local cache directory
+)
+
+# The Hub integration provides:
+# - Automatic versioning with git
+# - Public or private sharing
+# - Download caching for efficiency
+# - Integration with model repositories
+```
+
+### Loading with Overrides
+
+Sometimes you need to modify loaded processors for new environments or datasets.
+The override mechanism allows you to update specific processor configurations without modifying
+the saved files:
+
+```python
+# Load processor with configuration overrides
+processor = RobotProcessor.from_pretrained(
+    "./saved_processor",
+    overrides={
+        # Change device for different hardware
+        "device_processor": {"device": "cuda:1"},
+
+        # Update statistics for new dataset
+        "normalizer_processor": {"stats": new_dataset.meta.stats},
+
+        # Provide non-serializable objects (like tokenizers)
+        "tokenizer_processor": {"tokenizer": custom_tokenizer}
+    }
+)
+
+# Common override scenarios:
+# 1. Adapting to different hardware (GPU availability)
+# 2. Fine-tuning on new datasets with different statistics
+# 3. Providing runtime dependencies that can't be serialized
+# 4. Testing variations without creating new saved configs
+```
+
+## Creating Custom Processor Steps
+
+Build your own processor steps for specialized transformations.
+The key is implementing the required interface:
+
+### Basic Custom Step with Registration
+
+The registration mechanism allows your custom processors to be saved and loaded by name rather
+than by module path.
+This makes them more portable and easier to share:
+
+```python
+from dataclasses import dataclass
+from lerobot.processor.pipeline import ProcessorStepRegistry, ObservationProcessor
+
+# The @register decorator adds your processor to the global registry
+# Use a unique name, preferably namespaced to avoid conflicts
+@dataclass
+@ProcessorStepRegistry.register("my_company/gaussian_noise")
+class GaussianNoiseProcessor(ObservationProcessor):
+    """Add Gaussian noise to observations for robustness training."""
+
+    noise_std: float = 0.01
+    training_only: bool = True
+    is_training: bool = True
+
+    def observation(self, observation):
+        """Add noise to observation tensors."""
+        if not self.is_training and self.training_only:
+            return observation
+
+        noisy_obs = {}
+        for key, value in observation.items():
+            if isinstance(value, torch.Tensor) and "image" not in key:
+                # Add noise to non-image observations
+                noise = torch.randn_like(value) * self.noise_std
+                noisy_obs[key] = value + noise
+            else:
+                noisy_obs[key] = value
+
+        return noisy_obs
+
+    def get_config(self):
+        return {
+            "noise_std": self.noise_std,
+            "training_only": self.training_only,
+            "is_training": self.is_training
+        }
+
+# Why register?
+# 1. Enables saving by name: config saves "my_company/gaussian_noise" instead of full module path
+# 2. More portable: Others can use your processor without your exact module structure
+# 3. Version-safe: Module refactoring won't break saved configs
+# 4. Cleaner configs: JSON shows readable names instead of long import paths
+```
+
+### Using Base Classes for Common Patterns
+
+LeRobot provides base classes like `ObservationProcessor`, `ActionProcessor`, etc., that handle
+the boilerplate of extracting and reinserting specific components:
+
+```python
+from lerobot.processor import ActionProcessor
+
+@dataclass
+@ProcessorStepRegistry.register("my_company/action_clipper")
+class ActionClipProcessor(ActionProcessor):
+    """Clip actions to safe ranges."""
+
+    min_value: float = -1.0
+    max_value: float = 1.0
+
+    def action(self, action):
+        """Process only the action component."""
+        # No need to handle transition dict - base class does it
+        return torch.clamp(action, self.min_value, self.max_value)
+
+    def get_config(self):
+        return {"min_value": self.min_value, "max_value": self.max_value}
+```
+
+For more advanced processor patterns including stateful processors, see [Implement Your Own Processor](implement_your_own_processor.mdx).
+
+## Advanced Features
+
+### Debugging with Hooks
+
+Processors support hooks for monitoring and debugging without modifying the pipeline code:
+
+```python
+# Define monitoring hooks
+def log_shapes(step_idx: int, transition: EnvTransition):
+    """Log tensor shapes after each step."""
+    obs = transition.get(TransitionKey.OBSERVATION)
+    if obs:
+        print(f"Step {step_idx} shapes:")
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor):
+                print(f"  {key}: {value.shape}")
+
+def check_nans(step_idx: int, transition: EnvTransition):
+    """Check for NaN values."""
+    obs = transition.get(TransitionKey.OBSERVATION)
+    if obs:
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor) and torch.isnan(value).any():
+                print(f"Warning: NaN detected in {key} at step {step_idx}")
+
+# Register hooks
+processor.register_after_step_hook(log_shapes)
+processor.register_after_step_hook(check_nans)
+
+# Process data - hooks will be called after each step
+output = processor(input_data)
+
+# Remove hooks when done debugging
+processor.unregister_after_step_hook(log_shapes)
+processor.unregister_after_step_hook(check_nans)
+```
+
+### Step-by-Step Inspection
+
+Use `step_through()` for detailed debugging of the transformation pipeline:
+
+```python
+# Inspect data at each transformation stage
+for i, intermediate in enumerate(processor.step_through(data)):
+    print(f"\n=== After step {i} ===")
+
+    # Check observation shapes
+    obs = intermediate.get(TransitionKey.OBSERVATION)
+    if obs:
+        for key, value in obs.items():
+            if isinstance(value, torch.Tensor):
+                print(f"{key}: shape={value.shape}, "
+                      f"dtype={value.dtype}, "
+                      f"device={value.device}, "
+                      f"range=[{value.min():.3f}, {value.max():.3f}]")
+
+    # Check action if present
+    action = intermediate.get(TransitionKey.ACTION)
+    if action is not None and isinstance(action, torch.Tensor):
+        print(f"action: shape={action.shape}, range=[{action.min():.3f}, {action.max():.3f}]")
+```
+
+### Pipeline Slicing
+
+Extract subsets of a pipeline for testing or creating variations:
+
+```python
+# Get specific steps
+first_three_steps = processor[:3]  # Returns new RobotProcessor
+middle_step = processor[2]         # Returns single ProcessorStep
+
+# Test individual steps
+test_input = {...}
+step_output = processor[0](test_input)  # Test first step only
+
+# Create variations
+variant_processor = RobotProcessor(
+    steps=processor.steps[:-1] + [new_final_step],
+    name="variant"
+)
+```
+
+## Best Practices and Tips
+
+### 1. Order Matters
+
+The sequence of processors is crucial. Follow this general order:
+
+```python
+# Preprocessing: Raw → Model-ready
+1. Rename (standardize keys)
+2. Batch (add dimensions)
+3. Tokenize (text → tokens)
+4. Normalize (scale values)
+5. Device (move to GPU)
+
+# Postprocessing: Model → Robot-ready
+1. Device (move to CPU)
+2. Unnormalize (restore scale)
+3. Unbatch (remove dimensions if needed)
+```
+
+### 2. Registration Best Practices
+
+```python
+# Always register custom steps for better portability
+@ProcessorStepRegistry.register("my_company/special_processor")
+class SpecialProcessor:
+    ...
+
+# Use namespaced names to avoid conflicts
+# Good: "my_company/augmentation"
+# Bad: "augmentation" (too generic)
+
+# Check registered processors
+print(ProcessorStepRegistry.list())  # See all registered processors
+```
+
+### 3. Common Pitfalls and Solutions
+
+**Tensor Device Mismatch:**
+
+```python
+# Problem: RuntimeError: Expected all tensors on same device
+# Solution: Ensure DeviceProcessor is in pipeline
+preprocessor = RobotProcessor(
+    steps=[
+        NormalizerProcessor(...),
+        DeviceProcessor(device="cuda")  # Add this
+    ]
+)
+```
+
+**Missing Statistics:**
+
+```python
+# Problem: NormalizerProcessor has no stats
+# Solution 1: Compute stats from dataset
+from lerobot.datasets.compute_stats import compute_stats
+stats = compute_stats(dataset)
+
+# Solution 2: Load with overrides
+processor = RobotProcessor.from_pretrained(
+    "model_path",
+    overrides={"normalizer_processor": {"stats": dataset.meta.stats}}
+)
+```
+
+## Next Steps
+
+Now that you understand processors, explore these topics:
+
+- [**Implement Your Own Processor**](implement_your_own_processor.mdx) - Deep dive into creating custom processors with advanced features like stateful processing
+- [**Policy Documentation**](policies.mdx) - Learn how different policies use processors
+- [**Dataset Documentation**](datasets.mdx) - Understand the data format that processors transform
+- [**Training Guide**](training.mdx) - See processors in action during model training
+- [**Evaluation Guide**](evaluation.mdx) - Learn about processor usage during policy evaluation
+
+## Summary
+
+Processors are the unsung heroes of robotics pipelines, handling the critical transformations between raw sensor data and model-ready tensors. By understanding and effectively using processors, you can:
+
+- Build robust, reusable data pipelines
+- Share preprocessing configurations across projects
+- Debug data transformations systematically
+- Ensure consistency between training and deployment
+- Create custom transformations for specialized tasks
+
+Remember: good preprocessing is often the difference between a model that works in theory
+and one that works in practice!
+The modular pipeline approach ensures your transformations are testable, reproducible,
+and portable across different robots and environments.

docs/source/koch.mdx

@@ -31,7 +31,7 @@ pip install -e ".[dynamixel]"
 To find the port for each bus servo adapter, run this script:
 
 ```bash
-lerobot-find-port
+python -m lerobot.find_port
 ```
 
 <hfoptions id="example">
@@ -98,7 +98,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
 <hfoption id="Command">
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem575E0031751 # <- paste here the port found at previous step
 ```
@@ -174,7 +174,7 @@ Do the same steps for the leader arm but modify the command or script accordingl
 <hfoption id="Command">
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --teleop.type=koch_leader \
     --teleop.port=/dev/tty.usbmodem575E0031751 \  # <- paste here the port found at previous step
 ```
@@ -211,7 +211,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=koch_follower \
     --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --robot.id=my_awesome_follower_arm  # <- Give the robot a unique name
@@ -249,7 +249,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=koch_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --teleop.id=my_awesome_leader_arm  # <- Give the robot a unique name

docs/source/lekiwi.mdx

@@ -60,7 +60,7 @@ First, we will assemble the two SO100/SO101 arms. One to attach to the mobile ba
 To find the port for each bus servo adapter, run this script:
 
 ```bash
-lerobot-find-port
+python -m lerobot.find_port
 ```
 
 <hfoptions id="example">
@@ -116,7 +116,7 @@ The instructions for configuring the motors can be found in the SO101 [docs](./s
 You can run this command to setup motors for LeKiwi. It will first setup the motors for arm (id 6..1) and then setup motors for wheels (9,8,7)
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --robot.type=lekiwi \
     --robot.port=/dev/tty.usbmodem58760431551 # <- paste here the port found at previous step
 ```
@@ -174,7 +174,7 @@ The calibration process is very important because it allows a neural network tra
 Make sure the arm is connected to the Raspberry Pi and run this script or API example (on the Raspberry Pi via SSH) to launch calibration of the follower arm:
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=lekiwi \
     --robot.id=my_awesome_kiwi # <- Give the robot a unique name
 ```
@@ -193,7 +193,7 @@ Then, to calibrate the leader arm (which is attached to the laptop/pc). Run the
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

docs/source/phone_teleop.mdx

@@ -0,0 +1,195 @@
+# Phone
+
+Use your phone (iOS or Android) to control your robot.
+
+**In this guide you'll learn:**
+
+- How to connect an iOS/Android phone
+- How phone pose is mapped to robot end‑effector (EE) targets
+- How to tweak safety limits, gripper control, and IK settings
+
+To use phone to control your robot, install the relevant dependencies with:
+
+```bash
+pip install lerobot[phone]
+```
+
+## Get started
+
+### Supported platforms
+
+- iOS: Uses the HEBI Mobile I/O app (ARKit pose + buttons). Download the app first, open it and the examples will discover it on your network and stream the phone pose and inputs.
+- Android: Uses the `teleop` package (WebXR). When you start the Python process, it prints a local URL. Open the link on your phone, tap Start, then use Move to stream pose.
+
+Links:
+
+- Android WebXR library: [`teleop` on PyPI](https://pypi.org/project/teleop/)
+- iOS app: [HEBI Mobile I/O](https://docs.hebi.us/tools.html#mobile-io)
+
+### Phone orientation and controls
+
+- Orientation: hold the phone with the screen facing up and the top edge pointing in the same direction as the robot gripper. This ensures calibration aligns the phone’s frame with the robot frame so motion feels natural.
+- Enable/disable:
+  - iOS: Hold `B1` to enable teleoperation, release to stop. The first press captures a reference pose.
+  - Android: Press and hold the `Move` button, release to stop. The first press captures a reference pose.
+- Gripper control:
+  - iOS: Analog input `A3` controls the gripper as velocity input.
+  - Android: Buttons `A` and `B` act like increment/decrement (A opens, B closes). You can tune velocity in the `GripperVelocityToJoint` step.
+
+### Step 1: Choose the platform
+
+Modify the examples to use `PhoneOS.IOS` or `PhoneOS.ANDROID` in `PhoneConfig`. The API is identical across platforms, only the input source differs. All examples are under `examples/` and have `phone_so100_*.py` variants.
+
+Teleoperation example:
+
+```36:43:examples/phone_so100_teleop.py
+from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
+
+teleop_config = PhoneConfig(phone_os=PhoneOS.IOS)  # or PhoneOS.ANDROID
+teleop_device = Phone(teleop_config)
+```
+
+### Step 2: Connect and calibrate
+
+When `Phone(teleop_config)` is created and `connect()` is called, calibration is prompted automatically. Hold the phone in the orientation described above, then:
+
+- iOS: press and hold `B1` to capture the reference pose.
+- Android: press `Move` button on the WebXR page to capture the reference pose.
+
+Why calibrate? We capture the current pose so subsequent poses are expressed in a robot aligned frame. When you again press the button to enable control, the position is recaptured to avoid drift when your phone is repositioned while it was disabled.
+
+### Step 3: Run an example
+
+Run on of the examples scripts to teleoperate, record a dataset, replay a dataset or evaluate a policy.
+
+All scripts assume you configured your robot (e.g., SO-100 follower) and set the correct serial port.
+
+- Android: after starting the script, open the printed local URL on your phone, tap Start, then press and hold Move.
+- iOS: open HEBI Mobile I/O first; B1 enables motion. A3 controls the gripper.
+
+You can customize mapping or safety limits by editing the processor steps shown in the examples.
+
+You can also remap inputs (e.g., use a different analog input) or adapt the pipeline to other robots (e.g., LeKiwi) by modifying the input and kinematics steps. More about this in the [Processors for Robots and Teleoperators](./processors_robots_teleop.mdx) guide.
+
+- Run this example to teleoperate:
+
+  ```bash
+  python examples/phone_so100_teleop.py
+  ```
+
+- Run this example to record a dataset, which saves absolute end effector observations and actions:
+
+  ```bash
+  python examples/phone_so100_record.py
+  ```
+
+- Run this example to replay recorded episodes:
+
+  ```bash
+  python examples/phone_so100_replay.py
+  ```
+
+- Run this example to evaluate a pretrained policy:
+
+  ```bash
+  python examples/phone_so100_eval.py
+  ```
+
+### Important pipeline steps and options
+
+- Kinematics are used in multiple steps. We use [Placo](https://github.com/Rhoban/placo) which is a wrapper around Pinocchio for handling our kinematics. We construct the kinematics object by passing the robot's URDF and target frame. We set `target_frame_name` to the gripper frame.
+
+  ```44:49:examples/phone_so100_teleop.py
+  RobotKinematics(
+      urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
+      target_frame_name="gripper_frame_link",
+      joint_names=list(robot.bus.motors.keys()),
+  )
+  ```
+
+- The `MapPhoneActionToRobotAction` step converts the calibrated phone pose and inputs into target deltas and gripper commands, below is shown what the step outputs.
+
+  ```72:83:src/lerobot/teleoperators/phone/phone_processor.py
+  # Map calibrated phone pose to robot targets (enabled gates the motion)
+  act.update(
+      {
+          "action.enabled": enabled,
+          "action.target_x": -pos[1] if enabled else 0.0,
+          "action.target_y":  pos[0] if enabled else 0.0,
+          "action.target_z":  pos[2] if enabled else 0.0,
+          "action.target_wx": rotvec[1] if enabled else 0.0,
+          "action.target_wy": rotvec[0] if enabled else 0.0,
+          "action.target_wz": -rotvec[2] if enabled else 0.0,
+          "action.gripper":   gripper,
+      }
+  )
+  ```
+
+- The `EEReferenceAndDelta` step converts target deltas to an absolute desired EE pose, storing a reference on enable, the `end_effector_step_sizes` are the step sizes for the EE pose and can be modified to change the motion speed.
+
+  ```56:65:examples/phone_so100_teleop.py
+  EEReferenceAndDelta(
+      kinematics=kinematics_solver,
+      end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+      motor_names=list(robot.bus.motors.keys()),
+  )
+  ```
+
+- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` and `max_ee_twist_step_rad` are the step limits for the EE pose and can be modified to change the safety limits.
+
+  ```61:66:examples/phone_so100_teleop.py
+  EEBoundsAndSafety(
+      end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+      max_ee_step_m=0.10,
+      max_ee_twist_step_rad=0.50,
+  )
+  ```
+
+- The `GripperVelocityToJoint` step turns a velocity‑like gripper input into absolute gripper position using the current measured state. The `speed_factor` is the factor by which the velocity is multiplied.
+
+  ```78:81:examples/phone_so100_teleop.py
+  GripperVelocityToJoint(
+      motor_names=list(robot.bus.motors.keys()),
+      speed_factor=20.0,
+  )
+  ```
+
+#### Different IK initial guesses
+
+We use different IK initial guesses in the kinematic steps. As initial guess either the current measured joints or the previous IK solution is used.
+
+- Closed loop (used in record/eval): sets `initial_guess_current_joints=True` so IK starts from the measured joints each frame.
+
+  ```71:76:examples/phone_so100_eval.py
+  InverseKinematicsEEToJoints(
+      kinematics=kinematics_solver,
+      motor_names=list(robot.bus.motors.keys()),
+      initial_guess_current_joints=True,  # closed loop
+  )
+  ```
+
+- Open loop (used in replay): sets `initial_guess_current_joints=False` so IK continues from the previous IK solution rather than the measured state. This preserves action stability when we replay without feedback.
+
+  ```80:86:examples/phone_so100_replay.py
+  InverseKinematicsEEToJoints(
+      kinematics=kinematics_solver,
+      motor_names=list(robot.bus.motors.keys()),
+      initial_guess_current_joints=False,  # open loop
+  )
+  ```
+
+### Pipeline steps explained
+
+- MapPhoneActionToRobotAction: converts calibrated phone pose and inputs into target deltas and a gripper command. Motion is gated by an enable signal (B1 on iOS, Move on Android).
+- AddRobotObservationAsComplimentaryData: reads current robot joints and inserts them under `complementary_data.raw_joint_positions` for FK/IK steps to use.
+- EEReferenceAndDelta: latches a reference EE pose on enable and combines it with target deltas to produce an absolute desired EE pose each frame. When disabled, it keeps sending the last commanded pose.
+- EEBoundsAndSafety: clamps the EE pose to a workspace and rate‑limits jumps for safety. Also declares `action.ee.*` features.
+- InverseKinematicsEEToJoints: turns an EE pose into joint positions with IK. `initial_guess_current_joints=True` is recommended for closed‑loop control; set `False` for open‑loop replay for stability.
+- GripperVelocityToJoint: integrates a velocity‑like gripper input into an absolute gripper position using the current measured state.
+- ForwardKinematicsJointsToEE: computes `observation.state.ee.*` from observed joints for logging and training on EE state.
+
+### Troubleshooting
+
+- iOS not discovered: ensure HEBI Mobile I/O is open and your laptop/phone are on the same network.
+- Android URL not reachable: check local you used `https` instead of `http`, use the exact IP printed by the script and allow your browser to enter and ignore the certificate issue.
+- Motion feels inverted: adjust the sign flips in `MapPhoneActionToRobotAction` or swap axes to match your setup.

docs/source/processors_robots_teleop.mdx

@@ -0,0 +1,148 @@
+# Processors for Robots and Teleoperators
+
+This guide shows how to build and modify processing pipelines that connect teleoperators (e.g., phone) to robots and datasets. Pipelines standardize conversions between different action/observation spaces so you can swap teleops and robots without rewriting glue code.
+
+We use the Phone to SO‑100 follower examples for concreteness, but the same patterns apply to other robots.
+
+**What you'll learn**
+
+- Absolute vs. relative EE control: What each means, trade‑offs, and how to choose for your task.
+- Three-pipeline pattern: How to map teleop actions → dataset actions → robot commands, and robot observations → dataset observations.
+- Adapters (`to_transition` / `to_output`): How these convert raw dicts to `EnvTransition` and back to reduce boilerplate.
+- Dataset feature contracts: How steps declare features via `transform_features(...)`, and how to aggregate/merge them for recording.
+- Choosing a representation: When to store joints, absolute EE poses, or relative EE deltas—and how that affects training.
+- Pipeline customization guidance: How to swap robots/URDFs safely and tune bounds, step sizes, and options like IK initialization.
+
+### Absolute vs relative EE control
+
+The examples in this guide use absolute end effector (EE) poses because they are easy to reason about. In practice, relative EE deltas or joint position are often preferred as learning features.
+
+You can choose what you save and learn from the teleop and robot action spaces, joints, absolute EE, or relative EE by using/implementing the right steps (and `transform_features()`) in your pipelines.
+
+## Three pipelines
+
+We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
+Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
+
+1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
+2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
+3. Pipeline 3: Robot observation space → dataset observation space (joints → EE pose)
+
+Below is an example of the three pipelines that we use in the phone to SO-100 follower examples:
+
+```69:90:examples/phone_so100_record.py
+phone_to_robot_ee_pose = RobotProcessor(  # teleop -> dataset action
+    steps=[MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
+           AddRobotObservationAsComplimentaryData(robot=robot),
+            EEReferenceAndDelta(kinematics=kinematics_solver,
+                               end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+                               motor_names=list(robot.bus.motors.keys())),
+           EEBoundsAndSafety(end_effector_bounds={"min": [-1, -1, -1], "max": [1, 1, 1]},
+                             max_ee_step_m=0.20, max_ee_twist_step_rad=0.50)],
+    to_transition=to_transition_teleop_action,
+    to_output=lambda tr: tr,
+)
+
+robot_ee_to_joints = RobotProcessor(      # dataset action -> robot
+    steps=[InverseKinematicsEEToJoints(kinematics=kinematics_solver,
+                                       motor_names=list(robot.bus.motors.keys()),
+                                       initial_guess_current_joints=True),
+           GripperVelocityToJoint(motor_names=list(robot.bus.motors.keys()), speed_factor=20.0)],
+    to_transition=lambda tr: tr,
+    to_output=to_output_robot_action,
+)
+
+robot_joints_to_ee_pose = RobotProcessor( # robot obs -> dataset obs
+    steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver,
+                                       motor_names=list(robot.bus.motors.keys()))],
+    to_transition=to_transition_robot_observation,
+    to_output=lambda tr: tr,
+)
+```
+
+## Why to_transition / to_output
+
+To convert from robot/teleoperator to pipeline and back, we use the `to_transition` and `to_output` pipeline adapters.
+They standardize conversions to reduce boilerplate code, and form the bridge between the robot and teleoperators raw dicts and the pipeline’s `EnvTransition` format.
+In the phone to SO-100 follower examples we use the following adapters:
+
+- `to_transition_teleop_action`: transforms the teleop action dict to a pipeline transition (puts keys under `action.*`, converts scalars/arrays to tensors, keeps objects like `Rotation` intact)
+- `to_output_robot_action`: transforms the pipeline transition to a robot action dict (extracts keys ending with `.pos`/`.vel` and strips `action.` prefix)
+- `to_transition_robot_observation`: transforms the robot observation dict to a pipeline transition (splits state vs images; stores state under `observation.state.*` and images under `observation.images.*`)
+
+See `src/lerobot/processor/converters.py` for more details.
+
+## Dataset feature contracts
+
+Dataset features are the keys saved in the dataset. Each step can declare what its dataset features are via `transform_features(...)`. We can then aggregate features per pipeline with `aggregate_pipeline_dataset_features()` and merge multiple groups with `merge_features(...)`.
+
+Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
+
+```203:211:src/lerobot/robots/so100_follower/robot_kinematic_processor.py
+def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+    # Because this is last step we specify the dataset features of this step that we want to be stored in the dataset
+    features["action.ee.x"] = float
+    features["action.ee.y"] = float
+    features["action.ee.z"] = float
+    features["action.ee.wx"] = float
+    features["action.ee.wy"] = float
+    features["action.ee.wz"] = float
+    return features
+```
+
+Tip: declare features at the last step that produces them (e.g., `EEBoundsAndSafety` declares `action.ee.*`, `ForwardKinematicsJointsToEE` declares `observation.state.ee.*`).
+
+Below is an example of how we aggregate and merge features in the phone to SO-100 follower examples:
+
+```121:145:examples/phone_so100_record.py
+action_ee = aggregate_pipeline_dataset_features(
+    pipeline=phone_to_robot_ee_pose,
+    initial_features=phone.action_features,
+    use_videos=True,
+    patterns=["action.ee"],
+)
+
+gripper = aggregate_pipeline_dataset_features(
+    pipeline=robot_ee_to_joints,
+    initial_features={},
+    use_videos=True,
+    patterns=["action.gripper.pos", "observation.state.gripper.pos"],
+)
+
+observation_ee = aggregate_pipeline_dataset_features(
+    pipeline=robot_joints_to_ee_pose,
+    initial_features=robot.observation_features,
+    use_videos=True,
+    patterns=["observation.state.ee"],
+)
+
+dataset_features = merge_features(action_ee, gripper, observation_ee)
+```
+
+How it works:
+
+- `aggregate_pipeline_dataset_features(...)`: applies `transform_features` across the pipeline and filters by patterns (images included when `use_videos=True`).
+- `merge_features(...)`: combine multiple feature dicts.
+- Recording uses `to_dataset_frame(...)` to build frames consistent with `dataset.features` before we call `add_frame(...)` to add the frame to the dataset.
+
+## Guidance when customizing robot pipelines
+
+You can store any of the following features as your action/observation space:
+
+- Joint positions
+- Absolute EE poses
+- Relative EE deltas
+- Other features: joint velocity, etc.
+
+Pick what you want to use for your policy action and observation space and configure/modify the pipelines and steps accordingly.
+
+### Different robots
+
+- Swap `RobotKinematics` URDF and `motor_names`. Ensure `target_frame_name` points to your gripper/wrist.
+
+### Safety first
+
+- When changing pipelines, start with tight bounds, implement safety steps when working with real robots.
+- Its advised to start with simulation first and then move to real robots.
+
+Hope this guide helps you get started with customizing your robot pipelines, If you run into any issues at any point, jump into our [Discord community](https://discord.com/invite/s3KuuzsPFb) for support.

docs/source/smolvla.mdx

@@ -54,7 +54,7 @@ If you don't have a gpu device, you can train using our notebook on [![Google Co
 Pass your dataset to the training script using `--dataset.repo_id`. If you want to test your installation, run the following command where we use one of the datasets we collected for the [SmolVLA Paper](https://huggingface.co/papers/2506.01844).
 
 ```bash
-cd lerobot && lerobot-train \
+cd lerobot && python -m lerobot.scripts.train \
   --policy.path=lerobot/smolvla_base \
   --dataset.repo_id=${HF_USER}/mydataset \
   --batch_size=64 \
@@ -73,7 +73,7 @@ cd lerobot && lerobot-train \
 Fine-tuning is an art. For a complete overview of the options for finetuning, run
 
 ```bash
-lerobot-train --help
+python -m lerobot.scripts.train --help
 ```
 
 <p align="center">
@@ -97,7 +97,7 @@ Similarly for when recording an episode, it is recommended that you are logged i
 Once you are logged in, you can run inference in your setup by doing:
 
 ```bash
-lerobot-record \
+python -m lerobot.record \
   --robot.type=so101_follower \
   --robot.port=/dev/ttyACM0 \ # <- Use your port
   --robot.id=my_blue_follower_arm \ # <- Use your robot id

docs/source/so100.mdx

@@ -26,7 +26,7 @@ Unlike the SO-101, the motor connectors are not easily accessible once the arm i
 To find the port for each bus servo adapter, run this script:
 
 ```bash
-lerobot-find-port
+python -m lerobot.find_port
 ```
 
 <hfoptions id="example">
@@ -93,7 +93,7 @@ For a visual reference on how to set the motor ids please refer to [this video](
 <hfoption id="Command">
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
 ```
@@ -168,7 +168,7 @@ Do the same steps for the leader arm.
 <hfoptions id="setup_motors">
 <hfoption id="Command">
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
 ```
@@ -568,7 +568,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -606,7 +606,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

docs/source/so101.mdx

@@ -162,7 +162,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
 To find the port for each bus servo adapter, connect MotorBus to your computer via USB and power. Run the following script and disconnect the MotorBus when prompted:
 
 ```bash
-lerobot-find-port
+python -m lerobot.find_port
 ```
 
 <hfoptions id="example">
@@ -240,7 +240,7 @@ Connect the usb cable from your computer and the power supply to the follower ar
 <hfoption id="Command">
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem585A0076841  # <- paste here the port found at previous step
 ```
@@ -316,7 +316,7 @@ Do the same steps for the leader arm.
 <hfoption id="Command">
 
 ```bash
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem575E0031751  # <- paste here the port found at previous step
 ```
@@ -353,7 +353,7 @@ Run the following command or API example to calibrate the follower arm:
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --robot.id=my_awesome_follower_arm # <- Give the robot a unique name
@@ -402,7 +402,7 @@ Do the same steps to calibrate the leader arm, run the following command or API
 <hfoption id="Command">
 
 ```bash
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=so101_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \ # <- The port of your robot
     --teleop.id=my_awesome_leader_arm # <- Give the robot a unique name

examples/4_train_policy_with_script.md

@@ -62,7 +62,7 @@ By default, every field takes its default value specified in the dataclass. If a
 Let's say that we want to train [Diffusion Policy](../src/lerobot/policies/diffusion) on the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using the [gym_pusht](https://github.com/huggingface/gym-pusht) environment for evaluation. The command to do so would look like this:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --dataset.repo_id=lerobot/pusht \
     --policy.type=diffusion \
     --env.type=pusht
@@ -77,7 +77,7 @@ Let's break this down:
 Let's see another example. Let's say you've been training [ACT](../src/lerobot/policies/act) on [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) using the [gym-aloha](https://github.com/huggingface/gym-aloha) environment for evaluation with:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -90,7 +90,7 @@ We now want to train a different policy for aloha on another task. We'll change
 Looking at the [`AlohaEnv`](../src/lerobot/envs/configs.py) config, the task is `"AlohaInsertion-v0"` by default, which corresponds to the task we trained on in the command above. The [gym-aloha](https://github.com/huggingface/gym-aloha?tab=readme-ov-file#description) environment also has the `AlohaTransferCube-v0` task which corresponds to this other task we want to train on. Putting this together, we can train this new policy on this different task using:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -127,7 +127,7 @@ Now, let's assume that we want to reproduce the run just above. That run has pro
 We can then simply load the config values from this file using:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
 ```
@@ -137,7 +137,7 @@ lerobot-train \
 Similarly to Hydra, we can still override some parameters in the CLI if we want to, e.g.:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/act_aloha_transfer/checkpoints/last/pretrained_model/ \
     --output_dir=outputs/train/act_aloha_transfer_2
     --policy.n_action_steps=80
@@ -148,7 +148,7 @@ lerobot-train \
 `--config_path` can also accept the repo_id of a repo on the hub that contains a `train_config.json` file, e.g. running:
 
 ```bash
-lerobot-train --config_path=lerobot/diffusion_pusht
+python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
 ```
 
 will start a training run with the same configuration used for training [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)
@@ -160,7 +160,7 @@ Being able to resume a training run is important in case it crashed or aborted f
 Let's reuse the command from the previous run and add a few more options:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.type=act \
     --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
     --env.type=aloha \
@@ -179,7 +179,7 @@ INFO 2025-01-24 16:10:56 ts/train.py:263 Checkpoint policy after step 100
 Now let's simulate a crash by killing the process (hit `ctrl`+`c`). We can then simply resume this run from the last checkpoint available with:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true
 ```
@@ -190,7 +190,7 @@ Another reason for which you might want to resume a run is simply to extend trai
 You could double the number of steps of the previous run with:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=outputs/train/run_resumption/checkpoints/last/pretrained_model/ \
     --resume=true \
     --steps=200000
@@ -224,7 +224,7 @@ In addition to the features currently in Draccus, we've added a special `.path`
 For example, we could fine-tune a [policy pre-trained on the aloha transfer task](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) on the aloha insertion task. We can achieve this with:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \
@@ -270,7 +270,7 @@ We'll summarize here the main use cases to remember from this tutorial.
 #### Train a policy from scratch – CLI
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.type=act \  # <- select 'act' policy
     --env.type=pusht \  # <- select 'pusht' environment
     --dataset.repo_id=lerobot/pusht  # <- train on this dataset
@@ -279,7 +279,7 @@ lerobot-train \
 #### Train a policy from scratch - config file + CLI
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=path/to/pretrained_model \  # <- can also be a repo_id
     --policy.n_action_steps=80  # <- you may still override values
 ```
@@ -287,7 +287,7 @@ lerobot-train \
 #### Resume/continue a training run
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --config_path=checkpoint/pretrained_model/ \
     --resume=true \
     --steps=200000  # <- you can change some training parameters
@@ -296,7 +296,7 @@ lerobot-train \
 #### Fine-tuning
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
     --policy.path=lerobot/act_aloha_sim_transfer_cube_human \  # <- can also be a local path to a checkpoint
     --dataset.repo_id=lerobot/aloha_sim_insertion_human \
     --env.type=aloha \

examples/backward_compatibility/replay.py

@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
 Example:
 
 ```shell
-lerobot-replay \
+python -m lerobot.replay \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=black \

scripts/convert_videos_to_images.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+
+"""
+Convert video dataset to image dataset for faster training.
+This pre-extracts all frames from MP4 files to PNG images.
+"""
+
+import argparse
+from pathlib import Path
+import logging
+import shutil
+
+def convert_dataset_videos_to_images(repo_id: str, root: str | None = None):
+    """Convert all videos in a LeRobot dataset to individual image files."""
+    from lerobot.datasets.lerobot_dataset import LeRobotDataset
+    from lerobot.datasets.video_utils import decode_video_frames
+    import torch
+    
+    # Load dataset
+    dataset = LeRobotDataset(repo_id, root=root, download_videos=True)
+    
+    total_frames_processed = 0
+    
+    for ep_idx in range(dataset.meta.total_episodes):
+        logging.info(f"Processing episode {ep_idx}/{dataset.meta.total_episodes}")
+        
+        for vid_key in dataset.meta.video_keys:
+            video_path = dataset.root / dataset.meta.get_video_file_path(ep_idx, vid_key)
+            
+            if not video_path.exists():
+                logging.warning(f"Video not found: {video_path}")
+                continue
+                
+            # Create image directory  
+            img_dir = dataset.root / f"images/chunk-{dataset.meta.get_episode_chunk(ep_idx)}/{vid_key}"
+            img_dir.mkdir(parents=True, exist_ok=True)
+            
+            # Decode all frames from video
+            # Get episode length to decode all frames
+            ep_length = dataset.meta.episodes[ep_idx]["length"]
+            timestamps = [i / dataset.fps for i in range(ep_length)]
+            
+            try:
+                frames = decode_video_frames(video_path, timestamps, dataset.tolerance_s, dataset.video_backend)
+                
+                # Save each frame as PNG
+                for i, frame in enumerate(frames.squeeze(0)):
+                    img_path = img_dir / f"episode_{ep_idx:06d}_{i:06d}.png"
+                    # Convert tensor to PIL and save
+                    import torchvision.transforms as T
+                    to_pil = T.ToPILImage()
+                    pil_frame = to_pil(frame)
+                    pil_frame.save(img_path)
+                    
+                total_frames_processed += len(frames.squeeze(0))
+                logging.info(f"  Extracted {len(frames.squeeze(0))} frames to {img_dir}")
+                
+            except Exception as e:
+                logging.error(f"Failed to process {video_path}: {e}")
+                continue
+    
+    logging.info(f"Conversion complete! Processed {total_frames_processed} total frames")
+    logging.info(f"You can now use download_videos=False to use the extracted images")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert LeRobot video dataset to images")
+    parser.add_argument("repo_id", help="Dataset repo ID (e.g., 'kenmacken/record-test-2')")
+    parser.add_argument("--root", help="Local root directory", default=None)
+    
+    args = parser.parse_args()
+    
+    logging.basicConfig(level=logging.INFO)
+    
+    convert_dataset_videos_to_images(args.repo_id, args.root)

src/lerobot/calibrate.py

@@ -18,7 +18,7 @@ Helper to recalibrate your device (robot or teleoperator).
 Example:
 
 ```shell
-lerobot-calibrate \
+python -m lerobot.calibrate \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem58760431551 \
     --teleop.id=blue

src/lerobot/cameras/opencv/camera_opencv.py

@@ -60,7 +60,7 @@ class OpenCVCamera(Camera):
     or port changes, especially on Linux. Use the provided utility script to find
     available camera indices or paths:
     ```bash
-    lerobot-find-cameras opencv
+    python -m lerobot.find_cameras opencv
     ```
 
     The camera's default settings (FPS, resolution, color mode) are used unless
@@ -165,7 +165,8 @@ class OpenCVCamera(Camera):
             self.videocapture.release()
             self.videocapture = None
             raise ConnectionError(
-                f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
+                f"Failed to open {self}."
+                f"Run `python -m lerobot.find_cameras opencv` to find available cameras."
             )
 
         self._configure_capture_settings()

src/lerobot/cameras/realsense/camera_realsense.py

@@ -51,7 +51,7 @@ class RealSenseCamera(Camera):
 
     Use the provided utility script to find available camera indices and default profiles:
     ```bash
-    lerobot-find-cameras realsense
+    python -m lerobot.find_cameras realsense
     ```
 
     A `RealSenseCamera` instance requires a configuration object specifying the
@@ -176,7 +176,8 @@ class RealSenseCamera(Camera):
             self.rs_profile = None
             self.rs_pipeline = None
             raise ConnectionError(
-                f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
+                f"Failed to open {self}."
+                "Run `python -m lerobot.find_cameras realsense` to find available cameras."
             ) from e
 
         self._configure_capture_settings()

src/lerobot/configs/default.py

@@ -33,6 +33,8 @@ class DatasetConfig:
     # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | None = None
     episodes: list[int] | None = None
+    # Percentage of dataset to use (0-100). If set, overrides episodes parameter.
+    percentage: float | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
     revision: str | None = None
     use_imagenet_stats: bool = True

src/lerobot/constants.py

@@ -40,9 +40,6 @@ OPTIMIZER_STATE = "optimizer_state.safetensors"
 OPTIMIZER_PARAM_GROUPS = "optimizer_param_groups.json"
 SCHEDULER_STATE = "scheduler_state.json"
 
-PREPROCESSOR_DEFAULT_NAME = "robot_preprocessor"
-POSTPROCESSOR_DEFAULT_NAME = "robot_postprocessor"
-
 if "LEROBOT_HOME" in os.environ:
     raise ValueError(
         f"You have a 'LEROBOT_HOME' environment variable set to '{os.getenv('LEROBOT_HOME')}'.\n"

src/lerobot/datasets/factory.py

@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import logging
 from pprint import pformat
 
 import torch
@@ -87,10 +86,24 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
             cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
         )
         delta_timestamps = resolve_delta_timestamps(cfg.policy, ds_meta)
+
+        # Handle percentage parameter
+        episodes = cfg.dataset.episodes
+        if cfg.dataset.percentage is not None:
+            # Calculate episodes based on percentage
+            total_episodes = ds_meta.total_episodes
+            num_episodes_to_use = max(1, int(total_episodes * cfg.dataset.percentage / 100))
+            episodes = list(range(num_episodes_to_use))
+            import logging
+
+            logging.info(
+                f"Using {cfg.dataset.percentage}% of dataset: {num_episodes_to_use}/{total_episodes} episodes"
+            )
+
         dataset = LeRobotDataset(
             cfg.dataset.repo_id,
             root=cfg.dataset.root,
-            episodes=cfg.dataset.episodes,
+            episodes=episodes,
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
             revision=cfg.dataset.revision,

src/lerobot/datasets/v21/convert_dataset_v20_to_v21.py

@@ -13,20 +13,24 @@
 # limitations under the License.
 
 """
-This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 2.0 to
-2.1. It will:
+This script converts a LeRobot dataset already pushed to the Hub from codebase version 2.0 to 2.1.
+It downloads metadata from a SOURCE dataset repo, computes/validates per-episode stats, updates
+the codebase version in `info.json`, and uploads the result to a DESTINATION dataset repo.
+It will:
 
 - Generate per-episodes stats and writes them in `episodes_stats.jsonl`
 - Check consistency between these new stats and the old ones.
 - Remove the deprecated `stats.json`.
 - Update codebase_version in `info.json`.
-- Push this new version to the hub on the 'main' branch and tags it with "v2.1".
+- Push this new version to the destination repo/branch and tag it with the current codebase version.
 
 Usage:
 
 ```bash
 python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 \
-    --repo-id=aliberts/koch_tutorial
+    --source-repo-id=namespace/source_dataset \
+    --dest-repo-id=namespace/destination_dataset \
+    --branch=main
 ```
 
 """
@@ -54,48 +58,67 @@ class SuppressWarnings:
 
 
 def convert_dataset(
-    repo_id: str,
+    source_repo_id: str,
+    dest_repo_id: str,
     branch: str | None = None,
     num_workers: int = 4,
 ):
+    # Download metadata from the source repo at v2.0
     with SuppressWarnings():
-        dataset = LeRobotDataset(repo_id, revision=V20, force_cache_sync=True)
+        dataset = LeRobotDataset(source_repo_id, revision=V20, force_cache_sync=True)
 
+    # Ensure we recompute fresh episodes stats
     if (dataset.root / EPISODES_STATS_PATH).is_file():
         (dataset.root / EPISODES_STATS_PATH).unlink()
 
+    # Compute and validate stats
     convert_stats(dataset, num_workers=num_workers)
     ref_stats = load_stats(dataset.root)
     check_aggregate_stats(dataset, ref_stats)
 
+    # Update codebase version in info.json
     dataset.meta.info["codebase_version"] = CODEBASE_VERSION
     write_info(dataset.meta.info, dataset.root)
 
-    dataset.push_to_hub(branch=branch, tag_version=False, allow_patterns="meta/")
-
-    # delete old stats.json file
-    if (dataset.root / STATS_PATH).is_file:
+    # Remove deprecated stats.json locally so it won't be uploaded
+    if (dataset.root / STATS_PATH).is_file():
         (dataset.root / STATS_PATH).unlink()
 
+    # Push only meta/ to destination repo
     hub_api = HfApi()
-    if hub_api.file_exists(
-        repo_id=dataset.repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset"
-    ):
-        hub_api.delete_file(
-            path_in_repo=STATS_PATH, repo_id=dataset.repo_id, revision=branch, repo_type="dataset"
-        )
+    hub_api.create_repo(repo_id=dest_repo_id, private=False, repo_type="dataset", exist_ok=True)
+    if branch:
+        hub_api.create_branch(repo_id=dest_repo_id, branch=branch, repo_type="dataset", exist_ok=True)
 
-    hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
+    hub_api.upload_folder(
+        repo_id=dest_repo_id,
+        folder_path=str(dataset.root),
+        repo_type="dataset",
+        revision=branch,
+        allow_patterns="meta/",
+    )
+
+    # Ensure old stats.json is deleted on destination
+    if hub_api.file_exists(repo_id=dest_repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset"):
+        hub_api.delete_file(path_in_repo=STATS_PATH, repo_id=dest_repo_id, revision=branch, repo_type="dataset")
+
+    # Tag destination with current codebase version
+    hub_api.create_tag(dest_repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--repo-id",
+        "--source-repo-id",
         type=str,
         required=True,
-        help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset "
-        "(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
+        help="Source dataset repo id to download from (must be v2.0).",
+    )
+    parser.add_argument(
+        "--dest-repo-id",
+        type=str,
+        required=True,
+        help="Destination dataset repo id to upload the converted metadata to.",
     )
     parser.add_argument(
         "--branch",

src/lerobot/envs/configs.py

@@ -161,71 +161,33 @@ class XarmEnv(EnvConfig):
 
 
 @dataclass
-class ImagePreprocessingConfig:
-    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
-    resize_size: tuple[int, int] | None = None
+class VideoRecordConfig:
+    """Configuration for video recording in ManiSkill environments."""
+
+    enabled: bool = False
+    record_dir: str = "videos"
+    trajectory_name: str = "trajectory"
 
 
 @dataclass
-class RewardClassifierConfig:
-    """Configuration for reward classification."""
-
-    pretrained_path: str | None = None
-    success_threshold: float = 0.5
-    success_reward: float = 1.0
-
-
-@dataclass
-class InverseKinematicsConfig:
-    """Configuration for inverse kinematics processing."""
-
-    urdf_path: str | None = None
-    target_frame_name: str | None = None
-    end_effector_bounds: dict[str, list[float]] | None = None
-    end_effector_step_sizes: dict[str, float] | None = None
-
-
-@dataclass
-class ObservationConfig:
-    """Configuration for observation processing."""
+class EnvTransformConfig:
+    """Configuration for environment wrappers."""
 
+    # ee_action_space_params: EEActionSpaceConfig = field(default_factory=EEActionSpaceConfig)
+    control_mode: str = "gamepad"
+    display_cameras: bool = False
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
-    display_cameras: bool = False
-
-
-@dataclass
-class GripperConfig:
-    """Configuration for gripper control and penalties."""
-
-    use_gripper: bool = True
-    gripper_penalty: float = 0.0
-    gripper_penalty_in_reward: bool = False
-
-
-@dataclass
-class ResetConfig:
-    """Configuration for environment reset behavior."""
-
+    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
+    resize_size: tuple[int, int] | None = None
+    control_time_s: float = 20.0
     fixed_reset_joint_positions: Any | None = None
     reset_time_s: float = 5.0
-    control_time_s: float = 20.0
-    terminate_on_success: bool = True
-
-
-@dataclass
-class HILSerlProcessorConfig:
-    """Configuration for environment processing pipeline."""
-
-    control_mode: str = "gamepad"
-    observation: ObservationConfig | None = None
-    image_preprocessing: ImagePreprocessingConfig | None = None
-    gripper: GripperConfig | None = None
-    reset: ResetConfig | None = None
-    inverse_kinematics: InverseKinematicsConfig | None = None
-    reward_classifier: RewardClassifierConfig | None = None
-    max_gripper_pos: float | None = 100.0
+    use_gripper: bool = True
+    gripper_quantization_threshold: float | None = 0.8
+    gripper_penalty: float = 0.0
+    gripper_penalty_in_reward: bool = False
 
 
 @EnvConfig.register_subclass(name="gym_manipulator")
@@ -235,10 +197,77 @@ class HILSerlRobotEnvConfig(EnvConfig):
 
     robot: RobotConfig | None = None
     teleop: TeleoperatorConfig | None = None
-    processor: HILSerlProcessorConfig = field(default_factory=HILSerlProcessorConfig)
-
+    wrapper: EnvTransformConfig | None = None
+    fps: int = 10
     name: str = "real_robot"
+    mode: str | None = None  # Either "record", "replay", None
+    repo_id: str | None = None
+    dataset_root: str | None = None
+    task: str | None = ""
+    num_episodes: int = 10  # only for record mode
+    episode: int = 0
+    device: str = "cuda"
+    push_to_hub: bool = True
+    pretrained_policy_name_or_path: str | None = None
+    reward_classifier_pretrained_path: str | None = None
+    # For the reward classifier, to record more positive examples after a success
+    number_of_steps_after_success: int = 0
 
     @property
     def gym_kwargs(self) -> dict:
         return {}
+
+
+@EnvConfig.register_subclass("hil")
+@dataclass
+class HILEnvConfig(EnvConfig):
+    """Configuration for the HIL environment."""
+
+    name: str = "PandaPickCube"
+    task: str | None = "PandaPickCubeKeyboard-v0"
+    use_viewer: bool = True
+    gripper_penalty: float = 0.0
+    use_gamepad: bool = True
+    state_dim: int = 18
+    action_dim: int = 4
+    fps: int = 100
+    episode_length: int = 100
+    video_record: VideoRecordConfig = field(default_factory=VideoRecordConfig)
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)),
+            "observation.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128)),
+            "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(18,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            "action": ACTION,
+            "observation.image": OBS_IMAGE,
+            "observation.state": OBS_STATE,
+        }
+    )
+    ################# args from hilserlrobotenv
+    reward_classifier_pretrained_path: str | None = None
+    robot_config: RobotConfig | None = None
+    teleop_config: TeleoperatorConfig | None = None
+    wrapper: EnvTransformConfig | None = None
+    mode: str | None = None  # Either "record", "replay", None
+    repo_id: str | None = None
+    dataset_root: str | None = None
+    num_episodes: int = 10  # only for record mode
+    episode: int = 0
+    device: str = "cuda"
+    push_to_hub: bool = True
+    pretrained_policy_name_or_path: str | None = None
+    # For the reward classifier, to record more positive examples after a success
+    number_of_steps_after_success: int = 0
+    ############################
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {
+            "use_viewer": self.use_viewer,
+            "use_gamepad": self.use_gamepad,
+            "gripper_penalty": self.gripper_penalty,
+        }

src/lerobot/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.envs.configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv
+from lerobot.envs.configs import AlohaEnv, EnvConfig, HILEnvConfig, PushtEnv, XarmEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -27,6 +27,8 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         return PushtEnv(**kwargs)
     elif env_type == "xarm":
         return XarmEnv(**kwargs)
+    elif env_type == "hil":
+        return HILEnvConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{env_type}' is not available.")
 

src/lerobot/find_cameras.py

@@ -20,7 +20,7 @@ Helper to find the camera devices available in your system.
 Example:
 
 ```shell
-lerobot-find-cameras
+python -m lerobot.find_cameras
 ```
 """
 

src/lerobot/find_port.py

@@ -18,7 +18,7 @@ Helper to find the USB port associated with your MotorsBus.
 Example:
 
 ```shell
-lerobot-find-port
+python -m lerobot.find_port
 ```
 """
 

src/lerobot/motors/motors_bus.py

@@ -222,7 +222,7 @@ class MotorsBus(abc.ABC):
     A MotorsBus subclass instance requires a port (e.g. `FeetechMotorsBus(port="/dev/tty.usbmodem575E0031751"`)).
     To find the port, you can run our utility script:
     ```bash
-    lerobot-find-port.py
+    python -m lerobot.find_port.py
     >>> Finding all available ports for the MotorsBus.
     >>> ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
     >>> Remove the usb cable from your MotorsBus and press Enter when done.
@@ -446,7 +446,7 @@ class MotorsBus(abc.ABC):
         except (FileNotFoundError, OSError, serial.SerialException) as e:
             raise ConnectionError(
                 f"\nCould not connect on port '{self.port}'. Make sure you are using the correct port."
-                "\nTry running `lerobot-find-port`\n"
+                "\nTry running `python -m lerobot.find_port`\n"
             ) from e
 
     @abc.abstractmethod

src/lerobot/policies/__init__.py

@@ -16,6 +16,7 @@ from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0.processor_pi0 import Pi0NewLineProcessor
+from .rlearn.configuration_rlearn import RLearNConfig as RLearNConfig
 from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
 from .smolvla.processor_smolvla import SmolVLANewLineProcessor
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
@@ -28,4 +29,5 @@ __all__ = [
     "SmolVLAConfig",
     "TDMPCConfig",
     "VQBeTConfig",
+    "RLearNConfig",
 ]

src/lerobot/policies/act/processor_act.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -46,6 +45,6 @@ def make_act_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/diffusion/processor_diffusion.py

@@ -16,7 +16,6 @@
 # limitations under the License.
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -47,6 +46,6 @@ def make_diffusion_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/factory.py

@@ -34,6 +34,7 @@ from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rlearn.configuration_rlearn import RLearNConfig
 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
@@ -80,6 +81,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
+    elif name == "rlearn":
+        from lerobot.policies.rlearn.modeling_rlearn import RLearNPolicy
+
+        return RLearNPolicy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -103,6 +108,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return SmolVLAConfig(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
+    elif policy_type == "rlearn":
+        return RLearNConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 
@@ -140,6 +147,8 @@ def make_processor(
         NotImplementedError: If the policy type doesn't have a processor implemented.
     """
     if pretrained_path:
+        # Load a pretrained processor
+        # TODO(azouitine): Handle this case.
         return (
             RobotProcessor.from_pretrained(
                 pretrained_model_name_or_path=pretrained_path,
@@ -218,6 +227,13 @@ def make_processor(
             cast(SmolVLAConfig, policy_cfg), dataset_stats=kwargs.get("dataset_stats")
         )
 
+    elif policy_cfg.type == "rlearn":
+        from lerobot.policies.rlearn.processor_rlearn import make_rlearn_processor
+
+        processors = make_rlearn_processor(
+            cast(RLearNConfig, policy_cfg), dataset_stats=kwargs.get("dataset_stats")
+        )
+
     else:
         raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")
 
@@ -228,6 +244,7 @@ def make_policy(
     cfg: PreTrainedConfig,
     ds_meta: LeRobotDatasetMetadata | None = None,
     env_cfg: EnvConfig | None = None,
+    episode_data_index: dict | None = None,
 ) -> PreTrainedPolicy:
     """Make an instance of a policy class.
 
@@ -285,6 +302,10 @@ def make_policy(
     cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
     kwargs["config"] = cfg
 
+    # Pass episode_data_index for RLearN policy to calculate proper progress
+    if cfg.type == "rlearn" and episode_data_index is not None:
+        kwargs["episode_data_index"] = episode_data_index
+
     if cfg.pretrained_path:
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
         # hyperparameters that we want to vary).

src/lerobot/policies/normalize.py

@@ -0,0 +1,420 @@
+#!/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 numpy as np
+import torch
+from torch import Tensor, nn
+
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+
+
+def create_stats_buffers(
+    features: dict[str, PolicyFeature],
+    norm_map: dict[str, NormalizationMode],
+    stats: dict[str, dict[str, Tensor]] | None = None,
+) -> dict[str, dict[str, nn.ParameterDict]]:
+    """
+    Create buffers per modality (e.g. "observation.image", "action") containing their mean, std, min, max
+    statistics.
+
+    Args: (see Normalize and Unnormalize)
+
+    Returns:
+        dict: A dictionary where keys are modalities and values are `nn.ParameterDict` containing
+            `nn.Parameters` set to `requires_grad=False`, suitable to not be updated during backpropagation.
+    """
+    stats_buffers = {}
+
+    for key, ft in features.items():
+        norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY)
+        if norm_mode is NormalizationMode.IDENTITY:
+            continue
+
+        assert isinstance(norm_mode, NormalizationMode)
+
+        shape = tuple(ft.shape)
+
+        if ft.type is FeatureType.VISUAL:
+            # sanity checks
+            assert len(shape) == 3, f"number of dimensions of {key} != 3 ({shape=}"
+            c, h, w = shape
+            assert c < h and c < w, f"{key} is not channel first ({shape=})"
+            # override image shape to be invariant to height and width
+            shape = (c, 1, 1)
+
+        # Note: we initialize mean, std, min, max to infinity. They should be overwritten
+        # downstream by `stats` or `policy.load_state_dict`, as expected. During forward,
+        # we assert they are not infinity anymore.
+
+        buffer = {}
+        if norm_mode is NormalizationMode.MEAN_STD:
+            mean = torch.ones(shape, dtype=torch.float32) * torch.inf
+            std = torch.ones(shape, dtype=torch.float32) * torch.inf
+            buffer = nn.ParameterDict(
+                {
+                    "mean": nn.Parameter(mean, requires_grad=False),
+                    "std": nn.Parameter(std, requires_grad=False),
+                }
+            )
+        elif norm_mode is NormalizationMode.MIN_MAX:
+            min = torch.ones(shape, dtype=torch.float32) * torch.inf
+            max = torch.ones(shape, dtype=torch.float32) * torch.inf
+            buffer = nn.ParameterDict(
+                {
+                    "min": nn.Parameter(min, requires_grad=False),
+                    "max": nn.Parameter(max, requires_grad=False),
+                }
+            )
+
+        # TODO(aliberts, rcadene): harmonize this to only use one framework (np or torch)
+        if stats:
+            if isinstance(stats[key]["mean"], np.ndarray):
+                if norm_mode is NormalizationMode.MEAN_STD:
+                    buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32)
+                    buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32)
+                elif norm_mode is NormalizationMode.MIN_MAX:
+                    buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32)
+                    buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32)
+            elif isinstance(stats[key]["mean"], torch.Tensor):
+                # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
+                # tensors anywhere (for example, when we use the same stats for normalization and
+                # unnormalization). See the logic here
+                # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
+                if norm_mode is NormalizationMode.MEAN_STD:
+                    buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32)
+                    buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
+                elif norm_mode is NormalizationMode.MIN_MAX:
+                    buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32)
+                    buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
+            else:
+                type_ = type(stats[key]["mean"])
+                raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.")
+
+        stats_buffers[key] = buffer
+    return stats_buffers
+
+
+def _no_stats_error_str(name: str) -> str:
+    return (
+        f"`{name}` is infinity. You should either initialize with `stats` as an argument, or use a "
+        "pretrained model."
+    )
+
+
+class Normalize(nn.Module):
+    """Normalizes data (e.g. "observation.image") for more stable and faster convergence during training."""
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        """
+        Args:
+            shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values
+            are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing
+            mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape
+            is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format.
+            modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values
+                are their normalization modes among:
+                    - "mean_std": subtract the mean and divide by standard deviation.
+                    - "min_max": map to [-1, 1] range.
+            stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image")
+                and values are dictionaries of statistic types and their values (e.g.
+                `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for
+                training the model for the first time, these statistics will overwrite the default buffers. If
+                not provided, as expected for finetuning or evaluation, the default buffers should to be
+                overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the
+                dataset is not needed to get the stats, since they are already in the policy state_dict.
+        """
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+        self.stats = stats
+        stats_buffers = create_stats_buffers(features, norm_map, stats)
+        for key, buffer in stats_buffers.items():
+            setattr(self, "buffer_" + key.replace(".", "_"), buffer)
+
+    # TODO(rcadene): should we remove torch.no_grad?
+    @torch.no_grad()
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        # TODO: Remove this shallow copy
+        batch = dict(batch)  # shallow copy avoids mutating the input batch
+        for key, ft in self.features.items():
+            if key not in batch:
+                # FIXME(aliberts, rcadene): This might lead to silent fail!
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            buffer = getattr(self, "buffer_" + key.replace(".", "_"))
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = buffer["mean"]
+                std = buffer["std"]
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = (batch[key] - mean) / (std + 1e-8)
+            elif norm_mode is NormalizationMode.MIN_MAX:
+                min = buffer["min"]
+                max = buffer["max"]
+                assert not torch.isinf(min).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max).any(), _no_stats_error_str("max")
+                # normalize to [0,1]
+                batch[key] = (batch[key] - min) / (max - min + 1e-8)
+                # normalize to [-1, 1]
+                batch[key] = batch[key] * 2 - 1
+            else:
+                raise ValueError(norm_mode)
+        return batch
+
+
+class Unnormalize(nn.Module):
+    """
+    Similar to `Normalize` but unnormalizes output data (e.g. `{"action": torch.randn(b,c)}`) in their
+    original range used by the environment.
+    """
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        """
+        Args:
+            shapes (dict): A dictionary where keys are input modalities (e.g. "observation.image") and values
+            are their shapes (e.g. `[3,96,96]`]). These shapes are used to create the tensor buffer containing
+            mean, std, min, max statistics. If the provided `shapes` contain keys related to images, the shape
+            is adjusted to be invariant to height and width, assuming a channel-first (c, h, w) format.
+            modes (dict): A dictionary where keys are output modalities (e.g. "observation.image") and values
+                are their normalization modes among:
+                    - "mean_std": subtract the mean and divide by standard deviation.
+                    - "min_max": map to [-1, 1] range.
+            stats (dict, optional): A dictionary where keys are output modalities (e.g. "observation.image")
+                and values are dictionaries of statistic types and their values (e.g.
+                `{"mean": torch.randn(3,1,1)}, "std": torch.randn(3,1,1)}`). If provided, as expected for
+                training the model for the first time, these statistics will overwrite the default buffers. If
+                not provided, as expected for finetuning or evaluation, the default buffers should to be
+                overwritten by a call to `policy.load_state_dict(state_dict)`. That way, initializing the
+                dataset is not needed to get the stats, since they are already in the policy state_dict.
+        """
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+        self.stats = stats
+        # `self.buffer_observation_state["mean"]` contains `torch.tensor(state_dim)`
+        stats_buffers = create_stats_buffers(features, norm_map, stats)
+        for key, buffer in stats_buffers.items():
+            setattr(self, "buffer_" + key.replace(".", "_"), buffer)
+
+    # TODO(rcadene): should we remove torch.no_grad?
+    @torch.no_grad()
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        batch = dict(batch)  # shallow copy avoids mutating the input batch
+        for key, ft in self.features.items():
+            if key not in batch:
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            buffer = getattr(self, "buffer_" + key.replace(".", "_"))
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = buffer["mean"]
+                std = buffer["std"]
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = batch[key] * std + mean
+            elif norm_mode is NormalizationMode.MIN_MAX:
+                min = buffer["min"]
+                max = buffer["max"]
+                assert not torch.isinf(min).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max).any(), _no_stats_error_str("max")
+                batch[key] = (batch[key] + 1) / 2
+                batch[key] = batch[key] * (max - min) + min
+            else:
+                raise ValueError(norm_mode)
+        return batch
+
+
+# TODO (azouitine): We should replace all normalization on the policies with register_buffer normalization
+#       and remove the `Normalize` and `Unnormalize` classes.
+def _initialize_stats_buffers(
+    module: nn.Module,
+    features: dict[str, PolicyFeature],
+    norm_map: dict[str, NormalizationMode],
+    stats: dict[str, dict[str, Tensor]] | None = None,
+) -> None:
+    """Register statistics buffers (mean/std or min/max) on the given *module*.
+
+    The logic matches the previous constructors of `NormalizeBuffer` and `UnnormalizeBuffer`,
+    but is factored out so it can be reused by both classes and stay in sync.
+    """
+    for key, ft in features.items():
+        norm_mode = norm_map.get(ft.type, NormalizationMode.IDENTITY)
+        if norm_mode is NormalizationMode.IDENTITY:
+            continue
+
+        shape: tuple[int, ...] = tuple(ft.shape)
+        if ft.type is FeatureType.VISUAL:
+            # reduce spatial dimensions, keep channel dimension only
+            c, *_ = shape
+            shape = (c, 1, 1)
+
+        prefix = key.replace(".", "_")
+
+        if norm_mode is NormalizationMode.MEAN_STD:
+            mean = torch.full(shape, torch.inf, dtype=torch.float32)
+            std = torch.full(shape, torch.inf, dtype=torch.float32)
+
+            if stats and key in stats and "mean" in stats[key] and "std" in stats[key]:
+                mean_data = stats[key]["mean"]
+                std_data = stats[key]["std"]
+                if isinstance(mean_data, torch.Tensor):
+                    # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
+                    # tensors anywhere (for example, when we use the same stats for normalization and
+                    # unnormalization). See the logic here
+                    # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
+                    mean = mean_data.clone().to(dtype=torch.float32)
+                    std = std_data.clone().to(dtype=torch.float32)
+                else:
+                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
+
+            module.register_buffer(f"{prefix}_mean", mean)
+            module.register_buffer(f"{prefix}_std", std)
+            continue
+
+        if norm_mode is NormalizationMode.MIN_MAX:
+            min_val = torch.full(shape, torch.inf, dtype=torch.float32)
+            max_val = torch.full(shape, torch.inf, dtype=torch.float32)
+
+            if stats and key in stats and "min" in stats[key] and "max" in stats[key]:
+                min_data = stats[key]["min"]
+                max_data = stats[key]["max"]
+                if isinstance(min_data, torch.Tensor):
+                    min_val = min_data.clone().to(dtype=torch.float32)
+                    max_val = max_data.clone().to(dtype=torch.float32)
+                else:
+                    raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
+
+            module.register_buffer(f"{prefix}_min", min_val)
+            module.register_buffer(f"{prefix}_max", max_val)
+            continue
+
+        raise ValueError(norm_mode)
+
+
+class NormalizeBuffer(nn.Module):
+    """Same as `Normalize` but statistics are stored as registered buffers rather than parameters."""
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+
+        _initialize_stats_buffers(self, features, norm_map, stats)
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        batch = dict(batch)
+        for key, ft in self.features.items():
+            if key not in batch:
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            prefix = key.replace(".", "_")
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = getattr(self, f"{prefix}_mean")
+                std = getattr(self, f"{prefix}_std")
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = (batch[key] - mean) / (std + 1e-8)
+                continue
+
+            if norm_mode is NormalizationMode.MIN_MAX:
+                min_val = getattr(self, f"{prefix}_min")
+                max_val = getattr(self, f"{prefix}_max")
+                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
+                batch[key] = (batch[key] - min_val) / (max_val - min_val + 1e-8)
+                batch[key] = batch[key] * 2 - 1
+                continue
+
+            raise ValueError(norm_mode)
+
+        return batch
+
+
+class UnnormalizeBuffer(nn.Module):
+    """Inverse operation of `NormalizeBuffer`. Uses registered buffers for statistics."""
+
+    def __init__(
+        self,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[str, NormalizationMode],
+        stats: dict[str, dict[str, Tensor]] | None = None,
+    ):
+        super().__init__()
+        self.features = features
+        self.norm_map = norm_map
+
+        _initialize_stats_buffers(self, features, norm_map, stats)
+
+    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+        # batch = dict(batch)
+        for key, ft in self.features.items():
+            if key not in batch:
+                continue
+
+            norm_mode = self.norm_map.get(ft.type, NormalizationMode.IDENTITY)
+            if norm_mode is NormalizationMode.IDENTITY:
+                continue
+
+            prefix = key.replace(".", "_")
+
+            if norm_mode is NormalizationMode.MEAN_STD:
+                mean = getattr(self, f"{prefix}_mean")
+                std = getattr(self, f"{prefix}_std")
+                assert not torch.isinf(mean).any(), _no_stats_error_str("mean")
+                assert not torch.isinf(std).any(), _no_stats_error_str("std")
+                batch[key] = batch[key] * std + mean
+                continue
+
+            if norm_mode is NormalizationMode.MIN_MAX:
+                min_val = getattr(self, f"{prefix}_min")
+                max_val = getattr(self, f"{prefix}_max")
+                assert not torch.isinf(min_val).any(), _no_stats_error_str("min")
+                assert not torch.isinf(max_val).any(), _no_stats_error_str("max")
+                batch[key] = (batch[key] + 1) / 2
+                batch[key] = batch[key] * (max_val - min_val) + min_val
+                continue
+
+            raise ValueError(norm_mode)
+
+        return batch

src/lerobot/policies/pi0/modeling_pi0.py

@@ -30,7 +30,7 @@ pip install -e ".[pi0]"
 
 Example of finetuning the pi0 pretrained model (`pi0_base` in `openpi`):
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.path=lerobot/pi0 \
 --dataset.repo_id=danaaubakirova/koch_test
 ```
@@ -38,7 +38,7 @@ lerobot-train \
 Example of finetuning the pi0 neural network with PaliGemma and expert Gemma
 pretrained with VLM default parameters before pi0 finetuning:
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.type=pi0 \
 --dataset.repo_id=danaaubakirova/koch_test
 ```

src/lerobot/policies/pi0/processor_pi0.py

@@ -19,7 +19,6 @@ from typing import Any
 import torch
 
 from lerobot.configs.types import PolicyFeature
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
     DeviceProcessor,
@@ -116,6 +115,6 @@ def make_pi0_processor(
         ),
     ]
 
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/pi0fast/modeling_pi0fast.py

@@ -25,14 +25,14 @@ Disclaimer: It is not expected to perform as well as the original implementation
 
 Example of finetuning the pi0+FAST pretrained model (`pi0_fast_base` in `openpi`):
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.path=lerobot/pi0fast_base \
 --dataset.repo_id=danaaubakirova/koch_test
 ```
 
 Example of training the pi0+FAST neural network with from scratch:
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.type=pi0fast \
 --dataset.repo_id=danaaubakirova/koch_test
 ```

src/lerobot/policies/pi0fast/processor_pi0fast.py

@@ -16,7 +16,6 @@
 
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
     DeviceProcessor,
@@ -47,6 +46,6 @@ def make_pi0fast_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -0,0 +1,128 @@
+#!/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, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import NormalizationMode
+
+
+@PreTrainedConfig.register_subclass("rlearn")
+@dataclass
+class RLearNConfig(PreTrainedConfig):
+    """Configuration for a video-language conditioned reward model (RLearN).
+
+    Inputs:
+      - Visual frames (one or multiple cameras). Optionally a short sequence.
+      - A language instruction/goal string.
+
+    Output:
+      - Per-timestep reward logits or a single-step reward logit.
+
+    Notes:
+      - This follows the ReWiND paper architecture. It uses frozen vision/text encoders
+        (DINOv3 for vision, SigLIP2 for language) and trains a
+        lightweight temporal aggregator + head.
+    """
+
+    # Encoders - Use SigLIP2 for both vision and text (shared checkpoint)
+    vision_model_name: str = "google/siglip2-base-patch16-224"
+    text_model_name: str = "google/siglip2-base-patch16-224"
+    freeze_backbones: bool = True
+
+    # Sequence length, amount of past frames including current one to use in the temporal model
+    max_seq_len: int = 16
+    # Temporal sampling stride
+    temporal_sampling_stride: int = 3 # Open x mostly has fps 10, and rewind has seq len 16, ours is 30fps so 30/10 = 3 stride lenght to have same timeframe!
+
+    # Model dimensions and transformer
+    dim_model: int = 512
+    num_layers: int = 4
+    num_heads: int = 8
+    ff_mult: int = 4  # Feed-forward multiplier, hidden = dim_model * ff_mult
+    dropout: float = 0.05
+
+    # --- reward head options ---
+    use_categorical_rewards: bool = False      # classification over bins
+    num_reward_bins: int = 25
+    reward_min_value: float = 0.0              # for HL-Gauss range
+    reward_max_value: float = 1.0
+    use_hl_gauss_loss: bool = True             # if False -> plain regression
+    hl_gauss_num_bins: int = 25                # histogram resolution
+
+    # Inference-time subsampling and regularization
+    inference_stride: int = 1 # inference_stride is an extra, second downsampling applied in forward after window sampling/rewind. Keep it at 1 to disable extra skipping
+    frame_dropout_p: float = 0.10
+
+    # Training
+    learning_rate: float = 5e-4
+    weight_decay: float = 0.01
+    head_lr_multiplier: float = 5.0
+    logit_eps: float = 1e-4
+    regularizer_warmup_steps: int = 500
+    
+    # Performance optimizations
+    use_amp: bool = False
+    compile_model: bool = True
+
+    # ReWiND augmentation
+    rewind_prob: float = 0.3 #0.8
+    rewind_last3_prob: float = 0.0 #0.3
+    mismatch_prob: float = 0.0 #0.2
+    
+    # Normalization presets
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.MEAN_STD,
+        }
+    )
+
+    # Required path to episodes.jsonl for episode boundaries
+    episodes_jsonl_path: str | None = "meta/episodes.jsonl"
+
+    def validate_features(self) -> None:
+        # Require at least one image feature. Language is recommended but optional (can be blank).
+        if not self.image_features:
+            raise ValueError(
+                "You must provide at least one image feature for RLearN (e.g. 'observation.image')."
+            )
+
+    @property
+    def observation_delta_indices(self) -> list | None:
+        # Request a long enough context so in-window stride sampling can be >1.
+        # We ask for (max_seq_len * temporal_sampling_stride) frames ending at t=0.
+        # Example: max_seq_len=16, temporal_sampling_stride=3 → 48 deltas → ~46 frames available.
+        total_needed = self.max_seq_len * max(1, int(self.temporal_sampling_stride))
+        return list(range(1 - total_needed, 1))
+
+    @property
+    def action_delta_indices(self) -> list | None:
+        # Not an action chunking policy.
+        return None
+
+    @property
+    def reward_delta_indices(self) -> list | None:
+        # ReWiND generates progress labels on-the-fly, doesn't need reward data
+        return None
+
+    def get_optimizer_preset(self):  # type: ignore[override]
+        from lerobot.optim.optimizers import AdamWConfig
+
+        return AdamWConfig(lr=self.learning_rate, weight_decay=self.weight_decay)
+
+    def get_scheduler_preset(self):  # type: ignore[override]
+        # No scheduler by default.
+        return None

src/lerobot/policies/rlearn/eval_script.py

@@ -0,0 +1,392 @@
+#!/usr/bin/env python
+
+"""
+Standalone evaluation script for RLearN models.
+
+This script evaluates RLearN reward models on episodes from a dataset,
+generating comparison plots between ground truth rewards and model predictions.
+
+Usage:
+    python src/lerobot/policies/rlearn/eval_script.py --model MODEL_NAME --dataset DATASET_REPO --episodes N
+
+Example:
+    python src/lerobot/policies/rlearn/eval_script.py --model pepijn223/rlearn_18 --dataset pepijn223/phone_pipeline_pickup1 --episodes 2
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+# Add src to path for imports
+sys.path.append(str(Path(__file__).parent.parent.parent.parent))
+
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from scipy.stats import spearmanr
+from tqdm import tqdm
+
+warnings.filterwarnings("ignore")
+
+# LeRobot imports
+from lerobot.constants import OBS_IMAGE, OBS_IMAGES, OBS_LANGUAGE
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.rlearn.modeling_rlearn import RLearNPolicy
+
+
+def _to_chw_float01(img):
+    """Ensure CHW float in [0,1]."""
+    if isinstance(img, np.ndarray):
+        img = torch.from_numpy(img)
+    # HWC -> CHW if needed
+    if len(img.shape) == 3 and img.shape[-1] in (1, 3, 4):
+        img = img.permute(2, 0, 1)
+    if img.dtype == torch.uint8:
+        img = img.float() / 255.0
+    else:
+        img = img.float()
+    return torch.clamp(img, 0.0, 1.0)
+
+
+def _get_language(frame_data):
+    lang = None
+    if OBS_LANGUAGE in frame_data:
+        lang = frame_data[OBS_LANGUAGE]
+        if isinstance(lang, list) and len(lang) > 0:
+            lang = lang[0]
+    elif "task" in frame_data:
+        lang = frame_data["task"]
+    return lang if isinstance(lang, str) else "No language provided"
+
+
+def _get_ground_truth_reward(frame_data):
+    """Try common keys for ground-truth reward. Return None if unavailable."""
+    for key in ("reward", "rewards", "gt_reward", "progress"):
+        if key in frame_data:
+            r = frame_data[key]
+            # unwrap single-element lists/arrays
+            if isinstance(r, (list, np.ndarray)) and np.array(r).size == 1:
+                r = float(np.array(r).reshape(-1)[0])
+            try:
+                return float(r)
+            except Exception:
+                pass
+    return None
+
+
+def extract_episode_frames_and_gt(dataset, episode_idx):
+    """Load a full episode: frames (T, C, H, W), language (str), gt_rewards (np.ndarray or None)."""
+    ep_start = dataset.episode_data_index["from"][episode_idx].item()
+    ep_end = dataset.episode_data_index["to"][episode_idx].item()
+    T = ep_end - ep_start
+
+    frames = []
+    gt_rewards = []
+    language = None
+
+    for t in range(T):
+        item = dataset[ep_start + t]
+
+        # image(s)
+        if OBS_IMAGES in item:
+            img = item[OBS_IMAGES]
+        elif OBS_IMAGE in item:
+            img = item[OBS_IMAGE]
+        else:
+            # try to find an image-like key
+            img_keys = [k for k in item.keys() if "image" in k.lower()]
+            if not img_keys:
+                continue
+            img = item[img_keys[0]]
+
+        frames.append(_to_chw_float01(img))
+
+        # language once
+        if language is None:
+            language = _get_language(item)
+
+        # ground-truth reward (optional)
+        r = _get_ground_truth_reward(item)
+        gt_rewards.append(r)
+
+    if not frames:
+        return None, None, None
+
+    frames = torch.stack(frames)  # (T, C, H, W)
+
+    # If all GT entries are None, treat as missing
+    if all(r is None for r in gt_rewards):
+        gt_rewards = None
+    else:
+        # Replace None by forward filling
+        arr = np.array([np.nan if r is None else float(r) for r in gt_rewards], dtype=float)
+        # forward/back fill
+        if np.isnan(arr[0]):
+            first_valid = np.flatnonzero(~np.isnan(arr))
+            if len(first_valid) > 0:
+                arr[0] = arr[first_valid[0]]
+            else:
+                arr[0] = 0.0
+        for i in range(1, len(arr)):
+            if np.isnan(arr[i]):
+                arr[i] = arr[i - 1]
+        gt_rewards = arr
+
+    return frames, language or "No language provided", gt_rewards
+
+
+@torch.no_grad()
+def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=64, device="cuda", temporal_stride: int | None = None):
+    """
+    Sliding-window prediction: for each frame i, create a window [max(0, i-L+1) .. i],
+    left-pad by repeating the first frame to length L (<= 16), and take the prediction 
+    corresponding to the current frame's position in the window.
+    Returns np.ndarray of shape (T,).
+    """
+    T = frames.shape[0]
+    cfg = getattr(model, "config", object())
+    L = int(getattr(cfg, "max_seq_len", max_seq_len))
+    L = min(L, max_seq_len)  # hard-cap at 16
+    # Use the same temporal stride as training (skip s-1 frames, take 1)
+    if temporal_stride is None:
+        temporal_stride = int(getattr(cfg, "temporal_sampling_stride", 1))
+    temporal_stride = max(1, int(temporal_stride))
+
+    # Preprocessed tensor on device
+    frames = frames.to(device)
+
+    windows = []
+    frame_positions = []  # Track which temporal position each frame should use
+    left_pad_counts = []  # Number of left-pad (OOB) frames per window
+    
+    for i in range(T):
+        # Build indices with stride s: [..., i-3, i] etc., left-padded by clamping to 0
+        idxs = [i - (L - 1 - j) * temporal_stride for j in range(L)]
+        pad_needed = sum(1 for k in idxs if k < 0)
+        clamped = [0 if k < 0 else (T - 1 if k >= T else k) for k in idxs]
+        window = frames[clamped]  # (L, C, H, W)
+
+        # Use the last temporal position (current frame) for reading model output
+        frame_pos = L - 1
+
+        windows.append(window)
+        frame_positions.append(frame_pos)
+        left_pad_counts.append(pad_needed)
+
+    preds = np.zeros(T, dtype=float)
+
+    for s in range(0, T, batch_size):
+        e = min(s + batch_size, T)
+        batch_windows = torch.stack(windows[s:e])  # (B, L, C, H, W)
+        batch_positions = frame_positions[s:e]
+
+        batch = {OBS_IMAGES: batch_windows, OBS_LANGUAGE: [language] * (e - s)}  # expects (B, L, C, H, W)
+
+        # Model returns (B, L) predictions for each temporal position
+        values = model.predict_rewards(batch)  # torch.Tensor (B, L)
+
+        # Apply eval-time padding rule: predictions for left-padded (OOB) frames are zero
+        if values.dim() == 2 and len(left_pad_counts) >= (e - s):
+            for b_idx in range(e - s):
+                pad_n = left_pad_counts[s + b_idx]
+                if pad_n > 0:
+                    values[b_idx, :pad_n] = 0.0
+
+        # Debug output removed - issue was identified and fixed
+
+        if values.dim() == 2:
+            # Extract the prediction corresponding to each frame's position in its window
+            batch_preds = []
+            for b_idx, pos in enumerate(batch_positions):
+                batch_preds.append(values[b_idx, pos].item())
+            preds[s:e] = np.array(batch_preds)
+        else:
+            # Fallback: if model returns (B,), use as is
+            preds[s:e] = values.detach().float().cpu().numpy()
+
+    return preds
+
+
+def plot_episode_eval(episode_idx, gt, pred, language, save_path=None, show=False, title_prefix="RLearN Eval"):
+    """Plot GT vs Predicted over time. Saves PNG if save_path is provided."""
+    T = len(pred)
+    x = np.arange(T)
+
+    plt.figure(figsize=(14, 8))
+    plt.plot(x, pred, linewidth=2.5, marker="o", markersize=3, label="Predicted Reward", color="blue")
+
+    if gt is not None:
+        plt.plot(x, gt, linestyle="--", linewidth=2.5, label="Ground-Truth Reward", color="orange")
+        # Correlation between GT and Pred
+        corr, p = spearmanr(gt, pred)
+        corr_str = f"ρ(GT, Pred) = {0.0 if np.isnan(corr) else corr:.3f} (p={0.0 if np.isnan(p) else p:.3f})"
+    else:
+        expected = np.linspace(0, 1, T)
+        plt.plot(x, expected, linestyle="--", linewidth=2.5, label="Expected Progress (0→1)", color="orange")
+        corr, p = spearmanr(x, pred)
+        corr_str = f"VOC-S ρ(t, Pred) = {0.0 if np.isnan(corr) else corr:.3f} (p={0.0 if np.isnan(p) else p:.3f})"
+
+    plt.title(f"{title_prefix} — Episode {episode_idx}\n{language}\n{corr_str}", fontsize=14)
+    plt.xlabel("Frame Index", fontsize=12)
+    plt.ylabel("Reward / Progress", fontsize=12)
+    plt.legend(fontsize=11)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+
+    if save_path is not None:
+        plt.savefig(save_path, dpi=200, bbox_inches="tight")
+        print(f"Saved eval image to: {save_path}")
+
+    if show:
+        plt.show()
+    else:
+        plt.close()
+
+
+def eval_episode_sliding(
+    episode_idx, dataset, model, save_dir=".", device="cuda", max_seq_len=16, batch_size=64, title_prefix="RLearN Eval"
+):
+    """End-to-end: load episode, predict with sliding 16-frame windows, and save PNG."""
+    frames, language, gt = extract_episode_frames_and_gt(dataset, episode_idx)
+    if frames is None:
+        print(f"[Episode {episode_idx}] No frames found.")
+        return None
+
+    model.eval()
+
+    pred = predict_rewards_sliding(
+        model=model, frames=frames, language=language, max_seq_len=max_seq_len, batch_size=batch_size, device=device
+    )
+
+    # Basic stats
+    print(f"Episode {episode_idx}: T={len(pred)}, pred∈[{pred.min():.3f},{pred.max():.3f}]")
+    if gt is not None:
+        print(f"GT available: gt∈[{np.nanmin(gt):.3f},{np.nanmax(gt):.3f}]")
+
+    save_path = f"{save_dir}/episode_{episode_idx:04d}_eval.png"
+    plot_episode_eval(
+        episode_idx=episode_idx, gt=gt, pred=pred, language=language, save_path=save_path, show=False, title_prefix=title_prefix
+    )
+    return save_path
+
+
+def main():
+    """Main evaluation script for RLearN models."""
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description="Evaluate RLearN model on episodes with GT vs Predicted rewards")
+    parser.add_argument("--model", type=str, required=True, help="Model name/path (e.g., pepijn223/rlearn_mse5)")
+    parser.add_argument("--dataset", type=str, required=True, help="Dataset repo (e.g., pepijn223/phone_pipeline_pickup1)")
+    parser.add_argument("--episodes", type=int, default=5, help="Number of episodes to evaluate")
+    parser.add_argument("--output", type=str, default="./eval_results", help="Output directory for images")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu",
+        help="Device to use",
+    )
+    parser.add_argument("--batch_size", type=int, default=32, help="Batch size for sliding window evaluation")
+
+    args = parser.parse_args()
+
+    # Create output directory
+    output_dir = Path(args.output)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    print("🎯 RLearN Model Evaluation")
+    print("=" * 60)
+    print(f"Model: {args.model}")
+    print(f"Dataset: {args.dataset}")
+    print(f"Episodes: {args.episodes}")
+    print(f"Device: {args.device}")
+    print(f"Output: {output_dir}")
+    print("=" * 60)
+
+    try:
+        # Load dataset
+        print("📁 Loading dataset...")
+
+        dataset = LeRobotDataset(
+            repo_id=args.dataset,
+            episodes=list(range(min(args.episodes, 50))),  # Load enough episodes
+            download_videos=True,
+        )
+
+        print(f"✅ Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+        print(f"   Features: {list(dataset.features.keys())}")
+        print(f"   FPS: {dataset.fps}")
+
+        # Load model
+        print("\n🤖 Loading model...")
+
+        model = RLearNPolicy.from_pretrained(args.model)
+        model = model.to(args.device)
+        model.eval()
+
+        print(f"✅ Model loaded on {args.device}")
+        print(f"   Parameters: {sum(p.numel() for p in model.parameters()):,}")
+        print(f"   Trainable: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}")
+        print(f"   Max sequence length: {model.config.max_seq_len}")
+
+        # Select episodes to evaluate
+        total_available = min(dataset.num_episodes, args.episodes)
+        episode_indices = list(range(total_available))
+
+        print(f"\n📊 Evaluating {len(episode_indices)} episodes...")
+        print("=" * 60)
+
+        # Run sliding window evaluation on each episode
+        saved_paths = []
+        for i, ep_idx in enumerate(episode_indices):
+            print(f"\n[{i+1}/{len(episode_indices)}] Processing Episode {ep_idx}")
+            print("-" * 40)
+
+            try:
+                save_path = eval_episode_sliding(
+                    episode_idx=ep_idx,
+                    dataset=dataset,
+                    model=model,
+                    save_dir=str(output_dir),
+                    device=args.device,
+                    batch_size=args.batch_size,
+                    title_prefix="RLearN Ground Truth vs Predicted",
+                )
+
+                if save_path:
+                    saved_paths.append(save_path)
+
+            except Exception as e:
+                print(f"❌ Error processing episode {ep_idx}: {e}")
+                import traceback
+
+                traceback.print_exc()
+                continue
+
+        # Summary
+        print("\n" + "=" * 60)
+        print("✅ EVALUATION COMPLETE")
+        print(f"📈 Generated {len(saved_paths)} evaluation plots")
+        print(f"📁 Results saved to: {output_dir}")
+        print("\nGenerated files:")
+        for path in saved_paths:
+            print(f"  • {path}")
+
+        if saved_paths:
+            print(f"\n💡 View the plots to compare ground truth vs predicted rewards!")
+            print(f"   Each plot shows the model's sliding 16-frame window predictions")
+            print(f"   against available ground truth rewards over the episode timeline.")
+
+        return 0
+
+    except Exception as e:
+        print(f"❌ Error during evaluation: {e}")
+        import traceback
+
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == "__main__":
+    exit(main())

src/lerobot/policies/rlearn/processor_rlearn.py

@@ -0,0 +1,128 @@
+#!/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
+from typing import Any
+
+from lerobot.configs.types import PolicyFeature
+from lerobot.constants import OBS_LANGUAGE
+from lerobot.policies.rlearn.configuration_rlearn import RLearNConfig
+from lerobot.processor import (
+    DeviceProcessor,
+    NormalizerProcessor,
+    RenameProcessor,
+    RobotProcessor,
+    ToBatchProcessor,
+    TokenizerProcessor,
+    UnnormalizerProcessor,
+)
+from lerobot.processor.pipeline import (
+    ComplementaryDataProcessor,
+    EnvTransition,
+    ProcessorStepRegistry,
+    TransitionKey,
+)
+
+
+def make_rlearn_processor(
+    config: RLearNConfig, dataset_stats: dict[str, dict[str, Any]] | None = None
+) -> tuple[RobotProcessor, RobotProcessor]:
+    """Build pre/post processors for RLearN.
+
+    Responsibilities moved out of the model:
+      - Normalize inputs (images) using dataset stats
+      - Ensure batching
+      - Map complementary_data.task to observation.language when available
+      - Tokenize language into observation.language.tokens / attention_mask
+      - Move to/from device
+    """
+
+    input_steps = [
+        # No renaming by default, but keep for future extensibility
+        RenameProcessor(rename_map={}),
+        # Move heavy normalization to GPU after transfer for better parallelism
+        ToBatchProcessor(),
+        RLearnLanguageFromTaskProcessor(),
+        # Use SigLIP2 for tokenizer to keep vocab aligned with text tower
+        TokenizerProcessor(
+            tokenizer_name=config.text_model_name,
+            max_length=64,
+            padding="max_length",
+            truncation=True,
+            padding_side="right",
+        ),
+        DeviceProcessor(device=config.device),
+        # Move normalization after GPU transfer to use GPU acceleration
+        NormalizerProcessor(
+            features={**config.input_features, **config.output_features},
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        ),
+    ]
+
+    output_steps = [
+        DeviceProcessor(device="cpu"),
+        UnnormalizerProcessor(
+            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
+        ),
+    ]
+
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
+    )
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="rlearn_language_from_task")
+class RLearnLanguageFromTaskProcessor(ComplementaryDataProcessor):
+    """Copy complementary_data['task'] into observation['observation.language'] if present.
+
+    This ensures the model can consume a raw language string when tokenization is not used,
+    while TokenizerProcessor can still create tokenized fields.
+    """
+
+    task_key: str = "task"
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:  # type: ignore[override]
+        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
+        if not complementary_data or self.task_key not in complementary_data:
+            return transition
+
+        task = complementary_data.get(self.task_key)
+        if task is None:
+            return transition
+
+        # Normalize to list[str]
+        if isinstance(task, str):
+            task_list = [task]
+        elif isinstance(task, list) and all(isinstance(t, str) for t in task):
+            task_list = task
+        else:
+            return transition
+
+        observation = transition.get(TransitionKey.OBSERVATION) or {}
+        # Do not overwrite if user already provided observation.language
+        if OBS_LANGUAGE not in observation:
+            observation[OBS_LANGUAGE] = task_list
+            transition[TransitionKey.OBSERVATION] = observation
+        return transition
+
+    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:  # noqa: D401
+        # Adds nothing to features; only mirrors complementary_data.task into observation
+        return features
+
+    def get_config(self) -> dict[str, Any]:
+        return {"task_key": self.task_key}

src/lerobot/policies/rlearn/rlearn_plan.md

@@ -0,0 +1,101 @@
+## General Value/Reward Learning:
+
+I want to implement a general/universal vision and language value function or reward model for robotics/video tasks. Also called a video language conditioned reward model. Integrated with already existing LeRobot code if convenient, use the LeRobot Dataset for dataset and store the reward for a frame in the lerobot frame itself.
+
+Inspired by these papers:
+
+- ReWiND; https://arxiv.org/pdf/2505.10911 (Most applicable and main paper I want to implement ideas from) and code: https://github.com/lucidrains/rewind-reward-pytorch
+- LIV; https://arxiv.org/pdf/2306.00958 (Most applicable and 2nd main paper I want to implement ideas from) and code https://github.com/penn-pal-lab/LI
+- VLC: Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics: https://arxiv.org/pdf/2405.19988 (Most applicable and 3rd paper I want to implement ideas from) and code: https://github.com/minttusofia/video_language_critic
+
+And these papers which are also relevant:
+
+- https://www.dyna.co/dyna-1/research (Main company I want to reproduce the eventual results from)
+- vip; https://arxiv.org/pdf/2210.00030
+- uvd; https://arxiv.org/pdf/2310.08581
+- vlm in context; https://arxiv.org/pdf/2411.04549
+- https://www.youtube.com/watch?v=JfZYtpEisoM
+
+Little less relevant but still similar papers:
+
+- Learning Generalizable Robotic Reward Functions from “In-The-Wild” Human Videos,
+- XIRL: Cross-embodiment Inverse Reinforcement Learning,
+- Video-Language Critic: Transferable Reward https://arxiv.org/pdf/2405.19988
+- Functions for Language-Conditioned Robotics,
+- LORel, Language-Driven Representation Learning for Robotics https://sites.google.com/view/robotlorel
+- RoboCLIP: One Demonstration is Enough to Learn Robot Policies https://arxiv.org/pdf/2310.07899
+- Points2Rewards: learn first key points and then uses the keypoints to learn general value function/policy https://semrob.github.io/docs/2025_rss_semrob.github.io_paper20.pdf
+- Language-Driven Representation Learning for Robotics: https://arxiv.org/pdf/2302.12766v1
+- R3M: A Universal Visual Representation for Robot Manipulation: https://arxiv.org/pdf/2203.12601v3
+
+Input should be the current image or whole video and the task goal specified in text/language. Output is current reward.
+Archiutecture:
+_ inputs: video o1:T (or current o1:t), language z;
+_ DINO v3 ViT-B/16 (86M params): https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m for vision encoding
+\_ sentence-transformers/all-MiniLM-L12-v2: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2 for text encoding \* Temporal module: small causal transformer ("cross-modal sequential aggregator"), with first-frame positional embedding (to avoid position cheating), frame-dropout, and stride sampling; outputs per-timestep logits.
+
+Loss: See this chatgpt thread: https://chatgpt.com/s/t_68999a50a0b081919abc365cdd205e01
+
+Past images: (for example a reward method go to 3rd floor, has to know what floor it was on and what pas actions it did, can we attend or encorperate images of decision from history in one way?) Maybe via this paper: Learning Long-Context Diffusion Policies via Past-Token Prediction
+
+Amount of frames needed for test/generalization: 1M frames? or ~20% of IPEC-COMMUNITY/bc_z_lerobot
+
+Eval:
+Implement something like voc score , or ROC rank order correlation between reward leanredna and ev reward from sim, or use something else to do additional evaluation
+
+Ideas:
+
+- Incorporate training on multiple horizons: as in label same dataset for longer horizons: make a sandwich (long), put cheese on bread (medium) and even smaller horizons: go down or close gripper (small)
+- Incorporate navigation goals “walk towards the kitchen”, make sure we fix CLIP contrastive learning issue of positional text misunderstanding where model doesnnt learn difference between "horse right of cow" and "horse left of cow" “Move right” potentially train with more other data or even actionable world models such as Genie 3 (https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/)
+
+How to use a general reward model (use cases): - Train rl policy on it - Success detection - Do exploraion - Do task via planning and search to optimize reward - Filter out bad episodes in large datasets from imitation learning
+
+Potential Datasets: (start with dataset that is most clean for this and works best with chosen way of doing evals)
+_ Epic-Kitchens-100
+_ Something-Something v. 2 Dataset https://www.qualcomm.com/developer/software/something-something-v-2-dataset
+_ Ego4D (3000 hours)
+_ Open X-Embodiment (OXE)
+\_ Agi bot world: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha
+
+- GalexiAI dataset: https://opengalaxea.github.io/G0/
+  _ GTEA+ Gaze: https://cbs.ic.gatech.edu/fpv/
+  _ YouCook2 dataset
+  \_ HOWTO100M: https://www.di.ens.fr/willow/research/howto100m/
+- Genie generated dataset?
+
+### TODOs:
+
+- Implement first architecture [x]
+- Implement processors [x]
+- Choose right loss metric(s) [x]
+- Make dataset with script that generated the dataset (IPEC-COMMUNITY/bc_z_lerobot) ready in lerobot format (and be able to visualize in dataset visualizer)
+  - Annotate with ReWiND-style 0→1 progress rewards [x]
+  - Visualize to check [x]
+- Implement eval score or metric that is robust and can deal with generalization/is a good metric to try different architectures. And use it in an eval jupyter notebook with visalization of the live reward next to the video for part of the dataset: VOC score and score with correct and incorrect language captions [x]
+- Do first training [x]
+- Implement on-the-fly progress label generation (no need for pre-annotated rewards) [x]
+- Try different losses
+  - Only rewind loss [x]
+  - Exactly similar to: https://github.com/lucidrains/rewind-reward-pytorch/blob/main/rewind_reward_pytorch/rewind_reward.py#L11 [x]
+  - Try DINO v2 as encoder Base 86 M: with https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2 [x]
+  - Test rewind (evaluate) [x]
+- benchmark siglip 2 vs this implementation forward pass, debug speed [x]
+- use siglip 2 [x]
+- Fix evaluation bug !!! []
+- Fix sample episode padding bug !!! []
+- Overfit on one episode []
+- Cleanup code? [] + enable language loss
+- Convert python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 --repo-id=IPEC-COMMUNITY/bc_z_lerobot and train on 1 percent
+- Then on 10 percent []
+- Ablation 16 sucessive frame vs 16 frame samples with stride 2 or 4 []
+- Add more artificial text to dataset generated by vlm (google gemini) []
+  - See google gemini vlm caption [] https://gemini.google.com/app/7e332ffaf32580f2
+  - Multiple captions per video, creat method to generate as much data as possible etc [] https://arxiv.org/abs/2508.13446, https://arxiv.org/pdf/2412.04453
+- Add other datasets from OXE metioned in rewind []
+- Extend evaluation []
+- Ablation for size vision encoder, language encoder, temporal head []
+- Ablation one mlp head per frame or single mlp head []
+- Add other datasets metnioned here []
+- How can we improve spatial aware learning? solve issue of Contrastive learning and position []
+
+

src/lerobot/policies/sac/processor_sac.py

@@ -17,7 +17,6 @@
 
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -48,6 +47,6 @@ def make_sac_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -28,7 +28,7 @@ pip install -e ".[smolvla]"
 
 Example of finetuning the smolvla pretrained model (`smolvla_base`):
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.path=lerobot/smolvla_base \
 --dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
 --batch_size=64 \
@@ -38,7 +38,7 @@ lerobot-train \
 Example of finetuning a smolVLA. SmolVLA is composed of a pretrained VLM,
 and an action expert.
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
 --policy.type=smolvla \
 --dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
 --batch_size=64 \

src/lerobot/policies/smolvla/processor_smolvla.py

@@ -18,7 +18,6 @@ from typing import Any
 import torch
 
 from lerobot.configs.types import PolicyFeature
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -58,8 +57,8 @@ def make_smolvla_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )
 
 

src/lerobot/policies/tdmpc/processor_tdmpc.py

@@ -16,7 +16,6 @@
 # limitations under the License.
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -47,6 +46,6 @@ def make_tdmpc_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/policies/vqbet/processor_vqbet.py

@@ -17,7 +17,6 @@
 # limitations under the License.
 import torch
 
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.processor import (
     DeviceProcessor,
@@ -48,6 +47,6 @@ def make_vqbet_processor(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
     ]
-    return RobotProcessor(steps=input_steps, name=PREPROCESSOR_DEFAULT_NAME), RobotProcessor(
-        steps=output_steps, name=POSTPROCESSOR_DEFAULT_NAME
+    return RobotProcessor(steps=input_steps, name="robot_preprocessor"), RobotProcessor(
+        steps=output_steps, name="robot_postprocessor"
     )

src/lerobot/processor/__init__.py

@@ -15,20 +15,7 @@
 # limitations under the License.
 
 from .batch_processor import ToBatchProcessor
-from .delta_action_processor import MapDeltaActionToRobotAction
 from .device_processor import DeviceProcessor
-from .hil_processor import (
-    AddTeleopActionAsComplimentaryData,
-    AddTeleopEventsAsInfo,
-    GripperPenaltyProcessor,
-    ImageCropResizeProcessor,
-    InterventionActionProcessor,
-    Numpy2TorchActionProcessor,
-    RewardClassifierProcessor,
-    TimeLimitProcessor,
-    Torch2NumpyActionProcessor,
-)
-from .joint_observations_processor import JointVelocityProcessor, MotorCurrentProcessor
 from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor, hotswap_stats
 from .observation_processor import VanillaObservationProcessor
 from .pipeline import (
@@ -50,20 +37,11 @@ from .tokenizer_processor import TokenizerProcessor
 
 __all__ = [
     "ActionProcessor",
-    "AddTeleopActionAsComplimentaryData",
-    "AddTeleopEventsAsInfo",
     "DeviceProcessor",
     "DoneProcessor",
-    "MapDeltaActionToRobotAction",
     "EnvTransition",
-    "GripperPenaltyProcessor",
     "IdentityProcessor",
-    "ImageCropResizeProcessor",
     "InfoProcessor",
-    "InterventionActionProcessor",
-    "JointVelocityProcessor",
-    "MapDeltaActionToRobotAction",
-    "MotorCurrentProcessor",
     "NormalizerProcessor",
     "UnnormalizerProcessor",
     "hotswap_stats",
@@ -71,14 +49,10 @@ __all__ = [
     "ProcessorStep",
     "ProcessorStepRegistry",
     "RenameProcessor",
-    "RewardClassifierProcessor",
     "RewardProcessor",
     "RobotProcessor",
     "ToBatchProcessor",
     "TokenizerProcessor",
-    "TimeLimitProcessor",
-    "Numpy2TorchActionProcessor",
-    "Torch2NumpyActionProcessor",
     "TransitionKey",
     "TruncatedProcessor",
     "VanillaObservationProcessor",

src/lerobot/processor/delta_action_processor.py

@@ -1,125 +0,0 @@
-# !/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, field
-
-from torch import Tensor
-
-from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.processor.pipeline import ActionProcessor, ProcessorStepRegistry
-
-
-@ProcessorStepRegistry.register("map_delta_action_to_robot_action")
-@dataclass
-class MapDeltaActionToRobotAction(ActionProcessor):
-    """
-    Map delta actions from teleoperators (gamepad, keyboard) to robot target actions
-    for use with inverse kinematics processors.
-
-    Expected input ACTION keys:
-    {
-        "action.delta_x": float,
-        "action.delta_y": float,
-        "action.delta_z": float,
-        "action.gripper": float (optional),
-    }
-
-    Output ACTION keys:
-    {
-        "action.enabled": bool,
-        "action.target_x": float,
-        "action.target_y": float,
-        "action.target_z": float,
-        "action.target_wx": float,
-        "action.target_wy": float,
-        "action.target_wz": float,
-        "action.gripper": float,
-    }
-    """
-
-    # Scale factors for delta movements
-    position_scale: float = 1.0
-    rotation_scale: float = 0.0  # No rotation deltas for gamepad/keyboard
-    gripper_deadzone: float = 0.1  # Threshold for gripper activation
-    _prev_enabled: bool = field(default=False, init=False, repr=False)
-
-    def action(self, action: dict | Tensor | None) -> dict:
-        if action is None:
-            return {}
-
-        # NOTE (maractingi): Action can be a dict from the teleop_devices or a tensor from the policy
-        # TODO (maractingi): changing this target_xyz naming convention from the teleop_devices
-        if isinstance(action, dict):
-            delta_x = action.pop("action.delta_x", 0.0)
-            delta_y = action.pop("action.delta_y", 0.0)
-            delta_z = action.pop("action.delta_z", 0.0)
-            gripper = action.pop("action.gripper", 1.0)  # Default to "stay" (1.0)
-        else:
-            delta_x = action[0].item()
-            delta_y = action[1].item()
-            delta_z = action[2].item()
-            gripper = action[3].item()
-
-        # Determine if the teleoperator is actively providing input
-        # Consider enabled if any significant movement delta is detected
-        position_magnitude = abs(delta_x) + abs(delta_y) + abs(delta_z)
-        enabled = position_magnitude > 1e-6  # Small threshold to avoid noise
-
-        # Scale the deltas appropriately
-        scaled_delta_x = float(delta_x) * self.position_scale
-        scaled_delta_y = float(delta_y) * self.position_scale
-        scaled_delta_z = float(delta_z) * self.position_scale
-
-        # For gamepad/keyboard, we don't have rotation input, so set to 0
-        # These could be extended in the future for more sophisticated teleoperators
-        target_wx = 0.0
-        target_wy = 0.0
-        target_wz = 0.0
-
-        # Update action with robot target format
-        action = {
-            "action.enabled": enabled,
-            "action.target_x": scaled_delta_x,
-            "action.target_y": scaled_delta_y,
-            "action.target_z": scaled_delta_z,
-            "action.target_wx": target_wx,
-            "action.target_wy": target_wy,
-            "action.target_wz": target_wz,
-            "action.gripper": float(gripper),
-        }
-
-        self._prev_enabled = enabled
-        return action
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        """Transform features to match output format."""
-        # Update features to reflect the new action format
-        features.update(
-            {
-                "action.enabled": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_x": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_y": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_z": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_wx": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_wy": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.target_wz": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-                "action.gripper": PolicyFeature(type=FeatureType.ACTION, shape=(1,)),
-            }
-        )
-        return features
-
-    def reset(self):
-        self._prev_enabled = False

src/lerobot/processor/device_processor.py

@@ -66,26 +66,9 @@ class DeviceProcessor:
             self._target_float_dtype = None
 
     def _process_tensor(self, tensor: torch.Tensor) -> torch.Tensor:
-        """Process a tensor by moving to device and optionally converting float dtype.
-
-        If the tensor is already on a GPU and we're configured for a GPU, it preserves
-        that GPU placement (useful for multi-GPU training with Accelerate).
-        Otherwise, it moves to the configured device.
-        """
-        # Determine target device
-        if tensor.is_cuda and self._device.type == "cuda":
-            # Both tensor and target are on GPU - preserve tensor's GPU placement
-            # This handles multi-GPU scenarios where Accelerate has already placed
-            # tensors on the correct GPU for each process
-            target_device = tensor.device
-        else:
-            # Either tensor is on CPU, or we're configured for CPU
-            # In both cases, use the configured device
-            target_device = self._device
-
-        # Only move if necessary
-        if tensor.device != target_device:
-            tensor = tensor.to(target_device, non_blocking=self.non_blocking)
+        """Process a tensor by moving to device and optionally converting float dtype."""
+        # Move to device first
+        tensor = tensor.to(self.device, non_blocking=self.non_blocking)
 
         # Convert float dtype if specified and tensor is floating point
         if self._target_float_dtype is not None and tensor.is_floating_point():

src/lerobot/processor/hil_processor.py

@@ -1,418 +0,0 @@
-import time
-from dataclasses import dataclass
-from typing import Any
-
-import numpy as np
-import torch
-import torchvision.transforms.functional as F  # noqa: N812
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.processor.pipeline import (
-    ActionProcessor,
-    ComplementaryDataProcessor,
-    EnvTransition,
-    InfoProcessor,
-    ObservationProcessor,
-    ProcessorStepRegistry,
-    TransitionKey,
-)
-from lerobot.teleoperators.teleoperator import Teleoperator
-from lerobot.teleoperators.utils import TeleopEvents
-
-GRIPPER_KEY = "gripper"
-
-
-@ProcessorStepRegistry.register("add_teleop_action_as_complementary_data")
-@dataclass
-class AddTeleopActionAsComplimentaryData(ComplementaryDataProcessor):
-    """Add teleoperator action to transition complementary data."""
-
-    teleop_device: Teleoperator
-
-    def complementary_data(self, complementary_data: dict | None) -> dict:
-        complementary_data = {} if complementary_data is None else dict(complementary_data)
-        complementary_data["teleop_action"] = self.teleop_device.get_action()
-        return complementary_data
-
-
-@ProcessorStepRegistry.register("add_teleop_action_as_info")
-@dataclass
-class AddTeleopEventsAsInfo(InfoProcessor):
-    """Add teleoperator control events to transition info."""
-
-    teleop_device: Teleoperator
-
-    def info(self, info: dict | None) -> dict:
-        info = {} if info is None else dict(info)
-        teleop_events = getattr(self.teleop_device, "get_teleop_events", lambda: {})()
-        info.update(teleop_events)
-        return info
-
-
-@ProcessorStepRegistry.register("torch2numpy_action_processor")
-@dataclass
-class Torch2NumpyActionProcessor(ActionProcessor):
-    """Convert PyTorch tensor actions to NumPy arrays."""
-
-    squeeze_batch_dim: bool = True
-
-    def action(self, action: torch.Tensor | None) -> np.ndarray | None:
-        if action is None:
-            return None
-
-        if not isinstance(action, torch.Tensor):
-            raise TypeError(
-                f"Expected torch.Tensor or None, got {type(action).__name__}. "
-                "Use appropriate processor for non-tensor actions."
-            )
-
-        numpy_action = action.detach().cpu().numpy()
-
-        # Remove batch dimensions but preserve action dimensions
-        # Only squeeze if there's a batch dimension (first dim == 1)
-        if (
-            self.squeeze_batch_dim
-            and numpy_action.shape
-            and len(numpy_action.shape) > 1
-            and numpy_action.shape[0] == 1
-        ):
-            numpy_action = numpy_action.squeeze(0)
-
-        return numpy_action
-
-
-@ProcessorStepRegistry.register("numpy2torch_action_processor")
-@dataclass
-class Numpy2TorchActionProcessor(ActionProcessor):
-    """Convert NumPy array action to PyTorch tensor."""
-
-    def action(self, action: np.ndarray | None) -> torch.Tensor | None:
-        if action is None:
-            return None
-        if not isinstance(action, np.ndarray):
-            raise TypeError(
-                f"Expected np.ndarray or None, got {type(action).__name__}. "
-                "Use appropriate processor for non-tensor actions."
-            )
-        torch_action = torch.from_numpy(action)
-        return torch_action
-
-
-@ProcessorStepRegistry.register("image_crop_resize_processor")
-@dataclass
-class ImageCropResizeProcessor(ObservationProcessor):
-    """Crop and resize image observations."""
-
-    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
-    resize_size: tuple[int, int] | None = None
-
-    def observation(self, observation: dict | None) -> dict | None:
-        if observation is None:
-            return None
-
-        if self.resize_size is None and not self.crop_params_dict:
-            return observation
-
-        new_observation = dict(observation)
-
-        # Process all image keys in the observation
-        for key in observation:
-            if "image" not in key:
-                continue
-
-            image = observation[key]
-            device = image.device
-            # NOTE (maractingi): No mps kernel for crop and resize, so we need to move to cpu
-            if device.type == "mps":
-                image = image.cpu()
-            # Crop if crop params are provided for this key
-            if self.crop_params_dict is not None and key in self.crop_params_dict:
-                crop_params = self.crop_params_dict[key]
-                image = F.crop(image, *crop_params)
-            if self.resize_size is not None:
-                image = F.resize(image, self.resize_size)
-                image = image.clamp(0.0, 1.0)
-            new_observation[key] = image.to(device)
-
-        return new_observation
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "crop_params_dict": self.crop_params_dict,
-            "resize_size": self.resize_size,
-        }
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        if self.resize_size is None:
-            return features
-        for key in features:
-            if "image" in key:
-                features[key] = PolicyFeature(type=features[key].type, shape=self.resize_size)
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register("time_limit_processor")
-class TimeLimitProcessor:
-    """Track episode steps and enforce time limits."""
-
-    max_episode_steps: int
-    current_step: int = 0
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        truncated = transition.get(TransitionKey.TRUNCATED)
-        if truncated is None:
-            return transition
-
-        self.current_step += 1
-        if self.current_step >= self.max_episode_steps:
-            truncated = True
-        new_transition = transition.copy()
-        new_transition[TransitionKey.TRUNCATED] = truncated
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "max_episode_steps": self.max_episode_steps,
-        }
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {}
-
-    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
-        pass
-
-    def reset(self) -> None:
-        self.current_step = 0
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register("gripper_penalty_processor")
-class GripperPenaltyProcessor:
-    """Apply penalty for inappropriate gripper usage."""
-
-    penalty: float = -0.01
-    max_gripper_pos: float = 30.0
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Calculate gripper penalty and add to complementary data."""
-        action = transition.get(TransitionKey.ACTION)
-        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
-
-        if complementary_data is None or action is None:
-            return transition
-
-        current_gripper_pos = complementary_data.get("raw_joint_positions", None).get(GRIPPER_KEY, None)
-        if current_gripper_pos is None:
-            return transition
-
-        gripper_action = action[f"action.{GRIPPER_KEY}.pos"]
-        gripper_action_normalized = gripper_action / self.max_gripper_pos
-
-        # Normalize gripper state and action
-        gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
-
-        # Calculate penalty boolean as in original
-        gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
-            gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
-        )
-
-        gripper_penalty = self.penalty * int(gripper_penalty_bool)
-
-        # Add penalty information to complementary data
-        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-
-        # Create new complementary data with penalty info
-        new_complementary_data = dict(complementary_data)
-        new_complementary_data["discrete_penalty"] = gripper_penalty
-
-        # Create new transition with updated complementary data
-        new_transition = transition.copy()
-        existing_comp_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        existing_comp_data.update(new_complementary_data)
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = existing_comp_data  # type: ignore[misc]
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "penalty": self.penalty,
-            "max_gripper_pos": self.max_gripper_pos,
-        }
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {}
-
-    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
-        pass
-
-    def reset(self) -> None:
-        """Reset the processor state."""
-        self.last_gripper_state = None
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register("intervention_action_processor")
-class InterventionActionProcessor:
-    """Handle human intervention actions and episode termination."""
-
-    use_gripper: bool = False
-    terminate_on_success: bool = True
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition.get(TransitionKey.ACTION)
-        if action is None:
-            return transition
-
-        # Get intervention signals from complementary data
-        info = transition.get(TransitionKey.INFO, {})
-        teleop_action = info.get("teleop_action", {})
-        is_intervention = info.get(TeleopEvents.IS_INTERVENTION, False)
-        terminate_episode = info.get(TeleopEvents.TERMINATE_EPISODE, False)
-        success = info.get(TeleopEvents.SUCCESS, False)
-        rerecord_episode = info.get(TeleopEvents.RERECORD_EPISODE, False)
-
-        new_transition = transition.copy()
-
-        # Override action if intervention is active
-        if is_intervention and teleop_action is not None:
-            if isinstance(teleop_action, dict):
-                # Convert teleop_action dict to tensor format
-                action_list = [
-                    teleop_action.get("action.delta_x", 0.0),
-                    teleop_action.get("action.delta_y", 0.0),
-                    teleop_action.get("action.delta_z", 0.0),
-                ]
-                if self.use_gripper:
-                    action_list.append(teleop_action.get("gripper", 1.0))
-            elif isinstance(teleop_action, np.ndarray):
-                action_list = teleop_action.tolist()
-            else:
-                action_list = teleop_action
-
-            teleop_action_tensor = torch.tensor(action_list, dtype=action.dtype, device=action.device)
-            new_transition[TransitionKey.ACTION] = teleop_action_tensor
-
-        # Handle episode termination
-        new_transition[TransitionKey.DONE] = bool(terminate_episode) or (
-            self.terminate_on_success and success
-        )
-        new_transition[TransitionKey.REWARD] = float(success)
-
-        # Update info with intervention metadata
-        info = new_transition.get(TransitionKey.INFO, {})
-        info[TeleopEvents.IS_INTERVENTION] = is_intervention
-        info[TeleopEvents.RERECORD_EPISODE] = rerecord_episode
-        info[TeleopEvents.SUCCESS] = success
-        new_transition[TransitionKey.INFO] = info
-
-        # Update complementary data with teleop action
-        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        complementary_data["teleop_action"] = new_transition.get(TransitionKey.ACTION)
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
-
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "use_gripper": self.use_gripper,
-        }
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {}
-
-    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
-        pass
-
-    def reset(self) -> None:
-        pass
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register("reward_classifier_processor")
-class RewardClassifierProcessor:
-    """Apply reward classification to image observations."""
-
-    pretrained_path: str | None = None
-    device: str = "cpu"
-    success_threshold: float = 0.5
-    success_reward: float = 1.0
-    terminate_on_success: bool = True
-
-    reward_classifier: Any = None
-
-    def __post_init__(self):
-        """Initialize the reward classifier after dataclass initialization."""
-        if self.pretrained_path is not None:
-            from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
-
-            self.reward_classifier = Classifier.from_pretrained(self.pretrained_path)
-            self.reward_classifier.to(self.device)
-            self.reward_classifier.eval()
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if observation is None or self.reward_classifier is None:
-            return transition
-
-        # Extract images from observation
-        images = {key: value for key, value in observation.items() if "image" in key}
-
-        if not images:
-            return transition
-
-        # Run reward classifier
-        start_time = time.perf_counter()
-        with torch.inference_mode():
-            success = self.reward_classifier.predict_reward(images, threshold=self.success_threshold)
-
-        classifier_frequency = 1 / (time.perf_counter() - start_time)
-
-        # Calculate reward and termination
-        reward = transition.get(TransitionKey.REWARD, 0.0)
-        terminated = transition.get(TransitionKey.DONE, False)
-
-        if success == 1.0:
-            reward = self.success_reward
-            if self.terminate_on_success:
-                terminated = True
-
-        # Update transition
-        new_transition = transition.copy()
-        new_transition[TransitionKey.REWARD] = reward
-        new_transition[TransitionKey.DONE] = terminated
-
-        # Update info with classifier frequency
-        info = new_transition.get(TransitionKey.INFO, {})
-        info["reward_classifier_frequency"] = classifier_frequency
-        new_transition[TransitionKey.INFO] = info
-
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "device": self.device,
-            "success_threshold": self.success_threshold,
-            "success_reward": self.success_reward,
-            "terminate_on_success": self.terminate_on_success,
-        }
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        return {}
-
-    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
-        pass
-
-    def reset(self) -> None:
-        pass
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features

src/lerobot/processor/joint_observations_processor.py

@@ -1,116 +0,0 @@
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.processor.pipeline import (
-    ObservationProcessor,
-    ProcessorStepRegistry,
-)
-from lerobot.robots import Robot
-
-
-@dataclass
-@ProcessorStepRegistry.register("joint_velocity_processor")
-class JointVelocityProcessor:
-    """Add joint velocity information to observations."""
-
-    joint_velocity_limits: float = 100.0
-    dt: float = 1.0 / 10
-    num_dof: int | None = None
-
-    last_joint_positions: torch.Tensor | None = None
-
-    def observation(self, observation: dict | None) -> dict | None:
-        if observation is None:
-            return None
-
-        # Get current joint positions (assuming they're in observation.state)
-        current_positions = observation.get("observation.state")
-        if current_positions is None:
-            return observation
-
-        # Initialize last joint positions if not already set
-        if self.last_joint_positions is None:
-            self.last_joint_positions = current_positions.clone()
-
-        # Compute velocities
-        joint_velocities = (current_positions - self.last_joint_positions) / self.dt
-        self.last_joint_positions = current_positions.clone()
-
-        # Extend observation with velocities
-        extended_state = torch.cat([current_positions, joint_velocities], dim=-1)
-
-        # Create new observation dict
-        new_observation = dict(observation)
-        new_observation["observation.state"] = extended_state
-
-        return new_observation
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "joint_velocity_limits": self.joint_velocity_limits,
-            "dt": self.dt,
-        }
-
-    def reset(self) -> None:
-        self.last_joint_positions = None
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        if "observation.state" in features and self.num_dof is not None:
-            from lerobot.configs.types import PolicyFeature
-
-            original_feature = features["observation.state"]
-            # Double the shape to account for positions + velocities
-            new_shape = (original_feature.shape[0] + self.num_dof,) + original_feature.shape[1:]
-            features["observation.state"] = PolicyFeature(type=original_feature.type, shape=new_shape)
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register("current_processor")
-class MotorCurrentProcessor(ObservationProcessor):
-    """Add motor current information to observations."""
-
-    robot: Robot | None = None
-
-    def observation(self, observation: dict | None) -> dict | None:
-        if observation is None:
-            return None
-
-        # Get current values from robot state
-        if self.robot is None:
-            return observation
-        present_current_dict = self.robot.bus.sync_read("Present_Current")  # type: ignore[attr-defined]
-        motor_currents = torch.tensor(
-            [present_current_dict[name] for name in self.robot.bus.motors],  # type: ignore[attr-defined]
-            dtype=torch.float32,
-        ).unsqueeze(0)
-
-        current_state = observation.get("observation.state")
-        if current_state is None:
-            return observation
-
-        extended_state = torch.cat([current_state, motor_currents], dim=-1)
-
-        # Create new observation dict
-        new_observation = dict(observation)
-        new_observation["observation.state"] = extended_state
-
-        return new_observation
-
-    def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        if "observation.state" in features and self.robot is not None:
-            from lerobot.configs.types import PolicyFeature
-
-            original_feature = features["observation.state"]
-            # Add motor current dimensions to the original state shape
-            num_motors = 0
-            if hasattr(self.robot, "bus") and hasattr(self.robot.bus, "motors"):  # type: ignore[attr-defined]
-                num_motors = len(self.robot.bus.motors)  # type: ignore[attr-defined]
-
-            if num_motors > 0:
-                new_shape = (original_feature.shape[0] + num_motors,) + original_feature.shape[1:]
-                features["observation.state"] = PolicyFeature(type=original_feature.type, shape=new_shape)
-        return features

src/lerobot/processor/tokenizer_processor.py

@@ -134,19 +134,9 @@ class TokenizerProcessor:
         if task is None:
             return transition
 
-        # Tokenize the task (creates CPU tensors)
+        # Tokenize the task
         tokenized_prompt = self._tokenize_text(task)
 
-        # Detect device from existing tensors in the transition
-        target_device = self._detect_device(transition)
-
-        # Move tokenized tensors to match the device of other data
-        if target_device is not None:
-            tokenized_prompt = {
-                k: v.to(target_device) if isinstance(v, torch.Tensor) else v
-                for k, v in tokenized_prompt.items()
-            }
-
         # Get or create observation dict
         observation = transition.get(TransitionKey.OBSERVATION)
         if observation is None:
@@ -155,53 +145,17 @@ class TokenizerProcessor:
             observation = dict(observation)  # Make a copy
 
         # Add tokenized data to observation
-        observation[f"{OBS_LANGUAGE}.tokens"] = tokenized_prompt["input_ids"]
-        observation[f"{OBS_LANGUAGE}.attention_mask"] = tokenized_prompt["attention_mask"].to(
-            dtype=torch.bool
-        )
+        input_ids = tokenized_prompt["input_ids"]
+        attention_mask = tokenized_prompt.get("attention_mask")
+        if attention_mask is None:
+            # Some tokenizers (e.g., SigLIP text) may not return attention_mask; default to ones
+            attention_mask = torch.ones_like(input_ids)
+        observation[f"{OBS_LANGUAGE}.tokens"] = input_ids
+        observation[f"{OBS_LANGUAGE}.attention_mask"] = attention_mask.to(dtype=torch.bool)
 
         transition[TransitionKey.OBSERVATION.value] = observation  # type: ignore[misc]
         return transition
 
-    def _detect_device(self, transition: EnvTransition) -> torch.device | None:
-        """Detect device from existing tensors in the transition.
-
-        This allows the tokenized tensors to match the device of other data,
-        which is especially important for multi-GPU training with Accelerate.
-
-        Args:
-            transition: The transition to search for existing tensors.
-
-        Returns:
-            The device of the first tensor found, or None if no tensors exist.
-        """
-        # Check observation tensors first (most likely to exist)
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if observation:
-            for value in observation.values():
-                if isinstance(value, torch.Tensor):
-                    return value.device
-
-        # Check action tensor
-        action = transition.get(TransitionKey.ACTION)
-        if isinstance(action, torch.Tensor):
-            return action.device
-
-        # Check other tensor fields
-        for key in [TransitionKey.REWARD, TransitionKey.DONE, TransitionKey.TRUNCATED]:
-            value = transition.get(key)
-            if isinstance(value, torch.Tensor):
-                return value.device
-
-        # Check complementary data for tensors
-        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
-        if complementary_data:
-            for value in complementary_data.values():
-                if isinstance(value, torch.Tensor):
-                    return value.device
-
-        return None  # No tensors found, keep on CPU
-
     def _tokenize_text(self, text: str | list[str]) -> dict[str, torch.Tensor]:
         """Tokenize text using the configured tokenizer.
 

src/lerobot/record.py

@@ -18,7 +18,7 @@ Records a dataset. Actions for the robot can be either generated by teleoperatio
 Example:
 
 ```shell
-lerobot-record \
+python -m lerobot.record \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.cameras="{laptop: {type: opencv, camera_index: 0, width: 640, height: 480}}" \
@@ -36,7 +36,7 @@ lerobot-record \
 
 Example recording with bimanual so100:
 ```shell
-lerobot-record \
+python -m lerobot.record \
   --robot.type=bi_so100_follower \
   --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
   --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

src/lerobot/replay.py

@@ -18,7 +18,7 @@ Replays the actions of an episode from a dataset on a robot.
 Examples:
 
 ```shell
-lerobot-replay \
+python -m lerobot.replay \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=black \
@@ -28,7 +28,7 @@ lerobot-replay \
 
 Example replay with bimanual so100:
 ```shell
-lerobot-replay \
+python -m lerobot.replay \
   --robot.type=bi_so100_follower \
   --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
   --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

src/lerobot/robots/so100_follower/robot_kinematic_processor.py

@@ -53,9 +53,6 @@ class EEReferenceAndDelta:
     kinematics: RobotKinematics
     end_effector_step_sizes: dict
     motor_names: list[str]
-    use_latched_reference: bool = (
-        True  # If True, latch reference on enable; if False, always use current pose
-    )
 
     reference_ee_pose: np.ndarray | None = field(default=None, init=False, repr=False)
     _prev_enabled: bool = field(default=False, init=False, repr=False)
@@ -72,10 +69,7 @@ class EEReferenceAndDelta:
                 "raw_joint_positions is not in complementary data and is required for EEReferenceAndDelta"
             )
 
-        if "reference_joint_positions" in comp:
-            q = comp["reference_joint_positions"]
-        else:
-            q = np.array([float(raw[n]) for n in self.motor_names], dtype=float)
+        q = np.array([float(raw[n]) for n in self.motor_names], dtype=float)
 
         # Current pose from FK on measured joints
         t_curr = self.kinematics.forward_kinematics(q)
@@ -91,12 +85,11 @@ class EEReferenceAndDelta:
         desired = None
 
         if enabled:
-            ref = t_curr
-            if self.use_latched_reference:
-                # Latched reference mode: latch reference at the rising edge
-                if not self._prev_enabled or self.reference_ee_pose is None:
-                    self.reference_ee_pose = t_curr.copy()
-                ref = self.reference_ee_pose if self.reference_ee_pose is not None else t_curr
+            # Latch a reference at the rising edge; also be defensive if None
+            if not self._prev_enabled or self.reference_ee_pose is None:
+                self.reference_ee_pose = t_curr.copy()
+
+            ref = self.reference_ee_pose if self.reference_ee_pose is not None else t_curr
 
             delta_p = np.array(
                 [
@@ -107,6 +100,7 @@ class EEReferenceAndDelta:
                 dtype=float,
             )
             r_abs = Rotation.from_rotvec([wx, wy, wz]).as_matrix()
+
             desired = np.eye(4, dtype=float)
             desired[:3, :3] = ref[:3, :3] @ r_abs
             desired[:3, 3] = ref[:3, 3] + delta_p
@@ -298,8 +292,6 @@ class InverseKinematicsEEToJoints:
             else:
                 new_act[f"action.{name}.pos"] = float(q_target[i])
         transition[TransitionKey.ACTION] = new_act
-        if not self.initial_guess_current_joints:
-            transition[TransitionKey.COMPLEMENTARY_DATA]["reference_joint_positions"] = q_target
         return transition
 
     def transform_features(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
@@ -340,7 +332,6 @@ class GripperVelocityToJoint:
     speed_factor: float = 20.0
     clip_min: float = 0.0
     clip_max: float = 100.0
-    discrete_gripper: bool = False
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         obs = transition.get(TransitionKey.OBSERVATION) or {}
@@ -356,15 +347,6 @@ class GripperVelocityToJoint:
             transition[TransitionKey.ACTION] = new_act
             return transition
 
-        if self.discrete_gripper:
-            # Discrete gripper actions are in [0, 1, 2]
-            # 0: open, 1: close, 2: stay
-            # We need to shift them to [-1, 0, 1] and then scale them to clip_max
-            gripper_action = act.get("action.gripper", 1.0)
-            gripper_action = gripper_action - 1.0
-            gripper_action *= self.clip_max
-            act["action.gripper"] = gripper_action
-
         # Get current gripper position from complementary data
         raw = comp.get("raw_joint_positions") or {}
         curr_pos = float(raw.get("gripper"))

src/lerobot/robots/viperx/README.md

@@ -141,10 +141,10 @@ python lerobot/scripts/control_robot.py \
 
 ## 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:
+To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --dataset.repo_id=${HF_USER}/aloha_test \
   --policy.type=act \
   --output_dir=outputs/train/act_aloha_test \

src/lerobot/scripts/eval.py

@@ -21,7 +21,7 @@ You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/di
 for 10 episodes.
 
 ```
-lerobot-eval \
+python -m lerobot.scripts.eval \
     --policy.path=lerobot/diffusion_pusht \
     --env.type=pusht \
     --eval.batch_size=10 \
@@ -32,7 +32,7 @@ lerobot-eval \
 
 OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes.
 ```
-lerobot-eval \
+python -m lerobot.scripts.eval \
     --policy.path=outputs/train/diffusion_pusht/checkpoints/005000/pretrained_model \
     --env.type=pusht \
     --eval.batch_size=10 \

src/lerobot/scripts/rl/actor.py

@@ -62,16 +62,9 @@ from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.policies.factory import make_policy
 from lerobot.policies.sac.modeling_sac import SACPolicy
-from lerobot.processor.pipeline import TransitionKey
 from lerobot.robots import so100_follower  # noqa: F401
-from lerobot.scripts.rl.gym_manipulator import (
-    create_transition,
-    make_processors,
-    make_robot_env,
-    step_env_and_process_transition,
-)
+from lerobot.scripts.rl.gym_manipulator import make_robot_env
 from lerobot.teleoperators import gamepad, so101_leader  # noqa: F401
-from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.transport import services_pb2, services_pb2_grpc
 from lerobot.transport.utils import (
     bytes_to_state_dict,
@@ -243,8 +236,7 @@ def act_with_policy(
 
     logging.info("make_env online")
 
-    online_env, teleop_device = make_robot_env(cfg=cfg.env)
-    env_processor, action_processor = make_processors(online_env, teleop_device, cfg.env, cfg.policy.device)
+    online_env = make_robot_env(cfg=cfg.env)
 
     set_seed(cfg.seed)
     device = get_safe_torch_device(cfg.policy.device, log=True)
@@ -265,12 +257,6 @@ def act_with_policy(
     assert isinstance(policy, nn.Module)
 
     obs, info = online_env.reset()
-    env_processor.reset()
-    action_processor.reset()
-
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info)
-    transition = env_processor(transition)
 
     # NOTE: For the moment we will solely handle the case of a single environment
     sum_reward_episode = 0
@@ -288,61 +274,45 @@ def act_with_policy(
             logging.info("[ACTOR] Shutting down act_with_policy")
             return
 
-        observation = transition[TransitionKey.OBSERVATION]
+        if interaction_step >= cfg.policy.online_step_before_learning:
+            # Time policy inference and check if it meets FPS requirement
+            with policy_timer:
+                action = policy.select_action(batch=obs)
+            policy_fps = policy_timer.fps_last
 
-        # Time policy inference and check if it meets FPS requirement
-        with policy_timer:
-            # Extract observation from transition for policy
-            action = policy.select_action(batch=observation)
-        policy_fps = policy_timer.fps_last
+            log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
 
-        log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
+        else:
+            action = online_env.action_space.sample()
 
-        # Use the new step function
-        new_transition = step_env_and_process_transition(
-            env=online_env,
-            transition=transition,
-            action=action,
-            env_processor=env_processor,
-            action_processor=action_processor,
-        )
-
-        # Extract values from processed transition
-        next_observation = new_transition[TransitionKey.OBSERVATION]
-        executed_action = new_transition[TransitionKey.ACTION]
-        reward = new_transition[TransitionKey.REWARD]
-        done = new_transition.get(TransitionKey.DONE, False)
-        truncated = new_transition.get(TransitionKey.TRUNCATED, False)
+        next_obs, reward, done, truncated, info = online_env.step(action)
 
         sum_reward_episode += float(reward)
+        # Increment total steps counter for intervention rate
         episode_total_steps += 1
 
-        # Check for intervention from transition info
-        intervention_info = new_transition[TransitionKey.INFO]
-        if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
+        # NOTE: We override the action if the intervention is True, because the action applied is the intervention action
+        if "is_intervention" in info and info["is_intervention"]:
+            # NOTE: The action space for demonstration before hand is with the full action space
+            # but sometimes for example we want to deactivate the gripper
+            action = info["action_intervention"]
             episode_intervention = True
+            # Increment intervention steps counter
             episode_intervention_steps += 1
 
-        complementary_info = {
-            "discrete_penalty": torch.tensor(
-                [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
-            ),
-        }
-        # Create transition for learner (convert to old format)
         list_transition_to_send_to_learner.append(
             Transition(
-                state=observation,
-                action=executed_action,
+                state=obs,
+                action=action,
                 reward=reward,
-                next_state=next_observation,
+                next_state=next_obs,
                 done=done,
-                truncated=truncated,
-                complementary_info=complementary_info,
+                truncated=truncated,  # TODO: (azouitine) Handle truncation properly
+                complementary_info=info,
             )
         )
-
-        # Update transition for next iteration
-        transition = new_transition
+        # assign obs to the next obs and continue the rollout
+        obs = next_obs
 
         if done or truncated:
             logging.info(f"[ACTOR] Global step {interaction_step}: Episode reward: {sum_reward_episode}")
@@ -377,20 +347,12 @@ def act_with_policy(
                 )
             )
 
-            # Reset intervention counters and environment
+            # Reset intervention counters
             sum_reward_episode = 0.0
             episode_intervention = False
             episode_intervention_steps = 0
             episode_total_steps = 0
-
-            # Reset environment and processors
             obs, info = online_env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            # Process initial observation
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time

src/lerobot/scripts/rl/learner.py

@@ -75,7 +75,6 @@ from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so100_follower  # noqa: F401
 from lerobot.scripts.rl import learner_service
 from lerobot.teleoperators import gamepad, so101_leader  # noqa: F401
-from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.transport import services_pb2_grpc
 from lerobot.transport.utils import (
     MAX_MESSAGE_SIZE,
@@ -1175,7 +1174,7 @@ def process_transitions(
 
             # Add to offline buffer if it's an intervention
             if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
-                TeleopEvents.IS_INTERVENTION
+                "is_intervention"
             ):
                 offline_replay_buffer.add(**transition)
 

src/lerobot/scripts/train.py

@@ -14,19 +14,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
+import os
 import time
 from contextlib import nullcontext
 from pprint import pformat
 from typing import Any
 
 import torch
+
+# Fix tokenizer parallelism conflicts with multiprocessing
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
 from termcolor import colored
 from torch.amp import GradScaler
 from torch.optim import Optimizer
 
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.constants import POSTPROCESSOR_DEFAULT_NAME, PREPROCESSOR_DEFAULT_NAME
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.sampler import EpisodeAwareSampler
 from lerobot.datasets.utils import cycle
@@ -68,10 +72,18 @@ def update_policy(
     start_time = time.perf_counter()
     device = get_device_from_parameters(policy)
     policy.train()
+    
+    # Forward pass timing
+    forward_start = time.perf_counter()
     with torch.autocast(device_type=device.type) if use_amp else nullcontext():
         loss, output_dict = policy.forward(batch)
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
+    forward_time = time.perf_counter() - forward_start
+    
+    # Backward pass timing
+    backward_start = time.perf_counter()
     grad_scaler.scale(loss).backward()
+    backward_time = time.perf_counter() - backward_start
 
     # Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
     grad_scaler.unscale_(optimizer)
@@ -82,6 +94,9 @@ def update_policy(
         error_if_nonfinite=False,
     )
 
+    # Optimizer step timing
+    optim_start = time.perf_counter()
+    
     # Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
     # although it still skips optimizer.step() if the gradients contain infs or NaNs.
     with lock if lock is not None else nullcontext():
@@ -98,6 +113,47 @@ def update_policy(
     if has_method(policy, "update"):
         # To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
         policy.update()
+        
+    optim_time = time.perf_counter() - optim_start
+    total_time = time.perf_counter() - start_time
+
+    # Collect timing statistics for RLearN policy (averaged reporting every minute)
+    if getattr(policy, "name", None) == "rlearn":
+        # Initialize timing accumulator if not exists
+        if not hasattr(policy, '_train_timing_stats'):
+            policy._train_timing_stats = {
+                'forward_times': [],
+                'backward_times': [],
+                'optim_times': [],
+                'total_times': [],
+                'last_print_time': time.perf_counter()
+            }
+        
+        # Accumulate current step's timings
+        stats = policy._train_timing_stats
+        stats['forward_times'].append(forward_time * 1000)
+        stats['backward_times'].append(backward_time * 1000)
+        stats['optim_times'].append(optim_time * 1000)
+        stats['total_times'].append(total_time * 1000)
+        
+        # Print averaged stats every minute (60 seconds)
+        current_time = time.perf_counter()
+        if current_time - stats['last_print_time'] >= 60.0:
+            n_samples = len(stats['forward_times'])
+            if n_samples > 0:
+                print(f"\nTraining Step Average Timing (last {n_samples} steps):")
+                print(f"  Forward pass:       {sum(stats['forward_times'])/n_samples:.2f} ms")
+                print(f"  Backward pass:      {sum(stats['backward_times'])/n_samples:.2f} ms")
+                print(f"  Optimizer step:     {sum(stats['optim_times'])/n_samples:.2f} ms")
+                print(f"  Total update:       {sum(stats['total_times'])/n_samples:.2f} ms")
+                print(f"  Avg steps/sec:      {1000.0/(sum(stats['total_times'])/n_samples):.2f}")
+                print("-" * 50)
+            
+            # Reset stats for next minute
+            for key in stats:
+                if key != 'last_print_time':
+                    stats[key] = []
+            stats['last_print_time'] = current_time
 
     train_metrics.loss = loss.item()
     train_metrics.grad_norm = grad_norm.item()
@@ -126,6 +182,18 @@ def train(cfg: TrainPipelineConfig):
     torch.backends.cuda.matmul.allow_tf32 = True
 
     logging.info("Creating dataset")
+    
+    # Force PyAV backend for RLearN (proven to be fastest)
+    if getattr(cfg.policy, "type", None) == "rlearn":
+        # Override video backend to use PyAV
+        if hasattr(cfg.dataset, 'video_backend'):
+            original_backend = cfg.dataset.video_backend
+            cfg.dataset.video_backend = 'pyav'
+            logging.info(f"RLearN: Forcing video_backend from '{original_backend}' to 'pyav' for better performance")
+        else:
+            cfg.dataset.video_backend = 'pyav'
+            logging.info("RLearN: Setting video_backend to 'pyav' for better performance")
+    
     dataset = make_dataset(cfg)
 
     # Create environment used for evaluating checkpoints during training on simulation data.
@@ -137,10 +205,16 @@ def train(cfg: TrainPipelineConfig):
         eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
 
     logging.info("Creating policy")
+    # Pass episode_data_index for RLearN to calculate proper progress
+    episode_data_index = dataset.episode_data_index if hasattr(dataset, "episode_data_index") else None
     policy = make_policy(
         cfg=cfg.policy,
         ds_meta=dataset.meta,
+        episode_data_index=episode_data_index,
     )
+    
+
+    
     preprocessor, postprocessor = make_processor(
         policy_cfg=cfg.policy, pretrained_path=cfg.policy.pretrained_path, dataset_stats=dataset.meta.stats
     )
@@ -153,10 +227,6 @@ def train(cfg: TrainPipelineConfig):
 
     if cfg.resume:
         step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
-        preprocessor.from_pretrained(cfg.checkpoint_path, config_filename=f"{PREPROCESSOR_DEFAULT_NAME}.json")
-        postprocessor.from_pretrained(
-            cfg.checkpoint_path, config_filename=f"{POSTPROCESSOR_DEFAULT_NAME}.json"
-        )
 
     num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
     num_total_params = sum(p.numel() for p in policy.parameters())
@@ -178,6 +248,15 @@ def train(cfg: TrainPipelineConfig):
             drop_n_last_frames=cfg.policy.drop_n_last_frames,
             shuffle=True,
         )
+    elif cfg.policy.type == "rlearn":
+        # For RLearN, drop first 15 frames to avoid padding issues with temporal windows
+        shuffle = False
+        sampler = EpisodeAwareSampler(
+            dataset.episode_data_index,
+            drop_n_first_frames=15,  # Skip frames that would need padding
+            drop_n_last_frames=0,
+            shuffle=True,
+        )
     else:
         shuffle = True
         sampler = None
@@ -190,6 +269,9 @@ def train(cfg: TrainPipelineConfig):
         sampler=sampler,
         pin_memory=device.type == "cuda",
         drop_last=False,
+        persistent_workers=cfg.num_workers > 0,  # Keep workers alive between epochs
+        prefetch_factor=3,  # Prefetch for video pipeline
+        timeout=30,  # Prevent hanging on video decode errors
     )
     dl_iter = cycle(dataloader)
 
@@ -202,6 +284,12 @@ def train(cfg: TrainPipelineConfig):
         "update_s": AverageMeter("updt_s", ":.3f"),
         "dataloading_s": AverageMeter("data_s", ":.3f"),
     }
+    # RLearN-only: pixels per second throughput
+    try:
+        if getattr(policy, "name", None) == "rlearn":
+            train_metrics["pix_s"] = AverageMeter("pix/s", ":.1f")
+    except Exception:
+        pass
 
     train_tracker = MetricsTracker(
         cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step
@@ -209,10 +297,21 @@ def train(cfg: TrainPipelineConfig):
 
     logging.info("Start offline training on a fixed dataset")
     for _ in range(step, cfg.steps):
-        start_time = time.perf_counter()
+        # Data loading timing
+        data_start = time.perf_counter()
         batch = next(dl_iter)
+        data_loading_time = time.perf_counter() - data_start
+        
+        # Preprocessing timing  
+        preprocess_start = time.perf_counter()
         batch = preprocessor(batch)
-        train_tracker.dataloading_s = time.perf_counter() - start_time
+        preprocess_time = time.perf_counter() - preprocess_start
+        
+        train_tracker.dataloading_s = data_loading_time + preprocess_time
+
+        for key in batch:
+            if isinstance(batch[key], torch.Tensor):
+                batch[key] = batch[key].to(device, non_blocking=device.type == "cuda")
 
         train_tracker, output_dict = update_policy(
             train_tracker,
@@ -225,6 +324,73 @@ def train(cfg: TrainPipelineConfig):
             use_amp=cfg.policy.use_amp,
         )
 
+        # RLearN-only: compute pixel throughput (pixels per second)
+        if getattr(policy, "name", None) == "rlearn":
+            def _count_pixels(x: torch.Tensor) -> int:
+                # Expect shapes: (B,T,C,H,W) or (B,C,H,W)
+                if x.dim() == 5:
+                    b, t, _, h, w = x.shape
+                    return int(b * t * h * w)
+                if x.dim() == 4:
+                    b, _, h, w = x.shape
+                    return int(b * h * w)
+                return 0
+
+            total_pixels = 0
+            for k, v in batch.items():
+                if "image" not in k.lower():
+                    continue
+                if isinstance(v, torch.Tensor):
+                    total_pixels += _count_pixels(v)
+                elif isinstance(v, list) and len(v) > 0 and isinstance(v[0], torch.Tensor):
+                    # list of T tensors shaped (B,C,H,W)
+                    total_pixels += sum(_count_pixels(t) for t in v)
+
+            # Avoid div-by-zero
+            meter = train_tracker.update_s
+            upd_s = meter.val if isinstance(meter, AverageMeter) else float(meter)
+            upd_s = max(upd_s, 1e-8)
+            pix_per_s = float(total_pixels) / upd_s
+            try:
+                train_tracker.pix_s = pix_per_s
+            except Exception:
+                pass
+
+        # Collect data pipeline timing for RLearN (averaged reporting every minute)
+        if getattr(policy, "name", None) == "rlearn":
+            # Initialize data timing accumulator if not exists
+            if not hasattr(policy, '_data_timing_stats'):
+                policy._data_timing_stats = {
+                    'data_loading_times': [],
+                    'preprocess_times': [],
+                    'last_print_time': time.perf_counter()
+                }
+            
+            # Accumulate current step's data timings
+            data_stats = policy._data_timing_stats
+            data_stats['data_loading_times'].append(data_loading_time * 1000)
+            data_stats['preprocess_times'].append(preprocess_time * 1000)
+            
+            # Print averaged stats every minute (60 seconds)  
+            current_time = time.perf_counter()
+            if current_time - data_stats['last_print_time'] >= 60.0:
+                n_samples = len(data_stats['data_loading_times'])
+                if n_samples > 0:
+                    avg_data_loading = sum(data_stats['data_loading_times']) / n_samples
+                    avg_preprocessing = sum(data_stats['preprocess_times']) / n_samples
+                    
+                    print(f"\nData Pipeline Average Timing (last {n_samples} steps):")
+                    print(f"  Data loading:       {avg_data_loading:.2f} ms")
+                    print(f"  Preprocessing:      {avg_preprocessing:.2f} ms") 
+                    print(f"  Total data pipeline: {avg_data_loading + avg_preprocessing:.2f} ms")
+                    print("-" * 50)
+                
+                # Reset stats for next minute
+                for key in data_stats:
+                    if key != 'last_print_time':
+                        data_stats[key] = []
+                data_stats['last_print_time'] = current_time
+            
         # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
         # increment `step` here.
         step += 1
@@ -233,6 +399,7 @@ def train(cfg: TrainPipelineConfig):
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
         is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
 
+
         if is_log_step:
             logging.info(train_tracker)
             if wandb_logger:
@@ -245,9 +412,7 @@ def train(cfg: TrainPipelineConfig):
         if cfg.save_checkpoint and is_saving_step:
             logging.info(f"Checkpoint policy after step {step}")
             checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
-            save_checkpoint(
-                checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor, postprocessor
-            )
+            save_checkpoint(checkpoint_dir, step, cfg, policy, optimizer, lr_scheduler, preprocessor)
             update_last_checkpoint(checkpoint_dir)
             if wandb_logger:
                 wandb_logger.log_policy(checkpoint_dir)
@@ -285,14 +450,18 @@ def train(cfg: TrainPipelineConfig):
                 wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
                 wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
 
+
+
     if eval_env:
         eval_env.close()
     logging.info("End of training")
 
     if cfg.policy.push_to_hub:
         policy.push_model_to_hub(cfg)
-        preprocessor.push_to_hub(cfg.policy.repo_id)
-        postprocessor.push_to_hub(cfg.policy.repo_id)
+        if preprocessor:
+            preprocessor.push_to_hub(cfg.policy.repo_id)
+        if postprocessor:
+            postprocessor.push_to_hub(cfg.policy.repo_id)
 
 
 def main():

src/lerobot/scripts/train_accelerate.py

@@ -1,311 +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 logging
-import time
-from contextlib import nullcontext
-from pprint import pformat
-from typing import Any, Callable
-
-import accelerate
-import torch
-from termcolor import colored
-from torch.amp import GradScaler
-from torch.optim import Optimizer
-
-from lerobot.common.datasets.factory import make_dataset
-from lerobot.common.datasets.sampler import EpisodeAwareSampler
-from lerobot.common.datasets.utils import cycle
-from lerobot.common.envs.factory import make_env
-from lerobot.common.optim.factory import make_optimizer_and_scheduler
-from lerobot.common.policies.factory import make_policy
-from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.utils import get_device_from_parameters
-from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
-from lerobot.common.utils.random_utils import set_seed
-from lerobot.common.utils.train_utils import (
-    get_step_checkpoint_dir,
-    get_step_identifier,
-    load_training_state,
-    save_checkpoint,
-    update_last_checkpoint,
-)
-from lerobot.common.utils.utils import (
-    format_big_number,
-    get_safe_torch_device,
-    has_method,
-    init_logging,
-    is_launched_with_accelerate,
-)
-from lerobot.common.utils.wandb_utils import WandBLogger
-from lerobot.configs import parser
-from lerobot.configs.train import TrainPipelineConfig
-from lerobot.scripts.eval import eval_policy
-
-
-def update_policy(
-    train_metrics: MetricsTracker,
-    policy: PreTrainedPolicy,
-    batch: Any,
-    optimizer: Optimizer,
-    grad_clip_norm: float,
-    grad_scaler: GradScaler,
-    lr_scheduler=None,
-    use_amp: bool = False,
-    lock=None,
-    accelerator: Callable = None,
-) -> tuple[MetricsTracker, dict]:
-    start_time = time.perf_counter()
-
-    policy.train()
-
-    loss, output_dict = policy.forward(batch)
-
-    accelerator.backward(loss)
-    accelerator.unscale_gradients(optimizer=optimizer)
-    grad_norm = torch.nn.utils.clip_grad_norm_(
-        policy.parameters(),
-        grad_clip_norm,
-        error_if_nonfinite=False,
-    )
-    optimizer.step()
-
-    optimizer.zero_grad()
-
-    # Step through pytorch scheduler at every batch instead of epoch
-    if lr_scheduler is not None:
-        lr_scheduler.step()
-
-    if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
-        accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()
-
-    train_metrics.loss = loss.item()
-    train_metrics.grad_norm = grad_norm.item()
-    train_metrics.lr = optimizer.param_groups[0]["lr"]
-    train_metrics.update_s = time.perf_counter() - start_time
-    return train_metrics, output_dict
-
-
-@parser.wrap()
-def train(cfg: TrainPipelineConfig, accelerator: Callable):
-    cfg.validate()
-    logging.info(pformat(cfg.to_dict()))
-
-    if accelerator.is_main_process:
-        # Disable logging on non-main processes.
-        cfg.wandb.enable = False
-
-    if cfg.wandb.enable and cfg.wandb.project:
-        wandb_logger = WandBLogger(cfg)
-    else:
-        wandb_logger = None
-        logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
-
-    if cfg.seed is not None:
-        set_seed(cfg.seed, accelerator=accelerator)
-
-    # Check device is available
-    device = get_safe_torch_device(cfg.device, log=True, accelerator=accelerator)
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = True
-
-    logging.info("Creating dataset")
-    dataset = make_dataset(cfg)
-
-    # Create environment used for evaluating checkpoints during training on simulation data.
-    # On real-world data, no need to create an environment as evaluations are done outside train.py,
-    # using the eval.py instead, with gym_dora environment and dora-rs.
-    eval_env = None
-    if cfg.eval_freq > 0 and cfg.env is not None:
-        logging.info("Creating env")
-        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size)
-
-    logging.info("Creating policy")
-    policy = make_policy(
-        cfg=cfg.policy,
-        device=device,
-        ds_meta=dataset.meta,
-    )
-    policy.to(device)
-    logging.info("Creating optimizer and scheduler")
-    optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
-    grad_scaler = GradScaler(device, enabled=cfg.use_amp)
-
-    step = 0  # number of policy updates (forward + backward + optim)
-
-    if cfg.resume:
-        step, optimizer, lr_scheduler = load_training_state(cfg.checkpoint_path, optimizer, lr_scheduler)
-
-    num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
-    num_total_params = sum(p.numel() for p in policy.parameters())
-    if accelerator.is_main_process:
-        logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
-        if cfg.env is not None:
-            logging.info(f"{cfg.env.task=}")
-        logging.info(f"{cfg.steps=} ({format_big_number(cfg.steps)})")
-        logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
-        logging.info(f"{dataset.num_episodes=}")
-        logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
-        logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
-
-    # create dataloader for offline training
-    if hasattr(cfg.policy, "drop_n_last_frames"):
-        shuffle = False
-        sampler = EpisodeAwareSampler(
-            dataset.episode_data_index,
-            drop_n_last_frames=cfg.policy.drop_n_last_frames,
-            shuffle=True,
-        )
-    else:
-        shuffle = True
-        sampler = None
-
-    dataloader = torch.utils.data.DataLoader(
-        dataset,
-        num_workers=cfg.num_workers,
-        batch_size=cfg.batch_size,
-        shuffle=shuffle,
-        sampler=sampler,
-        pin_memory=device.type != "cpu",
-        drop_last=False,
-    )
-
-    policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
-        policy, optimizer, dataloader, lr_scheduler
-    )
-
-    dl_iter = cycle(dataloader)
-
-    policy.train()
-
-    train_metrics = {
-        "loss": AverageMeter("loss", ":.3f"),
-        "grad_norm": AverageMeter("grdn", ":.3f"),
-        "lr": AverageMeter("lr", ":0.1e"),
-        "update_s": AverageMeter("updt_s", ":.3f"),
-        "dataloading_s": AverageMeter("data_s", ":.3f"),
-    }
-
-    train_tracker = MetricsTracker(
-        cfg.batch_size,
-        dataset.num_frames,
-        dataset.num_episodes,
-        train_metrics,
-        initial_step=step,
-        accelerator=accelerator,
-    )
-    if accelerator.is_main_process:
-        logging.info("Start offline training on a fixed dataset")
-
-    for _ in range(step, cfg.steps):
-        start_time = time.perf_counter()
-        batch = next(dl_iter)
-        train_tracker.dataloading_s = time.perf_counter() - start_time
-
-        train_tracker, output_dict = update_policy(
-            train_tracker,
-            policy,
-            batch,
-            optimizer,
-            cfg.optimizer.grad_clip_norm,
-            grad_scaler=grad_scaler,
-            lr_scheduler=lr_scheduler,
-            use_amp=cfg.use_amp,
-            accelerator=accelerator,
-        )
-
-        # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
-        # increment `step` here.
-        step += 1
-        train_tracker.step()
-        is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and accelerator.is_main_process
-        is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps and accelerator.is_main_process
-        is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0 and accelerator.is_main_process
-
-        if is_log_step:
-            logging.info(train_tracker)
-            if wandb_logger:
-                wandb_log_dict = train_tracker.to_dict()
-                if output_dict:
-                    wandb_log_dict.update(output_dict)
-                wandb_logger.log_dict(wandb_log_dict, step)
-            train_tracker.reset_averages()
-
-        if cfg.save_checkpoint and is_saving_step:
-            logging.info(f"Checkpoint policy after step {step}")
-            checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, cfg.steps, step)
-            save_checkpoint(
-                checkpoint_dir,
-                step,
-                cfg,
-                accelerator.unwrap_model(policy),
-                optimizer,
-                lr_scheduler,
-            )
-            update_last_checkpoint(checkpoint_dir)
-            if wandb_logger:
-                wandb_logger.log_policy(checkpoint_dir)
-
-        accelerator.wait_for_everyone()
-
-        if cfg.env and is_eval_step:
-            step_id = get_step_identifier(step, cfg.steps)
-            logging.info(f"Eval policy at step {step}")
-
-            with torch.no_grad():
-                eval_info = eval_policy(
-                    env=eval_env,
-                    policy=accelerator.unwrap_model(policy),
-                    n_episodes=cfg.eval.n_episodes,
-                    videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
-                    max_episodes_rendered=4,
-                    start_seed=cfg.seed,
-                )
-
-            eval_metrics = {
-                "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),
-                "pc_success": AverageMeter("success", ":.1f"),
-                "eval_s": AverageMeter("eval_s", ":.3f"),
-            }
-            eval_tracker = MetricsTracker(
-                cfg.batch_size,
-                dataset.num_frames,
-                dataset.num_episodes,
-                eval_metrics,
-                initial_step=step,
-                accelerator=None,
-            )
-            eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s")
-            eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward")
-            eval_tracker.pc_success = eval_info["aggregated"].pop("pc_success")
-            logging.info(eval_tracker)
-            if wandb_logger:
-                wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
-                wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
-                wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
-
-    if eval_env:
-        eval_env.close()
-    if not accelerator or accelerator.is_main_process:
-        logging.info("End of training")
-
-
-if __name__ == "__main__":
-    init_logging()
-
-    # We set step_scheduler_with_optimizer False to prevent accelerate from
-    # adjusting the lr_scheduler steps based on the num_processes
-    accelerator = accelerate.Accelerator(step_scheduler_with_optimizer=False)
-    train(accelerator=accelerator)

src/lerobot/setup_motors.py

@@ -18,7 +18,7 @@ Helper to set motor ids and baudrate.
 Example:
 
 ```shell
-lerobot-setup-motors \
+python -m lerobot.setup_motors \
     --teleop.type=so100_leader \
     --teleop.port=/dev/tty.usbmodem575E0031751
 ```

src/lerobot/teleoperate.py

@@ -18,7 +18,7 @@ Simple script to control a robot from teleoperation.
 Example:
 
 ```shell
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
     --robot.type=so101_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
@@ -32,7 +32,7 @@ lerobot-teleoperate \
 Example teleoperation with bimanual so100:
 
 ```shell
-lerobot-teleoperate \
+python -m lerobot.teleoperate \
   --robot.type=bi_so100_follower \
   --robot.left_arm_port=/dev/tty.usbmodem5A460851411 \
   --robot.right_arm_port=/dev/tty.usbmodem5A460812391 \

src/lerobot/teleoperators/__init__.py

@@ -16,4 +16,4 @@
 
 from .config import TeleoperatorConfig
 from .teleoperator import Teleoperator
-from .utils import TeleopEvents, make_teleoperator_from_config
+from .utils import make_teleoperator_from_config

src/lerobot/teleoperators/gamepad/gamepad_utils.py

@@ -16,8 +16,6 @@
 
 import logging
 
-from ..utils import TeleopEvents
-
 
 class InputController:
     """Base class for input controllers that generate motion deltas."""
@@ -136,10 +134,10 @@ class KeyboardController(InputController):
                     return False
                 elif key == keyboard.Key.enter:
                     self.key_states["success"] = True
-                    self.episode_end_status = TeleopEvents.SUCCESS
+                    self.episode_end_status = "success"
                 elif key == keyboard.Key.backspace:
                     self.key_states["failure"] = True
-                    self.episode_end_status = TeleopEvents.FAILURE
+                    self.episode_end_status = "failure"
             except AttributeError:
                 pass
 
@@ -257,13 +255,13 @@ class GamepadController(InputController):
         for event in pygame.event.get():
             if event.type == pygame.JOYBUTTONDOWN:
                 if event.button == 3:
-                    self.episode_end_status = TeleopEvents.SUCCESS
+                    self.episode_end_status = "success"
                 # A button (1) for failure
                 elif event.button == 1:
-                    self.episode_end_status = TeleopEvents.FAILURE
+                    self.episode_end_status = "failure"
                 # X button (0) for rerecord
                 elif event.button == 0:
-                    self.episode_end_status = TeleopEvents.RERECORD_EPISODE
+                    self.episode_end_status = "rerecord_episode"
 
                 # RB button (6) for closing gripper
                 elif event.button == 6:
@@ -453,11 +451,11 @@ class GamepadControllerHID(InputController):
                 # Check if X/Square button (bit 5) is pressed for failure
                 # Check if A/Cross button (bit 4) is pressed for rerecording
                 if buttons & 1 << 7:
-                    self.episode_end_status = TeleopEvents.SUCCESS
+                    self.episode_end_status = "success"
                 elif buttons & 1 << 5:
-                    self.episode_end_status = TeleopEvents.FAILURE
+                    self.episode_end_status = "failure"
                 elif buttons & 1 << 4:
-                    self.episode_end_status = TeleopEvents.RERECORD_EPISODE
+                    self.episode_end_status = "rerecord_episode"
                 else:
                     self.episode_end_status = None
 

src/lerobot/teleoperators/gamepad/teleop_gamepad.py

@@ -21,7 +21,6 @@ from typing import Any
 import numpy as np
 
 from ..teleoperator import Teleoperator
-from ..utils import TeleopEvents
 from .configuration_gamepad import GamepadTeleopConfig
 
 
@@ -94,9 +93,9 @@ class GamepadTeleop(Teleoperator):
         gamepad_action = np.array([delta_x, delta_y, delta_z], dtype=np.float32)
 
         action_dict = {
-            "action.delta_x": gamepad_action[0],
-            "action.delta_y": gamepad_action[1],
-            "action.delta_z": gamepad_action[2],
+            "delta_x": gamepad_action[0],
+            "delta_y": gamepad_action[1],
+            "delta_z": gamepad_action[2],
         }
 
         # Default gripper action is to stay
@@ -108,48 +107,6 @@ class GamepadTeleop(Teleoperator):
 
         return action_dict
 
-    def get_teleop_events(self) -> dict[str, Any]:
-        """
-        Get extra control events from the gamepad such as intervention status,
-        episode termination, success indicators, etc.
-
-        Returns:
-            Dictionary containing:
-                - is_intervention: bool - Whether human is currently intervening
-                - terminate_episode: bool - Whether to terminate the current episode
-                - success: bool - Whether the episode was successful
-                - rerecord_episode: bool - Whether to rerecord the episode
-        """
-        if self.gamepad is None:
-            return {
-                TeleopEvents.IS_INTERVENTION: False,
-                TeleopEvents.TERMINATE_EPISODE: False,
-                TeleopEvents.SUCCESS: False,
-                TeleopEvents.RERECORD_EPISODE: False,
-            }
-
-        # Update gamepad state to get fresh inputs
-        self.gamepad.update()
-
-        # Check if intervention is active
-        is_intervention = self.gamepad.should_intervene()
-
-        # Get episode end status
-        episode_end_status = self.gamepad.get_episode_end_status()
-        terminate_episode = episode_end_status in [
-            TeleopEvents.RERECORD_EPISODE,
-            TeleopEvents.FAILURE,
-        ]
-        success = episode_end_status == TeleopEvents.SUCCESS
-        rerecord_episode = episode_end_status == TeleopEvents.RERECORD_EPISODE
-
-        return {
-            TeleopEvents.IS_INTERVENTION: is_intervention,
-            TeleopEvents.TERMINATE_EPISODE: terminate_episode,
-            TeleopEvents.SUCCESS: success,
-            TeleopEvents.RERECORD_EPISODE: rerecord_episode,
-        }
-
     def disconnect(self) -> None:
         """Disconnect from the gamepad."""
         if self.gamepad is not None:

src/lerobot/teleoperators/keyboard/teleop_keyboard.py

@@ -24,7 +24,6 @@ from typing import Any
 from lerobot.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..teleoperator import Teleoperator
-from ..utils import TeleopEvents
 from .configuration_keyboard import KeyboardEndEffectorTeleopConfig, KeyboardTeleopConfig
 
 PYNPUT_AVAILABLE = True
@@ -168,13 +167,13 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
             return {
                 "dtype": "float32",
                 "shape": (4,),
-                "names": {"action.delta_x": 0, "action.delta_y": 1, "action.delta_z": 2, "action.gripper": 3},
+                "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2, "gripper": 3},
             }
         else:
             return {
                 "dtype": "float32",
                 "shape": (3,),
-                "names": {"action.delta_x": 0, "action.delta_y": 1, "action.delta_z": 2},
+                "names": {"delta_x": 0, "delta_y": 1, "delta_z": 2},
             }
 
     def _on_press(self, key):
@@ -227,75 +226,12 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
         self.current_pressed.clear()
 
         action_dict = {
-            "action.delta_x": delta_x,
-            "action.delta_y": delta_y,
-            "action.delta_z": delta_z,
+            "delta_x": delta_x,
+            "delta_y": delta_y,
+            "delta_z": delta_z,
         }
 
         if self.config.use_gripper:
             action_dict["gripper"] = gripper_action
 
         return action_dict
-
-    def get_teleop_events(self) -> dict[str, Any]:
-        """
-        Get extra control events from the keyboard such as intervention status,
-        episode termination, success indicators, etc.
-
-        Keyboard mappings:
-        - Any movement keys pressed = intervention active
-        - 's' key = success (terminate episode successfully)
-        - 'r' key = rerecord episode (terminate and rerecord)
-        - 'q' key = quit episode (terminate without success)
-
-        Returns:
-            Dictionary containing:
-                - is_intervention: bool - Whether human is currently intervening
-                - terminate_episode: bool - Whether to terminate the current episode
-                - success: bool - Whether the episode was successful
-                - rerecord_episode: bool - Whether to rerecord the episode
-        """
-        if not self.is_connected:
-            return {
-                TeleopEvents.IS_INTERVENTION: False,
-                TeleopEvents.TERMINATE_EPISODE: False,
-                TeleopEvents.SUCCESS: False,
-                TeleopEvents.RERECORD_EPISODE: False,
-            }
-
-        # Check if any movement keys are currently pressed (indicates intervention)
-        movement_keys = [
-            keyboard.Key.up,
-            keyboard.Key.down,
-            keyboard.Key.left,
-            keyboard.Key.right,
-            keyboard.Key.shift,
-            keyboard.Key.shift_r,
-            keyboard.Key.ctrl_r,
-            keyboard.Key.ctrl_l,
-        ]
-        is_intervention = any(self.current_pressed.get(key, False) for key in movement_keys)
-
-        # Check for episode control commands from misc_keys_queue
-        terminate_episode = False
-        success = False
-        rerecord_episode = False
-
-        # Process any pending misc keys
-        while not self.misc_keys_queue.empty():
-            key = self.misc_keys_queue.get_nowait()
-            if key == "s":
-                success = True
-            elif key == "r":
-                terminate_episode = True
-                rerecord_episode = True
-            elif key == "q":
-                terminate_episode = True
-                success = False
-
-        return {
-            TeleopEvents.IS_INTERVENTION: is_intervention,
-            TeleopEvents.TERMINATE_EPISODE: terminate_episode,
-            TeleopEvents.SUCCESS: success,
-            TeleopEvents.RERECORD_EPISODE: rerecord_episode,
-        }

src/lerobot/teleoperators/utils.py

@@ -12,22 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from enum import Enum
-
 from .config import TeleoperatorConfig
 from .teleoperator import Teleoperator
 
 
-class TeleopEvents(Enum):
-    """Shared constants for teleoperator events across teleoperators."""
-
-    SUCCESS = "success"
-    FAILURE = "failure"
-    RERECORD_EPISODE = "rerecord_episode"
-    IS_INTERVENTION = "is_intervention"
-    TERMINATE_EPISODE = "terminate_episode"
-
-
 def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
     if config.type == "keyboard":
         from .keyboard import KeyboardTeleop

src/lerobot/templates/lerobot_modelcard_template.md

@@ -44,7 +44,7 @@ Below is the short version on how to train and run inference/eval:
 ### Train from scratch
 
 ```bash
-lerobot-train \
+python -m lerobot.scripts.train \
   --dataset.repo_id=${HF_USER}/<dataset> \
   --policy.type=act \
   --output_dir=outputs/train/<desired_policy_repo_id> \
@@ -59,7 +59,7 @@ _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
 ### Evaluate the policy/run inference
 
 ```bash
-lerobot-record \
+python -m lerobot.record \
   --robot.type=so100_follower \
   --dataset.repo_id=<hf_user>/eval_<dataset> \
   --policy.path=<hf_user>/<desired_policy_repo_id> \

src/lerobot/utils/logging_utils.py

@@ -13,7 +13,7 @@
 # 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 typing import Any, Callable
+from typing import Any
 
 from lerobot.utils.utils import format_big_number
 
@@ -84,7 +84,6 @@ class MetricsTracker:
         "samples",
         "episodes",
         "epochs",
-        "accelerator",
     ]
 
     def __init__(
@@ -94,14 +93,12 @@ class MetricsTracker:
         num_episodes: int,
         metrics: dict[str, AverageMeter],
         initial_step: int = 0,
-        accelerator: Callable | None = None,
     ):
         self.__dict__.update(dict.fromkeys(self.__keys__))
         self._batch_size = batch_size
         self._num_frames = num_frames
         self._avg_samples_per_ep = num_frames / num_episodes
         self.metrics = metrics
-        self.accelerator = accelerator
 
         self.steps = initial_step
         # A sample is an (observation,action) pair, where observation and action
@@ -131,7 +128,7 @@ class MetricsTracker:
         Updates metrics that depend on 'step' for one step.
         """
         self.steps += 1
-        self.samples += self._batch_size * (self.accelerator.num_processes if self.accelerator else 1)
+        self.samples += self._batch_size
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
 

src/lerobot/utils/random_utils.py

@@ -17,7 +17,7 @@ import random
 from collections.abc import Generator
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Any, Callable, Generator
+from typing import Any
 
 import numpy as np
 import torch
@@ -164,7 +164,7 @@ def set_rng_state(random_state_dict: dict[str, Any]):
         torch.cuda.random.set_rng_state(random_state_dict["torch_cuda_random_state"])
 
 
-def set_seed(seed: int, accelerator: Callable | None = None) -> None:
+def set_seed(seed) -> None:
     """Set seed for reproducibility."""
     random.seed(seed)
     np.random.seed(seed)
@@ -172,11 +172,6 @@ def set_seed(seed: int, accelerator: Callable | None = None) -> None:
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
 
-    if accelerator:
-        from accelerate.utils import set_seed as accelerate_set_seed
-
-        accelerate_set_seed(seed)
-
 
 @contextmanager
 def seeded_context(seed: int) -> Generator[None, None, None]:

src/lerobot/utils/robot_utils.py

@@ -17,9 +17,10 @@ import time
 
 
 def busy_wait(seconds):
-    if platform.system() == "Darwin" or platform.system() == "Windows":
-        # On Mac and Windows, `time.sleep` is not accurate and we need to use this while loop trick,
+    if platform.system() == "Darwin":
+        # On Mac, `time.sleep` is not accurate and we need to use this while loop trick,
         # but it consumes CPU cycles.
+        # TODO(rcadene): find an alternative: from python 11, time.sleep is precise
         end_time = time.perf_counter() + seconds
         while time.perf_counter() < end_time:
             pass

src/lerobot/utils/train_utils.py

@@ -32,7 +32,6 @@ from lerobot.datasets.utils import load_json, write_json
 from lerobot.optim.optimizers import load_optimizer_state, save_optimizer_state
 from lerobot.optim.schedulers import load_scheduler_state, save_scheduler_state
 from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.processor.pipeline import RobotProcessor
 from lerobot.utils.random_utils import load_rng_state, save_rng_state
 
 
@@ -75,8 +74,7 @@ def save_checkpoint(
     policy: PreTrainedPolicy,
     optimizer: Optimizer,
     scheduler: LRScheduler | None = None,
-    preprocessor: RobotProcessor | None = None,
-    postprocessor: RobotProcessor | None = None,
+    preprocessor=None,
 ) -> None:
     """This function creates the following directory structure:
 
@@ -107,8 +105,6 @@ def save_checkpoint(
     cfg.save_pretrained(pretrained_dir)
     if preprocessor is not None:
         preprocessor.save_pretrained(pretrained_dir)
-    if postprocessor is not None:
-        postprocessor.save_pretrained(pretrained_dir)
     save_training_state(checkpoint_dir, step, optimizer, scheduler)
 
 

src/lerobot/utils/utils.py

@@ -24,7 +24,6 @@ import time
 from copy import copy, deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Callable
 from statistics import mean
 
 import numpy as np
@@ -57,15 +56,13 @@ def auto_select_torch_device() -> torch.device:
 
 
 # TODO(Steven): Remove log. log shouldn't be an argument, this should be handled by the logger level
-def get_safe_torch_device(
-    try_device: str, log: bool = False, accelerator: Callable | None = None
-) -> torch.device:
+def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
     """Given a string, return a torch.device with checks on whether the device is available."""
     try_device = str(try_device)
     match try_device:
         case "cuda":
             assert torch.cuda.is_available()
-            device = accelerator.device if accelerator else torch.device("cuda")
+            device = torch.device("cuda")
         case "mps":
             assert torch.backends.mps.is_available()
             device = torch.device("mps")
@@ -119,7 +116,6 @@ def init_logging(
     display_pid: bool = False,
     console_level: str = "INFO",
     file_level: str = "DEBUG",
-    accelerator: Callable | None = None,
 ):
     def custom_format(record: logging.LogRecord) -> str:
         dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -156,11 +152,6 @@ def init_logging(
         file_handler.setLevel(file_level.upper())
         logger.addHandler(file_handler)
 
-    if accelerator is not None and not accelerator.is_main_process:
-        # Disable duplicate logging on non-main processes
-        logging.info(f"Setting logging level on non-main process {accelerator.process_index} to WARNING.")
-        logging.getLogger().setLevel(logging.WARNING)
-
 
 def format_big_number(num, precision=0):
     suffixes = ["", "K", "M", "B", "T", "Q"]
@@ -174,10 +165,6 @@ def format_big_number(num, precision=0):
     return num
 
 
-def is_launched_with_accelerate() -> bool:
-    return "ACCELERATE_MIXED_PRECISION" in os.environ
-
-
 def _relative_path_between(path1: Path, path2: Path) -> Path:
     """Returns path1 relative to path2."""
     path1 = path1.absolute()

test_video_backends.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+
+"""
+Quick benchmark to test video decoding speed across different backends.
+"""
+
+import time
+from pathlib import Path
+import torch
+
+def test_video_backend(video_path, backend_name, num_frames=10):
+    """Test video decoding speed for a specific backend."""
+    try:
+        from lerobot.datasets.video_utils import decode_video_frames
+        
+        # Create timestamps for first N frames
+        fps = 30  # Assume 30fps, adjust if needed
+        timestamps = [i / fps for i in range(num_frames)]
+        
+        # Time the decoding
+        start_time = time.perf_counter()
+        frames = decode_video_frames(video_path, timestamps, tolerance_s=1e-4, backend=backend_name)
+        decode_time = time.perf_counter() - start_time
+        
+        frames_decoded = frames.shape[1] if frames.dim() > 1 else frames.shape[0]
+        ms_per_frame = (decode_time * 1000) / max(frames_decoded, 1)
+        
+        print(f"✅ {backend_name:12} | {decode_time*1000:6.1f}ms total | {ms_per_frame:6.1f}ms/frame | {frames_decoded} frames")
+        return decode_time, frames_decoded
+        
+    except Exception as e:
+        print(f"❌ {backend_name:12} | ERROR: {str(e)[:50]}...")
+        return float('inf'), 0
+
+def main():
+    print("📦 Downloading dataset to get video file locations...")
+    
+    try:
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+        
+        # Download the dataset - this will tell us exactly where it's stored
+        dataset = LeRobotDataset("kenmacken/record-test-2", download_videos=True)
+        
+        print(f"✅ Dataset downloaded to: {dataset.root}")
+        print(f"   Video keys: {dataset.meta.video_keys}")
+        print(f"   Total episodes: {dataset.meta.total_episodes}")
+        
+        # Get actual video file paths from the dataset
+        video_files = []
+        for ep_idx in range(min(2, dataset.meta.total_episodes)):  # Test first 2 episodes max
+            for vid_key in dataset.meta.video_keys:
+                video_path = dataset.root / dataset.meta.get_video_file_path(ep_idx, vid_key)
+                if video_path.exists():
+                    video_files.append(video_path)
+                    break  # Just need one video file for testing
+            if video_files:
+                break
+        
+        if not video_files:
+            print("❌ No video files found after download!")
+            return
+            
+    except Exception as e:
+        print(f"❌ Error downloading dataset: {e}")
+        # Fallback to manual search
+        possible_paths = [
+            Path.home() / ".cache/huggingface/lerobot/kenmacken/record-test-2",
+            Path("/tmp/huggingface/lerobot/kenmacken/record-test-2"),
+            Path("./datasets/record-test-2"),
+        ]
+        
+        video_files = []
+        print("Trying fallback search...")
+        for path in possible_paths:
+            print(f"  Checking: {path}")
+            if path.exists():
+                files = list(path.rglob("*.mp4"))
+                if files:
+                    video_files = files
+                    print(f"  ✅ Found {len(files)} video files!")
+                    break
+        
+        if not video_files:
+            print("❌ No video files found!")
+            return
+        
+    test_video = video_files[0]
+    print(f"Testing video: {test_video.name}")
+    print(f"File size: {test_video.stat().st_size / 1024 / 1024:.1f} MB")
+    print("-" * 60)
+    
+    backends = ["torchcodec", "pyav", "video_reader"]
+    results = {}
+    
+    for backend in backends:
+        decode_time, frames = test_video_backend(test_video, backend)
+        results[backend] = (decode_time, frames)
+    
+    print("-" * 60)
+    print("RECOMMENDATION:")
+    
+    # Find fastest backend
+    valid_results = {k: v for k, v in results.items() if v[0] != float('inf')}
+    if valid_results:
+        fastest = min(valid_results.items(), key=lambda x: x[1][0])
+        print(f"🚀 Use '{fastest[0]}' - fastest backend!")
+        print(f"   Add to your config: video_backend: \"{fastest[0]}\"")
+        
+        slowest_time = max(valid_results.values())[0]
+        speedup = slowest_time / fastest[1][0]
+        print(f"   Speedup vs slowest: {speedup:.1f}x faster")
+    else:
+        print("❌ No backends worked!")
+
+if __name__ == "__main__":
+    main()

tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors

@@ -1,3 +1,3 @@
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tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors

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-size 31672
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+size 33400

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@@ -1,3 +1,3 @@
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tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors

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tests/artifacts/policies/pusht_diffusion_/actions.safetensors

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tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors

@@ -1,3 +1,3 @@
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tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors

@@ -1,3 +1,3 @@
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-size 47424
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+size 49120

tests/artifacts/policies/save_policy_to_safetensors.py

@@ -23,8 +23,7 @@ from lerobot.configs.default import DatasetConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.datasets.factory import make_dataset
 from lerobot.optim.factory import make_optimizer_and_scheduler
-from lerobot.policies.factory import make_policy, make_policy_config, make_processor
-from lerobot.processor import TransitionKey
+from lerobot.policies.factory import make_policy, make_policy_config
 from lerobot.utils.random_utils import set_seed
 
 
@@ -38,9 +37,7 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
     train_cfg.validate()  # Needed for auto-setting some parameters
 
     dataset = make_dataset(train_cfg)
-    dataset_stats = dataset.meta.stats
     policy = make_policy(train_cfg.policy, ds_meta=dataset.meta)
-    preprocessor, postprocessor = make_processor(train_cfg.policy, dataset_stats=dataset_stats)
     policy.train()
 
     optimizer, _ = make_optimizer_and_scheduler(train_cfg, policy)
@@ -52,9 +49,7 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
     )
 
     batch = next(iter(dataloader))
-    batch = preprocessor(batch)
     loss, output_dict = policy.forward(batch)
-
     if output_dict is not None:
         output_dict = {k: v for k, v in output_dict.items() if isinstance(v, torch.Tensor)}
         output_dict["loss"] = loss
@@ -101,12 +96,7 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
     else:
         actions_queue = train_cfg.policy.n_action_repeats
 
-    actions = {}
-    for i in range(actions_queue):
-        unnormalized_action = policy.select_action(obs).contiguous()
-        action_robot = postprocessor({TransitionKey.ACTION: unnormalized_action}).get(TransitionKey.ACTION)
-        actions[str(i)] = action_robot
-
+    actions = {str(i): policy.select_action(obs).contiguous() for i in range(actions_queue)}
     return output_dict, grad_stats, param_stats, actions
 
 

tests/artifacts/policies/xarm_lift_medium_tdmpc_use_mpc/actions.safetensors

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