also have pipeline for feedback_features and action_features

* chore(docstrings): fixing deprecated `root` argument docstrings in LeRobotDataset class

* chore(draccus): updating draccus CLI help

* chore(revert): reverting changes in lerobot_dataset_viz.py

---------

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

.github/workflows/fast_tests.yml

@@ -61,7 +61,6 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -90,10 +89,5 @@ jobs:
       - name: Install lerobot with test extras
         run: uv sync --extra "test"
 
-      - name: Login to Hugging Face
-        run: |
-          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          uv run hf auth whoami
-
       - name: Run pytest
         run: uv run pytest tests -vv --maxfail=10

.github/workflows/full_tests.yml

@@ -60,7 +60,6 @@ jobs:
       MUJOCO_GL: egl
       HF_HOME: /mnt/cache/.cache/huggingface
       HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     steps:
       - uses: actions/checkout@v6
         with:
@@ -88,11 +87,6 @@ jobs:
       - name: Install lerobot with all extras
         run: uv sync --extra all # TODO(Steven): Make flash-attn optional
 
-      - name: Login to Hugging Face
-        run: |
-          uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          uv run hf auth whoami
-
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv --maxfail=10
 
@@ -168,7 +162,6 @@ jobs:
       HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
       TORCH_HOME: /home/user_lerobot/.cache/torch
       TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
     container:
       image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
       options: --gpus all --shm-size "16gb"
@@ -180,10 +173,8 @@ jobs:
         shell: bash
         working-directory: /lerobot
     steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
+      - name: Fix ptxas permissions
+        run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests

AI_POLICY.md

@@ -0,0 +1,25 @@
+# AI Usage Policy
+
+The LeRobot project welcomes contributions from everyone, and we have a few guidelines regarding AI usage to ensure high code quality, clear communication, and a healthy open-source ecosystem:
+
+- **Please disclose significant AI assistance.** If you used AI tools (e.g., Copilot, Claude, Cursor, ChatGPT) to generate a substantial portion of your code or text, let us know in your PR description. Transparency helps us review your changes more effectively.
+- **Own your code (The Human-in-the-Loop).** You must fully understand all the changes you are proposing. If you cannot explain what your AI-assisted code does or how it interacts with LeRobot's broader architecture, please take the time to learn and test it before submitting.
+- **Keep issues and discussions focused.** You are welcome to use AI to help draft issues or PR descriptions, but please review and edit them carefully before posting. AI can often be overly verbose; trimming the noise and getting straight to the point helps our maintainers address your needs faster.
+
+Our core maintainers also use AI tools to aid their workflows, but they do so while bringing deep contextual knowledge of the LeRobot codebase to validate the output. We ask all contributors to apply that same level of rigor.
+
+## Remember the Human Maintainers
+
+Please remember that LeRobot is maintained by a dedicated team of humans.
+
+Every discussion, issue, and pull request is read and reviewed by real people. While AI tools can generate thousands of lines of code in seconds, reviewing that code still takes human time and energy. Submitting unverified or low-effort AI output puts an unfair burden on our maintainers.
+
+Today, the quality of the AI output still heavily depends on the developer driving the tool. We ask that you respect our maintainers' time by thoroughly vetting, testing, and refining your submissions.
+
+## AI is Welcome Here
+
+LeRobot operates at the cutting edge of AI and robotics, and many of our maintainers actively embrace AI coding assistants as valuable productivity tools. We are a pro-AI project!
+
+Our reason for having an AI policy is not an anti-AI stance. Rather, it exists to ensure that AI is used to enhance human contributions, not replace them with unverified noise. It's about how the tools are used, not the tools themselves.
+
+We value the unique human insight you bring to the LeRobot community. Let AI empower your workflow, but always let your own judgment take the wheel.

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
 
-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md).
+Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
 
 ## Ways to Contribute
 

MANIFEST.in

@@ -1,2 +1,3 @@
 include src/lerobot/templates/lerobot_modelcard_template.md
 include src/lerobot/datasets/card_template.md
+include src/lerobot/envs/metaworld_config.json

docker/Dockerfile.internal

@@ -85,6 +85,8 @@ RUN if [ "$UNBOUND_DEPS" = "true" ]; then \
 
 RUN uv pip install --no-cache ".[all]"
 
+RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
+
 # Copy the rest of the application source code
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .

docs/source/pi0fast.mdx

@@ -52,7 +52,7 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
 
 You have two options for the FAST tokenizer:
 
-1. **Use the pre-trained tokenizer**: The `lerobot/fast-action-tokenizer` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
+1. **Use the pre-trained tokenizer**: The `physical-intelligence/fast` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
 
 2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
 
@@ -114,15 +114,15 @@ lerobot-train \
 
 ### Key Training Parameters
 
-| Parameter                              | Description                                        | Default                         |
-| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
-| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                         |
-| `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                       |
-| `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `50`                            |
-| `--policy.n_action_steps`              | Number of action steps to execute                  | `50`                            |
-| `--policy.max_action_tokens`           | Maximum number of FAST tokens per action chunk     | `256`                           |
-| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `lerobot/fast-action-tokenizer` |
-| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                         |
+| Parameter                              | Description                                        | Default                      |
+| -------------------------------------- | -------------------------------------------------- | ---------------------------- |
+| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false`                      |
+| `--policy.dtype=bfloat16`              | Use mixed precision training for efficiency        | `float32`                    |
+| `--policy.chunk_size`                  | Number of action steps to predict (action horizon) | `50`                         |
+| `--policy.n_action_steps`              | Number of action steps to execute                  | `50`                         |
+| `--policy.max_action_tokens`           | Maximum number of FAST tokens per action chunk     | `256`                        |
+| `--policy.action_tokenizer_name`       | FAST tokenizer to use                              | `physical-intelligence/fast` |
+| `--policy.compile_model=true`          | Enable torch.compile for faster training           | `false`                      |
 
 ## Inference
 

examples/backward_compatibility/replay.py

@@ -57,7 +57,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30

examples/lekiwi/evaluate.py

@@ -18,7 +18,6 @@ from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import hw_to_dataset_features
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
-from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi import LeKiwiClient, LeKiwiClientConfig
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.constants import ACTION, OBS_STR
@@ -71,9 +70,6 @@ def main():
     # To connect you already should have this script running on LeKiwi: `python -m lerobot.robots.lekiwi.lekiwi_host --robot.id=my_awesome_kiwi`
     robot.connect()
 
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
     # Initialize the keyboard listener and rerun visualization
     listener, events = init_keyboard_listener()
     init_rerun(session_name="lekiwi_evaluate")
@@ -99,9 +95,6 @@ def main():
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -116,9 +109,6 @@ def main():
                     control_time_s=EPISODE_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
                 )
 
             if events["rerecord_episode"]:

examples/lekiwi/record.py

@@ -16,7 +16,6 @@
 
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.processor import make_default_processors
 from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
 from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
 from lerobot.scripts.lerobot_record import record_loop
@@ -46,9 +45,6 @@ def main():
     leader_arm = SO100Leader(leader_arm_config)
     keyboard = KeyboardTeleop(keyboard_config)
 
-    # TODO(Steven): Update this example to use pipelines
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
     # Configure the dataset features
     action_features = hw_to_dataset_features(robot.action_features, ACTION)
     obs_features = hw_to_dataset_features(robot.observation_features, OBS_STR)
@@ -93,9 +89,6 @@ def main():
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -111,9 +104,6 @@ def main():
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
                 )
 
             if events["rerecord_episode"]:

examples/phone_to_so100/evaluate.py

@@ -17,30 +17,16 @@
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import combine_feature_dicts
-from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
-from lerobot.processor import (
-    RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    make_default_teleop_action_processor,
-)
-from lerobot.processor.converters import (
-    observation_to_transition,
-    robot_action_observation_to_transition,
-    transition_to_observation,
-    transition_to_robot_action,
-)
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
-    InverseKinematicsEEToJoints,
+from lerobot.robots.so_follower.pipelines import (
+    make_so10x_fk_observation_pipeline,
+    make_so10x_ik_action_pipeline,
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.pipeline_utils import build_dataset_features
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 
@@ -51,6 +37,10 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+# https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+URDF_PATH = "./SO101/so101_new_calib.urdf"
+
 
 def main():
     # Create the robot configuration & robot
@@ -64,68 +54,31 @@ def main():
 
     robot = SO100Follower(robot_config)
 
-    # Create policy
-    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+    # Attach FK/IK pipelines so the robot works in EE space
+    motor_names = list(robot.bus.motors.keys())
+    robot.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+    robot.set_input_pipeline(make_so10x_ik_action_pipeline(URDF_PATH, motor_names))
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert joints observation to EE observation
-    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
+    # Create the dataset — obs auto-derived from FK pipeline, EE action spec explicit
     dataset = LeRobotDataset.create(
         repo_id=HF_DATASET_ID,
         fps=FPS,
-        features=combine_feature_dicts(
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose_processor,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
-            # User for now should be explicit on the feature keys that were used for record
-            # Alternatively, the user can pass the processor step that has the right features
-            aggregate_pipeline_dataset_features(
-                pipeline=make_default_teleop_action_processor(),
-                initial_features=create_initial_features(
-                    action={
-                        f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
-                        for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
-                    }
-                ),
-                use_videos=True,
-            ),
+        features=build_dataset_features(
+            robot,
+            use_videos=True,
+            action_features={
+                f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+            },
         ),
         robot_type=robot.name,
         use_videos=True,
         image_writer_threads=4,
     )
 
+    # Create policy
+    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+
     # Build Policy Processors
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=policy,
@@ -151,21 +104,18 @@ def main():
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
+            # Main record loop — pipelines applied internally by robot
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
                 policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
+                preprocessor=preprocessor,
                 postprocessor=postprocessor,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -180,9 +130,6 @@ def main():
                     control_time_s=EPISODE_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
                 )
 
             if events["rerecord_episode"]:

examples/phone_to_so100/record.py

@@ -16,21 +16,17 @@
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import combine_feature_dicts
-from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
 from lerobot.processor.converters import (
-    observation_to_transition,
     robot_action_observation_to_transition,
-    transition_to_observation,
+    robot_action_to_transition,
     transition_to_robot_action,
 )
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+from lerobot.robots.so_follower.pipelines import make_so10x_fk_observation_pipeline
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     EEBoundsAndSafety,
     EEReferenceAndDelta,
-    ForwardKinematicsJointsToEE,
     GripperVelocityToJoint,
     InverseKinematicsEEToJoints,
 )
@@ -39,6 +35,7 @@ from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
 from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
 from lerobot.teleoperators.phone.teleop_phone import Phone
 from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.pipeline_utils import build_dataset_features
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 
@@ -49,6 +46,10 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+# https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+URDF_PATH = "./SO101/so101_new_calib.urdf"
+
 
 def main():
     # Create the robot and teleoperator configurations
@@ -65,77 +66,59 @@ def main():
     robot = SO100Follower(robot_config)
     phone = Phone(teleop_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+    motor_names = list(robot.bus.motors.keys())
+
+    from lerobot.model.kinematics import RobotKinematics
+
     kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
+        urdf_path=URDF_PATH,
         target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
+        joint_names=motor_names,
     )
 
-    # Build pipeline to convert phone action to EE action
-    phone_to_robot_ee_pose_processor = RobotProcessorPipeline[
-        tuple[RobotAction, RobotObservation], RobotAction
-    ](
-        steps=[
-            MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
-            EEReferenceAndDelta(
-                kinematics=kinematics_solver,
-                end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
-                motor_names=list(robot.bus.motors.keys()),
-                use_latched_reference=True,
-            ),
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.20,
-            ),
-            GripperVelocityToJoint(speed_factor=20.0),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
+    # Phone output pipeline: map raw phone gesture to EE delta (no robot obs needed)
+    phone.set_output_pipeline(
+        RobotProcessorPipeline[RobotAction, RobotAction](
+            steps=[MapPhoneActionToRobotAction(platform=teleop_config.phone_os)],
+            to_transition=robot_action_to_transition,
+            to_output=transition_to_robot_action,
+        )
     )
 
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
+    # Robot FK observation pipeline: joints → EE pose
+    robot.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+
+    # Robot input pipeline: EE delta + current robot obs → joint commands
+    robot.set_input_pipeline(
+        RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+            steps=[
+                EEReferenceAndDelta(
+                    kinematics=kinematics_solver,
+                    end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
+                    motor_names=motor_names,
+                    use_latched_reference=True,
+                ),
+                EEBoundsAndSafety(
+                    end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
+                    max_ee_step_m=0.20,
+                ),
+                GripperVelocityToJoint(speed_factor=20.0),
+                InverseKinematicsEEToJoints(
+                    kinematics=kinematics_solver,
+                    motor_names=motor_names,
+                    initial_guess_current_joints=True,
+                ),
+            ],
+            to_transition=robot_action_observation_to_transition,
+            to_output=transition_to_robot_action,
+        )
     )
 
-    # Build pipeline to convert joint observation to EE observation
-    robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
+    # Dataset features auto-derived from robot's FK obs pipeline and phone's mapped action pipeline
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
-        features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
-            aggregate_pipeline_dataset_features(
-                pipeline=phone_to_robot_ee_pose_processor,
-                initial_features=create_initial_features(action=phone.action_features),
-                use_videos=True,
-            ),
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
-        ),
+        features=build_dataset_features(robot, phone, use_videos=True),
         robot_type=robot.name,
         use_videos=True,
         image_writer_threads=4,
@@ -158,7 +141,7 @@ def main():
         while episode_idx < NUM_EPISODES and not events["stop_recording"]:
             log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
+            # Main record loop — pipelines applied internally by robot and phone
             record_loop(
                 robot=robot,
                 events=events,
@@ -168,9 +151,6 @@ def main():
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=phone_to_robot_ee_pose_processor,
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -186,9 +166,6 @@ def main():
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=phone_to_robot_ee_pose_processor,
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose,
                 )
 
             if events["rerecord_episode"]:

examples/rtc/eval_with_real_robot.py

@@ -87,8 +87,8 @@ from lerobot.policies.rtc.action_queue import ActionQueue
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
 from lerobot.policies.rtc.latency_tracker import LatencyTracker
 from lerobot.processor.factory import (
-    make_default_robot_action_processor,
-    make_default_robot_observation_processor,
+    _make_identity_observation_pipeline as make_default_robot_observation_processor,
+    _make_identity_robot_action_pipeline as make_default_robot_action_processor,
 )
 from lerobot.rl.process import ProcessSignalHandler
 from lerobot.robots import (  # noqa: F401

examples/so100_to_so100_EE/evaluate.py

@@ -17,30 +17,16 @@
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import combine_feature_dicts
-from lerobot.model.kinematics import RobotKinematics
 from lerobot.policies.act.modeling_act import ACTPolicy
 from lerobot.policies.factory import make_pre_post_processors
-from lerobot.processor import (
-    RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    make_default_teleop_action_processor,
-)
-from lerobot.processor.converters import (
-    observation_to_transition,
-    robot_action_observation_to_transition,
-    transition_to_observation,
-    transition_to_robot_action,
-)
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
-    InverseKinematicsEEToJoints,
+from lerobot.robots.so_follower.pipelines import (
+    make_so10x_fk_observation_pipeline,
+    make_so10x_ik_action_pipeline,
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.pipeline_utils import build_dataset_features
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 
@@ -51,6 +37,10 @@ TASK_DESCRIPTION = "My task description"
 HF_MODEL_ID = "<hf_username>/<model_repo_id>"
 HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
 
+# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo:
+# https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+URDF_PATH = "./SO101/so101_new_calib.urdf"
+
 
 def main():
     # Create the robot configuration & robot
@@ -64,68 +54,31 @@ def main():
 
     robot = SO100Follower(robot_config)
 
-    # Create policy
-    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+    # Attach FK/IK pipelines so the robot works in EE space
+    motor_names = list(robot.bus.motors.keys())
+    robot.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+    robot.set_input_pipeline(make_so10x_ik_action_pipeline(URDF_PATH, motor_names))
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(robot.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert EE action to joints action
-    robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            InverseKinematicsEEToJoints(
-                kinematics=kinematics_solver,
-                motor_names=list(robot.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert joints observation to EE observation
-    robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
-            )
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Create the dataset
+    # Create the dataset — obs auto-derived from FK pipeline, EE action spec explicit
     dataset = LeRobotDataset.create(
         repo_id=HF_DATASET_ID,
         fps=FPS,
-        features=combine_feature_dicts(
-            aggregate_pipeline_dataset_features(
-                pipeline=robot_joints_to_ee_pose_processor,
-                initial_features=create_initial_features(observation=robot.observation_features),
-                use_videos=True,
-            ),
-            # User for now should be explicit on the feature keys that were used for record
-            # Alternatively, the user can pass the processor step that has the right features
-            aggregate_pipeline_dataset_features(
-                pipeline=make_default_teleop_action_processor(),
-                initial_features=create_initial_features(
-                    action={
-                        f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
-                        for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
-                    }
-                ),
-                use_videos=True,
-            ),
+        features=build_dataset_features(
+            robot,
+            use_videos=True,
+            action_features={
+                f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+            },
         ),
         robot_type=robot.name,
         use_videos=True,
         image_writer_threads=4,
     )
 
+    # Create policy
+    policy = ACTPolicy.from_pretrained(HF_MODEL_ID)
+
     # Build Policy Processors
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=policy,
@@ -135,7 +88,7 @@ def main():
         preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
     )
 
-    # Connect the robot and teleoperator
+    # Connect the robot
     robot.connect()
 
     # Initialize the keyboard listener and rerun visualization
@@ -151,21 +104,18 @@ def main():
         for episode_idx in range(NUM_EPISODES):
             log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
+            # Main record loop — pipelines applied internally by robot
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
                 policy=policy,
-                preprocessor=preprocessor,  # Pass the pre and post policy processors
+                preprocessor=preprocessor,
                 postprocessor=postprocessor,
                 dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=make_default_teleop_action_processor(),
-                robot_action_processor=robot_ee_to_joints_processor,
-                robot_observation_processor=robot_joints_to_ee_pose_processor,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -180,9 +130,6 @@ def main():
                     control_time_s=EPISODE_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=make_default_teleop_action_processor(),
-                    robot_action_processor=robot_ee_to_joints_processor,
-                    robot_observation_processor=robot_joints_to_ee_pose_processor,
                 )
 
             if events["rerecord_episode"]:

examples/so100_to_so100_EE/record.py

@@ -17,25 +17,20 @@
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import combine_feature_dicts
-from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
-from lerobot.processor.converters import (
-    observation_to_transition,
-    robot_action_observation_to_transition,
-    transition_to_observation,
-    transition_to_robot_action,
-)
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    EEBoundsAndSafety,
-    ForwardKinematicsJointsToEE,
-    InverseKinematicsEEToJoints,
+from lerobot.robots.so_follower.pipelines import (
+    make_so10x_fk_observation_pipeline,
+    make_so10x_ik_action_pipeline,
 )
 from lerobot.scripts.lerobot_record import record_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.teleoperators.so_leader.pipelines import make_so10x_leader_fk_pipeline
 from lerobot.utils.control_utils import init_keyboard_listener
+from lerobot.utils.pipeline_utils import (
+    build_dataset_features,
+    check_action_space_compatibility,
+    check_observation_space_compatibility,
+)
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
 
@@ -46,6 +41,10 @@ RESET_TIME_SEC = 30
 TASK_DESCRIPTION = "My task description"
 HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
 
+# NOTE: Use the URDF from the SO-ARM100 repo:
+# https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+URDF_PATH = "./SO101/so101_new_calib.urdf"
+
 
 def main():
     # Create the robot and teleoperator configurations
@@ -62,77 +61,17 @@ def main():
     follower = SO100Follower(follower_config)
     leader = SO100Leader(leader_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    follower_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(follower.bus.motors.keys()),
-    )
+    # Attach EE-space pipelines to the objects
+    motor_names = list(follower.bus.motors.keys())
+    follower.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+    follower.set_input_pipeline(make_so10x_ik_action_pipeline(URDF_PATH, motor_names))
+    leader.set_output_pipeline(make_so10x_leader_fk_pipeline(URDF_PATH, list(leader.bus.motors.keys())))
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    leader_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(leader.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert follower joints to EE observation
-    follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=follower_kinematics_solver, motor_names=list(follower.bus.motors.keys())
-            ),
-        ],
-        to_transition=observation_to_transition,
-        to_output=transition_to_observation,
-    )
-
-    # Build pipeline to convert leader joints to EE action
-    leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Build pipeline to convert EE action to follower joints
-    ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.10,
-            ),
-            InverseKinematicsEEToJoints(
-                kinematics=follower_kinematics_solver,
-                motor_names=list(follower.bus.motors.keys()),
-                initial_guess_current_joints=True,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # Create the dataset
+    # Dataset features are derived automatically from robot/teleop pipelines
     dataset = LeRobotDataset.create(
         repo_id=HF_REPO_ID,
         fps=FPS,
-        features=combine_feature_dicts(
-            # Run the feature contract of the pipelines
-            # This tells you how the features would look like after the pipeline steps
-            aggregate_pipeline_dataset_features(
-                pipeline=leader_joints_to_ee,
-                initial_features=create_initial_features(action=leader.action_features),
-                use_videos=True,
-            ),
-            aggregate_pipeline_dataset_features(
-                pipeline=follower_joints_to_ee,
-                initial_features=create_initial_features(observation=follower.observation_features),
-                use_videos=True,
-            ),
-        ),
+        features=build_dataset_features(follower, leader, use_videos=True),
         robot_type=follower.name,
         use_videos=True,
         image_writer_threads=4,
@@ -142,9 +81,13 @@ def main():
     leader.connect()
     follower.connect()
 
+    # Verify action/observation space alignment (warns on mismatch)
+    check_action_space_compatibility(leader, follower)
+    check_observation_space_compatibility(follower, leader)
+
     # Initialize the keyboard listener and rerun visualization
     listener, events = init_keyboard_listener()
-    init_rerun(session_name="recording_phone")
+    init_rerun(session_name="recording_ee")
 
     try:
         if not leader.is_connected or not follower.is_connected:
@@ -155,7 +98,8 @@ def main():
         while episode_idx < NUM_EPISODES and not events["stop_recording"]:
             log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
 
-            # Main record loop
+            # Pipelines applied automatically inside robot.get_observation(),
+            # teleop.get_action(), and robot.send_action()
             record_loop(
                 robot=follower,
                 events=events,
@@ -165,9 +109,6 @@ def main():
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
-                teleop_action_processor=leader_joints_to_ee,
-                robot_action_processor=ee_to_follower_joints,
-                robot_observation_processor=follower_joints_to_ee,
             )
 
             # Reset the environment if not stopping or re-recording
@@ -183,9 +124,6 @@ def main():
                     control_time_s=RESET_TIME_SEC,
                     single_task=TASK_DESCRIPTION,
                     display_data=True,
-                    teleop_action_processor=leader_joints_to_ee,
-                    robot_action_processor=ee_to_follower_joints,
-                    robot_observation_processor=follower_joints_to_ee,
                 )
 
             if events["rerecord_episode"]:

examples/so100_to_so100_EE/teleoperate.py

@@ -14,27 +14,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import time
-
-from lerobot.model.kinematics import RobotKinematics
-from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
-from lerobot.processor.converters import (
-    robot_action_observation_to_transition,
-    robot_action_to_transition,
-    transition_to_robot_action,
-)
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.robots.so_follower.robot_kinematic_processor import (
-    EEBoundsAndSafety,
-    ForwardKinematicsJointsToEE,
-    InverseKinematicsEEToJoints,
+from lerobot.robots.so_follower.pipelines import (
+    make_so10x_fk_observation_pipeline,
+    make_so10x_ik_action_pipeline,
 )
+from lerobot.scripts.lerobot_teleoperate import teleop_loop
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
-from lerobot.utils.robot_utils import precise_sleep
-from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+from lerobot.teleoperators.so_leader.pipelines import make_so10x_leader_fk_pipeline
+from lerobot.utils.pipeline_utils import check_action_space_compatibility
+from lerobot.utils.visualization_utils import init_rerun
 
 FPS = 30
 
+# NOTE: Use the URDF from the SO-ARM100 repo:
+# https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
+URDF_PATH = "./SO101/so101_new_calib.urdf"
+
 
 def main():
     # Initialize the robot and teleoperator config
@@ -47,47 +43,14 @@ def main():
     follower = SO100Follower(follower_config)
     leader = SO100Leader(leader_config)
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    follower_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(follower.bus.motors.keys()),
-    )
+    # Attach EE-space pipelines to the objects
+    motor_names = list(follower.bus.motors.keys())
+    follower.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+    follower.set_input_pipeline(make_so10x_ik_action_pipeline(URDF_PATH, motor_names))
+    leader.set_output_pipeline(make_so10x_leader_fk_pipeline(URDF_PATH, list(leader.bus.motors.keys())))
 
-    # NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
-    leader_kinematics_solver = RobotKinematics(
-        urdf_path="./SO101/so101_new_calib.urdf",
-        target_frame_name="gripper_frame_link",
-        joint_names=list(leader.bus.motors.keys()),
-    )
-
-    # Build pipeline to convert teleop joints to EE action
-    leader_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
-        steps=[
-            ForwardKinematicsJointsToEE(
-                kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
-            ),
-        ],
-        to_transition=robot_action_to_transition,
-        to_output=transition_to_robot_action,
-    )
-
-    # build pipeline to convert EE action to robot joints
-    ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        [
-            EEBoundsAndSafety(
-                end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
-                max_ee_step_m=0.10,
-            ),
-            InverseKinematicsEEToJoints(
-                kinematics=follower_kinematics_solver,
-                motor_names=list(follower.bus.motors.keys()),
-                initial_guess_current_joints=False,
-            ),
-        ],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
+    # Verify action space alignment (warns if leader EE ≠ follower action_features)
+    check_action_space_compatibility(leader, follower)
 
     # Connect to the robot and teleoperator
     follower.connect()
@@ -97,28 +60,12 @@ def main():
     init_rerun(session_name="so100_so100_EE_teleop")
 
     print("Starting teleop loop...")
-    while True:
-        t0 = time.perf_counter()
-
-        # Get robot observation
-        robot_obs = follower.get_observation()
-
-        # Get teleop observation
-        leader_joints_obs = leader.get_action()
-
-        # teleop joints -> teleop EE action
-        leader_ee_act = leader_to_ee(leader_joints_obs)
-
-        # teleop EE -> robot joints
-        follower_joints_act = ee_to_follower_joints((leader_ee_act, robot_obs))
-
-        # Send action to robot
-        _ = follower.send_action(follower_joints_act)
-
-        # Visualize
-        log_rerun_data(observation=leader_ee_act, action=follower_joints_act)
-
-        precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+    try:
+        # Pipelines applied automatically inside teleop.get_action() and robot.send_action()
+        teleop_loop(teleop=leader, robot=follower, fps=FPS, display_data=True)
+    finally:
+        follower.disconnect()
+        leader.disconnect()
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.4"
+version = "0.4.5"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
@@ -61,7 +61,7 @@ dependencies = [
     # Hugging Face dependencies
     "datasets>=4.0.0,<5.0.0",
     "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub[cli]>=1.0.0,<2.0.0",
+    "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
     "accelerate>=1.10.0,<2.0.0",
 
     # Core dependencies
@@ -96,7 +96,7 @@ dependencies = [
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.10.0"]
-transformers-dep = ["transformers>=5.1.0,<6.0.0"]
+transformers-dep = ["transformers>=4.57.1,<5.0.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 
@@ -129,13 +129,13 @@ phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
 
 # Policies
 wallx = [
-    "lerobot[transformers-dep]",
-    "peft>=0.18.0,<1.0.0",
-    "scipy==1.15.3", # TODO: Relax version
-    "torchdiffeq==0.2.5", # TODO: Relax version
-    "qwen-vl-utils==0.0.11" # TODO: Relax version
+    "transformers==4.49.0",
+    "peft==0.17.1",
+    "scipy==1.15.3",
+    "torchdiffeq==0.2.5",
+    "qwen_vl_utils==0.0.11"
 ]
-pi = ["lerobot[transformers-dep]", "scipy==1.15.3"] # TODO: Relax scipy version
+pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi", "scipy>=1.10.1,<1.15"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
 groot = [
     "lerobot[transformers-dep]",
@@ -148,7 +148,7 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.11,<0.1.0"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.14,<0.1.0"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
@@ -176,8 +176,8 @@ all = [
     "lerobot[reachy2]",
     "lerobot[kinematics]",
     "lerobot[intelrealsense]",
-    "lerobot[wallx]",
-    "lerobot[pi]",
+    # "lerobot[wallx]",
+    # "lerobot[pi]", TODO(Pepijn): Update pi to transformers v5
     "lerobot[smolvla]",
     # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
     "lerobot[xvla]",
@@ -214,6 +214,9 @@ lerobot-edit-dataset="lerobot.scripts.lerobot_edit_dataset:main"
 lerobot-setup-can="lerobot.scripts.lerobot_setup_can:main"
 
 # ---------------- Tool Configurations ----------------
+[tool.setuptools.package-data]
+lerobot = ["envs/*.json"]
+
 [tool.setuptools.packages.find]
 where = ["src"]
 
@@ -394,3 +397,85 @@ ignore_errors = false
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"
 # ignore_errors = false
+
+[tool.uv]
+# wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
+conflicts = [
+    [
+        { extra = "wallx" },
+        { extra = "transformers-dep" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "pi" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "smolvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "groot" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "xvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "sarm" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "hilserl" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "libero" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "peft" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "all" },
+    ],
+    # pi uses custom branch which conflicts with transformers-dep
+    [
+        { extra = "pi" },
+        { extra = "transformers-dep" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "smolvla" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "groot" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "xvla" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "sarm" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "hilserl" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "libero" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "peft" },
+    ],
+    [
+        { extra = "pi" },
+        { extra = "all" },
+    ],
+]

src/lerobot/async_inference/robot_client.py

@@ -49,23 +49,18 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.robots import (  # noqa: F401
-    Robot,
-    RobotConfig,
-    bi_so_follower,
-    koch_follower,
+from lerobot.robots import (
+    RobotConfig,  # noqa: F401
     make_robot_from_config,
-    omx_follower,
-    so_follower,
 )
 from lerobot.transport import (
     services_pb2,  # type: ignore
     services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
+from lerobot.utils.import_utils import register_third_party_plugins
 
 from .configs import RobotClientConfig
-from .constants import SUPPORTED_ROBOTS
 from .helpers import (
     Action,
     FPSTracker,
@@ -485,8 +480,9 @@ class RobotClient:
 def async_client(cfg: RobotClientConfig):
     logging.info(pformat(asdict(cfg)))
 
-    if cfg.robot.type not in SUPPORTED_ROBOTS:
-        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
+    # TODO: Assert if checking robot support is still needed with the plugin system
+    # if cfg.robot.type not in SUPPORTED_ROBOTS:
+    #     raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
 
     client = RobotClient(cfg)
 
@@ -512,4 +508,5 @@ def async_client(cfg: RobotClientConfig):
 
 
 if __name__ == "__main__":
+    register_third_party_plugins()
     async_client()  # run the client

src/lerobot/configs/default.py

@@ -27,7 +27,7 @@ class DatasetConfig:
     # "dataset_index" into the returned item. The index mapping is made according to the order in which the
     # datasets are provided.
     repo_id: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | None = None
     episodes: list[int] | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)

src/lerobot/datasets/card_template.md

@@ -7,6 +7,13 @@
 
 This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
 
+{% if repo_id is defined and repo_id %}
+<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path={{ repo_id }}">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
+</a>
+{% endif %}
+
 ## Dataset Description
 
 {{ dataset_description | default("", true) }}

src/lerobot/datasets/dataset_tools.py

@@ -567,20 +567,22 @@ def _copy_and_reindex_data(
 def _keep_episodes_from_video_with_av(
     input_path: Path,
     output_path: Path,
-    episodes_to_keep: list[tuple[float, float]],
+    episodes_to_keep: list[tuple[int, int]],
     fps: float,
     vcodec: str = "libsvtav1",
     pix_fmt: str = "yuv420p",
 ) -> None:
     """Keep only specified episodes from a video file using PyAV.
 
-    This function decodes frames from specified time ranges and re-encodes them with
+    This function decodes frames from specified frame ranges and re-encodes them with
     properly reset timestamps to ensure monotonic progression.
 
     Args:
         input_path: Source video file path.
         output_path: Destination video file path.
-        episodes_to_keep: List of (start_time, end_time) tuples for episodes to keep.
+        episodes_to_keep: List of (start_frame, end_frame) tuples for episodes to keep.
+            Ranges are half-open intervals: [start_frame, end_frame), where start_frame
+            is inclusive and end_frame is exclusive.
         fps: Frame rate of the video.
         vcodec: Video codec to use for encoding.
         pix_fmt: Pixel format for output video.
@@ -622,9 +624,10 @@ def _keep_episodes_from_video_with_av(
 
     # Create set of (start, end) ranges for fast lookup.
     # Convert to a sorted list for efficient checking.
-    time_ranges = sorted(episodes_to_keep)
+    frame_ranges = sorted(episodes_to_keep)
 
     # Track frame index for setting PTS and current range being processed.
+    src_frame_count = 0
     frame_count = 0
     range_idx = 0
 
@@ -634,21 +637,20 @@ def _keep_episodes_from_video_with_av(
             if frame is None:
                 continue
 
-            # Get frame timestamp.
-            frame_time = float(frame.pts * frame.time_base) if frame.pts is not None else 0.0
-
-            # Check if frame is in any of our desired time ranges.
+            # Check if frame is in any of our desired frame ranges.
             # Skip ranges that have already passed.
-            while range_idx < len(time_ranges) and frame_time >= time_ranges[range_idx][1]:
+            while range_idx < len(frame_ranges) and src_frame_count >= frame_ranges[range_idx][1]:
                 range_idx += 1
 
             # If we've passed all ranges, stop processing.
-            if range_idx >= len(time_ranges):
+            if range_idx >= len(frame_ranges):
                 break
 
             # Check if frame is in current range.
-            start_ts, end_ts = time_ranges[range_idx]
-            if frame_time < start_ts:
+            start_frame = frame_ranges[range_idx][0]
+
+            if src_frame_count < start_frame:
+                src_frame_count += 1
                 continue
 
             # Frame is in range - create a new frame with reset timestamps.
@@ -661,6 +663,7 @@ def _keep_episodes_from_video_with_av(
             for pkt in v_out.encode(new_frame):
                 out.mux(pkt)
 
+            src_frame_count += 1
             frame_count += 1
 
     # Flush encoder.
@@ -749,15 +752,17 @@ def _copy_and_reindex_videos(
                         f"videos/{video_key}/to_timestamp"
                     ]
             else:
-                # Build list of time ranges to keep, in sorted order.
+                # Build list of frame ranges to keep, in sorted order.
                 sorted_keep_episodes = sorted(episodes_in_file, key=lambda x: episode_mapping[x])
-                episodes_to_keep_ranges: list[tuple[float, float]] = []
-
+                episodes_to_keep_ranges: list[tuple[int, int]] = []
                 for old_idx in sorted_keep_episodes:
                     src_ep = src_dataset.meta.episodes[old_idx]
-                    from_ts = src_ep[f"videos/{video_key}/from_timestamp"]
-                    to_ts = src_ep[f"videos/{video_key}/to_timestamp"]
-                    episodes_to_keep_ranges.append((from_ts, to_ts))
+                    from_frame = round(src_ep[f"videos/{video_key}/from_timestamp"] * src_dataset.meta.fps)
+                    to_frame = round(src_ep[f"videos/{video_key}/to_timestamp"] * src_dataset.meta.fps)
+                    assert src_ep["length"] == to_frame - from_frame, (
+                        f"Episode length mismatch: {src_ep['length']} vs {to_frame - from_frame}"
+                    )
+                    episodes_to_keep_ranges.append((from_frame, to_frame))
 
                 # Use PyAV filters to efficiently re-encode only the desired segments.
                 assert src_dataset.meta.video_path is not None

src/lerobot/datasets/lerobot_dataset.py

@@ -664,11 +664,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         for the README).
 
         Args:
-            repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
-                will be stored under root/repo_id.
-            root (Path | None, optional): Local directory to use for downloading/writing files. You can also
-                set the HF_LEROBOT_HOME environment variable to point to a different location. Defaults to
-                '~/.cache/huggingface/lerobot'.
+            repo_id (str): This is the repo id that will be used to fetch the dataset.
+            root (Path | None, optional): Local directory where the dataset will be downloaded and
+                stored. If set, all dataset files will be stored directly under this path. If not set, the
+                dataset files will be stored under $HF_LEROBOT_HOME/repo_id (configurable via the
+                HF_LEROBOT_HOME environment variable).
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
             image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
@@ -747,7 +747,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             # Check if cached dataset contains all requested episodes
             if not self._check_cached_episodes_sufficient():
                 raise FileNotFoundError("Cached dataset doesn't contain all requested episodes")
-        except (AssertionError, FileNotFoundError, NotADirectoryError):
+        except (FileNotFoundError, NotADirectoryError):
             if is_valid_version(self.revision):
                 self.revision = get_safe_version(self.repo_id, self.revision)
             self.download(download_videos)
@@ -839,7 +839,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             hub_api.upload_folder(**upload_kwargs)
 
         card = create_lerobot_dataset_card(
-            tags=tags, dataset_info=self.meta.info, license=license, **card_kwargs
+            tags=tags, dataset_info=self.meta.info, license=license, repo_id=self.repo_id, **card_kwargs
         )
         card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch)
 
@@ -1771,11 +1771,12 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
             )
         for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
             extra_keys = set(ds.features).difference(intersection_features)
-            logging.warning(
-                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
-                "other datasets."
-            )
-            self.disabled_features.update(extra_keys)
+            if extra_keys:
+                logging.warning(
+                    f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                    "other datasets."
+                )
+                self.disabled_features.update(extra_keys)
 
         self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps

src/lerobot/datasets/pipeline_features.py

@@ -12,14 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-from collections.abc import Sequence
-from typing import Any
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
 
 from lerobot.configs.types import PipelineFeatureType
-from lerobot.datasets.utils import hw_to_dataset_features
-from lerobot.processor import DataProcessorPipeline, RobotAction, RobotObservation
-from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
+
+if TYPE_CHECKING:
+    from lerobot.processor import RobotAction, RobotObservation
 
 
 def create_initial_features(
@@ -41,99 +41,3 @@ def create_initial_features(
     if observation:
         features[PipelineFeatureType.OBSERVATION] = observation
     return features
-
-
-# Helper to filter state/action keys based on regex patterns.
-def should_keep(key: str, patterns: tuple[str]) -> bool:
-    if patterns is None:
-        return True
-    return any(re.search(pat, key) for pat in patterns)
-
-
-def strip_prefix(key: str, prefixes_to_strip: tuple[str]) -> str:
-    for prefix in prefixes_to_strip:
-        if key.startswith(prefix):
-            return key[len(prefix) :]
-    return key
-
-
-# Define prefixes to strip from feature keys for clean names.
-# Handles both fully qualified (e.g., "action.state") and short (e.g., "state") forms.
-PREFIXES_TO_STRIP = tuple(
-    f"{token}." for const in (ACTION, OBS_STATE, OBS_IMAGES) for token in (const, const.split(".")[-1])
-)
-
-
-def aggregate_pipeline_dataset_features(
-    pipeline: DataProcessorPipeline,
-    initial_features: dict[PipelineFeatureType, dict[str, Any]],
-    *,
-    use_videos: bool = True,
-    patterns: Sequence[str] | None = None,
-) -> dict[str, dict]:
-    """
-    Aggregates and filters pipeline features to create a dataset-ready features dictionary.
-
-    This function transforms initial features using the pipeline, categorizes them as action or observations
-    (image or state), filters them based on `use_videos` and `patterns`, and finally
-    formats them for use with a Hugging Face LeRobot Dataset.
-
-    Args:
-        pipeline: The DataProcessorPipeline to apply.
-        initial_features: A dictionary of raw feature specs for actions and observations.
-        use_videos: If False, image features are excluded.
-        patterns: A sequence of regex patterns to filter action and state features.
-                  Image features are not affected by this filter.
-
-    Returns:
-        A dictionary of features formatted for a Hugging Face LeRobot Dataset.
-    """
-    all_features = pipeline.transform_features(initial_features)
-
-    # Intermediate storage for categorized and filtered features.
-    processed_features: dict[str, dict[str, Any]] = {
-        ACTION: {},
-        OBS_STR: {},
-    }
-    images_token = OBS_IMAGES.split(".")[-1]
-
-    # Iterate through all features transformed by the pipeline.
-    for ptype, feats in all_features.items():
-        if ptype not in [PipelineFeatureType.ACTION, PipelineFeatureType.OBSERVATION]:
-            continue
-
-        for key, value in feats.items():
-            # 1. Categorize the feature.
-            is_action = ptype == PipelineFeatureType.ACTION
-            # Observations are classified as images if their key matches image-related tokens or if the shape of the feature is 3.
-            # All other observations are treated as state.
-            is_image = not is_action and (
-                (isinstance(value, tuple) and len(value) == 3)
-                or (
-                    key.startswith(f"{OBS_IMAGES}.")
-                    or key.startswith(f"{images_token}.")
-                    or f".{images_token}." in key
-                )
-            )
-
-            # 2. Apply filtering rules.
-            if is_image and not use_videos:
-                continue
-            if not is_image and not should_keep(key, patterns):
-                continue
-
-            # 3. Add the feature to the appropriate group with a clean name.
-            name = strip_prefix(key, PREFIXES_TO_STRIP)
-            if is_action:
-                processed_features[ACTION][name] = value
-            else:
-                processed_features[OBS_STR][name] = value
-
-    # Convert the processed features into the final dataset format.
-    dataset_features = {}
-    if processed_features[ACTION]:
-        dataset_features.update(hw_to_dataset_features(processed_features[ACTION], ACTION, use_videos))
-    if processed_features[OBS_STR]:
-        dataset_features.update(hw_to_dataset_features(processed_features[OBS_STR], OBS_STR, use_videos))
-
-    return dataset_features

src/lerobot/datasets/video_utils.py

@@ -227,16 +227,17 @@ def decode_video_frames_torchvision(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    assert is_within_tol.all(), (
-        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
-        "It means that the closest frame that can be loaded from the video is too far away in time."
-        "This might be due to synchronization issues with timestamps during data collection."
-        "To be safe, we advise to ignore this item during training."
-        f"\nqueried timestamps: {query_ts}"
-        f"\nloaded timestamps: {loaded_ts}"
-        f"\nvideo: {video_path}"
-        f"\nbackend: {backend}"
-    )
+    if not is_within_tol.all():
+        raise FrameTimestampError(
+            f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
+            " It means that the closest frame that can be loaded from the video is too far away in time."
+            " This might be due to synchronization issues with timestamps during data collection."
+            " To be safe, we advise to ignore this item during training."
+            f"\nqueried timestamps: {query_ts}"
+            f"\nloaded timestamps: {loaded_ts}"
+            f"\nvideo: {video_path}"
+            f"\nbackend: {backend}"
+        )
 
     # get closest frames to the query timestamps
     closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
@@ -248,7 +249,11 @@ def decode_video_frames_torchvision(
     # convert to the pytorch format which is float32 in [0,1] range (and channel first)
     closest_frames = closest_frames.type(torch.float32) / 255
 
-    assert len(timestamps) == len(closest_frames)
+    if len(timestamps) != len(closest_frames):
+        raise FrameTimestampError(
+            f"Number of retrieved frames ({len(closest_frames)}) does not match "
+            f"number of queried timestamps ({len(timestamps)})"
+        )
     return closest_frames
 
 
@@ -353,15 +358,16 @@ def decode_video_frames_torchcodec(
     min_, argmin_ = dist.min(1)
 
     is_within_tol = min_ < tolerance_s
-    assert is_within_tol.all(), (
-        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
-        "It means that the closest frame that can be loaded from the video is too far away in time."
-        "This might be due to synchronization issues with timestamps during data collection."
-        "To be safe, we advise to ignore this item during training."
-        f"\nqueried timestamps: {query_ts}"
-        f"\nloaded timestamps: {loaded_ts}"
-        f"\nvideo: {video_path}"
-    )
+    if not is_within_tol.all():
+        raise FrameTimestampError(
+            f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
+            " It means that the closest frame that can be loaded from the video is too far away in time."
+            " This might be due to synchronization issues with timestamps during data collection."
+            " To be safe, we advise to ignore this item during training."
+            f"\nqueried timestamps: {query_ts}"
+            f"\nloaded timestamps: {loaded_ts}"
+            f"\nvideo: {video_path}"
+        )
 
     # get closest frames to the query timestamps
     closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -55,10 +55,16 @@ class DiffusionConfig(PreTrainedConfig):
         normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
             a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
-        crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
-            within the image size. If None, no cropping is done.
-        crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
-            mode).
+        resize_shape: (H, W) shape to resize images to as a preprocessing step for the vision
+            backbone. If None, no resizing is done and the original image resolution is used.
+        crop_ratio: Ratio in (0, 1] used to derive the crop size from resize_shape
+            (crop_h = int(resize_shape[0] * crop_ratio), likewise for width).
+            Set to 1.0 to disable cropping. Only takes effect when resize_shape is not None.
+        crop_shape: (H, W) shape to crop images to. When resize_shape is set and crop_ratio < 1.0,
+            this is computed automatically. Can also be set directly for legacy configs that use
+            crop-only (without resize). If None and no derivation applies, no cropping is done.
+        crop_is_random: Whether the crop should be random at training time (it's always a center
+            crop in eval mode).
         pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
@@ -114,7 +120,9 @@ class DiffusionConfig(PreTrainedConfig):
     # Architecture / modeling.
     # Vision backbone.
     vision_backbone: str = "resnet18"
-    crop_shape: tuple[int, int] | None = (84, 84)
+    resize_shape: tuple[int, int] | None = None
+    crop_ratio: float = 1.0
+    crop_shape: tuple[int, int] | None = None
     crop_is_random: bool = True
     pretrained_backbone_weights: str | None = None
     use_group_norm: bool = True
@@ -139,6 +147,10 @@ class DiffusionConfig(PreTrainedConfig):
     # Inference
     num_inference_steps: int | None = None
 
+    # Optimization
+    compile_model: bool = False
+    compile_mode: str = "reduce-overhead"
+
     # Loss computation
     do_mask_loss_for_padding: bool = False
 
@@ -171,6 +183,25 @@ class DiffusionConfig(PreTrainedConfig):
                 f"Got {self.noise_scheduler_type}."
             )
 
+        if self.resize_shape is not None and (
+            len(self.resize_shape) != 2 or any(d <= 0 for d in self.resize_shape)
+        ):
+            raise ValueError(f"`resize_shape` must be a pair of positive integers. Got {self.resize_shape}.")
+        if not (0 < self.crop_ratio <= 1.0):
+            raise ValueError(f"`crop_ratio` must be in (0, 1]. Got {self.crop_ratio}.")
+
+        if self.resize_shape is not None:
+            if self.crop_ratio < 1.0:
+                self.crop_shape = (
+                    int(self.resize_shape[0] * self.crop_ratio),
+                    int(self.resize_shape[1] * self.crop_ratio),
+                )
+            else:
+                # Explicitly disable cropping for resize+ratio path when crop_ratio == 1.0.
+                self.crop_shape = None
+        if self.crop_shape is not None and (self.crop_shape[0] <= 0 or self.crop_shape[1] <= 0):
+            raise ValueError(f"`crop_shape` must have positive dimensions. Got {self.crop_shape}.")
+
         # Check that the horizon size and U-Net downsampling is compatible.
         # U-Net downsamples by 2 with each stage.
         downsampling_factor = 2 ** len(self.down_dims)
@@ -198,13 +229,12 @@ class DiffusionConfig(PreTrainedConfig):
         if len(self.image_features) == 0 and self.env_state_feature is None:
             raise ValueError("You must provide at least one image or the environment state among the inputs.")
 
-        if self.crop_shape is not None:
+        if self.resize_shape is None and self.crop_shape is not None:
             for key, image_ft in self.image_features.items():
                 if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                     raise ValueError(
-                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
-                        f"for `crop_shape` and {image_ft.shape} for "
-                        f"`{key}`."
+                        f"`crop_shape` should fit within the image shapes. Got {self.crop_shape} "
+                        f"for `crop_shape` and {image_ft.shape} for `{key}`."
                     )
 
         # Check that all input images have the same shape.

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -142,6 +142,9 @@ class DiffusionPolicy(PreTrainedPolicy):
         """Run the batch through the model and compute the loss for training or validation."""
         if self.config.image_features:
             batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            for key in self.config.image_features:
+                if self.config.n_obs_steps == 1 and batch[key].ndim == 4:
+                    batch[key] = batch[key].unsqueeze(1)
             batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
         loss = self.diffusion.compute_loss(batch)
         # no output_dict so returning None
@@ -182,6 +185,11 @@ class DiffusionModel(nn.Module):
 
         self.unet = DiffusionConditionalUnet1d(config, global_cond_dim=global_cond_dim * config.n_obs_steps)
 
+        if config.compile_model:
+            # Compile the U-Net. "reduce-overhead" is preferred for the small-batch repetitive loops
+            # common in diffusion inference.
+            self.unet = torch.compile(self.unet, mode=config.compile_mode)
+
         self.noise_scheduler = _make_noise_scheduler(
             config.noise_scheduler_type,
             num_train_timesteps=config.num_train_timesteps,
@@ -446,12 +454,18 @@ class DiffusionRgbEncoder(nn.Module):
     def __init__(self, config: DiffusionConfig):
         super().__init__()
         # Set up optional preprocessing.
-        if config.crop_shape is not None:
+        if config.resize_shape is not None:
+            self.resize = torchvision.transforms.Resize(config.resize_shape)
+        else:
+            self.resize = None
+
+        crop_shape = config.crop_shape
+        if crop_shape is not None:
             self.do_crop = True
             # Always use center crop for eval
-            self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
+            self.center_crop = torchvision.transforms.CenterCrop(crop_shape)
             if config.crop_is_random:
-                self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
+                self.maybe_random_crop = torchvision.transforms.RandomCrop(crop_shape)
             else:
                 self.maybe_random_crop = self.center_crop
         else:
@@ -477,13 +491,16 @@ class DiffusionRgbEncoder(nn.Module):
 
         # Set up pooling and final layers.
         # Use a dry run to get the feature map shape.
-        # The dummy input should take the number of image channels from `config.image_features` and it should
-        # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the
-        # height and width from `config.image_features`.
+        # The dummy shape mirrors the runtime preprocessing order: resize -> crop.
 
         # Note: we have a check in the config class to make sure all images have the same shape.
         images_shape = next(iter(config.image_features.values())).shape
-        dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:]
+        if config.crop_shape is not None:
+            dummy_shape_h_w = config.crop_shape
+        elif config.resize_shape is not None:
+            dummy_shape_h_w = config.resize_shape
+        else:
+            dummy_shape_h_w = images_shape[1:]
         dummy_shape = (1, images_shape[0], *dummy_shape_h_w)
         feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:]
 
@@ -499,7 +516,10 @@ class DiffusionRgbEncoder(nn.Module):
         Returns:
             (B, D) image feature.
         """
-        # Preprocess: maybe crop (if it was set up in the __init__).
+        # Preprocess: resize if configured, then crop if configured.
+
+        if self.resize is not None:
+            x = self.resize(x)
         if self.do_crop:
             if self.training:  # noqa: SIM108
                 x = self.maybe_random_crop(x)

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

@@ -14,7 +14,7 @@ from transformers.image_processing_utils import (
 )
 from transformers.image_processing_utils_fast import (
     BaseImageProcessorFast,
-    ImagesKwargs,
+    DefaultFastImageProcessorKwargs,
     group_images_by_shape,
     reorder_images,
 )
@@ -77,7 +77,7 @@ def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> tor
     return img[:, top:bottom, left:right]
 
 
-class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
+class Eagle25VLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
     max_dynamic_tiles: int | None
     min_dynamic_tiles: int | None
     use_thumbnail: bool | None

src/lerobot/policies/pi0/modeling_pi0.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 import builtins
-import copy
 import logging
 import math
 from collections import deque
@@ -33,21 +32,13 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
     from transformers.models.auto import CONFIG_MAPPING
     from transformers.models.gemma import modeling_gemma
-
-    from lerobot.policies.pi_gemma import (
-        PaliGemmaForConditionalGenerationWithPiGemma,
-        PiGemmaForCausalLM,
-        _gated_residual,
-        layernorm_forward,
-    )
+    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
 else:
     CONFIG_MAPPING = None
     modeling_gemma = None
-    PiGemmaForCausalLM = None
-    _gated_residual = None
-    layernorm_forward = None
-    PaliGemmaForConditionalGenerationWithPiGemma = None
-
+    GemmaForCausalLM = None
+    PaliGemmaForConditionalGeneration = None
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0.configuration_pi0 import DEFAULT_IMAGE_SIZE, PI0Config
@@ -200,7 +191,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -211,7 +202,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -230,14 +221,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
     layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.model.language_model, gemma_expert.model]
+    models = [paligemma.language_model, gemma_expert.model]
     query_states = []
     key_states = []
     value_states = []
     gates = []
     for i, hidden_states in enumerate(inputs_embeds):
         layer = models[i].layers[layer_idx]
-        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
+        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
         gates.append(gate)
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -263,10 +254,10 @@ def compute_layer_complete(
         query_states, key_states, cos, sin, unsqueeze_dim=1
     )
     batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
     # Attention computation
     att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma.language_model.layers[layer_idx].self_attn,
         query_states,
         key_states,
         value_states,
@@ -274,7 +265,7 @@ def compute_layer_complete(
         scaling,
     )
     # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
     att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
     # Process layer outputs
     outputs_embeds = []
@@ -286,15 +277,15 @@ def compute_layer_complete(
             att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
         out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
         # first residual
-        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
+        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
         after_first_residual = out_emb.clone()
-        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
+        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
         # Convert to bfloat16 if the next layer (mlp) uses bfloat16
         if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
             out_emb = out_emb.to(dtype=torch.bfloat16)
         out_emb = layer.mlp(out_emb)
         # second residual
-        out_emb = _gated_residual(after_first_residual, out_emb, gate)
+        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
         outputs_embeds.append(out_emb)
         start_pos = end_pos
     return outputs_embeds
@@ -367,7 +358,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -375,7 +366,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
 
         action_expert_config_hf = CONFIG_MAPPING["gemma"](
             head_dim=action_expert_config.head_dim,
@@ -386,13 +377,13 @@ class PaliGemmaWithExpertModel(
             num_key_value_heads=action_expert_config.num_kv_heads,
             vocab_size=257152,
             hidden_activation="gelu_pytorch_tanh",
-            dtype="float32",
+            torch_dtype="float32",
             use_adarms=use_adarms[1],
             adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
         )
 
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
-        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
         self.gemma_expert.model.embed_tokens = None
 
         self.to_bfloat16_for_selected_params(precision)
@@ -407,11 +398,10 @@ class PaliGemmaWithExpertModel(
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
-        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
-        # "same dtype" error). Align with PI05.
         params_to_keep_float32 = [
-            "vision_tower",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -423,8 +413,8 @@ class PaliGemmaWithExpertModel(
 
     def _set_requires_grad(self):
         if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
-            for param in self.paligemma.model.vision_tower.parameters():
+            self.paligemma.vision_tower.eval()
+            for param in self.paligemma.vision_tower.parameters():
                 param.requires_grad = False
         if self.train_expert_only:
             self.paligemma.eval()
@@ -434,23 +424,15 @@ class PaliGemmaWithExpertModel(
     def train(self, mode: bool = True):
         super().train(mode)
         if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
         if self.train_expert_only:
             self.paligemma.eval()
 
     def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
-        out_dtype = image.dtype
-        if image.dtype != torch.float32:
-            image = image.to(torch.float32)
-        image_outputs = self.paligemma.model.get_image_features(image)
-        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
-        if features.dtype != out_dtype:
-            features = features.to(out_dtype)
-        return features
+        return self.paligemma.model.get_image_features(image)
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -464,7 +446,7 @@ class PaliGemmaWithExpertModel(
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -488,7 +470,7 @@ class PaliGemmaWithExpertModel(
             prefix_output = None
             prefix_past_key_values = None
         else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
+            models = [self.paligemma.language_model, self.gemma_expert.model]
             num_layers = self.paligemma.config.text_config.num_hidden_layers
 
             # Check if gradient checkpointing is enabled for any of the models
@@ -528,7 +510,7 @@ class PaliGemmaWithExpertModel(
             def compute_final_norms(inputs_embeds, adarms_cond):
                 outputs_embeds = []
                 for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
                     outputs_embeds.append(out_emb)
                 return outputs_embeds
 
@@ -594,19 +576,29 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             # Also compile the main forward pass used during training
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
+        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
+
+        try:
+            from transformers.models.siglip import check
+
+            if not check.check_whether_transformers_replace_is_installed_correctly():
+                raise ValueError(msg)
+        except ImportError:
+            raise ValueError(msg) from None
+
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
         logging.info("Enabled gradient checkpointing for PI0Pytorch model")
 
     def gradient_checkpointing_disable(self):
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
         logging.info("Disabled gradient checkpointing for PI0Pytorch model")
 
@@ -768,7 +760,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, time)
 
         if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -842,7 +834,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
 
         prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
         _, past_key_values = self.paligemma_with_expert.forward(
             attention_mask=prefix_att_2d_masks_4d,
@@ -916,7 +908,6 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
-        past_key_values = copy.deepcopy(past_key_values)
         outputs_embeds, _ = self.paligemma_with_expert.forward(
             attention_mask=full_att_2d_masks_4d,
             position_ids=position_ids,
@@ -1006,12 +997,14 @@ class PI0Policy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
         try:
+            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
+                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -1019,7 +1012,7 @@ class PI0Policy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -1032,7 +1025,7 @@ class PI0Policy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
 
             # Then add "model." prefix for all keys that don't already have it
@@ -1077,7 +1070,7 @@ class PI0Policy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
 
         return model
 
@@ -1127,14 +1120,6 @@ class PI0Policy(PreTrainedPolicy):
                 # Some checkpoints might have this, but current model expects different structure
                 logging.warning(f"Vision embedding key might need handling: {key}")
 
-            if (
-                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
-                or key == "paligemma_with_expert.paligemma.lm_head.weight"
-            ):
-                fixed_state_dict[
-                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
-                ] = value.clone()
-
             fixed_state_dict[new_key] = value
 
         return fixed_state_dict

src/lerobot/policies/pi05/modeling_pi05.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 import builtins
-import copy
 import logging
 import math
 from collections import deque
@@ -33,20 +32,14 @@ from lerobot.utils.import_utils import _transformers_available
 if TYPE_CHECKING or _transformers_available:
     from transformers.models.auto import CONFIG_MAPPING
     from transformers.models.gemma import modeling_gemma
-
-    from lerobot.policies.pi_gemma import (
-        PaliGemmaForConditionalGenerationWithPiGemma,
-        PiGemmaForCausalLM,
-        _gated_residual,
-        layernorm_forward,
-    )
+    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
 else:
     CONFIG_MAPPING = None
     modeling_gemma = None
-    PiGemmaForCausalLM = None
-    _gated_residual = None
-    layernorm_forward = None
-    PaliGemmaForConditionalGenerationWithPiGemma = None
+    GemmaForCausalLM = None
+    PaliGemmaForConditionalGeneration = None
+
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi05.configuration_pi05 import DEFAULT_IMAGE_SIZE, PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
@@ -196,7 +189,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -207,7 +200,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -226,14 +219,14 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
 def compute_layer_complete(
     layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
 ):
-    models = [paligemma.model.language_model, gemma_expert.model]
+    models = [paligemma.language_model, gemma_expert.model]
     query_states = []
     key_states = []
     value_states = []
     gates = []
     for i, hidden_states in enumerate(inputs_embeds):
         layer = models[i].layers[layer_idx]
-        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
+        hidden_states, gate = layer.input_layernorm(hidden_states, cond=adarms_cond[i])  # noqa: PLW2901
         gates.append(gate)
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
@@ -259,10 +252,10 @@ def compute_layer_complete(
         query_states, key_states, cos, sin, unsqueeze_dim=1
     )
     batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    scaling = paligemma.language_model.layers[layer_idx].self_attn.scaling
     # Attention computation
     att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma.language_model.layers[layer_idx].self_attn,
         query_states,
         key_states,
         value_states,
@@ -270,7 +263,7 @@ def compute_layer_complete(
         scaling,
     )
     # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma.language_model.layers[layer_idx].self_attn.head_dim
     att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
     # Process layer outputs
     outputs_embeds = []
@@ -282,15 +275,15 @@ def compute_layer_complete(
             att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
         out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
         # first residual
-        out_emb = _gated_residual(hidden_states, out_emb, gates[i])
+        out_emb = modeling_gemma._gated_residual(hidden_states, out_emb, gates[i])  # noqa: SLF001
         after_first_residual = out_emb.clone()
-        out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
+        out_emb, gate = layer.post_attention_layernorm(out_emb, cond=adarms_cond[i])
         # Convert to bfloat16 if the next layer (mlp) uses bfloat16
         if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
             out_emb = out_emb.to(dtype=torch.bfloat16)
         out_emb = layer.mlp(out_emb)
         # second residual
-        out_emb = _gated_residual(after_first_residual, out_emb, gate)
+        out_emb = modeling_gemma._gated_residual(after_first_residual, out_emb, gate)  # noqa: SLF001
         outputs_embeds.append(out_emb)
         start_pos = end_pos
     return outputs_embeds
@@ -363,7 +356,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
@@ -371,7 +364,7 @@ class PaliGemmaWithExpertModel(
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
 
         action_expert_config_hf = CONFIG_MAPPING["gemma"](
             head_dim=action_expert_config.head_dim,
@@ -382,13 +375,13 @@ class PaliGemmaWithExpertModel(
             num_key_value_heads=action_expert_config.num_kv_heads,
             vocab_size=257152,
             hidden_activation="gelu_pytorch_tanh",
-            dtype="float32",
+            torch_dtype="float32",
             use_adarms=use_adarms[1],
             adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
         )
 
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
-        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
         self.gemma_expert.model.embed_tokens = None
 
         self.to_bfloat16_for_selected_params(precision)
@@ -403,11 +396,10 @@ class PaliGemmaWithExpertModel(
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
-        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
-        # "same dtype" error). Saves memory vs full float32; more memory than only 3 params.
         params_to_keep_float32 = [
-            "vision_tower",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -419,8 +411,8 @@ class PaliGemmaWithExpertModel(
 
     def _set_requires_grad(self):
         if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
-            for param in self.paligemma.model.vision_tower.parameters():
+            self.paligemma.vision_tower.eval()
+            for param in self.paligemma.vision_tower.parameters():
                 param.requires_grad = False
         if self.train_expert_only:
             self.paligemma.eval()
@@ -430,23 +422,15 @@ class PaliGemmaWithExpertModel(
     def train(self, mode: bool = True):
         super().train(mode)
         if self.freeze_vision_encoder:
-            self.paligemma.model.vision_tower.eval()
+            self.paligemma.vision_tower.eval()
         if self.train_expert_only:
             self.paligemma.eval()
 
     def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32).
-        out_dtype = image.dtype
-        if image.dtype != torch.float32:
-            image = image.to(torch.float32)
-        image_outputs = self.paligemma.model.get_image_features(image)
-        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
-        if features.dtype != out_dtype:
-            features = features.to(out_dtype)
-        return features
+        return self.paligemma.model.get_image_features(image)
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -460,7 +444,7 @@ class PaliGemmaWithExpertModel(
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -484,7 +468,7 @@ class PaliGemmaWithExpertModel(
             prefix_output = None
             prefix_past_key_values = None
         else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
+            models = [self.paligemma.language_model, self.gemma_expert.model]
             num_layers = self.paligemma.config.text_config.num_hidden_layers
 
             # Check if gradient checkpointing is enabled for any of the models
@@ -524,7 +508,7 @@ class PaliGemmaWithExpertModel(
             def compute_final_norms(inputs_embeds, adarms_cond):
                 outputs_embeds = []
                 for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = models[i].norm(hidden_states, cond=adarms_cond[i])
                     outputs_embeds.append(out_emb)
                 return outputs_embeds
 
@@ -589,19 +573,29 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
             # Also compile the main forward pass used during training
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
+        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
+
+        try:
+            from transformers.models.siglip import check
+
+            if not check.check_whether_transformers_replace_is_installed_correctly():
+                raise ValueError(msg)
+        except ImportError:
+            raise ValueError(msg) from None
+
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = True
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
         logging.info("Enabled gradient checkpointing for PI05Pytorch model")
 
     def gradient_checkpointing_disable(self):
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing = False
         self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
         logging.info("Disabled gradient checkpointing for PI05Pytorch model")
 
@@ -743,7 +737,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(x_t, time)
 
         if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
@@ -814,7 +808,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
 
         prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
-        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+        self.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
         _, past_key_values = self.paligemma_with_expert.forward(
             attention_mask=prefix_att_2d_masks_4d,
@@ -886,7 +880,6 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
         full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
-        past_key_values = copy.deepcopy(past_key_values)
         outputs_embeds, _ = self.paligemma_with_expert.forward(
             attention_mask=full_att_2d_masks_4d,
             position_ids=position_ids,
@@ -976,12 +969,14 @@ class PI05Policy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
         try:
+            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
+                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -989,7 +984,7 @@ class PI05Policy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -1002,7 +997,7 @@ class PI05Policy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
 
             # Then add "model." prefix for all keys that don't already have it
@@ -1014,6 +1009,8 @@ class PI05Policy(PreTrainedPolicy):
                     new_key = f"model.{key}"
                     remapped_state_dict[new_key] = value
                     remap_count += 1
+                    if remap_count <= 10:  # Only print first 10 to avoid spam
+                        print(f"Remapped: {key} -> {new_key}")
                 else:
                     remapped_state_dict[key] = value
 
@@ -1047,7 +1044,7 @@ class PI05Policy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
 
         return model
 
@@ -1101,14 +1098,6 @@ class PI05Policy(PreTrainedPolicy):
                 # Some checkpoints might have this, but current model expects different structure
                 logging.warning(f"Vision embedding key might need handling: {key}")
 
-            if (
-                key == "model.paligemma_with_expert.paligemma.lm_head.weight"
-                or key == "paligemma_with_expert.paligemma.lm_head.weight"
-            ):
-                fixed_state_dict[
-                    "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
-                ] = value.clone()
-
             fixed_state_dict[new_key] = value
 
         return fixed_state_dict

src/lerobot/policies/pi05/processor_pi05.py

@@ -23,6 +23,7 @@ import torch
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.pi05.modeling_pi05 import pad_vector
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
@@ -67,6 +68,9 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
         # TODO: check if this necessary
         state = deepcopy(state)
 
+        # Prepare state (pad to max_state_dim)
+        state = pad_vector(state, self.max_state_dim)
+
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()

src/lerobot/policies/pi0_fast/configuration_pi0_fast.py

@@ -54,7 +54,7 @@ class PI0FastConfig(PreTrainedConfig):
 
     tokenizer_max_length: int = 200  # see openpi `__post_init__`
     text_tokenizer_name: str = "google/paligemma-3b-pt-224"
-    action_tokenizer_name: str = "lerobot/fast-action-tokenizer"
+    action_tokenizer_name: str = "physical-intelligence/fast"
     temperature: float = 0.0
     max_decoding_steps: int = 256
     fast_skip_tokens: int = 128

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -38,16 +38,11 @@ else:
 if TYPE_CHECKING or _transformers_available:
     from transformers import AutoTokenizer
     from transformers.models.auto import CONFIG_MAPPING
-
-    from lerobot.policies.pi_gemma import (
-        PaliGemmaForConditionalGenerationWithPiGemma,
-        PiGemmaModel,
-    )
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
 else:
     CONFIG_MAPPING = None
+    PaliGemmaForConditionalGeneration = None
     AutoTokenizer = None
-    PiGemmaModel = None
-    PaliGemmaForConditionalGenerationWithPiGemma = None
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
@@ -126,7 +121,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     if images.dtype == torch.uint8:
         resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
     elif images.dtype == torch.float32:
-        resized_images = resized_images.clamp(0.0, 1.0)
+        resized_images = resized_images.clamp(-1.0, 1.0)
     else:
         raise ValueError(f"Unsupported image dtype: {images.dtype}")
 
@@ -137,7 +132,7 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)
     pad_w1 = pad_w0 + remainder_w
 
     # Pad
-    constant_value = 0 if images.dtype == torch.uint8 else 0.0
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
     padded_images = F.pad(
         resized_images,
         (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
@@ -211,22 +206,16 @@ class PI0FastPaliGemma(nn.Module):
         vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
         vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
         vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
-        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.torch_dtype = "float32"
         vlm_config_hf.text_config.vocab_size = 257152
         vlm_config_hf.text_config.use_adarms = use_adarms[0]
         vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
         vlm_config_hf.vision_config.intermediate_size = 4304
         vlm_config_hf.vision_config.projection_dim = 2048
         vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
-        vlm_config_hf.vision_config.dtype = "float32"
+        vlm_config_hf.vision_config.torch_dtype = "float32"
 
-        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
-
-        # Use PI Gemma (AdaRMS) as language model when use_adarms[0] is True so that
-        # forward(..., adarms_cond=...) is supported (same as pi0/pi05).
-        if use_adarms[0]:
-            text_config = self.paligemma.config.text_config
-            self.paligemma.model.language_model = PiGemmaModel(text_config)
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
 
         self.to_bfloat16_for_selected_params(precision)
 
@@ -239,11 +228,10 @@ class PI0FastPaliGemma(nn.Module):
         else:
             raise ValueError(f"Invalid precision: {precision}")
 
-        # Keep full vision path in float32 so we never toggle (toggle causes optimizer
-        # "same dtype" error). Align with PI05.
         params_to_keep_float32 = [
-            "vision_tower",
-            "multi_modal_projector",
+            "vision_tower.vision_model.embeddings.patch_embedding.weight",
+            "vision_tower.vision_model.embeddings.patch_embedding.bias",
+            "vision_tower.vision_model.embeddings.position_embedding.weight",
             "input_layernorm",
             "post_attention_layernorm",
             "model.norm",
@@ -254,18 +242,10 @@ class PI0FastPaliGemma(nn.Module):
                 param.data = param.data.to(dtype=torch.float32)
 
     def embed_image(self, image: torch.Tensor):
-        # Vision tower and multi_modal_projector are kept in float32 (params_to_keep_float32). Align with PI05.
-        out_dtype = image.dtype
-        if image.dtype != torch.float32:
-            image = image.to(torch.float32)
-        image_outputs = self.paligemma.model.get_image_features(image)
-        features = image_outputs.pooler_output * self.paligemma.config.text_config.hidden_size**0.5
-        if features.dtype != out_dtype:
-            features = features.to(out_dtype)
-        return features
+        return self.paligemma.model.get_image_features(image)
 
     def embed_language_tokens(self, tokens: torch.Tensor):
-        return self.paligemma.model.language_model.embed_tokens(tokens)
+        return self.paligemma.language_model.embed_tokens(tokens)
 
     def forward(
         self,
@@ -279,7 +259,7 @@ class PI0FastPaliGemma(nn.Module):
         if adarms_cond is None:
             adarms_cond = [None, None]
         if inputs_embeds[1] is None:
-            prefix_output = self.paligemma.model.language_model.forward(
+            prefix_output = self.paligemma.language_model.forward(
                 inputs_embeds=inputs_embeds[0],
                 attention_mask=attention_mask,
                 position_ids=position_ids,
@@ -326,14 +306,24 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
             self.sample_actions_fast = torch.compile(self.sample_actions_fast, mode=config.compile_mode)
             self.forward = torch.compile(self.forward, mode=config.compile_mode)
 
+        msg = """An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues"""
+
+        try:
+            from transformers.models.siglip import check
+
+            if not check.check_whether_transformers_replace_is_installed_correctly():
+                raise ValueError(msg)
+        except ImportError:
+            raise ValueError(msg) from None
+
     def gradient_checkpointing_enable(self):
         """Enable gradient checkpointing for memory optimization."""
         self.gradient_checkpointing_enabled = True
         # Call the proper gradient_checkpointing_enable() method with use_reentrant=False for better memory efficiency
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_enable(
             gradient_checkpointing_kwargs={"use_reentrant": False}
         )
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_enable(
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_enable(
             gradient_checkpointing_kwargs={"use_reentrant": False}
         )
         logging.info("Enabled gradient checkpointing for PI0FastPytorch model")
@@ -342,8 +332,8 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         """Disable gradient checkpointing."""
         self.gradient_checkpointing_enabled = False
         # Call the proper gradient_checkpointing_disable() method
-        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing_disable()
-        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.language_model.gradient_checkpointing_disable()
+        self.paligemma_with_expert.paligemma.vision_tower.gradient_checkpointing_disable()
         logging.info("Disabled gradient checkpointing for PI0FastPytorch model")
 
     def _apply_checkpoint(self, func, *args, **kwargs):
@@ -533,7 +523,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         # Convert embeddings to bfloat16 if needed
         if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -626,7 +616,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         )
 
         if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -724,7 +714,7 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
         # Ensure correct precision (bfloat16/float32)
         if (
-            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            self.paligemma_with_expert.paligemma.language_model.layers[0].self_attn.q_proj.weight.dtype
             == torch.bfloat16
         ):
             prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
@@ -907,12 +897,14 @@ class PI0FastPolicy(PreTrainedPolicy):
         # Check if dataset_stats were provided in kwargs
         model = cls(config, **kwargs)
 
-        # Load state dict (expects keys with "model." prefix)
+        # Now manually load and remap the state dict
         try:
+            # Try to load the pytorch_model.bin or model.safetensors file
             print(f"Loading model from: {pretrained_name_or_path}")
             try:
                 from transformers.utils import cached_file
 
+                # Try safetensors first
                 resolved_file = cached_file(
                     pretrained_name_or_path,
                     "model.safetensors",
@@ -920,7 +912,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                     force_download=kwargs.get("force_download", False),
                     resume_download=kwargs.get("resume_download"),
                     proxies=kwargs.get("proxies"),
-                    token=kwargs.get("token"),
+                    use_auth_token=kwargs.get("use_auth_token"),
                     revision=kwargs.get("revision"),
                     local_files_only=kwargs.get("local_files_only", False),
                 )
@@ -933,9 +925,8 @@ class PI0FastPolicy(PreTrainedPolicy):
                 print("Returning model without loading pretrained weights")
                 return model
 
-            # First, fix any key differences (see openpi model.py, _fix_pytorch_state_dict_keys)
+            # First, fix any key differences # see openpi `model.py, _fix_pytorch_state_dict_keys`
             fixed_state_dict = model._fix_pytorch_state_dict_keys(original_state_dict, model.config)
-
             # Then add "model." prefix for all keys that don't already have it
             remapped_state_dict = {}
             remap_count = 0
@@ -945,6 +936,8 @@ class PI0FastPolicy(PreTrainedPolicy):
                     new_key = f"model.{key}"
                     remapped_state_dict[new_key] = value
                     remap_count += 1
+                    if remap_count <= 10:  # Only print first 10 to avoid spam
+                        print(f"Remapped: {key} -> {new_key}")
                 else:
                     remapped_state_dict[key] = value
 
@@ -978,7 +971,7 @@ class PI0FastPolicy(PreTrainedPolicy):
                 print("All keys loaded successfully!")
 
         except Exception as e:
-            print(f"Warning: Could not load state dict: {e}")
+            print(f"Warning: Could not remap state dict keys: {e}")
 
         return model
 

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -23,6 +23,7 @@ import torch
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
+from lerobot.policies.pi0_fast.modeling_pi0_fast import pad_vector
 from lerobot.processor import (
     ActionTokenizerProcessorStep,
     AddBatchDimensionProcessorStep,
@@ -68,6 +69,9 @@ class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
         # TODO: check if this necessary
         state = deepcopy(state)
 
+        # Prepare state (pad to max_state_dim)
+        state = pad_vector(state, self.max_state_dim)
+
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()

src/lerobot/policies/pi_gemma.py

@@ -1,363 +0,0 @@
-# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import torch
-from torch import nn
-
-from lerobot.utils.import_utils import _transformers_available
-
-if TYPE_CHECKING or _transformers_available:
-    from transformers.cache_utils import DynamicCache
-    from transformers.masking_utils import create_causal_mask
-    from transformers.modeling_layers import GradientCheckpointingLayer
-    from transformers.modeling_outputs import BaseModelOutputWithPast
-    from transformers.models.gemma.modeling_gemma import (
-        GemmaAttention,
-        GemmaConfig,
-        GemmaForCausalLM,
-        GemmaMLP,
-        GemmaModel,
-    )
-    from transformers.models.paligemma.modeling_paligemma import (
-        PaliGemmaForConditionalGeneration,
-        PaliGemmaModel,
-    )
-else:
-    GemmaAttention = None
-    GemmaConfig = None
-    GemmaForCausalLM = None
-    GemmaMLP = None
-    GemmaModel = None
-    PaliGemmaModel = None
-    PaliGemmaForConditionalGeneration = None
-    DynamicCache = None
-    GradientCheckpointingLayer = None
-    BaseModelOutputWithPast = None
-    create_causal_mask = None
-
-
-def _gated_residual(
-    x: torch.Tensor | None,
-    y: torch.Tensor | None,
-    gate: torch.Tensor | None,
-) -> torch.Tensor | None:
-    """Gated residual: x + y when gate is None, else x + y * gate."""
-    if x is None and y is None:
-        return None
-    if x is None or y is None:
-        return x if x is not None else y
-    if gate is None:
-        return x + y
-    return x + y * gate
-
-
-def layernorm_forward(
-    layernorm: nn.Module,
-    x: torch.Tensor,
-    cond: torch.Tensor | None = None,
-):
-    """
-    call layernorm and return hidden states and gate
-    if cond is not None, use conditional norm
-    otherwise, use normal gemma norm
-    """
-    if cond is not None:
-        return layernorm(x, cond=cond)
-    else:
-        return layernorm(x)
-
-
-class PiGemmaRMSNorm(nn.Module):
-    """
-    Adaptive RMSNorm for PI Gemma (AdaRMS).
-    When cond_dim is set, uses cond to modulate scale/shift/gate; otherwise behaves like standard GemmaRMSNorm.
-    forward(x, cond=None) returns (output, gate) for use with _gated_residual.
-    """
-
-    def __init__(self, dim: int, eps: float = 1e-6, cond_dim: int | None = None):
-        super().__init__()
-        self.eps = eps
-        self.dim = dim
-        self.cond_dim = cond_dim
-        if cond_dim is not None:
-            self.dense = nn.Linear(cond_dim, dim * 3, bias=True)
-            nn.init.zeros_(self.dense.weight)
-        else:
-            self.weight = nn.Parameter(torch.zeros(dim))
-            self.dense = None
-
-    def _norm(self, x):
-        # Compute variance in float32 (like the source implementation)
-        var = torch.mean(torch.square(x.float()), dim=-1, keepdim=True)
-        # Compute normalization in float32
-        normed_inputs = x * torch.rsqrt(var + self.eps)
-        return normed_inputs
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        cond: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
-        dtype = x.dtype
-        normed = self._norm(x)
-        if cond is None or self.dense is None:
-            normed = normed * (1.0 + self.weight.float())
-            return normed.type_as(x), None
-        if cond.shape[-1] != self.cond_dim:
-            raise ValueError(f"Expected cond dim {self.cond_dim}, got {cond.shape[-1]}")
-        modulation = self.dense(cond)
-        if len(x.shape) == 3:
-            modulation = modulation.unsqueeze(1)
-        scale, shift, gate = modulation.chunk(3, dim=-1)
-        normed = normed * (1 + scale.float()) + shift.float()
-        return normed.to(dtype), gate.to(dtype)
-
-    def extra_repr(self) -> str:
-        if self.dense is not None:
-            return f"dim={self.dim}, eps={self.eps}, adaptive=True, cond_dim={self.cond_dim}"
-        return f"dim={self.dim}, eps={self.eps}"
-
-
-def _get_pi_gemma_decoder_layer_base():
-    """base for PiGemmaDecoderLayer"""
-
-    class _PiGemmaDecoderLayerBase(GradientCheckpointingLayer):
-        """Decoder layer that uses PiGemmaRMSNorm and _gated_residual, compatible with v5 Gemma."""
-
-        def __init__(self, config: GemmaConfig, layer_idx: int):
-            super().__init__()
-            self.hidden_size = config.hidden_size
-            self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
-            self.mlp = GemmaMLP(config)
-            cond_dim = (
-                getattr(config, "adarms_cond_dim", None) if getattr(config, "use_adarms", False) else None
-            )
-            self.input_layernorm = PiGemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
-            )
-            self.post_attention_layernorm = PiGemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim
-            )
-
-        def forward(
-            self,
-            hidden_states: torch.Tensor,
-            attention_mask: torch.Tensor | None = None,
-            position_ids: torch.LongTensor | None = None,
-            past_key_values=None,
-            use_cache: bool = False,
-            cache_position: torch.LongTensor | None = None,
-            position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
-            adarms_cond: torch.Tensor | None = None,
-            **kwargs,
-        ) -> torch.Tensor:
-            residual = hidden_states
-            hidden_states, gate = self.input_layernorm(hidden_states, cond=adarms_cond)
-            hidden_states, _ = self.self_attn(
-                hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **kwargs,
-            )
-
-            hidden_states = _gated_residual(residual, hidden_states, gate)
-
-            residual = hidden_states
-            hidden_states, gate = self.post_attention_layernorm(hidden_states, cond=adarms_cond)
-            hidden_states = self.mlp(hidden_states)
-            hidden_states = _gated_residual(residual, hidden_states, gate)
-            return hidden_states
-
-    return _PiGemmaDecoderLayerBase
-
-
-class PiGemmaModel(GemmaModel):  # type: ignore[misc]
-    """
-    GemmaModel extended with AdaRMS (adaptive RMSNorm) and gated residuals when config.use_adarms is True.
-    """
-
-    def __init__(self, config: GemmaConfig, **kwargs):
-        super().__init__(config, **kwargs)
-        # if not getattr(config, "use_adarms", False):
-        #     return
-        cond_dim = getattr(config, "adarms_cond_dim", None)
-        pi_gemma_decoder_layer_base = _get_pi_gemma_decoder_layer_base()
-        self.layers = nn.ModuleList(
-            [pi_gemma_decoder_layer_base(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = PiGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps, cond_dim=cond_dim)
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        past_key_values: DynamicCache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        cache_position: torch.LongTensor | None = None,
-        adarms_cond: torch.Tensor | None = None,
-        **kwargs,
-    ) -> BaseModelOutputWithPast:
-        """
-        adarms_cond (`torch.Tensor` of shape `(batch_size, cond_dim)`, *optional*):
-            Condition for ADARMS.
-        """
-        output_attentions = (
-            output_attentions if output_attentions is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            import logging
-
-            logging.warning(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = create_causal_mask(
-            config=self.config,
-            input_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            past_key_values=past_key_values,
-            position_ids=position_ids,
-        )
-
-        # embed positions
-        hidden_states = inputs_embeds
-        # Convert to bfloat16 if the first layer uses bfloat16
-        if len(self.layers) > 0 and self.layers[0].self_attn.q_proj.weight.dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(torch.bfloat16)
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # normalized
-        # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
-        # See https://github.com/huggingface/transformers/pull/29402
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                adarms_cond=adarms_cond,
-                **kwargs,
-            )
-
-            hidden_states = layer_outputs
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states, _ = self.norm(hidden_states, adarms_cond)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-
-class PiGemmaForCausalLM(GemmaForCausalLM):  # type: ignore[misc]
-    """
-    Causal LM wrapper using PiGemmaModel as the backbone, for consistency with GemmaForCausalLM
-    and the language model used in pi0_fast. Use this for the action expert in pi0/pi05.
-    """
-
-    def __init__(self, config: GemmaConfig, **kwargs):
-        super().__init__(config, **kwargs)
-        self.model = PiGemmaModel(config)
-
-
-class PaliGemmaModelWithPiGemma(PaliGemmaModel):
-    """PaliGemmaModel whose language_model is PiGemmaModel (custom decoder with PiGemmaRMSNorm and gated residuals)."""
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.language_model = PiGemmaModel(config.text_config)
-
-
-class PaliGemmaForConditionalGenerationWithPiGemma(PaliGemmaForConditionalGeneration):
-    """PaliGemmaForConditionalGeneration using PiGemma decoder for the language model."""
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = PaliGemmaModelWithPiGemma(config)
-
-    # Make modules available through conditional class for BC
-    @property
-    def language_model(self):
-        return self.model.language_model
-
-
-__all__ = [
-    "PiGemmaModel",
-    "PiGemmaForCausalLM",
-    "PiGemmaRMSNorm",
-    "_gated_residual",
-    "layernorm_forward",
-    "PaliGemmaModelWithPiGemma",
-    "PaliGemmaForConditionalGenerationWithPiGemma",
-]

src/lerobot/policies/sac/reward_model/configuration_classifier.py

@@ -33,7 +33,7 @@ class RewardClassifierConfig(PreTrainedConfig):
     latent_dim: int = 256
     image_embedding_pooling_dim: int = 8
     dropout_rate: float = 0.1
-    model_name: str = "helper2424/resnet10"  # TODO: This needs to be updated. The model on the Hub doesn't call self.post_init() in its __init__, which is required by transformers v5 to set all_tied_weights_keys. The from_pretrained call fails when it tries to access this attribute during _finalize_model_loading.
+    model_name: str = "helper2424/resnet10"
     device: str = "cpu"
     model_type: str = "cnn"  # "transformer" or "cnn"
     num_cameras: int = 2

src/lerobot/policies/sarm/processor_sarm.py

@@ -277,9 +277,7 @@ class SARMEncodingProcessorStep(ProcessorStep):
 
         # When language is perturbed, targets are zero so perturbed samples don't contribute to progress loss
         if self.dataset_meta is not None:
-            episodes_df = None
-            if self.sparse_subtask_names != ["task"]:
-                episodes_df = self.dataset_meta.episodes.to_pandas()
+            episodes_df = self.dataset_meta.episodes.to_pandas()
 
             # Generate sparse targets
             if self.sparse_temporal_proportions is not None:

src/lerobot/policies/smolvla/configuration_smolvla.py

@@ -106,6 +106,9 @@ class SmolVLAConfig(PreTrainedConfig):
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 
+    compile_model: bool = False  # Whether to use torch.compile for model optimization
+    compile_mode: str = "max-autotune"  # Torch compile mode
+
     def __post_init__(self):
         super().__post_init__()
 

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -593,6 +593,12 @@ class VLAFlowMatching(nn.Module):
         self.prefix_length = self.config.prefix_length
         self.rtc_processor = rtc_processor
 
+        # Compile model if requested
+        if config.compile_model:
+            torch.set_float32_matmul_precision("high")
+            self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
+            self.forward = torch.compile(self.forward, mode=config.compile_mode)
+
     def _rtc_enabled(self):
         return self.config.rtc_config is not None and self.config.rtc_config.enabled
 

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -77,7 +77,6 @@ class SmolVLMWithExpertModel(nn.Module):
             print(f"Loading  {model_id} weights ...")
             self.vlm = AutoModelForImageTextToText.from_pretrained(
                 model_id,
-                device_map=device,
                 torch_dtype="bfloat16",
                 low_cpu_mem_usage=True,
             )

src/lerobot/policies/wall_x/modeling_wall_x.py

@@ -261,15 +261,10 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
     and optional LoRA fine-tuning support.
     """
 
-    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tied_weights_keys = ["lm_head.weight"]
     config_class = Qwen2_5_VLConfig
     _no_split_modules = ["Qwen2_5_VLDecoderLayer_with_MoE", "Qwen2_5_VLVisionBlock"]
 
-    def init_weights(self):
-        if getattr(self.model, "language_model", None) is not None:
-            return
-        super().init_weights()
-
     @classmethod
     def from_pretrained(
         cls,
@@ -317,11 +312,6 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
             processor.action_processor = action_tokenizer
         else:
             action_tokenizer = None
-
-        # add pad_token_id to config
-        config.pad_token_id = processor.tokenizer.pad_token_id
-        config.text_config.pad_token_id = processor.tokenizer.pad_token_id
-
         # Initialize model with configuration and processor
         model = cls(config, processor=processor, action_tokenizer=action_tokenizer, **kwargs)
 
@@ -341,7 +331,7 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
                 force_download=kwargs.get("force_download", False),
                 resume_download=kwargs.get("resume_download"),
                 proxies=kwargs.get("proxies"),
-                token=kwargs.get("token"),
+                use_auth_token=kwargs.get("use_auth_token"),
                 revision=kwargs.get("revision"),
                 local_files_only=kwargs.get("local_files_only", False),
             )

src/lerobot/policies/wall_x/qwen_model/configuration_qwen2_5_vl.py

@@ -21,7 +21,6 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
         window_size=112,
         out_hidden_size=3584,
         fullatt_block_indexes=[7, 15, 23, 31],
-        initializer_range=0.02,
         **kwargs,
     ):
         super().__init__(**kwargs)
@@ -39,7 +38,6 @@ class Qwen2_5_VLVisionConfig(PretrainedConfig):
         self.window_size = window_size
         self.fullatt_block_indexes = fullatt_block_indexes
         self.out_hidden_size = out_hidden_size
-        self.initializer_range = initializer_range
 
 
 class Qwen2_5_VLConfig(PretrainedConfig):

src/lerobot/policies/wall_x/qwen_model/qwen2_5_vl_moe.py

@@ -11,6 +11,7 @@ from transformers.activations import ACT2FN
 from transformers.cache_utils import (
     Cache,
     DynamicCache,
+    SlidingWindowCache,
     StaticCache,
 )
 from transformers.generation import GenerationMixin
@@ -30,15 +31,6 @@ from transformers.utils import (
 
 from .configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
 
-
-# TODO(Steven): SlidingWindowCache was removed in transformers v5. Define a placeholder so isinstance checks
-# always return False (which is the correct behavior when no sliding window cache is in use).
-class _SlidingWindowCachePlaceholder:
-    pass
-
-
-SlidingWindowCache = _SlidingWindowCachePlaceholder
-
 if is_flash_attn_2_available():
     from flash_attn import flash_attn_func, flash_attn_varlen_func
     from flash_attn.layers.rotary import apply_rotary_emb
@@ -602,40 +594,19 @@ class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel):
         return hidden_states
 
 
-def _compute_default_rope_parameters_qwen2_5_vl(config, device=None):
-    """
-    compute default rope parameters for Qwen2_5_VL
-    """
-    base = config.text_config.rope_parameters["rope_theta"]
-    dim = config.hidden_size // config.num_attention_heads
-    inv_freq = 1.0 / (
-        base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
-    )
-    return inv_freq, 1.0
-
-
 class Qwen2_5_VLRotaryEmbedding(nn.Module):
     def __init__(self, config: Qwen2_5_VLConfig, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
         if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
             self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        elif hasattr(config, "rope_parameters") and config.rope_parameters is not None:
-            self.rope_type = config.rope_parameters.get("rope_type", "default")
         else:
             self.rope_type = "default"
         self.max_seq_len_cached = config.max_position_embeddings
         self.original_max_seq_len = config.max_position_embeddings
 
         self.config = config
-
-        if self.rope_type == "default":
-            self.rope_init_fn = _compute_default_rope_parameters_qwen2_5_vl
-            self.rope_kwargs = {}
-        else:
-            rope_type_key = "linear" if self.rope_type == "linear" else self.rope_type
-            self.rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type_key]
-            self.rope_kwargs = {}
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
 
         inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
         self.register_buffer("inv_freq", inv_freq, persistent=False)

src/lerobot/policies/wall_x/utils.py

@@ -144,7 +144,7 @@ def preprocesser_call(
     """
     # Process image inputs
     if images is not None and len(images) > 0:
-        image_inputs = processor.image_processor(images=images, return_tensors=return_tensors)
+        image_inputs = processor.image_processor(images=images, videos=None, return_tensors=return_tensors)
         image_grid_thw = image_inputs["image_grid_thw"]
     else:
         image_inputs = {}
@@ -152,7 +152,7 @@ def preprocesser_call(
 
     # Process video inputs
     if videos is not None:
-        videos_inputs = processor.image_processor(videos=videos, return_tensors=return_tensors)
+        videos_inputs = processor.image_processor(images=None, videos=videos, return_tensors=return_tensors)
         video_grid_thw = videos_inputs["video_grid_thw"]
     else:
         videos_inputs = {}

src/lerobot/policies/xvla/configuration_florence2.py

@@ -13,9 +13,12 @@
 import warnings
 
 from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
 
 """ Florence-2 configuration"""
 
+logger = logging.get_logger(__name__)
+
 
 class Florence2VisionConfig(PretrainedConfig):
     r"""
@@ -273,8 +276,6 @@ class Florence2LanguageConfig(PretrainedConfig):
         )
 
         # ensure backward compatibility for BART CNN models
-        if not hasattr(self, "forced_bos_token_id"):
-            self.forced_bos_token_id = None
         if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
             self.forced_bos_token_id = self.bos_token_id
             warnings.warn(

src/lerobot/policies/xvla/modeling_florence2.py

@@ -46,6 +46,7 @@ from transformers.utils import (
     add_start_docstrings_to_model_forward,
     is_flash_attn_2_available,
     is_flash_attn_greater_or_equal_2_10,
+    logging,
     replace_return_docstrings,
 )
 
@@ -56,6 +57,8 @@ if is_flash_attn_2_available():
     from flash_attn import flash_attn_func, flash_attn_varlen_func
     from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
 
+logger = logging.get_logger(__name__)
+
 _CONFIG_FOR_DOC = "Florence2Config"
 
 
@@ -989,6 +992,12 @@ class Florence2FlashAttention2(Florence2Attention):
             else:
                 target_dtype = self.q_proj.weight.dtype
 
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
             query_states = query_states.to(target_dtype)
             key_states = key_states.to(target_dtype)
             value_states = value_states.to(target_dtype)
@@ -1126,6 +1135,11 @@ class Florence2SdpaAttention(Florence2Attention):
     ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
         """Input shape: Batch x Time x Channel"""
         if output_attentions or layer_head_mask is not None:
+            # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Florence2Model is using Florence2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
+                ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
             return super().forward(
                 hidden_states,
                 key_value_states=key_value_states,
@@ -1846,6 +1860,9 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel):
         hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
 
         if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
             use_cache = False
 
         # decoder layers
@@ -1934,10 +1951,7 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel):
 
 
 class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
-    _tied_weights_keys = {
-        "encoder.embed_tokens.weight": "shared.weight",
-        "decoder.embed_tokens.weight": "shared.weight",
-    }
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
 
     def __init__(self, config: Florence2LanguageConfig):
         super().__init__(config)
@@ -2062,10 +2076,7 @@ class Florence2LanguageModel(Florence2LanguagePreTrainedModel):
 
 class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
-    _tied_weights_keys = {
-        "model.encoder.embed_tokens.weight": "model.shared.weight",
-        "model.decoder.embed_tokens.weight": "model.shared.weight",
-    }
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
 
     def __init__(self, config: Florence2LanguageConfig):
@@ -2143,6 +2154,8 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if labels is not None:
+            if use_cache:
+                logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
             use_cache = False
             if decoder_input_ids is None and decoder_inputs_embeds is None:
                 decoder_input_ids = shift_tokens_right(
@@ -2423,10 +2436,11 @@ FLORENCE2_INPUTS_DOCSTRING = r"""
     FLORENCE2_START_DOCSTRING,
 )
 class Florence2ForConditionalGeneration(Florence2PreTrainedModel):
-    _tied_weights_keys = {
-        "language_model.model.encoder.embed_tokens.weight": "language_model.model.shared.weight",
-        "language_model.model.decoder.embed_tokens.weight": "language_model.model.shared.weight",
-    }
+    _tied_weights_keys = [
+        "language_model.encoder.embed_tokens.weight",
+        "language_model.decoder.embed_tokens.weight",
+        "language_model.lm_head.weight",
+    ]
 
     def __init__(self, config: Florence2Config):
         super().__init__(config)

src/lerobot/processor/__init__.py

@@ -30,12 +30,6 @@ from .core import (
 )
 from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
 from .device_processor import DeviceProcessorStep
-from .factory import (
-    make_default_processors,
-    make_default_robot_action_processor,
-    make_default_robot_observation_processor,
-    make_default_teleop_action_processor,
-)
 from .gym_action_processor import (
     Numpy2TorchActionProcessorStep,
     Torch2NumpyActionProcessorStep,
@@ -95,11 +89,7 @@ __all__ = [
     "ImageCropResizeProcessorStep",
     "InfoProcessorStep",
     "InterventionActionProcessorStep",
-    "make_default_processors",
-    "make_default_teleop_action_processor",
-    "make_default_robot_action_processor",
-    "make_default_robot_observation_processor",
-    "MapDeltaActionToRobotActionStep",
+"MapDeltaActionToRobotActionStep",
     "MapTensorToDeltaActionDictStep",
     "NormalizerProcessorStep",
     "Numpy2TorchActionProcessorStep",

src/lerobot/processor/factory.py

@@ -17,6 +17,7 @@
 from .converters import (
     observation_to_transition,
     robot_action_observation_to_transition,
+    robot_action_to_transition,
     transition_to_observation,
     transition_to_robot_action,
 )
@@ -24,39 +25,44 @@ from .core import RobotAction, RobotObservation
 from .pipeline import IdentityProcessorStep, RobotProcessorPipeline
 
 
-def make_default_teleop_action_processor() -> RobotProcessorPipeline[
-    tuple[RobotAction, RobotObservation], RobotAction
-]:
-    teleop_action_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[IdentityProcessorStep()],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-    return teleop_action_processor
+# ── Internal identity pipeline helpers (used by Robot/Teleoperator base classes) ──────────────────
 
 
-def make_default_robot_action_processor() -> RobotProcessorPipeline[
-    tuple[RobotAction, RobotObservation], RobotAction
-]:
-    robot_action_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
-        steps=[IdentityProcessorStep()],
-        to_transition=robot_action_observation_to_transition,
-        to_output=transition_to_robot_action,
-    )
-    return robot_action_processor
-
-
-def make_default_robot_observation_processor() -> RobotProcessorPipeline[RobotObservation, RobotObservation]:
-    robot_observation_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
+def _make_identity_observation_pipeline() -> RobotProcessorPipeline[RobotObservation, RobotObservation]:
+    """Identity pipeline for robot observations (get_observation output pipeline)."""
+    return RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[IdentityProcessorStep()],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+
+def _make_identity_robot_action_pipeline() -> RobotProcessorPipeline[
+    tuple[RobotAction, RobotObservation], RobotAction
+]:
+    """Identity pipeline for robot action input (send_action input pipeline, takes (action, obs) tuple)."""
+    return RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[IdentityProcessorStep()],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+
+def _make_identity_teleop_action_pipeline() -> RobotProcessorPipeline[RobotAction, RobotAction]:
+    """Identity pipeline for teleop action output (get_action output pipeline, takes just action)."""
+    return RobotProcessorPipeline[RobotAction, RobotAction](
+        steps=[IdentityProcessorStep()],
+        to_transition=robot_action_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+
+def _make_identity_feedback_pipeline() -> RobotProcessorPipeline[dict, dict]:
+    """Identity pipeline for teleop feedback input (send_feedback input pipeline)."""
+    return RobotProcessorPipeline[dict, dict](
         steps=[IdentityProcessorStep()],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
     )
-    return robot_observation_processor
 
 
-def make_default_processors():
-    teleop_action_processor = make_default_teleop_action_processor()
-    robot_action_processor = make_default_robot_action_processor()
-    robot_observation_processor = make_default_robot_observation_processor()
-    return (teleop_action_processor, robot_action_processor, robot_observation_processor)

src/lerobot/processor/normalize_processor.py

@@ -19,15 +19,17 @@ from __future__ import annotations
 
 from copy import deepcopy
 from dataclasses import dataclass, field
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import torch
 from torch import Tensor
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PipelineFeatureType, PolicyFeature
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.constants import ACTION
 
+if TYPE_CHECKING:
+    from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
 from .converters import from_tensor_to_numpy, to_tensor
 from .core import EnvTransition, PolicyAction, TransitionKey
 from .pipeline import PolicyProcessorPipeline, ProcessorStep, ProcessorStepRegistry, RobotObservation

src/lerobot/processor/tokenizer_processor.py

@@ -43,12 +43,9 @@ from lerobot.utils.import_utils import _transformers_available
 from .core import EnvTransition, RobotObservation, TransitionKey
 from .pipeline import ActionProcessorStep, ObservationProcessorStep, ProcessorStepRegistry
 
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
+# Type-checking only import — do NOT import transformers at module level (it loads TF which blocks)
+if TYPE_CHECKING:
     from transformers import AutoProcessor, AutoTokenizer
-else:
-    AutoProcessor = None
-    AutoTokenizer = None
 
 
 @dataclass
@@ -106,8 +103,7 @@ class TokenizerProcessorStep(ObservationProcessorStep):
             # Use provided tokenizer object directly
             self.input_tokenizer = self.tokenizer
         elif self.tokenizer_name is not None:
-            if AutoTokenizer is None:
-                raise ImportError("AutoTokenizer is not available")
+            from transformers import AutoTokenizer  # lazy import to avoid TF deadlock at module load
             self.input_tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name)
         else:
             raise ValueError(
@@ -336,7 +332,7 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
     Requires the `transformers` library to be installed.
 
     Attributes:
-        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "lerobot/fast-action-tokenizer").
+        tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "physical-intelligence/fast").
         tokenizer: A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is ignored.
         trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
         action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.
@@ -370,12 +366,12 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
                 "Please install it with `pip install 'lerobot[transformers-dep]'` to use ActionTokenizerProcessorStep."
             )
 
+        from transformers import AutoProcessor, AutoTokenizer  # lazy import to avoid TF deadlock at module load
+
         if self.action_tokenizer_input_object is not None:
             self.action_tokenizer = self.action_tokenizer_input_object
 
         elif self.action_tokenizer_name is not None:
-            if AutoProcessor is None:
-                raise ImportError("AutoProcessor is not available")
             self.action_tokenizer = AutoProcessor.from_pretrained(
                 self.action_tokenizer_name, trust_remote_code=self.trust_remote_code
             )

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -102,11 +102,11 @@ class BiOpenArmFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -136,7 +136,7 @@ class BiOpenArmFollower(Robot):
         )
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         obs_dict = {}
 
         # Add "left_" prefix
@@ -150,7 +150,7 @@ class BiOpenArmFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(
+    def _send_action(
         self,
         action: RobotAction,
         custom_kp: dict[str, float] | None = None,

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -86,11 +86,11 @@ class BiSOFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -119,7 +119,7 @@ class BiSOFollower(Robot):
         self.right_arm.setup_motors()
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         obs_dict = {}
 
         # Add "left_" prefix
@@ -133,7 +133,7 @@ class BiSOFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         # Remove "left_" prefix
         left_action = {
             key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_")

src/lerobot/robots/earthrover_mini_plus/robot_earthrover_mini_plus.py

@@ -147,7 +147,7 @@ class EarthRoverMiniPlus(Robot):
         pass
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         """Define the observation space for dataset recording.
 
         Returns:
@@ -184,7 +184,7 @@ class EarthRoverMiniPlus(Robot):
         }
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         """Define the action space.
 
         Returns:
@@ -198,7 +198,7 @@ class EarthRoverMiniPlus(Robot):
         }
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         """Get current robot observation from SDK.
 
         Returns:
@@ -255,7 +255,7 @@ class EarthRoverMiniPlus(Robot):
         return observation
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Send action to robot via SDK.
 
         Args:

src/lerobot/robots/hope_jr/hope_jr_arm.py

@@ -71,11 +71,11 @@ class HopeJrArm(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -128,7 +128,7 @@ class HopeJrArm(Robot):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
         obs_dict = self.bus.sync_read("Present_Position", self.other_motors)
@@ -147,7 +147,7 @@ class HopeJrArm(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
 
         # Cap goal position when too far away from present position.

src/lerobot/robots/hope_jr/hope_jr_hand.py

@@ -107,11 +107,11 @@ class HopeJrHand(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -158,7 +158,7 @@ class HopeJrHand(Robot):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         obs_dict = {}
 
         # Read hand position
@@ -178,7 +178,7 @@ class HopeJrHand(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
         self.bus.sync_write("Goal_Position", goal_pos)
         return action

src/lerobot/robots/koch_follower/koch_follower.py

@@ -73,11 +73,11 @@ class KochFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -182,7 +182,7 @@ class KochFollower(Robot):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
         obs_dict = self.bus.sync_read("Present_Position")
@@ -200,7 +200,7 @@ class KochFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Command arm to move to a target joint configuration.
 
         The relative action magnitude may be clipped depending on the configuration parameter

src/lerobot/robots/lekiwi/lekiwi.py

@@ -98,11 +98,11 @@ class LeKiwi(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._state_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._state_ft
 
     @property
@@ -338,7 +338,7 @@ class LeKiwi(Robot):
         }  # m/s and deg/s
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         # Read actuators position for arm and vel for base
         start = time.perf_counter()
         arm_pos = self.bus.sync_read("Present_Position", self.arm_motors)
@@ -367,7 +367,7 @@ class LeKiwi(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Command lekiwi to move to a target joint configuration.
 
         The relative action magnitude may be clipped depending on the configuration parameter

src/lerobot/robots/lekiwi/lekiwi_client.py

@@ -98,11 +98,11 @@ class LeKiwiClient(Robot):
         return {name: (cfg.height, cfg.width, 3) for name, cfg in self.config.cameras.items()}
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._state_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._state_ft
 
     @property
@@ -250,7 +250,7 @@ class LeKiwiClient(Robot):
         return new_frames, new_state
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         """
         Capture observations from the remote robot: current follower arm positions,
         present wheel speeds (converted to body-frame velocities: x, y, theta),
@@ -304,7 +304,7 @@ class LeKiwiClient(Robot):
         pass
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Command lekiwi to move to a target joint configuration. Translates to motor space + sends over ZMQ
 
         Args:

src/lerobot/robots/omx_follower/omx_follower.py

@@ -73,11 +73,11 @@ class OmxFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -165,7 +165,7 @@ class OmxFollower(Robot):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
         obs_dict = self.bus.sync_read("Present_Position")
@@ -183,7 +183,7 @@ class OmxFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Command arm to move to a target joint configuration.
 
         The relative action magnitude may be clipped depending on the configuration parameter

src/lerobot/robots/openarm_follower/openarm_follower.py

@@ -105,12 +105,12 @@ class OpenArmFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         """Combined observation features from motors and cameras."""
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         """Action features."""
         return self._motors_ft
 
@@ -219,7 +219,7 @@ class OpenArmFollower(Robot):
         )
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         """
         Get current observation from robot including position, velocity, and torque.
 
@@ -251,7 +251,7 @@ class OpenArmFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(
+    def _send_action(
         self,
         action: RobotAction,
         custom_kp: dict[str, float] | None = None,

src/lerobot/robots/reachy2/robot_reachy2.py

@@ -95,11 +95,11 @@ class Reachy2Robot(Robot):
         self.joints_dict: dict[str, str] = self._generate_joints_dict()
 
     @property
-    def observation_features(self) -> dict[str, Any]:
+    def raw_observation_features(self) -> dict[str, Any]:
         return {**self.motors_features, **self.camera_features}
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self.motors_features
 
     @property
@@ -170,7 +170,7 @@ class Reachy2Robot(Robot):
         else:
             return {}
 
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         obs_dict: RobotObservation = {}
 
         # Read Reachy 2 state
@@ -184,7 +184,7 @@ class Reachy2Robot(Robot):
 
         return obs_dict
 
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         if self.reachy is not None:
             if not self.is_connected:
                 raise ConnectionError()

src/lerobot/robots/robot.py

@@ -18,8 +18,11 @@ from pathlib import Path
 
 import draccus
 
+from lerobot.configs.types import PipelineFeatureType
 from lerobot.motors import MotorCalibration
-from lerobot.processor import RobotAction, RobotObservation
+from lerobot.processor.core import RobotAction, RobotObservation
+from lerobot.processor.factory import _make_identity_observation_pipeline, _make_identity_robot_action_pipeline
+from lerobot.processor.pipeline import RobotProcessorPipeline
 from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, ROBOTS
 
 from .config import RobotConfig
@@ -34,6 +37,10 @@ class Robot(abc.ABC):
     This class provides a standardized interface for interacting with physical robots.
     Subclasses must implement all abstract methods and properties to be usable.
 
+    Pipelines are first-class citizens: every robot carries an optional output pipeline
+    (applied in get_observation()) and an optional input pipeline (applied in send_action()).
+    Both default to identity (no-op), so existing robots work without any changes.
+
     Attributes:
         config_class (RobotConfig): The expected configuration class for this robot.
         name (str): The unique robot name used to identify this robot type.
@@ -55,6 +62,12 @@ class Robot(abc.ABC):
         if self.calibration_fpath.is_file():
             self._load_calibration()
 
+        # Pipeline interface — default to identity (no-op), swap via set_output/input_pipeline()
+        self._output_pipeline: RobotProcessorPipeline = _make_identity_observation_pipeline()
+        self._input_pipeline: RobotProcessorPipeline = _make_identity_robot_action_pipeline()
+        # Cache of most recent raw observation; used by input_pipeline for IK initial guess
+        self._last_raw_obs: RobotObservation = {}
+
     def __str__(self) -> str:
         return f"{self.id} {self.__class__.__name__}"
 
@@ -84,40 +97,117 @@ class Robot(abc.ABC):
         except Exception:  # nosec B110
             pass
 
-    # TODO(aliberts): create a proper Feature class for this that links with datasets
+    # ── Pipeline interface ────────────────────────────────────────────────────
+
+    def output_pipeline(self) -> RobotProcessorPipeline:
+        """
+        Pipeline applied inside get_observation() to transform raw hardware observations.
+        Default: identity (no-op). Override via set_output_pipeline() or subclassing.
+
+        Example: set a forward-kinematics pipeline to convert joint positions to EE pose.
+        """
+        return self._output_pipeline
+
+    def input_pipeline(self) -> RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]:
+        """
+        Pipeline applied inside send_action() to transform incoming actions before hardware write.
+        Default: identity (no-op). Override via set_input_pipeline() or subclassing.
+
+        The pipeline receives a (action, last_raw_obs) tuple so IK solvers can use the
+        current joint configuration as an initial guess.
+
+        Example: set an inverse-kinematics pipeline to convert EE commands to joint positions.
+        """
+        return self._input_pipeline
+
+    def set_output_pipeline(self, pipeline: RobotProcessorPipeline) -> None:
+        """Set the observation output pipeline (applied in get_observation())."""
+        self._output_pipeline = pipeline
+
+    def set_input_pipeline(self, pipeline: RobotProcessorPipeline) -> None:
+        """Set the action input pipeline (applied in send_action())."""
+        self._input_pipeline = pipeline
+
+    # ── Feature properties ────────────────────────────────────────────────────
+
     @property
-    @abc.abstractmethod
     def observation_features(self) -> dict:
         """
-        A dictionary describing the structure and types of the observations produced by the robot.
-        Its structure (keys) should match the structure of what is returned by :pymeth:`get_observation`.
-        Values for the dict should either be:
-            - The type of the value if it's a simple value, e.g. `float` for single proprioceptive value (a joint's position/velocity)
-            - A tuple representing the shape if it's an array-type value, e.g. `(height, width, channel)` for images
+        Pipeline-transformed observation features.
 
-        Note: this property should be able to be called regardless of whether the robot is connected or not.
+        Applies output_pipeline().transform_features() to raw_observation_features so the
+        returned dict matches what get_observation() actually returns to callers.
+
+        Use raw_observation_features to inspect hardware-level feature shapes.
+
+        Note: this property should be able to be called regardless of whether the robot
+        is connected or not.
+        """
+        from lerobot.datasets.pipeline_features import create_initial_features  # lazy import
+
+        initial = create_initial_features(observation=self.raw_observation_features)
+        transformed = self.output_pipeline().transform_features(initial)
+        return transformed.get(PipelineFeatureType.OBSERVATION, {})
+
+    @property
+    @abc.abstractmethod
+    def raw_observation_features(self) -> dict:
+        """
+        Hardware-level observation features (before any pipeline transformation).
+
+        A dictionary describing the structure and types of the observations produced
+        directly by the robot hardware. Its structure (keys) should match the structure
+        of what is returned by :pymeth:`_get_observation`. Values should be:
+            - The type if it's a simple value, e.g. ``float`` for joint position
+            - A tuple representing the shape for array values, e.g. ``(H, W, C)`` for images
+
+        Note: this property should be able to be called regardless of whether the robot
+        is connected or not.
         """
         pass
 
     @property
     @abc.abstractmethod
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         """
-        A dictionary describing the structure and types of the actions expected by the robot. Its structure
-        (keys) should match the structure of what is passed to :pymeth:`send_action`. Values for the dict
-        should be the type of the value if it's a simple value, e.g. `float` for single proprioceptive value
-        (a joint's goal position/velocity)
+        Hardware-level action features (before any pipeline transformation).
 
-        Note: this property should be able to be called regardless of whether the robot is connected or not.
+        A dictionary describing the structure and types of the actions accepted directly
+        by the robot hardware (i.e. what :pymeth:`_send_action` receives). Its structure
+        (keys) should match the structure of what is expected by :pymeth:`_send_action`.
+        Values should be the type of the value if it's a simple value, e.g. ``float`` for
+        single proprioceptive value (a joint's goal position/velocity).
+
+        Note: this property should be able to be called regardless of whether the robot
+        is connected or not.
         """
         pass
 
+    @property
+    def action_features(self) -> dict:
+        """
+        Pipeline-transformed action features.
+
+        Applies input_pipeline().transform_features() to raw_action_features so the
+        returned dict reflects what the input pipeline outputs to hardware.
+
+        Use raw_action_features to inspect hardware-level action feature shapes.
+
+        Note: this property should be able to be called regardless of whether the robot
+        is connected or not.
+        """
+        from lerobot.datasets.pipeline_features import create_initial_features  # lazy import
+
+        initial = create_initial_features(action=self.raw_action_features)
+        transformed = self.input_pipeline().transform_features(initial)
+        return transformed.get(PipelineFeatureType.ACTION, {})
+
     @property
     @abc.abstractmethod
     def is_connected(self) -> bool:
         """
-        Whether the robot is currently connected or not. If `False`, calling :pymeth:`get_observation` or
-        :pymeth:`send_action` should raise an error.
+        Whether the robot is currently connected or not. If ``False``, calling
+        :pymeth:`get_observation` or :pymeth:`send_action` should raise an error.
         """
         pass
 
@@ -135,7 +225,7 @@ class Robot(abc.ABC):
     @property
     @abc.abstractmethod
     def is_calibrated(self) -> bool:
-        """Whether the robot is currently calibrated or not. Should be always `True` if not applicable"""
+        """Whether the robot is currently calibrated or not. Should be always ``True`` if not applicable"""
         pass
 
     @abc.abstractmethod
@@ -153,7 +243,7 @@ class Robot(abc.ABC):
         Helper to load calibration data from the specified file.
 
         Args:
-            fpath (Path | None): Optional path to the calibration file. Defaults to `self.calibration_fpath`.
+            fpath (Path | None): Optional path to the calibration file. Defaults to ``self.calibration_fpath``.
         """
         fpath = self.calibration_fpath if fpath is None else fpath
         with open(fpath) as f, draccus.config_type("json"):
@@ -164,7 +254,7 @@ class Robot(abc.ABC):
         Helper to save calibration data to the specified file.
 
         Args:
-            fpath (Path | None): Optional path to save the calibration file. Defaults to `self.calibration_fpath`.
+            fpath (Path | None): Optional path to save the calibration file. Defaults to ``self.calibration_fpath``.
         """
         fpath = self.calibration_fpath if fpath is None else fpath
         with open(fpath, "w") as f, draccus.config_type("json"):
@@ -178,30 +268,64 @@ class Robot(abc.ABC):
         """
         pass
 
-    @abc.abstractmethod
+    # ── Template methods (concrete, call pipeline internally) ─────────────────
+
     def get_observation(self) -> RobotObservation:
         """
-        Retrieve the current observation from the robot.
+        Retrieve the current observation from the robot and apply the output pipeline.
+
+        Calls :pymeth:`_get_observation` to get raw hardware data, caches it for use as
+        IK initial guess in :pymeth:`send_action`, then applies :pymeth:`output_pipeline`.
 
         Returns:
-            RobotObservation: A flat dictionary representing the robot's current sensory state. Its structure
-                should match :pymeth:`observation_features`.
+            RobotObservation: Pipeline-transformed observation. With the default identity
+                pipeline this equals the raw observation from :pymeth:`_get_observation`.
         """
-
-        pass
+        raw = self._get_observation()
+        self._last_raw_obs = raw
+        return self.output_pipeline()(raw)
 
     @abc.abstractmethod
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _get_observation(self) -> RobotObservation:
         """
-        Send an action command to the robot.
-
-        Args:
-            action (RobotAction): Dictionary representing the desired action. Its structure should match
-                :pymeth:`action_features`.
+        Retrieve the raw observation directly from robot hardware.
 
         Returns:
-            RobotAction: The action actually sent to the motors potentially clipped or modified, e.g. by
-                safety limits on velocity.
+            RobotObservation: A flat dictionary representing the robot's current sensory
+                state. Its structure should match :pymeth:`raw_observation_features`.
+        """
+        pass
+
+    def send_action(self, action: RobotAction) -> RobotAction:
+        """
+        Apply the input pipeline and send the resulting action to robot hardware.
+
+        The input pipeline receives ``(action, last_raw_obs)`` so IK solvers can use the
+        cached joint configuration as an initial guess. With the default identity pipeline,
+        the action is forwarded unchanged.
+
+        Args:
+            action (RobotAction): Dictionary representing the desired action. Its structure
+                should match :pymeth:`action_features`.
+
+        Returns:
+            RobotAction: The action actually sent to the motors, potentially clipped or
+                modified by the pipeline or hardware safety limits.
+        """
+        transformed = self.input_pipeline()((action, self._last_raw_obs))
+        return self._send_action(transformed)
+
+    @abc.abstractmethod
+    def _send_action(self, action: RobotAction) -> RobotAction:
+        """
+        Send an action command directly to robot hardware.
+
+        Args:
+            action (RobotAction): Dictionary of motor-level commands. Its structure should
+                match what the hardware expects (typically motor positions/velocities).
+
+        Returns:
+            RobotAction: The action actually sent, potentially clipped by safety limits.
         """
         pass
 

src/lerobot/robots/so_follower/pipelines/__init__.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .ee_space import make_so10x_fk_observation_pipeline, make_so10x_ik_action_pipeline
+
+__all__ = ["make_so10x_fk_observation_pipeline", "make_so10x_ik_action_pipeline"]

src/lerobot/robots/so_follower/pipelines/ee_space.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+End-effector space pipelines for SO-100/101 follower robots.
+
+These factory functions return ready-to-use pipelines that convert between joint space
+and Cartesian end-effector space. Attach them to a robot with ``set_output_pipeline`` /
+``set_input_pipeline`` to enable EE-space recording and teleoperation.
+
+Example::
+
+    from lerobot.robots.so_follower.pipelines import (
+        make_so10x_fk_observation_pipeline,
+        make_so10x_ik_action_pipeline,
+    )
+
+    motor_names = list(follower.bus.motors.keys())
+    follower.set_output_pipeline(make_so10x_fk_observation_pipeline(URDF_PATH, motor_names))
+    follower.set_input_pipeline(make_so10x_ik_action_pipeline(URDF_PATH, motor_names))
+"""
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    EEBoundsAndSafety,
+    ForwardKinematicsJointsToEE,
+    InverseKinematicsEEToJoints,
+)
+
+_DEFAULT_EE_BOUNDS = {"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}
+_DEFAULT_GRIPPER_FRAME = "gripper_frame_link"
+
+
+def make_so10x_fk_observation_pipeline(
+    urdf_path: str,
+    motor_names: list[str],
+    *,
+    target_frame_name: str = _DEFAULT_GRIPPER_FRAME,
+) -> RobotProcessorPipeline[RobotObservation, RobotObservation]:
+    """
+    Create a forward-kinematics observation pipeline for SO-100/101 follower robots.
+
+    Converts raw joint positions (observation) into end-effector pose (position + orientation).
+    Attach this to a follower robot via ``set_output_pipeline`` so that ``get_observation()``
+    returns EE coordinates instead of raw joint angles.
+
+    Args:
+        urdf_path: Path to the SO-100/101 URDF file used for kinematics.
+        motor_names: Ordered list of motor names matching the URDF joint names.
+        target_frame_name: Name of the end-effector frame in the URDF.
+
+    Returns:
+        A RobotProcessorPipeline that maps joint observations to EE observations.
+
+    Example::
+
+        follower.set_output_pipeline(
+            make_so10x_fk_observation_pipeline("./so101.urdf", motor_names)
+        )
+        obs = follower.get_observation()  # now contains ee.x, ee.y, ee.z, ...
+    """
+    kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=target_frame_name,
+        joint_names=motor_names,
+    )
+    return RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics, motor_names=motor_names)],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+
+
+def make_so10x_ik_action_pipeline(
+    urdf_path: str,
+    motor_names: list[str],
+    *,
+    target_frame_name: str = _DEFAULT_GRIPPER_FRAME,
+    end_effector_bounds: dict | None = None,
+    max_ee_step_m: float = 0.10,
+) -> RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction]:
+    """
+    Create an inverse-kinematics action pipeline for SO-100/101 follower robots.
+
+    Converts incoming end-effector pose commands into joint positions, applying safety
+    bounds and step-size limits before solving IK. The current joint positions are used
+    as the IK initial guess (taken from the cached ``_last_raw_obs``).
+
+    Attach this to a follower robot via ``set_input_pipeline`` so that ``send_action()``
+    receives EE commands and translates them to motor positions before the hardware write.
+
+    Args:
+        urdf_path: Path to the SO-100/101 URDF file used for kinematics.
+        motor_names: Ordered list of motor names matching the URDF joint names.
+        target_frame_name: Name of the end-effector frame in the URDF.
+        end_effector_bounds: Dict with ``"min"`` and ``"max"`` lists (3D position bounds in metres).
+            Defaults to ``{"min": [-1, -1, -1], "max": [1, 1, 1]}``.
+        max_ee_step_m: Maximum allowed EE position change per step in metres.
+
+    Returns:
+        A RobotProcessorPipeline that maps (EE action, raw obs) to joint action.
+
+    Example::
+
+        follower.set_input_pipeline(
+            make_so10x_ik_action_pipeline("./so101.urdf", motor_names)
+        )
+        # send_action() now accepts ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_vel
+    """
+    kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=target_frame_name,
+        joint_names=motor_names,
+    )
+    bounds = end_effector_bounds or _DEFAULT_EE_BOUNDS
+    return RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            EEBoundsAndSafety(end_effector_bounds=bounds, max_ee_step_m=max_ee_step_m),
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics,
+                motor_names=motor_names,
+                initial_guess_current_joints=True,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )

src/lerobot/robots/so_follower/so_follower.py

@@ -74,11 +74,11 @@ class SOFollower(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return self._motors_ft
 
     @property
@@ -176,7 +176,7 @@ class SOFollower(Robot):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
         obs_dict = self.bus.sync_read("Present_Position")
@@ -194,7 +194,7 @@ class SOFollower(Robot):
         return obs_dict
 
     @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         """Command arm to move to a target joint configuration.
 
         The relative action magnitude may be clipped depending on the configuration parameter

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -170,7 +170,7 @@ class UnitreeG1(Robot):
             time.sleep(sleep_time)
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
 
     def calibrate(self) -> None:  # robot is already calibrated
@@ -273,7 +273,7 @@ class UnitreeG1(Robot):
         for cam in self._cameras.values():
             cam.disconnect()
 
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
         lowstate = self._lowstate
         if lowstate is None:
             return {}
@@ -351,10 +351,10 @@ class UnitreeG1(Robot):
         }
 
     @cached_property
-    def observation_features(self) -> dict[str, type | tuple]:
+    def raw_observation_features(self) -> dict[str, type | tuple]:
         return {**self._motors_ft, **self._cameras_ft}
 
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
         for motor in G1_29_JointIndex:
             key = f"{motor.name}.q"
             if key in action:
@@ -421,7 +421,7 @@ class UnitreeG1(Robot):
             num_steps = int(total_time / control_dt)
 
             # get current state
-            obs = self.get_observation()
+            obs = self._get_observation()
 
             # record current positions
             init_dof_pos = np.zeros(29, dtype=np.float32)
@@ -439,7 +439,7 @@ class UnitreeG1(Robot):
                     interp_pos = init_dof_pos[motor.value] * (1 - alpha) + target_pos * alpha
                     action_dict[f"{motor.name}.q"] = float(interp_pos)
 
-                self.send_action(action_dict)
+                self._send_action(action_dict)
 
                 # Maintain constant control rate
                 elapsed = time.time() - start_time

src/lerobot/scripts/lerobot_calibrate.py

@@ -56,6 +56,7 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
+    openarm_mini,
     so_leader,
     unitree_g1,
 )

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -61,6 +61,7 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
+    openarm_mini,
     so_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep

src/lerobot/scripts/lerobot_record.py

@@ -74,6 +74,8 @@ from pathlib import Path
 from pprint import pformat
 from typing import Any
 
+import torch
+
 from lerobot.cameras import (  # noqa: F401
     CameraConfig,  # noqa: F401
 )
@@ -85,19 +87,16 @@ from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.datasets.image_writer import safe_stop_image_writer
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts
+from lerobot.datasets.utils import build_dataset_frame
 from lerobot.datasets.video_utils import VideoEncodingManager
 from lerobot.policies.factory import make_policy, make_pre_post_processors
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionInterpolator
 from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     PolicyAction,
     PolicyProcessorPipeline,
     RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    make_default_processors,
 )
 from lerobot.processor.rename_processor import rename_stats
 from lerobot.robots import (  # noqa: F401
@@ -125,6 +124,7 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
+    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,
@@ -139,6 +139,11 @@ from lerobot.utils.control_utils import (
     sanity_check_dataset_robot_compatibility,
 )
 from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.pipeline_utils import (
+    build_dataset_features,
+    check_action_space_compatibility,
+    check_observation_space_compatibility,
+)
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import (
     get_safe_torch_device,
@@ -225,6 +230,9 @@ class RecordConfig:
     play_sounds: bool = True
     # Resume recording on an existing dataset.
     resume: bool = False
+    # Action interpolation multiplier for smoother policy control (1=off, 2=2x, 3=3x)
+    # Only applies when using a policy (not teleop)
+    interpolation_multiplier: int = 1
 
     def __post_init__(self):
         # HACK: We parse again the cli args here to get the pretrained path if there was one.
@@ -248,28 +256,23 @@ class RecordConfig:
 """ --------------- record_loop() data flow --------------------------
        [ Robot ]
            V
-     [ robot.get_observation() ] ---> raw_obs
-           V
-     [ robot_observation_processor ] ---> processed_obs
+     [ robot.get_observation() ]  → applies output_pipeline internally → obs
            V
      .-----( ACTION LOGIC )------------------.
      V                                       V
      [ From Teleoperator ]                   [ From Policy ]
      |                                       |
-     |  [teleop.get_action] -> raw_action    |   [predict_action]
-     |          |                            |          |
-     |          V                            |          V
-     | [teleop_action_processor]             |          |
-     |          |                            |          |
-     '---> processed_teleop_action           '---> processed_policy_action
+     |  teleop.get_action()                  |   predict_action(obs)
+     |  (output_pipeline applied internally) |          |
+     |          |                            |          V
+     '----> action                           '---> policy_action_dict
      |                                       |
      '-------------------------.-------------'
                                V
-                  [ robot_action_processor ] --> robot_action_to_send
+                  [ robot.send_action(action) ]
+                  (input_pipeline applied internally)
                                V
-                    [ robot.send_action() ] -- (Robot Executes)
-                               V
-                    ( Save to Dataset )
+                    ( Save action + obs to Dataset )
                                V
                   ( Rerun Log / Loop Wait )
 """
@@ -280,15 +283,6 @@ def record_loop(
     robot: Robot,
     events: dict,
     fps: int,
-    teleop_action_processor: RobotProcessorPipeline[
-        tuple[RobotAction, RobotObservation], RobotAction
-    ],  # runs after teleop
-    robot_action_processor: RobotProcessorPipeline[
-        tuple[RobotAction, RobotObservation], RobotAction
-    ],  # runs before robot
-    robot_observation_processor: RobotProcessorPipeline[
-        RobotObservation, RobotObservation
-    ],  # runs after robot
     dataset: LeRobotDataset | None = None,
     teleop: Teleoperator | list[Teleoperator] | None = None,
     policy: PreTrainedPolicy | None = None,
@@ -297,8 +291,30 @@ def record_loop(
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
+    interpolator: ActionInterpolator | None = None,
     display_compressed_images: bool = False,
 ):
+    """
+    Core recording loop. Robot and teleoperator pipelines are applied internally —
+    no explicit processor arguments are needed.
+
+    Args:
+        robot: The robot instance. Its output_pipeline() transforms observations and
+            its input_pipeline() transforms actions before hardware write.
+        events: Control events dict (exit_early, stop_recording, rerecord_episode).
+        fps: Target control loop frequency.
+        dataset: If provided, frames are written here each step.
+        teleop: Teleoperator or list of teleoperators. Its output_pipeline() transforms
+            actions (e.g., joint → EE) before they are sent to the robot.
+        policy: Optional pre-trained policy for closed-loop control.
+        preprocessor: Policy input pre-processor.
+        postprocessor: Policy output post-processor.
+        control_time_s: Episode duration in seconds.
+        single_task: Task description string saved with each frame.
+        display_data: If True, log observations and actions to Rerun.
+        interpolator: Optional action interpolator for smoother policy control.
+        display_compressed_images: If True, compress images before Rerun display.
+    """
     if dataset is not None and dataset.fps != fps:
         raise ValueError(f"The dataset fps should be equal to requested fps ({dataset.fps} != {fps}).")
 
@@ -333,6 +349,17 @@ def record_loop(
         preprocessor.reset()
         postprocessor.reset()
 
+    # Reset interpolator if provided
+    if interpolator is not None:
+        interpolator.reset()
+
+    # Calculate control interval based on interpolation
+    use_interpolation = interpolator is not None and interpolator.enabled and policy is not None
+    control_interval = interpolator.get_control_interval(fps) if interpolator else 1 / fps
+    # Pre-compute once — action features don't change during a recording episode
+    action_keys = sorted(robot.action_features) if use_interpolation else []
+
+    no_action_count = 0
     timestamp = 0
     start_episode_t = time.perf_counter()
     while timestamp < control_time_s:
@@ -342,65 +369,85 @@ def record_loop(
             events["exit_early"] = False
             break
 
-        # Get robot observation
+        # Get robot observation (output_pipeline applied internally)
         obs = robot.get_observation()
 
-        # Applies a pipeline to the raw robot observation, default is IdentityProcessor
-        obs_processed = robot_observation_processor(obs)
-
         if policy is not None or dataset is not None:
-            observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
+            observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
 
         # Get action from either policy or teleop
         if policy is not None and preprocessor is not None and postprocessor is not None:
-            action_values = predict_action(
-                observation=observation_frame,
-                policy=policy,
-                device=get_safe_torch_device(policy.config.device),
-                preprocessor=preprocessor,
-                postprocessor=postprocessor,
-                use_amp=policy.config.use_amp,
-                task=single_task,
-                robot_type=robot.robot_type,
-            )
+            # With interpolation: only call policy when interpolator needs new action
+            if use_interpolation:
+                if interpolator.needs_new_action():
+                    action_values = predict_action(
+                        observation=observation_frame,
+                        policy=policy,
+                        device=get_safe_torch_device(policy.config.device),
+                        preprocessor=preprocessor,
+                        postprocessor=postprocessor,
+                        use_amp=policy.config.use_amp,
+                        task=single_task,
+                        robot_type=robot.robot_type,
+                    )
+                    act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
+                    # send_action applies input_pipeline (e.g. IK) internally;
+                    # capture the actually-sent joint action for interpolation
+                    sent_joint_action = robot.send_action(act_processed_policy)
 
-            act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
+                    # Build interpolation tensor from the motor-level joint action
+                    action_tensor = torch.tensor([sent_joint_action[k] for k in action_keys])
+                    interpolator.add(action_tensor)
+
+                # Get interpolated action (in joint/motor space)
+                interp_action = interpolator.get()
+                if interp_action is not None:
+                    action_values = {k: interp_action[i].item() for i, k in enumerate(action_keys)}
+                    # Interpolated values are already in joint space; bypass IK pipeline
+                    robot._send_action(action_values)
+                else:
+                    # No action available yet, skip this iteration
+                    continue
+            else:
+                action_values = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=get_safe_torch_device(policy.config.device),
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.use_amp,
+                    task=single_task,
+                    robot_type=robot.robot_type,
+                )
+                act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
+                # send_action applies input_pipeline (e.g. IK) internally
+                robot.send_action(act_processed_policy)
+                action_values = act_processed_policy
 
         elif policy is None and isinstance(teleop, Teleoperator):
-            act = teleop.get_action()
-
-            # Applies a pipeline to the raw teleop action, default is IdentityProcessor
-            act_processed_teleop = teleop_action_processor((act, obs))
+            # get_action applies output_pipeline (e.g. FK) internally
+            action_values = teleop.get_action()
+            # send_action applies input_pipeline (e.g. IK) internally
+            robot.send_action(action_values)
 
         elif policy is None and isinstance(teleop, list):
-            arm_action = teleop_arm.get_action()
+            # LeKiwi multi-teleop path
+            arm_action = teleop_arm.get_action()  # output_pipeline applied internally
             arm_action = {f"arm_{k}": v for k, v in arm_action.items()}
             keyboard_action = teleop_keyboard.get_action()
             base_action = robot._from_keyboard_to_base_action(keyboard_action)
-            act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
-            act_processed_teleop = teleop_action_processor((act, obs))
+            action_values = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
+            robot.send_action(action_values)  # input_pipeline applied internally
         else:
-            logging.info(
-                "No policy or teleoperator provided, skipping action generation."
-                "This is likely to happen when resetting the environment without a teleop device."
-                "The robot won't be at its rest position at the start of the next episode."
-            )
+            no_action_count += 1
+            if no_action_count == 1 or no_action_count % 10 == 0:
+                logging.warning(
+                    "No policy or teleoperator provided, skipping action generation. "
+                    "This is likely to happen when resetting the environment without a teleop device. "
+                    "The robot won't be at its rest position at the start of the next episode."
+                )
             continue
 
-        # Applies a pipeline to the action, default is IdentityProcessor
-        if policy is not None and act_processed_policy is not None:
-            action_values = act_processed_policy
-            robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-        else:
-            action_values = act_processed_teleop
-            robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
-
-        # Send action to robot
-        # Action can eventually be clipped using `max_relative_target`,
-        # so action actually sent is saved in the dataset. action = postprocessor.process(action)
-        # TODO(steven, pepijn, adil): we should use a pipeline step to clip the action, so the sent action is the action that we input to the robot.
-        _sent_action = robot.send_action(robot_action_to_send)
-
         # Write to dataset
         if dataset is not None:
             action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
@@ -409,12 +456,12 @@ def record_loop(
 
         if display_data:
             log_rerun_data(
-                observation=obs_processed, action=action_values, compress_images=display_compressed_images
+                observation=obs, action=action_values, compress_images=display_compressed_images
             )
 
         dt_s = time.perf_counter() - start_loop_t
 
-        sleep_time_s: float = 1 / fps - dt_s
+        sleep_time_s: float = control_interval - dt_s
         if sleep_time_s < 0:
             logging.warning(
                 f"Record loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
@@ -440,22 +487,9 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
     robot = make_robot_from_config(cfg.robot)
     teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None
 
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
-    dataset_features = combine_feature_dicts(
-        aggregate_pipeline_dataset_features(
-            pipeline=teleop_action_processor,
-            initial_features=create_initial_features(
-                action=robot.action_features
-            ),  # TODO(steven, pepijn): in future this should be come from teleop or policy
-            use_videos=cfg.dataset.video,
-        ),
-        aggregate_pipeline_dataset_features(
-            pipeline=robot_observation_processor,
-            initial_features=create_initial_features(observation=robot.observation_features),
-            use_videos=cfg.dataset.video,
-        ),
-    )
+    # Dataset features derived automatically from robot/teleop pipelines.
+    # When teleop is None (policy-only recording), only observation features are included.
+    dataset_features = build_dataset_features(robot, teleop, use_videos=cfg.dataset.video)
 
     dataset = None
     listener = None
@@ -501,6 +535,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
         policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
         preprocessor = None
         postprocessor = None
+        interpolator = None
         if cfg.policy is not None:
             preprocessor, postprocessor = make_pre_post_processors(
                 policy_cfg=cfg.policy,
@@ -511,11 +546,19 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
                 },
             )
+            # Create interpolator for smoother policy control
+            if cfg.interpolation_multiplier > 1:
+                interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
+                logging.info(f"Action interpolation enabled: {cfg.interpolation_multiplier}x control rate")
 
         robot.connect()
         if teleop is not None:
             teleop.connect()
 
+        if teleop is not None:
+            check_action_space_compatibility(teleop, robot)
+            check_observation_space_compatibility(robot, teleop)
+
         listener, events = init_keyboard_listener()
 
         if not cfg.dataset.streaming_encoding:
@@ -531,9 +574,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     robot=robot,
                     events=events,
                     fps=cfg.dataset.fps,
-                    teleop_action_processor=teleop_action_processor,
-                    robot_action_processor=robot_action_processor,
-                    robot_observation_processor=robot_observation_processor,
                     teleop=teleop,
                     policy=policy,
                     preprocessor=preprocessor,
@@ -542,6 +582,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
+                    interpolator=interpolator,
                     display_compressed_images=display_compressed_images,
                 )
 
@@ -560,9 +601,6 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                         robot=robot,
                         events=events,
                         fps=cfg.dataset.fps,
-                        teleop_action_processor=teleop_action_processor,
-                        robot_action_processor=robot_action_processor,
-                        robot_observation_processor=robot_observation_processor,
                         teleop=teleop,
                         control_time_s=cfg.dataset.reset_time_s,
                         single_task=cfg.dataset.single_task,

src/lerobot/scripts/lerobot_replay.py

@@ -47,9 +47,6 @@ from pprint import pformat
 
 from lerobot.configs import parser
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.processor import (
-    make_default_robot_action_processor,
-)
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
@@ -80,7 +77,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path').
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30
@@ -99,8 +96,6 @@ def replay(cfg: ReplayConfig):
     init_logging()
     logging.info(pformat(asdict(cfg)))
 
-    robot_action_processor = make_default_robot_action_processor()
-
     robot = make_robot_from_config(cfg.robot)
     dataset = LeRobotDataset(cfg.dataset.repo_id, root=cfg.dataset.root, episodes=[cfg.dataset.episode])
 
@@ -120,11 +115,10 @@ def replay(cfg: ReplayConfig):
             for i, name in enumerate(dataset.features[ACTION]["names"]):
                 action[name] = action_array[i]
 
-            robot_obs = robot.get_observation()
+            # Update cached observation so the robot's input pipeline can use it (e.g. for IK)
+            robot.get_observation()
 
-            processed_action = robot_action_processor((action, robot_obs))
-
-            _ = robot.send_action(processed_action)
+            _ = robot.send_action(action)
 
             dt_s = time.perf_counter() - start_episode_t
             precise_sleep(max(1 / dataset.fps - dt_s, 0.0))

src/lerobot/scripts/lerobot_setup_motors.py

@@ -43,6 +43,7 @@ from lerobot.teleoperators import (  # noqa: F401
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
+    openarm_mini,
     so_leader,
 )
 
@@ -51,6 +52,7 @@ COMPATIBLE_DEVICES = [
     "koch_leader",
     "omx_follower",
     "omx_leader",
+    "openarm_mini",
     "so100_follower",
     "so100_leader",
     "so101_follower",

src/lerobot/scripts/lerobot_teleoperate.py

@@ -61,12 +61,6 @@ import rerun as rr
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
 from lerobot.configs import parser
-from lerobot.processor import (
-    RobotAction,
-    RobotObservation,
-    RobotProcessorPipeline,
-    make_default_processors,
-)
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
@@ -94,11 +88,13 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
+    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,
 )
 from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.pipeline_utils import check_action_space_compatibility, check_observation_space_compatibility
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import init_logging, move_cursor_up
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
@@ -126,28 +122,28 @@ def teleop_loop(
     teleop: Teleoperator,
     robot: Robot,
     fps: int,
-    teleop_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction],
-    robot_action_processor: RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction],
-    robot_observation_processor: RobotProcessorPipeline[RobotObservation, RobotObservation],
     display_data: bool = False,
     duration: float | None = None,
     display_compressed_images: bool = False,
 ):
     """
-    This function continuously reads actions from a teleoperation device, processes them through optional
-    pipelines, sends them to a robot, and optionally displays the robot's state. The loop runs at a
-    specified frequency until a set duration is reached or it is manually interrupted.
+    Continuously reads actions from a teleoperation device, sends them to a robot,
+    and optionally displays the robot's state. Pipelines are applied internally by
+    the robot and teleoperator objects.
+
+    The loop runs at the specified frequency until a set duration is reached or it
+    is manually interrupted.
 
     Args:
         teleop: The teleoperator device instance providing control actions.
         robot: The robot instance being controlled.
         fps: The target frequency for the control loop in frames per second.
-        display_data: If True, fetches robot observations and displays them in the console and Rerun.
-        display_compressed_images: If True, compresses images before sending them to Rerun for display.
-        duration: The maximum duration of the teleoperation loop in seconds. If None, the loop runs indefinitely.
-        teleop_action_processor: An optional pipeline to process raw actions from the teleoperator.
-        robot_action_processor: An optional pipeline to process actions before they are sent to the robot.
-        robot_observation_processor: An optional pipeline to process raw observations from the robot.
+        display_data: If True, fetches robot observations and displays them in the
+            console and Rerun.
+        display_compressed_images: If True, compresses images before sending them
+            to Rerun for display.
+        duration: The maximum duration of the teleoperation loop in seconds.
+            If None, the loop runs indefinitely.
     """
 
     display_len = max(len(key) for key in robot.action_features)
@@ -156,40 +152,29 @@ def teleop_loop(
     while True:
         loop_start = time.perf_counter()
 
-        # Get robot observation
-        # Not really needed for now other than for visualization
-        # teleop_action_processor can take None as an observation
-        # given that it is the identity processor as default
-        obs = robot.get_observation()
+        # Get teleop action (output_pipeline applied internally)
+        action = teleop.get_action()
 
-        # Get teleop action
-        raw_action = teleop.get_action()
-
-        # Process teleop action through pipeline
-        teleop_action = teleop_action_processor((raw_action, obs))
-
-        # Process action for robot through pipeline
-        robot_action_to_send = robot_action_processor((teleop_action, obs))
-
-        # Send processed action to robot (robot_action_processor.to_output should return RobotAction)
-        _ = robot.send_action(robot_action_to_send)
+        # Send action to robot (input_pipeline applied internally)
+        robot_action_sent = robot.send_action(action)
 
         if display_data:
-            # Process robot observation through pipeline
-            obs_transition = robot_observation_processor(obs)
+            # Get robot observation (output_pipeline applied internally)
+            obs = robot.get_observation()
+            teleop.send_feedback(obs)
 
             log_rerun_data(
-                observation=obs_transition,
-                action=teleop_action,
+                observation=obs,
+                action=action,
                 compress_images=display_compressed_images,
             )
 
             print("\n" + "-" * (display_len + 10))
             print(f"{'NAME':<{display_len}} | {'NORM':>7}")
-            # Display the final robot action that was sent
-            for motor, value in robot_action_to_send.items():
-                print(f"{motor:<{display_len}} | {value:>7.2f}")
-            move_cursor_up(len(robot_action_to_send) + 3)
+            for motor, value in robot_action_sent.items():
+                if isinstance(value, float | int):
+                    print(f"{motor:<{display_len}} | {value:>7.2f}")
+            move_cursor_up(len(robot_action_sent) + 3)
 
         dt_s = time.perf_counter() - loop_start
         precise_sleep(max(1 / fps - dt_s, 0.0))
@@ -215,11 +200,13 @@ def teleoperate(cfg: TeleoperateConfig):
 
     teleop = make_teleoperator_from_config(cfg.teleop)
     robot = make_robot_from_config(cfg.robot)
-    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
 
     teleop.connect()
     robot.connect()
 
+    check_action_space_compatibility(teleop, robot)
+    check_observation_space_compatibility(robot, teleop)
+
     try:
         teleop_loop(
             teleop=teleop,
@@ -227,9 +214,6 @@ def teleoperate(cfg: TeleoperateConfig):
             fps=cfg.fps,
             display_data=cfg.display_data,
             duration=cfg.teleop_time_s,
-            teleop_action_processor=teleop_action_processor,
-            robot_action_processor=robot_action_processor,
-            robot_observation_processor=robot_observation_processor,
             display_compressed_images=display_compressed_images,
         )
     except KeyboardInterrupt:

src/lerobot/scripts/lerobot_train.py

@@ -24,6 +24,7 @@ import torch
 from accelerate import Accelerator
 from termcolor import colored
 from torch.optim import Optimizer
+from tqdm import tqdm
 
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
@@ -51,6 +52,7 @@ from lerobot.utils.utils import (
     format_big_number,
     has_method,
     init_logging,
+    inside_slurm,
 )
 
 
@@ -378,10 +380,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         "dataloading_s": AverageMeter("data_s", ":.3f"),
     }
 
-    # Use effective batch size for proper epoch calculation in distributed training
+    # Keep global batch size for logging; MetricsTracker handles world size internally.
     effective_batch_size = cfg.batch_size * accelerator.num_processes
     train_tracker = MetricsTracker(
-        effective_batch_size,
+        cfg.batch_size,
         dataset.num_frames,
         dataset.num_episodes,
         train_metrics,
@@ -390,6 +392,14 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     )
 
     if is_main_process:
+        progbar = tqdm(
+            total=cfg.steps - step,
+            desc="Training",
+            unit="step",
+            disable=inside_slurm(),
+            position=0,
+            leave=True,
+        )
         logging.info(
             f"Start offline training on a fixed dataset, with effective batch size: {effective_batch_size}"
         )
@@ -414,6 +424,8 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
         # increment `step` here.
         step += 1
+        if is_main_process:
+            progbar.update(1)
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and is_main_process
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
@@ -507,6 +519,9 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
             accelerator.wait_for_everyone()
 
+    if is_main_process:
+        progbar.close()
+
     if eval_env:
         close_envs(eval_env)
 

src/lerobot/scripts/lerobot_train_tokenizer.py

@@ -306,7 +306,7 @@ def train_fast_tokenizer(
 
     # download the tokenizer source code (not pretrained weights)
     # we'll train a new tokenizer on our own data
-    base_tokenizer = AutoProcessor.from_pretrained("lerobot/fast-action-tokenizer", trust_remote_code=True)
+    base_tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
 
     # convert action_chunks array to list of arrays (expected by .fit())
     action_data_list = [action_chunks[i] for i in range(len(action_chunks))]

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -72,9 +72,9 @@ class BiOpenArmLeader(Teleoperator):
         self.right_arm = OpenArmLeader(right_arm_config)
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
-        left_arm_features = self.left_arm.action_features
-        right_arm_features = self.right_arm.action_features
+    def raw_action_features(self) -> dict[str, type]:
+        left_arm_features = self.left_arm.raw_action_features
+        right_arm_features = self.right_arm.raw_action_features
 
         return {
             **{f"left_{k}": v for k, v in left_arm_features.items()},
@@ -82,7 +82,7 @@ class BiOpenArmLeader(Teleoperator):
         }
 
     @cached_property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -112,7 +112,7 @@ class BiOpenArmLeader(Teleoperator):
         )
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         action_dict = {}
 
         # Add "left_" prefix
@@ -125,7 +125,7 @@ class BiOpenArmLeader(Teleoperator):
 
         return action_dict
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
 

src/lerobot/teleoperators/bi_so_leader/bi_so_leader.py

@@ -55,9 +55,9 @@ class BiSOLeader(Teleoperator):
         self.right_arm = SOLeader(right_arm_config)
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
-        left_arm_features = self.left_arm.action_features
-        right_arm_features = self.right_arm.action_features
+    def raw_action_features(self) -> dict[str, type]:
+        left_arm_features = self.left_arm.raw_action_features
+        right_arm_features = self.right_arm.raw_action_features
 
         return {
             **{f"left_{k}": v for k, v in left_arm_features.items()},
@@ -65,7 +65,7 @@ class BiSOLeader(Teleoperator):
         }
 
     @cached_property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -94,7 +94,7 @@ class BiSOLeader(Teleoperator):
         self.right_arm.setup_motors()
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         action_dict = {}
 
         # Add "left_" prefix
@@ -107,7 +107,7 @@ class BiSOLeader(Teleoperator):
 
         return action_dict
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
 

src/lerobot/teleoperators/gamepad/teleop_gamepad.py

@@ -57,7 +57,7 @@ class GamepadTeleop(Teleoperator):
         self.gamepad = None
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         if self.config.use_gripper:
             return {
                 "dtype": "float32",
@@ -72,7 +72,7 @@ class GamepadTeleop(Teleoperator):
             }
 
     @property
-    def feedback_features(self) -> dict:
+    def raw_feedback_features(self) -> dict:
         return {}
 
     def connect(self) -> None:
@@ -87,7 +87,7 @@ class GamepadTeleop(Teleoperator):
         self.gamepad.start()
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         # Update the controller to get fresh inputs
         self.gamepad.update()
 
@@ -180,7 +180,7 @@ class GamepadTeleop(Teleoperator):
         # No additional configuration needed
         pass
 
-    def send_feedback(self, feedback: dict) -> None:
+    def _send_feedback(self, feedback: dict) -> None:
         """Send feedback to the gamepad."""
         # Gamepad doesn't support feedback
         pass

src/lerobot/teleoperators/homunculus/homunculus_arm.py

@@ -81,11 +81,11 @@ class HomunculusArm(Teleoperator):
         self.state_lock = threading.Lock()
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         return {f"{joint}.pos": float for joint in self.joints}
 
     @property
-    def feedback_features(self) -> dict:
+    def raw_feedback_features(self) -> dict:
         return {}
 
     @property
@@ -298,11 +298,11 @@ class HomunculusArm(Teleoperator):
                 logger.debug(f"Error reading frame in background thread for {self}: {e}")
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         joint_positions = self._read()
         return {f"{joint}.pos": pos for joint, pos in joint_positions.items()}
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError
 
     @check_if_not_connected

src/lerobot/teleoperators/homunculus/homunculus_glove.py

@@ -107,11 +107,11 @@ class HomunculusGlove(Teleoperator):
         self.state_lock = threading.Lock()
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         return {f"{joint}.pos": float for joint in self.joints}
 
     @property
-    def feedback_features(self) -> dict:
+    def raw_feedback_features(self) -> dict:
         return {}
 
     @property
@@ -324,13 +324,13 @@ class HomunculusGlove(Teleoperator):
                 logger.debug(f"Error reading frame in background thread for {self}: {e}")
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         joint_positions = self._read()
         return homunculus_glove_to_hope_jr_hand(
             {f"{joint}.pos": pos for joint, pos in joint_positions.items()}
         )
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError
 
     @check_if_not_connected

src/lerobot/teleoperators/keyboard/teleop_keyboard.py

@@ -67,7 +67,7 @@ class KeyboardTeleop(Teleoperator):
         self.logs = {}
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         return {
             "dtype": "float32",
             "shape": (len(self.arm),),
@@ -75,7 +75,7 @@ class KeyboardTeleop(Teleoperator):
         }
 
     @property
-    def feedback_features(self) -> dict:
+    def raw_feedback_features(self) -> dict:
         return {}
 
     @property
@@ -122,7 +122,7 @@ class KeyboardTeleop(Teleoperator):
         pass
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         before_read_t = time.perf_counter()
 
         self._drain_pressed_keys()
@@ -133,7 +133,7 @@ class KeyboardTeleop(Teleoperator):
 
         return dict.fromkeys(action, None)
 
-    def send_feedback(self, feedback: dict[str, Any]) -> None:
+    def _send_feedback(self, feedback: dict[str, Any]) -> None:
         pass
 
     @check_if_not_connected
@@ -157,7 +157,7 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
         self.misc_keys_queue = Queue()
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         if self.config.use_gripper:
             return {
                 "dtype": "float32",
@@ -172,7 +172,7 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
             }
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         self._drain_pressed_keys()
         delta_x = 0.0
         delta_y = 0.0
@@ -338,7 +338,7 @@ class KeyboardRoverTeleop(KeyboardTeleop):
         self.current_angular_speed = config.angular_speed
 
     @property
-    def action_features(self) -> dict:
+    def raw_action_features(self) -> dict:
         """Return action format for rover (linear and angular velocities)."""
         return {
             "linear.vel": float,
@@ -361,7 +361,7 @@ class KeyboardRoverTeleop(KeyboardTeleop):
                 self.current_pressed.pop(key_char, None)
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         """
         Get the current action based on pressed keys.
 

src/lerobot/teleoperators/koch_leader/koch_leader.py

@@ -58,11 +58,11 @@ class KochLeader(Teleoperator):
         )
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {f"{motor}.pos": float for motor in self.bus.motors}
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -160,7 +160,7 @@ class KochLeader(Teleoperator):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         start = time.perf_counter()
         action = self.bus.sync_read("Present_Position")
         action = {f"{motor}.pos": val for motor, val in action.items()}
@@ -168,7 +168,7 @@ class KochLeader(Teleoperator):
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO(rcadene, aliberts): Implement force feedback
         raise NotImplementedError
 

src/lerobot/teleoperators/omx_leader/omx_leader.py

@@ -57,11 +57,11 @@ class OmxLeader(Teleoperator):
         )
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {f"{motor}.pos": float for motor in self.bus.motors}
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -149,7 +149,7 @@ class OmxLeader(Teleoperator):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         start = time.perf_counter()
         action = self.bus.sync_read("Present_Position")
         action = {f"{motor}.pos": val for motor, val in action.items()}
@@ -157,7 +157,7 @@ class OmxLeader(Teleoperator):
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO(rcadene, aliberts): Implement force feedback
         raise NotImplementedError
 

src/lerobot/teleoperators/openarm_leader/openarm_leader.py

@@ -65,7 +65,7 @@ class OpenArmLeader(Teleoperator):
         )
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         """Features produced by this teleoperator."""
         features: dict[str, type] = {}
         for motor in self.bus.motors:
@@ -75,7 +75,7 @@ class OpenArmLeader(Teleoperator):
         return features
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         """Feedback features (not implemented for OpenArms)."""
         return {}
 
@@ -183,7 +183,7 @@ class OpenArmLeader(Teleoperator):
         )
 
     @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
         """
         Get current action from the leader arm.
 
@@ -209,7 +209,7 @@ class OpenArmLeader(Teleoperator):
 
         return action_dict
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError("Feedback is not yet implemented for OpenArm leader.")
 
     @check_if_not_connected

src/lerobot/teleoperators/openarm_mini/__init__.py

@@ -0,0 +1,20 @@
+#!/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 .config_openarm_mini import OpenArmMiniConfig
+from .openarm_mini import OpenArmMini
+
+__all__ = ["OpenArmMini", "OpenArmMiniConfig"]

src/lerobot/teleoperators/openarm_mini/config_openarm_mini.py

@@ -0,0 +1,30 @@
+#!/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 ..config import TeleoperatorConfig
+
+
+@TeleoperatorConfig.register_subclass("openarm_mini")
+@dataclass
+class OpenArmMiniConfig(TeleoperatorConfig):
+    """Configuration for OpenArm Mini teleoperator with Feetech motors (dual arms)."""
+
+    port_right: str = "/dev/ttyUSB0"
+    port_left: str = "/dev/ttyUSB1"
+
+    use_degrees: bool = True

src/lerobot/teleoperators/openarm_mini/openarm_mini.py

@@ -0,0 +1,372 @@
+#!/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.
+
+import logging
+import time
+from typing import Any
+
+from lerobot.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.motors.feetech import (
+    FeetechMotorsBus,
+    OperatingMode,
+)
+from lerobot.processor import RobotAction
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+from ..teleoperator import Teleoperator
+from .config_openarm_mini import OpenArmMiniConfig
+
+logger = logging.getLogger(__name__)
+
+# Motors whose direction is inverted on the leader side.
+LEFT_MOTORS_TO_FLIP = {"joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"}
+RIGHT_MOTORS_TO_FLIP = {"joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"}
+# Leader(OpenArmMini) -> Follower(OpenArms) joint remap
+JOINT_REMAP_TO_OPENARMS = {"joint_6": "joint_7", "joint_7": "joint_6"}
+# Follower(OpenArms) -> Leader(OpenArmMini) joint remap
+JOINT_REMAP_TO_MINI = {"joint_7": "joint_6", "joint_6": "joint_7"}
+OPENARMS_GRIPPER_MIN = -65.0
+OPENARMS_GRIPPER_MAX = 0.0
+MINI_GRIPPER_MIN = 0.0
+MINI_GRIPPER_MAX = 100.0
+
+
+class OpenArmMini(Teleoperator):
+    """
+    OpenArm Mini Teleoperator with dual Feetech-based arms (8 motors per arm).
+
+    Each arm has 7 joints plus a gripper, using Feetech STS3215 servos.
+    """
+
+    config_class = OpenArmMiniConfig
+    name = "openarm_mini"
+
+    def __init__(self, config: OpenArmMiniConfig):
+        super().__init__(config)
+        self.config = config
+
+        norm_mode_body = MotorNormMode.DEGREES
+
+        motors_right = {
+            "joint_1": Motor(1, "sts3215", norm_mode_body),
+            "joint_2": Motor(2, "sts3215", norm_mode_body),
+            "joint_3": Motor(3, "sts3215", norm_mode_body),
+            "joint_4": Motor(4, "sts3215", norm_mode_body),
+            "joint_5": Motor(5, "sts3215", norm_mode_body),
+            "joint_6": Motor(6, "sts3215", norm_mode_body),
+            "joint_7": Motor(7, "sts3215", norm_mode_body),
+            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
+        }
+
+        motors_left = {
+            "joint_1": Motor(1, "sts3215", norm_mode_body),
+            "joint_2": Motor(2, "sts3215", norm_mode_body),
+            "joint_3": Motor(3, "sts3215", norm_mode_body),
+            "joint_4": Motor(4, "sts3215", norm_mode_body),
+            "joint_5": Motor(5, "sts3215", norm_mode_body),
+            "joint_6": Motor(6, "sts3215", norm_mode_body),
+            "joint_7": Motor(7, "sts3215", norm_mode_body),
+            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
+        }
+
+        cal_right = {
+            k.replace("right_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("right_")
+        }
+        cal_left = {
+            k.replace("left_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("left_")
+        }
+
+        self.bus_right = FeetechMotorsBus(
+            port=self.config.port_right,
+            motors=motors_right,
+            calibration=cal_right,
+        )
+
+        self.bus_left = FeetechMotorsBus(
+            port=self.config.port_left,
+            motors=motors_left,
+            calibration=cal_left,
+        )
+
+    @staticmethod
+    def _mini_gripper_to_openarms(value: float) -> float:
+        """Convert OpenArmMini gripper range [0, 100] to OpenArms gripper range [-65, 0]."""
+        mapped = OPENARMS_GRIPPER_MAX + (
+            (value - MINI_GRIPPER_MIN)
+            * (OPENARMS_GRIPPER_MIN - OPENARMS_GRIPPER_MAX)
+            / (MINI_GRIPPER_MAX - MINI_GRIPPER_MIN)
+        )
+        return max(min(mapped, OPENARMS_GRIPPER_MAX), OPENARMS_GRIPPER_MIN)
+
+    @staticmethod
+    def _openarms_gripper_to_mini(value: float) -> float:
+        """Convert OpenArms gripper range [-65, 0] to OpenArmMini gripper range [0, 100]."""
+        clipped = max(min(value, OPENARMS_GRIPPER_MAX), OPENARMS_GRIPPER_MIN)
+        return MINI_GRIPPER_MIN + (
+            (OPENARMS_GRIPPER_MAX - clipped)
+            * (MINI_GRIPPER_MAX - MINI_GRIPPER_MIN)
+            / (OPENARMS_GRIPPER_MAX - OPENARMS_GRIPPER_MIN)
+        )
+
+    @property
+    def raw_action_features(self) -> dict[str, type]:
+        features: dict[str, type] = {}
+        for motor in self.bus_right.motors:
+            features[f"right_{motor}.pos"] = float
+        for motor in self.bus_left.motors:
+            features[f"left_{motor}.pos"] = float
+        return features
+
+    @property
+    def raw_feedback_features(self) -> dict[str, type]:
+        return {}
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus_right.is_connected and self.bus_left.is_connected
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        logger.info(f"Connecting right arm on {self.config.port_right}...")
+        self.bus_right.connect()
+        logger.info(f"Connecting left arm on {self.config.port_left}...")
+        self.bus_left.connect()
+
+        if calibrate:
+            self.calibrate()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus_right.is_calibrated and self.bus_left.is_calibrated
+
+    def calibrate(self) -> None:
+        """
+        Run calibration procedure for OpenArm Mini.
+
+        1. Disable torque
+        2. Ask user to position arms in hanging position with grippers closed
+        3. Set this as zero position via half-turn homing
+        4. Interactive gripper calibration (open/close positions)
+        5. Save calibration
+        """
+        if self.calibration:
+            user_input = input(
+                f"Press ENTER to use existing calibration for {self.id}, "
+                f"or type 'c' and press ENTER to run new calibration: "
+            )
+            if user_input.strip().lower() != "c":
+                logger.info(f"Using existing calibration for {self.id}")
+                cal_right = {
+                    k.replace("right_", ""): v for k, v in self.calibration.items() if k.startswith("right_")
+                }
+                cal_left = {
+                    k.replace("left_", ""): v for k, v in self.calibration.items() if k.startswith("left_")
+                }
+                self.bus_right.write_calibration(cal_right)
+                self.bus_left.write_calibration(cal_left)
+                return
+
+        logger.info(f"\nRunning calibration for {self}")
+
+        self._calibrate_arm("right", self.bus_right)
+        self._calibrate_arm("left", self.bus_left)
+
+        self._save_calibration()
+        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
+
+    def _calibrate_arm(self, arm_name: str, bus: FeetechMotorsBus) -> None:
+        """Calibrate a single arm with Feetech motors."""
+        logger.info(f"\n=== Calibrating {arm_name.upper()} arm ===")
+
+        bus.disable_torque()
+
+        logger.info(f"Setting Phase to 12 for all motors in {arm_name.upper()} arm...")
+        for motor in bus.motors:
+            bus.write("Phase", motor, 12)
+
+        for motor in bus.motors:
+            bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+        input(
+            f"\nCalibration: Zero Position ({arm_name.upper()} arm)\n"
+            "Position the arm in the following configuration:\n"
+            "  - Arm hanging straight down\n"
+            "  - Gripper closed\n"
+            "Press ENTER when ready..."
+        )
+
+        homing_offsets = bus.set_half_turn_homings()
+        logger.info(f"{arm_name.capitalize()} arm zero position set.")
+
+        print(f"\nSetting motor ranges for {arm_name.upper()} arm\n")
+
+        if self.calibration is None:
+            self.calibration = {}
+
+        motor_resolution = bus.model_resolution_table[list(bus.motors.values())[0].model]
+        max_res = motor_resolution - 1
+
+        for motor_name, motor in bus.motors.items():
+            prefixed_name = f"{arm_name}_{motor_name}"
+
+            if motor_name == "gripper":
+                input(
+                    f"\nGripper Calibration ({arm_name.upper()} arm)\n"
+                    f"Step 1: CLOSE the gripper fully\n"
+                    f"Press ENTER when gripper is closed..."
+                )
+                closed_pos = bus.read("Present_Position", motor_name, normalize=False)
+                logger.info(f"  Gripper closed position recorded: {closed_pos}")
+
+                input("\nStep 2: OPEN the gripper fully\nPress ENTER when gripper is fully open...")
+                open_pos = bus.read("Present_Position", motor_name, normalize=False)
+                logger.info(f"  Gripper open position recorded: {open_pos}")
+
+                if closed_pos < open_pos:
+                    range_min = int(closed_pos)
+                    range_max = int(open_pos)
+                    drive_mode = 0
+                else:
+                    range_min = int(open_pos)
+                    range_max = int(closed_pos)
+                    drive_mode = 1
+
+                logger.info(
+                    f"  {prefixed_name}: range set to [{range_min}, {range_max}] "
+                    f"(0=closed, 100=open, drive_mode={drive_mode})"
+                )
+            else:
+                range_min = 0
+                range_max = max_res
+                drive_mode = 0
+                logger.info(f"  {prefixed_name}: range set to [0, {max_res}] (full motor range)")
+
+            self.calibration[prefixed_name] = MotorCalibration(
+                id=motor.id,
+                drive_mode=drive_mode,
+                homing_offset=homing_offsets[motor_name],
+                range_min=range_min,
+                range_max=range_max,
+            )
+
+        cal_for_bus = {
+            k.replace(f"{arm_name}_", ""): v
+            for k, v in self.calibration.items()
+            if k.startswith(f"{arm_name}_")
+        }
+        bus.write_calibration(cal_for_bus)
+
+    def configure(self) -> None:
+        self.bus_right.disable_torque()
+        self.bus_right.configure_motors()
+        for motor in self.bus_right.motors:
+            self.bus_right.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+        self.bus_left.disable_torque()
+        self.bus_left.configure_motors()
+        for motor in self.bus_left.motors:
+            self.bus_left.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+
+    def setup_motors(self) -> None:
+        print("\nSetting up RIGHT arm motors...")
+        for motor in reversed(self.bus_right.motors):
+            input(f"Connect the controller board to the RIGHT '{motor}' motor only and press enter.")
+            self.bus_right.setup_motor(motor)
+            print(f"RIGHT '{motor}' motor id set to {self.bus_right.motors[motor].id}")
+
+        print("\nSetting up LEFT arm motors...")
+        for motor in reversed(self.bus_left.motors):
+            input(f"Connect the controller board to the LEFT '{motor}' motor only and press enter.")
+            self.bus_left.setup_motor(motor)
+            print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")
+
+    @check_if_not_connected
+    def _get_action(self) -> RobotAction:
+        """Get current action from both arms (read positions from all motors)."""
+        start = time.perf_counter()
+
+        right_positions = self.bus_right.sync_read("Present_Position")
+        left_positions = self.bus_left.sync_read("Present_Position")
+
+        action: dict[str, Any] = {}
+        for motor, val in right_positions.items():
+            target_motor = JOINT_REMAP_TO_OPENARMS.get(motor, motor)
+            mapped_val = -val if motor in RIGHT_MOTORS_TO_FLIP else val
+            if target_motor == "gripper":
+                mapped_val = self._mini_gripper_to_openarms(mapped_val)
+            action[f"right_{target_motor}.pos"] = mapped_val
+        for motor, val in left_positions.items():
+            target_motor = JOINT_REMAP_TO_OPENARMS.get(motor, motor)
+            mapped_val = -val if motor in LEFT_MOTORS_TO_FLIP else val
+            if target_motor == "gripper":
+                mapped_val = self._mini_gripper_to_openarms(mapped_val)
+            action[f"left_{target_motor}.pos"] = mapped_val
+
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read action: {dt_ms:.1f}ms")
+        return action
+
+    @check_if_not_connected
+    def enable_torque(self) -> None:
+        """Enable torque on both arms for active motion commands."""
+        self.bus_right.enable_torque()
+        self.bus_left.enable_torque()
+
+    @check_if_not_connected
+    def disable_torque(self) -> None:
+        """Disable torque on both arms for manual teleoperation."""
+        self.bus_right.disable_torque()
+        self.bus_left.disable_torque()
+
+    @check_if_not_connected
+    def write_goal_positions(self, action: dict[str, float]) -> None:
+        """Send normalized bilateral goal positions to the underlying Feetech buses."""
+        right_goals: dict[str, float] = {}
+        left_goals: dict[str, float] = {}
+
+        for key, value in action.items():
+            if not key.endswith(".pos"):
+                continue
+
+            if key.startswith("right_"):
+                openarms_motor = key.removeprefix("right_").removesuffix(".pos")
+                mini_motor = JOINT_REMAP_TO_MINI.get(openarms_motor, openarms_motor)
+                mapped_val = self._openarms_gripper_to_mini(value) if openarms_motor == "gripper" else value
+                right_goals[mini_motor] = -mapped_val if mini_motor in RIGHT_MOTORS_TO_FLIP else mapped_val
+            elif key.startswith("left_"):
+                openarms_motor = key.removeprefix("left_").removesuffix(".pos")
+                mini_motor = JOINT_REMAP_TO_MINI.get(openarms_motor, openarms_motor)
+                mapped_val = self._openarms_gripper_to_mini(value) if openarms_motor == "gripper" else value
+                left_goals[mini_motor] = -mapped_val if mini_motor in LEFT_MOTORS_TO_FLIP else mapped_val
+
+        if right_goals:
+            self.bus_right.sync_write("Goal_Position", right_goals)
+        if left_goals:
+            self.bus_left.sync_write("Goal_Position", left_goals)
+
+    @check_if_not_connected
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
+        """Route feedback position commands through the same OpenArms/OpenArmMini mapping."""
+        self.write_goal_positions(feedback)
+
+    @check_if_not_connected
+    def disconnect(self) -> None:
+        self.bus_right.disconnect()
+        self.bus_left.disconnect()
+        logger.info(f"{self} disconnected.")

src/lerobot/teleoperators/phone/teleop_phone.py

@@ -47,7 +47,7 @@ class BasePhone:
         return (self._calib_pos is not None) and (self._calib_rot_inv is not None)
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {
             "phone.pos": np.ndarray,  # shape (3,)
             "phone.rot": Rotation,  # scipy.spatial.transform.Rotation
@@ -56,15 +56,15 @@ class BasePhone:
         }
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         # No haptic or other feedback implemented yet
-        pass
+        return {}
 
     def configure(self) -> None:
         # No additional configuration required for phone teleop
         pass
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # We could add haptic feedback (vibrations) here, but it's not implemented yet
         raise NotImplementedError
 
@@ -163,7 +163,7 @@ class IOSPhone(BasePhone, Teleoperator):
         return True, pos, rot, pose
 
     @check_if_not_connected
-    def get_action(self) -> dict:
+    def _get_action(self) -> dict:
         has_pose, raw_position, raw_rotation, fb_pose = self._read_current_pose()
         if not has_pose or not self.is_calibrated:
             return {}
@@ -314,7 +314,7 @@ class AndroidPhone(BasePhone, Teleoperator):
             self._latest_message = message
 
     @check_if_not_connected
-    def get_action(self) -> dict:
+    def _get_action(self) -> dict:
         ok, raw_pos, raw_rot, pose = self._read_current_pose()
         if not ok or not self.is_calibrated:
             return {}
@@ -395,21 +395,21 @@ class Phone(Teleoperator):
         return self._phone_impl.is_calibrated
 
     @property
-    def action_features(self) -> dict[str, type]:
-        return self._phone_impl.action_features
+    def raw_action_features(self) -> dict[str, type]:
+        return self._phone_impl.raw_action_features
 
     @property
-    def feedback_features(self) -> dict[str, type]:
-        return self._phone_impl.feedback_features
+    def raw_feedback_features(self) -> dict[str, type]:
+        return self._phone_impl.raw_feedback_features
 
     def configure(self) -> None:
         return self._phone_impl.configure()
 
-    def get_action(self) -> dict:
-        return self._phone_impl.get_action()
+    def _get_action(self) -> dict:
+        return self._phone_impl._get_action()
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
-        return self._phone_impl.send_feedback(feedback)
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
+        return self._phone_impl._send_feedback(feedback)
 
     def disconnect(self) -> None:
         return self._phone_impl.disconnect()

src/lerobot/teleoperators/reachy2_teleoperator/reachy2_teleoperator.py

@@ -104,7 +104,7 @@ class Reachy2Teleoperator(Teleoperator):
         return joints
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         if self.config.with_mobile_base:
             return {
                 **dict.fromkeys(
@@ -120,7 +120,7 @@ class Reachy2Teleoperator(Teleoperator):
             return dict.fromkeys(self.joints_dict.keys(), float)
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -146,7 +146,7 @@ class Reachy2Teleoperator(Teleoperator):
         pass
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         start = time.perf_counter()
 
         joint_action: dict[str, float] = {}
@@ -168,7 +168,7 @@ class Reachy2Teleoperator(Teleoperator):
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return {**joint_action, **vel_action}
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError
 
     def disconnect(self) -> None:

src/lerobot/teleoperators/so_leader/pipelines/__init__.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .ee_space import make_so10x_leader_fk_pipeline
+
+__all__ = ["make_so10x_leader_fk_pipeline"]

src/lerobot/teleoperators/so_leader/pipelines/ee_space.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Forward-kinematics pipeline for SO-100/101 leader (teleoperator) arm.
+
+Converts raw leader joint positions into end-effector pose. Attach this to a leader
+via ``set_output_pipeline`` so that ``get_action()`` returns EE coordinates instead of
+raw joint angles.
+
+Example::
+
+    from lerobot.teleoperators.so_leader.pipelines import make_so10x_leader_fk_pipeline
+
+    motor_names = list(leader.bus.motors.keys())
+    leader.set_output_pipeline(make_so10x_leader_fk_pipeline(URDF_PATH, motor_names))
+    action = leader.get_action()  # now contains ee.x, ee.y, ee.z, ...
+"""
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import RobotAction, RobotProcessorPipeline
+from lerobot.processor.converters import (
+    robot_action_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots.so_follower.robot_kinematic_processor import ForwardKinematicsJointsToEE
+
+_DEFAULT_GRIPPER_FRAME = "gripper_frame_link"
+
+
+def make_so10x_leader_fk_pipeline(
+    urdf_path: str,
+    motor_names: list[str],
+    *,
+    target_frame_name: str = _DEFAULT_GRIPPER_FRAME,
+) -> RobotProcessorPipeline[RobotAction, RobotAction]:
+    """
+    Create a forward-kinematics action pipeline for SO-100/101 leader teleoperators.
+
+    Converts raw leader joint positions (action) into end-effector pose (position +
+    orientation + gripper). Attach this to a leader via ``set_output_pipeline`` so that
+    ``get_action()`` returns EE coordinates instead of raw joint angles.
+
+    Args:
+        urdf_path: Path to the SO-100/101 URDF file used for kinematics.
+        motor_names: Ordered list of motor names matching the URDF joint names.
+        target_frame_name: Name of the end-effector frame in the URDF.
+
+    Returns:
+        A RobotProcessorPipeline that maps joint actions to EE actions.
+
+    Example::
+
+        motor_names = list(leader.bus.motors.keys())
+        leader.set_output_pipeline(
+            make_so10x_leader_fk_pipeline("./so101.urdf", motor_names)
+        )
+        action = leader.get_action()  # returns ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_vel
+    """
+    kinematics = RobotKinematics(
+        urdf_path=urdf_path,
+        target_frame_name=target_frame_name,
+        joint_names=motor_names,
+    )
+    return RobotProcessorPipeline[RobotAction, RobotAction](
+        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics, motor_names=motor_names)],
+        to_transition=robot_action_to_transition,
+        to_output=transition_to_robot_action,
+    )

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -55,11 +55,11 @@ class SOLeader(Teleoperator):
         )
 
     @property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {f"{motor}.pos": float for motor in self.bus.motors}
 
     @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -138,7 +138,7 @@ class SOLeader(Teleoperator):
             print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         start = time.perf_counter()
         action = self.bus.sync_read("Present_Position")
         action = {f"{motor}.pos": val for motor, val in action.items()}
@@ -146,7 +146,7 @@ class SOLeader(Teleoperator):
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
 

src/lerobot/teleoperators/teleoperator.py

@@ -12,17 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import abc
 import builtins
 from pathlib import Path
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import draccus
 
+from lerobot.configs.types import PipelineFeatureType
 from lerobot.motors.motors_bus import MotorCalibration
-from lerobot.processor import RobotAction
 from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS
 
+if TYPE_CHECKING:
+    from lerobot.processor.core import RobotAction
+    from lerobot.processor.pipeline import RobotProcessorPipeline
+
 from .config import TeleoperatorConfig
 
 
@@ -33,6 +39,10 @@ class Teleoperator(abc.ABC):
     This class provides a standardized interface for interacting with physical teleoperators.
     Subclasses must implement all abstract methods and properties to be usable.
 
+    Pipelines are first-class citizens: every teleoperator carries an optional output pipeline
+    (applied in get_action()) and an optional input pipeline (applied in send_feedback()).
+    Both default to identity (no-op), so existing teleoperators work without any changes.
+
     Attributes:
         config_class (RobotConfig): The expected configuration class for this teleoperator.
         name (str): The unique name used to identify this teleoperator type.
@@ -55,6 +65,14 @@ class Teleoperator(abc.ABC):
         if self.calibration_fpath.is_file():
             self._load_calibration()
 
+        # Pipeline interface — default to identity (no-op), swap via set_output/input_pipeline()
+        # Lazy import: factory is in lerobot.processor which loads after teleoperators at module init time,
+        # but __init__ runs at instance-creation time when lerobot.processor is fully loaded.
+        from lerobot.processor.factory import _make_identity_feedback_pipeline, _make_identity_teleop_action_pipeline
+
+        self._output_pipeline: RobotProcessorPipeline = _make_identity_teleop_action_pipeline()
+        self._input_pipeline: RobotProcessorPipeline = _make_identity_feedback_pipeline()
+
     def __str__(self) -> str:
         return f"{self.id} {self.__class__.__name__}"
 
@@ -84,38 +102,114 @@ class Teleoperator(abc.ABC):
         except Exception:  # nosec B110
             pass
 
+    # ── Pipeline interface ────────────────────────────────────────────────────
+
+    def output_pipeline(self) -> RobotProcessorPipeline:
+        """
+        Pipeline applied inside get_action() to transform raw hardware actions.
+        Default: identity (no-op). Override via set_output_pipeline() or subclassing.
+
+        Example: set a forward-kinematics pipeline to convert leader joint positions to EE pose.
+        """
+        return self._output_pipeline
+
+    def input_pipeline(self) -> RobotProcessorPipeline:
+        """
+        Pipeline applied inside send_feedback() to transform incoming feedback.
+        Default: identity (no-op). Override via set_input_pipeline() or subclassing.
+        """
+        return self._input_pipeline
+
+    def set_output_pipeline(self, pipeline: RobotProcessorPipeline) -> None:
+        """Set the action output pipeline (applied in get_action())."""
+        self._output_pipeline = pipeline
+
+    def set_input_pipeline(self, pipeline: RobotProcessorPipeline) -> None:
+        """Set the feedback input pipeline (applied in send_feedback())."""
+        self._input_pipeline = pipeline
+
+    # ── Feature properties ────────────────────────────────────────────────────
+
     @property
-    @abc.abstractmethod
     def action_features(self) -> dict:
         """
-        A dictionary describing the structure and types of the actions produced by the teleoperator. Its
-        structure (keys) should match the structure of what is returned by :pymeth:`get_action`. Values for
-        the dict should be the type of the value if it's a simple value, e.g. `float` for single
-        proprioceptive value (a joint's goal position/velocity)
+        Pipeline-transformed action features.
 
-        Note: this property should be able to be called regardless of whether the robot is connected or not.
+        Applies output_pipeline().transform_features() to raw_action_features so the
+        returned dict matches what get_action() actually produces for callers.
+
+        Use raw_action_features to inspect hardware-level feature shapes.
+
+        Note: this property should be able to be called regardless of whether the
+        teleoperator is connected or not.
+        """
+        from lerobot.datasets.pipeline_features import create_initial_features  # lazy import
+
+        initial = create_initial_features(action=self.raw_action_features)
+        transformed = self.output_pipeline().transform_features(initial)
+        return transformed.get(PipelineFeatureType.ACTION, {})
+
+    @property
+    @abc.abstractmethod
+    def raw_action_features(self) -> dict:
+        """
+        Hardware-level action features (before any pipeline transformation).
+
+        A dictionary describing the structure and types of the actions produced
+        directly by the teleoperator hardware. Its structure (keys) should match
+        the structure of what is returned by :pymeth:`_get_action`. Values should be
+        the type of the value if it's a simple value, e.g. ``float`` for single
+        proprioceptive value (a joint's goal position/velocity).
+
+        Note: this property should be able to be called regardless of whether the
+        teleoperator is connected or not.
         """
         pass
 
     @property
     @abc.abstractmethod
-    def feedback_features(self) -> dict:
+    def raw_feedback_features(self) -> dict:
         """
-        A dictionary describing the structure and types of the feedback actions expected by the robot. Its
-        structure (keys) should match the structure of what is passed to :pymeth:`send_feedback`. Values for
-        the dict should be the type of the value if it's a simple value, e.g. `float` for single
-        proprioceptive value (a joint's goal position/velocity)
+        Hardware-level feedback features (before any pipeline transformation).
 
-        Note: this property should be able to be called regardless of whether the robot is connected or not.
+        A dictionary describing the structure and types of the feedback accepted directly
+        by the teleoperator hardware (i.e. what :pymeth:`_send_feedback` receives). Its
+        structure (keys) should match the structure of what is expected by
+        :pymeth:`_send_feedback`. Values should be the type of the value if it's a simple
+        value, e.g. ``float`` for single proprioceptive value.
+
+        Return an empty dict if this teleoperator does not support feedback.
+
+        Note: this property should be able to be called regardless of whether the
+        teleoperator is connected or not.
         """
         pass
 
+    @property
+    def feedback_features(self) -> dict:
+        """
+        Pipeline-transformed feedback features.
+
+        Applies input_pipeline().transform_features() to raw_feedback_features so the
+        returned dict reflects what the input pipeline outputs to the teleoperator hardware.
+
+        Use raw_feedback_features to inspect hardware-level feedback feature shapes.
+
+        Note: this property should be able to be called regardless of whether the
+        teleoperator is connected or not.
+        """
+        from lerobot.datasets.pipeline_features import create_initial_features  # lazy import
+
+        initial = create_initial_features(observation=self.raw_feedback_features)
+        transformed = self.input_pipeline().transform_features(initial)
+        return transformed.get(PipelineFeatureType.OBSERVATION, {})
+
     @property
     @abc.abstractmethod
     def is_connected(self) -> bool:
         """
-        Whether the teleoperator is currently connected or not. If `False`, calling :pymeth:`get_action`
-        or :pymeth:`send_feedback` should raise an error.
+        Whether the teleoperator is currently connected or not. If ``False``, calling
+        :pymeth:`get_action` or :pymeth:`send_feedback` should raise an error.
         """
         pass
 
@@ -133,7 +227,7 @@ class Teleoperator(abc.ABC):
     @property
     @abc.abstractmethod
     def is_calibrated(self) -> bool:
-        """Whether the teleoperator is currently calibrated or not. Should be always `True` if not applicable"""
+        """Whether the teleoperator is currently calibrated or not. Should be always ``True`` if not applicable"""
         pass
 
     @abc.abstractmethod
@@ -151,7 +245,7 @@ class Teleoperator(abc.ABC):
         Helper to load calibration data from the specified file.
 
         Args:
-            fpath (Path | None): Optional path to the calibration file. Defaults to `self.calibration_fpath`.
+            fpath (Path | None): Optional path to the calibration file. Defaults to ``self.calibration_fpath``.
         """
         fpath = self.calibration_fpath if fpath is None else fpath
         with open(fpath) as f, draccus.config_type("json"):
@@ -162,7 +256,7 @@ class Teleoperator(abc.ABC):
         Helper to save calibration data to the specified file.
 
         Args:
-            fpath (Path | None): Optional path to save the calibration file. Defaults to `self.calibration_fpath`.
+            fpath (Path | None): Optional path to save the calibration file. Defaults to ``self.calibration_fpath``.
         """
         fpath = self.calibration_fpath if fpath is None else fpath
         with open(fpath, "w") as f, draccus.config_type("json"):
@@ -176,29 +270,51 @@ class Teleoperator(abc.ABC):
         """
         pass
 
-    @abc.abstractmethod
+    # ── Template methods (concrete, call pipeline internally) ─────────────────
+
     def get_action(self) -> RobotAction:
         """
-        Retrieve the current action from the teleoperator.
+        Retrieve the current action from the teleoperator and apply the output pipeline.
+
+        Calls :pymeth:`_get_action` to get raw hardware data, then applies
+        :pymeth:`output_pipeline`.
 
         Returns:
-            RobotAction: A flat dictionary representing the teleoperator's current actions. Its
-                structure should match :pymeth:`observation_features`.
+            RobotAction: Pipeline-transformed action. With the default identity pipeline
+                this equals the raw action from :pymeth:`_get_action`.
+        """
+        raw = self._get_action()
+        return self.output_pipeline()(raw)
+
+    @abc.abstractmethod
+    def _get_action(self) -> RobotAction:
+        """
+        Retrieve the raw action directly from teleoperator hardware.
+
+        Returns:
+            RobotAction: A flat dictionary representing the teleoperator's current actions.
+                Its structure should match :pymeth:`raw_action_features`.
         """
         pass
 
-    @abc.abstractmethod
     def send_feedback(self, feedback: dict[str, Any]) -> None:
         """
-        Send a feedback action command to the teleoperator.
+        Apply the input pipeline and send the resulting feedback to teleoperator hardware.
 
         Args:
-            feedback (dict[str, Any]): Dictionary representing the desired feedback. Its structure should match
-                :pymeth:`feedback_features`.
+            feedback (dict[str, Any]): Dictionary representing the desired feedback.
+                Its structure should match :pymeth:`feedback_features`.
+        """
+        transformed = self.input_pipeline()(feedback)
+        self._send_feedback(transformed)
 
-        Returns:
-            dict[str, Any]: The action actually sent to the motors potentially clipped or modified, e.g. by
-                safety limits on velocity.
+    @abc.abstractmethod
+    def _send_feedback(self, feedback: dict[str, Any]) -> None:
+        """
+        Send feedback directly to teleoperator hardware.
+
+        Args:
+            feedback (dict[str, Any]): Dictionary of hardware-level feedback commands.
         """
         pass
 

src/lerobot/teleoperators/unitree_g1/unitree_g1.py

@@ -72,11 +72,11 @@ class UnitreeG1Teleoperator(Teleoperator):
         self.ik_helper: ExoskeletonIKHelper | None = None
 
     @cached_property
-    def action_features(self) -> dict[str, type]:
+    def raw_action_features(self) -> dict[str, type]:
         return {f"{name}.q": float for name in self._g1_joint_names}
 
     @cached_property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
         return {}
 
     @property
@@ -114,12 +114,12 @@ class UnitreeG1Teleoperator(Teleoperator):
     def configure(self) -> None:
         pass
 
-    def get_action(self) -> dict[str, float]:
+    def _get_action(self) -> dict[str, float]:
         left_angles = self.left_arm.get_angles()
         right_angles = self.right_arm.get_angles()
         return self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles)
 
-    def send_feedback(self, feedback: dict[str, float]) -> None:
+    def _send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError("Exoskeleton arms do not support feedback")
 
     def disconnect(self) -> None:

src/lerobot/teleoperators/utils.py

@@ -95,6 +95,10 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
         from .bi_openarm_leader import BiOpenArmLeader
 
         return BiOpenArmLeader(config)
+    elif config.type == "openarm_mini":
+        from .openarm_mini import OpenArmMini
+
+        return OpenArmMini(config)
     else:
         try:
             return cast("Teleoperator", make_device_from_device_class(config))

src/lerobot/utils/control_utils.py

@@ -189,7 +189,7 @@ def sanity_check_dataset_name(repo_id, policy_cfg):
     # Check if dataset_name starts with "eval_" but policy is missing
     if dataset_name.startswith("eval_") and policy_cfg is None:
         raise ValueError(
-            f"Your dataset name begins with 'eval_' ({dataset_name}), but no policy is provided ({policy_cfg.type})."
+            f"Your dataset name begins with 'eval_' ({dataset_name}), but no policy is provided."
         )
 
     # Check if dataset_name does not start with "eval_" but policy is provided

src/lerobot/utils/logging_utils.py

@@ -104,9 +104,10 @@ class MetricsTracker:
         self.metrics = metrics
 
         self.steps = initial_step
+        world_size = accelerator.num_processes if accelerator else 1
         # A sample is an (observation,action) pair, where observation and action
         # can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
-        self.samples = self.steps * self._batch_size
+        self.samples = self.steps * self._batch_size * world_size
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
         self.accelerator = accelerator
@@ -132,7 +133,8 @@ 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)
+        world_size = self.accelerator.num_processes if self.accelerator else 1
+        self.samples += self._batch_size * world_size
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
 

src/lerobot/utils/pipeline_utils.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Utilities for building dataset features from robot/teleoperator pipelines and for
+checking action/observation space compatibility between teleops and robots.
+"""
+
+import logging
+import re
+from collections.abc import Sequence
+
+from lerobot.datasets.utils import combine_feature_dicts, hw_to_dataset_features
+from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
+
+# Prefixes stripped from feature keys to produce clean dataset names.
+# Handles both fully-qualified (e.g. "observation.state.ee.x") and short (e.g. "state.ee.x") forms.
+_PREFIXES_TO_STRIP = tuple(
+    f"{token}."
+    for const in (ACTION, OBS_STATE, OBS_IMAGES)
+    for token in (const, const.split(".")[-1])
+)
+
+_IMAGES_TOKEN = OBS_IMAGES.split(".")[-1]
+
+
+def _should_keep(key: str, patterns: Sequence[str] | None) -> bool:
+    if patterns is None:
+        return True
+    return any(re.search(pat, key) for pat in patterns)
+
+
+def _strip_prefix(key: str) -> str:
+    for prefix in _PREFIXES_TO_STRIP:
+        if key.startswith(prefix):
+            return key[len(prefix) :]
+    return key
+
+
+def _features_to_dataset_spec(
+    features: dict,
+    *,
+    is_action: bool,
+    use_videos: bool,
+    patterns: Sequence[str] | None = None,
+) -> dict:
+    """
+    Convert a flat feature dict (as returned by ``robot.observation_features`` or
+    ``teleop.action_features``) into a LeRobot dataset feature specification.
+
+    Args:
+        features: Flat dict mapping feature key → type or shape.
+        is_action: True when ``features`` describes actions; False for observations.
+        use_videos: When False, image observation features are excluded entirely.
+        patterns: Optional regex patterns to filter state/action features.
+                  Image features are not affected by this filter.
+
+    Returns:
+        A dict suitable for passing to ``LeRobotDataset.create(..., features=...)``.
+    """
+    categorized: dict = {}
+    for key, value in features.items():
+        is_image = not is_action and (
+            (isinstance(value, tuple) and len(value) == 3)
+            or key.startswith(f"{OBS_IMAGES}.")
+            or key.startswith(f"{_IMAGES_TOKEN}.")
+            or f".{_IMAGES_TOKEN}." in key
+        )
+
+        if is_image and not use_videos:
+            continue
+        if not is_image and not _should_keep(key, patterns):
+            continue
+
+        categorized[_strip_prefix(key)] = value
+
+    if not categorized:
+        return {}
+
+    prefix = ACTION if is_action else OBS_STR
+    return hw_to_dataset_features(categorized, prefix, use_videos)
+
+
+def build_dataset_features(
+    robot,
+    teleop=None,
+    *,
+    use_videos: bool = True,
+    action_features: dict | None = None,
+) -> dict:
+    """
+    Derive dataset feature specifications from robot and teleoperator pipelines.
+
+    Reads ``robot.observation_features`` (which already reflects the robot's output
+    pipeline transformation) and, when provided, ``teleop.action_features`` or an
+    explicit ``action_features`` dict to determine what the dataset will store.
+
+    This replaces the old pattern of manually calling ``aggregate_pipeline_dataset_features``
+    with explicit processor objects.
+
+    Args:
+        robot: The robot instance (must have ``observation_features``).
+        teleop: The teleoperator instance. When ``None`` and ``action_features`` is also
+            ``None`` (policy-only recording), only observation features are returned.
+        use_videos: If True, image observations are included as video features.
+        action_features: Explicit action feature dict, used when no teleop is available
+            (e.g. evaluate/inference mode) but the dataset must match a specific action
+            space (e.g. EE coordinates from a previously recorded dataset).
+
+    Returns:
+        A combined feature dict suitable for passing to ``LeRobotDataset.create(..., features=...)``.
+
+    Example::
+
+        # Teleop recording
+        features = build_dataset_features(follower, leader, use_videos=True)
+
+        # Policy-only recording (no teleop)
+        features = build_dataset_features(robot, use_videos=True)
+
+        # Evaluate with explicit EE action space
+        features = build_dataset_features(
+            robot,
+            use_videos=True,
+            action_features={
+                f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+                for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
+            },
+        )
+    """
+    obs_ds = _features_to_dataset_spec(robot.observation_features, is_action=False, use_videos=use_videos)
+
+    if action_features is not None:
+        act_ds = _features_to_dataset_spec(action_features, is_action=True, use_videos=False)
+    elif teleop is not None:
+        act_ds = _features_to_dataset_spec(teleop.action_features, is_action=True, use_videos=False)
+    else:
+        return obs_ds
+
+    return combine_feature_dicts(act_ds, obs_ds)
+
+
+def check_action_space_compatibility(teleop, robot) -> None:
+    """
+    Warn if the teleoperator's pipeline-transformed action features don't match the robot's
+    declared ``action_features``.
+
+    This is a soft check — a mismatch produces a warning but does not raise. It is intended
+    to catch obvious misconfigurations (e.g., sending EE actions to a robot expecting joints)
+    before the control loop starts.
+
+    Args:
+        teleop: The teleoperator whose ``action_features`` describe what it sends.
+        robot: The robot whose ``action_features`` describe what it expects.
+    """
+    teleop_out = set(teleop.action_features.keys())
+    robot_in = set(robot.action_features.keys())
+    if teleop_out != robot_in:
+        import warnings
+
+        warnings.warn(
+            f"Action space mismatch between teleop and robot.\n"
+            f"  Teleop sends: {sorted(teleop_out)}\n"
+            f"  Robot expects: {sorted(robot_in)}\n"
+            "Ensure pipelines map between these spaces correctly.",
+            UserWarning,
+            stacklevel=2,
+        )
+    else:
+        logging.debug("Action space compatibility check passed.")
+
+
+def check_observation_space_compatibility(robot, teleop) -> None:
+    """
+    Warn if the robot's observation features don't cover what the teleoperator's
+    ``feedback_features`` expects.
+
+    A non-empty ``feedback_features`` that is not a subset of the robot's observation keys
+    will produce a warning.
+
+    Args:
+        robot: The robot whose ``observation_features`` describe what it produces.
+        teleop: The teleoperator whose ``feedback_features`` describe what it expects as feedback.
+    """
+    robot_obs = set(robot.observation_features.keys())
+    teleop_feedback = set(teleop.feedback_features.keys())
+    if teleop_feedback and not teleop_feedback.issubset(robot_obs):
+        import warnings
+
+        warnings.warn(
+            f"Observation/feedback space mismatch.\n"
+            f"  Robot obs: {sorted(robot_obs)}\n"
+            f"  Teleop feedback expects: {sorted(teleop_feedback)}\n"
+            "Ensure the robot observation pipeline covers all feedback keys.",
+            UserWarning,
+            stacklevel=2,
+        )
+    else:
+        logging.debug("Observation/feedback space compatibility check passed.")

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