also have pipeline for feedback_features and action_features

also refactor and remove use of aggregate_pipeline_dataset_features() as we already aggregate expected features on robot and teleop classes
make lerobot record easier
2026-07-06 09:37:06 +00:00 · 2026-03-02 22:07:13 +01:00 · 2026-03-02 21:05:58 +01:00 · 2026-03-02 20:17:50 +01:00 · 2026-03-02 13:09:35 +01:00
100 changed files with 2421 additions and 1990 deletions
@@ -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
@@ -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: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -180,10 +173,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$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
@@ -119,7 +119,6 @@ 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: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --shm-size "16gb"
@@ -131,10 +130,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
      - name: Run pytest on CPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -151,7 +146,6 @@ 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: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -163,10 +157,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
      - name: Run pytest on GPU
        run: pytest tests -vv --maxfail=10
      - name: Run end-to-end tests
@@ -184,7 +174,6 @@ jobs:
      TORCH_HOME: /home/user_lerobot/.cache/torch
      TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
      CUDA_VISIBLE_DEVICES: "0,1,2,3"
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
    container:
      image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -196,10 +185,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
      - name: Verify GPU availability
        run: |
          nvidia-smi
@@ -208,3 +193,4 @@ jobs:
      - name: Run multi-GPU training tests
      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
+        timeout-minutes: 10
@@ -48,7 +48,6 @@ 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:
@@ -80,10 +79,7 @@ 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

@@ -141,7 +137,6 @@ 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: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
      options: --gpus all --shm-size "16gb"
@@ -153,10 +148,6 @@ jobs:
        shell: bash
        working-directory: /lerobot
    steps:
-      - name: Login to Hugging Face
-        run: |
-          hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
-          hf auth whoami
      - name: Run pytest on GPU
        run: pytest tests -vv
      - name: Run end-to-end tests
@@ -48,7 +48,7 @@ python -m lerobot.async_inference.robot_client \
    --task="dummy" \ # POLICY: The task to run the policy on (`Fold my t-shirt`). Not necessarily defined for all policies, such as `act`
    --policy_type=your_policy_type \ # POLICY: the type of policy to run (smolvla, act, etc)
    --pretrained_name_or_path=user/model \ # POLICY: the model name/path on server to the checkpoint to run (e.g., lerobot/smolvla_base)
-    --policy_device=mps \ # POLICY: the device to run the policy on, on the server (cuda, mps, xpu, cpu)
+    --policy_device=mps \ # POLICY: the device to run the policy on, on the server
    --actions_per_chunk=50 \ # POLICY: the number of actions to output at once
    --chunk_size_threshold=0.5 \ # CLIENT: the threshold for the chunk size before sending a new observation to the server
    --aggregate_fn_name=weighted_average \ # CLIENT: the function to aggregate actions on overlapping portions
@@ -170,13 +170,13 @@ Once you can drive the robot well, you can start recording data to train AI mode
 We use Hugging Face to store your data online. First, log in with your token from [Hugging Face settings](https://huggingface.co/settings/tokens):

 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```

 Store your Hugging Face username:

 ```bash
-HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```

@@ -155,10 +155,10 @@ Upload your repository to Hugging Face:
 pip install huggingface_hub

 # Login to Hugging Face
-hf auth login
+huggingface-cli login

 # Create a new repository
-hf repo create my-org/my-custom-env
+huggingface-cli repo create my-custom-env --type space --org my-org

 # Initialize git and push
 git init
@@ -159,7 +159,7 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:

 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```

 Then store your Hugging Face repository name in a variable:
@@ -327,7 +327,7 @@ You can look for other LeRobot datasets on the hub by searching for `LeRobot` [t
 You can also push your local dataset to the Hub manually, running:

 ```bash
-hf upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
+huggingface-cli upload ${HF_USER}/record-test ~/.cache/huggingface/lerobot/{repo-id} --repo-type dataset
 ```

 #### Record function
@@ -491,7 +491,7 @@ If your local computer doesn't have a powerful GPU you could utilize Google Cola
 Once training is done, upload the latest checkpoint with:

 ```bash
-hf upload ${HF_USER}/act_so101_test \
+huggingface-cli upload ${HF_USER}/act_so101_test \
  outputs/train/act_so101_test/checkpoints/last/pretrained_model
 ```

@@ -499,7 +499,7 @@ You can also upload intermediate checkpoints with:

 ```bash
 CKPT=010000
-hf upload ${HF_USER}/act_so101_test${CKPT} \
+huggingface-cli upload ${HF_USER}/act_so101_test${CKPT} \
  outputs/train/act_so101_test/checkpoints/${CKPT}/pretrained_model
 ```

@@ -279,13 +279,13 @@ We use the Hugging Face hub features for uploading your dataset. If you haven't
 Add your token to the CLI by running this command:

 ```bash
-hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
 ```

 Then store your Hugging Face repository name in a variable:

 ```bash
-HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+HF_USER=$(huggingface-cli whoami | head -n 1)
 echo $HF_USER
 ```

@@ -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

@@ -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"]:
@@ -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"]:
@@ -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"]:
@@ -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"]:
@@ -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
@@ -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"]:
@@ -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"]:
@@ -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__":
@@ -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
@@ -96,12 +96,9 @@ dependencies = [
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.10.0"]
-transformers-dep = ["transformers>=5.3.0,<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"]
-peft-dep = ["peft>=0.18.0,<1.0.0"]
-scipy-dep = ["scipy>=1.14.0,<2.0.0"]
-qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]

 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
@@ -132,17 +129,17 @@ phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]

 # Policies
 wallx = [
-    "lerobot[transformers-dep]",
-    "lerobot[peft]",
-    "lerobot[scipy-dep]",
-    "torchdiffeq>=0.2.4,<0.3.0",
-    "lerobot[qwen-vl-utils-dep]",
+    "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]", "lerobot[scipy-dep]"]
+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]",
-    "lerobot[peft]",
+    "peft>=0.13.0,<1.0.0",
    "dm-tree>=0.1.8,<1.0.0",
    "timm>=1.0.0,<1.1.0",
    "safetensors>=0.4.3,<1.0.0",
@@ -151,13 +148,13 @@ 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", "lerobot[qwen-vl-utils-dep]"]
+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]"]

 # Features
 async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
-peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
+peft = ["lerobot[transformers-dep]", "peft>=0.18.0,<1.0.0"]

 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
@@ -179,8 +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]",
@@ -400,3 +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" },
+    ],
+]
@@ -289,9 +289,7 @@ def aggregate_datasets(

    logging.info("Find all tasks")
    unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
-    dst_meta.tasks = pd.DataFrame(
-        {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
-    )
+    dst_meta.tasks = pd.DataFrame({"task_index": range(len(unique_tasks))}, index=unique_tasks)

    meta_idx = {"chunk": 0, "file": 0}
    data_idx = {"chunk": 0, "file": 0}
@@ -89,8 +89,8 @@ def delete_episodes(
    Args:
        dataset: The source LeRobotDataset.
        episode_indices: List of episode indices to delete.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.
    """
    if not episode_indices:
        raise ValueError("No episodes to delete")
@@ -152,7 +152,7 @@ def split_dataset(
        dataset: The source LeRobotDataset to split.
        splits: Either a dict mapping split names to episode indices, or a dict mapping
                split names to fractions (must sum to <= 1.0).
-        output_dir: Root directory where the split datasets will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
+        output_dir: Base directory for output datasets. If None, uses default location.

    Examples:
      Split by specific episodes
@@ -243,8 +243,8 @@ def merge_datasets(

    Args:
        datasets: List of LeRobotDatasets to merge.
-        output_repo_id: Merged dataset identifier.
-        output_dir: Root directory where the merged dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/output_repo_id.
+        output_repo_id: Repository ID for the merged dataset.
+        output_dir: Directory to save the merged dataset. If None, uses default location.
    """
    if not datasets:
        raise ValueError("No datasets to merge")
@@ -288,8 +288,8 @@ def modify_features(
        dataset: The source LeRobotDataset.
        add_features: Optional dict mapping feature names to (feature_values, feature_info) tuples.
        remove_features: Optional feature name(s) to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.

    Returns:
        New dataset with features modified.
@@ -390,8 +390,8 @@ def add_features(
    Args:
        dataset: The source LeRobotDataset.
        features: Dictionary mapping feature names to (feature_values, feature_info) tuples.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.

    Returns:
        New dataset with all features added.
@@ -427,8 +427,8 @@ def remove_feature(
    Args:
        dataset: The source LeRobotDataset.
        feature_names: Name(s) of features to remove. Can be a single string or list.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_modified" to original.

    Returns:
        New dataset with features removed.
@@ -1475,9 +1475,7 @@ def modify_tasks(

    # Collect all unique tasks and create new task mapping
    unique_tasks = sorted(set(episode_to_task.values()))
-    new_task_df = pd.DataFrame(
-        {"task_index": list(range(len(unique_tasks)))}, index=pd.Index(unique_tasks, name="task")
-    )
+    new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks)
    task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}

    logging.info(f"Modifying tasks in {dataset.repo_id}")
@@ -1531,7 +1529,7 @@ def modify_tasks(

 def convert_image_to_video_dataset(
    dataset: LeRobotDataset,
-    output_dir: Path | None = None,
+    output_dir: Path,
    repo_id: str | None = None,
    vcodec: str = "libsvtav1",
    pix_fmt: str = "yuv420p",
@@ -1550,8 +1548,8 @@ def convert_image_to_video_dataset(

    Args:
        dataset: The source LeRobot dataset with images
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+        output_dir: Directory to save the new video dataset
+        repo_id: Repository ID for the new dataset (default: original_id + "_video")
        vcodec: Video codec (default: libsvtav1)
        pix_fmt: Pixel format (default: yuv420p)
        g: Group of pictures size (default: 2)
@@ -1602,7 +1600,6 @@ def convert_image_to_video_dataset(
            # Video info will be updated after episodes are encoded

    # Create new metadata for video dataset
-    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
    new_meta = LeRobotDatasetMetadata.create(
        repo_id=repo_id,
        fps=dataset.meta.fps,
@@ -314,7 +314,7 @@ class LeRobotDatasetMetadata:
        if self.tasks is None:
            new_tasks = tasks
            task_indices = range(len(tasks))
-            self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
+            self.tasks = pd.DataFrame({"task_index": task_indices}, index=tasks)
        else:
            new_tasks = [task for task in tasks if task not in self.tasks.index]
            new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
@@ -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
@@ -341,7 +341,6 @@ def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:

 def load_tasks(local_dir: Path) -> pandas.DataFrame:
    tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
-    tasks.index.name = "task"
    return tasks


@@ -36,11 +36,8 @@ Convert a local dataset (works in place):
 ```bash
 python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
    --repo-id=lerobot/pusht \
-    --root=/path/to/local/dataset/directory \
+    --root=/path/to/local/dataset/directory
    --push-to-hub=false
-
-N.B. Path semantics (v2): --root is the exact dataset folder containing
-meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
 ```

 """
@@ -108,7 +105,7 @@ episodes.jsonl
 {"episode_index": 1, "tasks": ["Put the blue block in the green bowl"], "length": 266}

 NEW
-meta/episodes/chunk-000/file_000.parquet
+meta/episodes/chunk-000/episodes_000.parquet
 episode_index | video_chunk_index | video_file_index | data_chunk_index | data_file_index | tasks | length
 -------------------------
 OLD
@@ -116,16 +113,15 @@ tasks.jsonl
 {"task_index": 1, "task": "Put the blue block in the green bowl"}

 NEW
-meta/tasks.parquet
+meta/tasks/chunk-000/file_000.parquet
 task_index | task
 -------------------------
 OLD
 episodes_stats.jsonl
-{"episode_index": 1, "stats": {"feature_name": {"min": ..., "max": ..., "mean": ..., "std": ..., "count": ...}}}

 NEW
-meta/episodes/chunk-000/file_000.parquet
-episode_index | feature_name/min | feature_name/max | feature_name/mean | feature_name/std | feature_name/count
+meta/episodes_stats/chunk-000/file_000.parquet
+episode_index | mean | std | min | max
 -------------------------
 UPDATE
 meta/info.json
@@ -174,7 +170,7 @@ def convert_tasks(root, new_root):
    tasks, _ = legacy_load_tasks(root)
    task_indices = tasks.keys()
    task_strings = tasks.values()
-    df_tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(task_strings, name="task"))
+    df_tasks = pd.DataFrame({"task_index": task_indices}, index=task_strings)
    write_tasks(df_tasks, new_root)


@@ -205,6 +201,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):

    image_keys = get_image_keys(root)

+    ep_idx = 0
    chunk_idx = 0
    file_idx = 0
    size_in_mb = 0
@@ -214,23 +211,9 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):

    logging.info(f"Converting data files from {len(ep_paths)} episodes")

-    for ep_idx, ep_path in enumerate(tqdm.tqdm(ep_paths, desc="convert data files")):
+    for ep_path in tqdm.tqdm(ep_paths, desc="convert data files"):
        ep_size_in_mb = get_parquet_file_size_in_mb(ep_path)
        ep_num_frames = get_parquet_num_frames(ep_path)
-
-        # Check if we need to start a new file BEFORE creating metadata
-        if size_in_mb + ep_size_in_mb >= data_file_size_in_mb and len(paths_to_cat) > 0:
-            # Write the accumulated data files
-            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
-
-            # Move to next file
-            chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)
-
-            # Reset for the next file
-            size_in_mb = 0
-            paths_to_cat = []
-
-        # Now create metadata with correct chunk/file indices
        ep_metadata = {
            "episode_index": ep_idx,
            "data/chunk_index": chunk_idx,
@@ -241,7 +224,20 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
        size_in_mb += ep_size_in_mb
        num_frames += ep_num_frames
        episodes_metadata.append(ep_metadata)
-        paths_to_cat.append(ep_path)
+        ep_idx += 1
+
+        if size_in_mb < data_file_size_in_mb:
+            paths_to_cat.append(ep_path)
+            continue
+
+        if paths_to_cat:
+            concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys)
+
+        # Reset for the next file
+        size_in_mb = ep_size_in_mb
+        paths_to_cat = [ep_path]
+
+        chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, DEFAULT_CHUNK_SIZE)

    # Write remaining data if any
    if paths_to_cat:
@@ -473,7 +469,7 @@ def convert_dataset(

    # Set root based on whether local dataset path is provided
    use_local_dataset = False
-    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root)
+    root = HF_LEROBOT_HOME / repo_id if root is None else Path(root) / repo_id
    if root.exists():
        validate_local_dataset_version(root)
        use_local_dataset = True
@@ -557,7 +553,7 @@ if __name__ == "__main__":
        "--root",
        type=str,
        default=None,
-        help="Local directory to use for downloading/writing the dataset. Defaults to $HF_LEROBOT_HOME/repo_id.",
+        help="Local directory to use for downloading/writing the dataset.",
    )
    parser.add_argument(
        "--push-to-hub",
@@ -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
@@ -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
@@ -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
@@ -99,11 +92,10 @@ def create_sinusoidal_pos_embedding(  # see openpi `create_sinusoidal_pos_embedd


 def sample_beta(alpha, beta, bsize, device):  # see openpi `sample_beta` (exact copy)
-    # Beta sampling uses _sample_dirichlet which isn't implemented for MPS, so sample on CPU
-    alpha_t = torch.tensor(alpha, dtype=torch.float32)
-    beta_t = torch.tensor(beta, dtype=torch.float32)
+    alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device)
+    beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device)
    dist = torch.distributions.Beta(alpha_t, beta_t)
-    return dist.sample((bsize,)).to(device)
+    return dist.sample((bsize,))


 def make_att_2d_masks(pad_masks, att_masks):  # see openpi `make_att_2d_masks` (exact copy)
@@ -197,7 +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}")

@@ -208,7 +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
@@ -227,14 +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)
@@ -260,10 +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,
@@ -271,7 +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 = []
@@ -283,15 +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
@@ -364,7 +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
@@ -372,7 +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,
@@ -383,13 +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)
@@ -404,11 +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",
@@ -420,8 +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()
@@ -431,23 +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,
@@ -461,7 +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,
@@ -485,7 +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
@@ -525,7 +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

@@ -590,19 +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")

@@ -744,7 +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)
@@ -815,7 +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,
@@ -887,7 +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,
@@ -977,12 +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",
@@ -990,7 +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),
                )
@@ -1003,7 +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
@@ -1015,6 +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

@@ -1048,7 +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

@@ -1102,14 +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
@@ -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()
@@ -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
@@ -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

@@ -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()
@@ -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,
-            inputs_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",
-]
@@ -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
@@ -55,7 +55,7 @@ class WallXConfig(PreTrainedConfig):
    pretrained_name_or_path: str = "x-square-robot/wall-oss-flow"

    # Tokenizer settings
-    action_tokenizer_path: str | None = "lerobot/fast-action-tokenizer"
+    action_tokenizer_path: str | None = "physical-intelligence/fast"

    # Action prediction mode: "diffusion" or "fast"
    prediction_mode: str = "diffusion"
@@ -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),
            )
@@ -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):
@@ -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)
@@ -1596,7 +1567,7 @@ QWEN2_5_VL_INPUTS_DOCSTRING = r"""


 class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin):
-    _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", "Qwen2_5_VLVisionBlock"]

@@ -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 = {}
@@ -276,8 +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(
@@ -1951,10 +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)
@@ -2079,10 +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):
@@ -2442,10 +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)
@@ -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",
@@ -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)
@@ -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
@@ -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
            )
@@ -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,
@@ -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_")
@@ -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:
@@ -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.
@@ -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
@@ -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
@@ -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
@@ -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:
@@ -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
@@ -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,
@@ -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()
@@ -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

@@ -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"]
@@ -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,
+    )
@@ -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
@@ -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
@@ -132,13 +132,10 @@ def visualize_dataset(

    logging.info("Logging to Rerun")

-    first_index = None
    for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
-        if first_index is None:
-            first_index = batch["index"][0].item()
        # iterate over the batch
        for i in range(len(batch["index"])):
-            rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
+            rr.set_time("frame_index", sequence=batch["frame_index"][i].item())
            rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())

            # display each camera image
@@ -21,9 +21,6 @@ This script allows you to delete episodes, split datasets, merge datasets,
 remove features, modify tasks, and convert image datasets to video format.
 When new_repo_id is specified, creates a new dataset.

-Path semantics (v2): --root and --new_root are exact dataset folders containing
-meta/, data/, videos/. When omitted, defaults to $HF_LEROBOT_HOME/{repo_id}.
-
 Usage Examples:

 Delete episodes 0, 2, and 5 from a dataset:
@@ -32,34 +29,19 @@ Delete episodes 0, 2, and 5 from a dataset:
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"

-Delete episodes from a local dataset at a specific path:
-    lerobot-edit-dataset \
-        --repo_id lerobot/pusht \
-        --root /path/to/pusht \
-        --operation.type delete_episodes \
-        --operation.episode_indices "[0, 2, 5]"
-
-Delete episodes and save to a new dataset at a specific path and with a new repo_id:
+Delete episodes and save to a new dataset:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --new_repo_id lerobot/pusht_filtered \
-        --new_root /path/to/pusht_filtered \
        --operation.type delete_episodes \
        --operation.episode_indices "[0, 2, 5]"

-Split dataset by fractions (pusht_train, pusht_val):
+Split dataset by fractions:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --operation.type split \
        --operation.splits '{"train": 0.8, "val": 0.2}'

-Split dataset by fractions and save split datasets to a specific folder (base_folder/train, base_folder/val):
-    lerobot-edit-dataset \
-        --repo_id lerobot/pusht \
-        --new_root /path/to/base_folder \
-        --operation.type split \
-        --operation.splits '{"train": 0.8, "val": 0.2}'
-
 Split dataset by episode indices:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
@@ -74,29 +56,15 @@ Split into more than two splits:

 Merge multiple datasets:
    lerobot-edit-dataset \
-        --new_repo_id lerobot/pusht_merged \
+        --repo_id lerobot/pusht_merged \
        --operation.type merge \
        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"

-Merge multiple datasets to a specific output path:
-    lerobot-edit-dataset \
-        --new_repo_id lerobot/pusht_merged \
-        --new_root /path/to/pusht_merged \
-        --operation.type merge \
-        --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
-
-Merge multiple datasets from a list of local dataset paths:
-    lerobot-edit-dataset \
-        --new_repo_id lerobot/pusht_merged \
-        --operation.type merge \
-        --operation.repo_ids "['pusht_train', 'pusht_val']" \
-        --operation.roots "['/path/to/pusht_train', '/path/to/pusht_val']"
-
 Remove camera feature:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --operation.type remove_feature \
-        --operation.feature_names "['observation.image']"
+        --operation.feature_names "['observation.images.top']"

 Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
    lerobot-edit-dataset \
@@ -120,8 +88,8 @@ Modify tasks - set default task with overrides for specific episodes (WARNING: m
 Convert image dataset to video format and save locally:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht_image \
-        --new_root /path/to/output/pusht_video \
-        --operation.type convert_image_to_video
+        --operation.type convert_image_to_video \
+        --operation.output_dir /path/to/output/pusht_video

 Convert image dataset to video format and save with new repo_id:
    lerobot-edit-dataset \
@@ -199,7 +167,6 @@ class SplitConfig(OperationConfig):
@dataclass
 class MergeConfig(OperationConfig):
    repo_ids: list[str] | None = None
-    roots: list[str] | None = None


@OperationConfig.register_subclass("remove_feature")
@@ -233,46 +200,36 @@ class ConvertImageToVideoConfig(OperationConfig):
@OperationConfig.register_subclass("info")
@dataclass
 class InfoConfig(OperationConfig):
+    type: str = "info"
    show_features: bool = False


@dataclass
 class EditDatasetConfig:
-    # Operation configuration.
+    repo_id: str
    operation: OperationConfig
-    # Input dataset identifier. Always required unless for Merge operation.
-    repo_id: str | None = None
-    # Root directory where the input dataset is stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id.
    root: str | None = None
-    # Edited dataset identifier. When both new_repo_id (resp. new_root) and repo_id (resp. root) are identical, modifications are applied in-place and a backup of the original dataset is created. Required for Merge operation.
    new_repo_id: str | None = None
-    # Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/new_repo_id. For Split operation, this is the base directory for the split datasets.
-    new_root: str | None = None
-    # Upload dataset to Hugging Face hub.
    push_to_hub: bool = False


-def get_output_path(
-    repo_id: str,
-    new_repo_id: str | None,
-    root: Path | str | None,
-    new_root: Path | str | None,
-) -> tuple[str, Path]:
-    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
+def get_output_path(repo_id: str, new_repo_id: str | None, root: Path | None) -> tuple[str, Path]:
+    if new_repo_id:
+        output_repo_id = new_repo_id
+        output_dir = root / new_repo_id if root else HF_LEROBOT_HOME / new_repo_id
+    else:
+        output_repo_id = repo_id
+        dataset_path = root / repo_id if root else HF_LEROBOT_HOME / repo_id
+        old_path = Path(str(dataset_path) + "_old")

-    output_repo_id = new_repo_id if new_repo_id else repo_id
-    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
+        if dataset_path.exists():
+            if old_path.exists():
+                shutil.rmtree(old_path)
+            shutil.move(str(dataset_path), str(old_path))

-    # In case of in-place modification, create a backup of the original dataset (if it exists)
-    if output_path == input_path:
-        backup_path = input_path.with_name(input_path.name + "_old")
+        output_dir = dataset_path

-        if input_path.exists():
-            if backup_path.exists():
-                shutil.rmtree(backup_path)
-            shutil.move(input_path, backup_path)
-
-    return output_repo_id, output_path
+    return output_repo_id, output_dir


 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -284,15 +241,11 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
-        cfg.repo_id,
-        new_repo_id=cfg.new_repo_id,
-        root=cfg.root,
-        new_root=cfg.new_root,
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
    )

-    # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if cfg.new_repo_id is None:
+        dataset.root = Path(str(dataset.root) + "_old")

    logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
    new_dataset = delete_episodes(
@@ -319,27 +272,19 @@ def handle_split(cfg: EditDatasetConfig) -> None:
            "splits dict must be specified with split names as keys and fractions/episode lists as values"
        )

-    if cfg.new_repo_id is not None:
-        logging.warning(
-            "split uses the original dataset identifier --repo_id to generate split names. The --new_repo_id parameter is ignored."
-        )
-
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)

    logging.info(f"Splitting dataset {cfg.repo_id} with splits: {cfg.operation.splits}")
-    split_datasets = split_dataset(
-        dataset,
-        splits=cfg.operation.splits,
-        output_dir=cfg.new_root,
-    )
+    split_datasets = split_dataset(dataset, splits=cfg.operation.splits)

    for split_name, split_ds in split_datasets.items():
+        split_repo_id = f"{cfg.repo_id}_{split_name}"
        logging.info(
            f"{split_name}: {split_ds.meta.total_episodes} episodes, {split_ds.meta.total_frames} frames"
        )

        if cfg.push_to_hub:
-            logging.info(f"Pushing {split_name} split to hub as {split_ds.repo_id}")
+            logging.info(f"Pushing {split_name} split to hub as {split_repo_id}")
            LeRobotDataset(split_ds.repo_id, root=split_ds.root).push_to_hub()


@@ -350,29 +295,18 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
    if not cfg.operation.repo_ids:
        raise ValueError("repo_ids must be specified for merge operation")

-    if cfg.repo_id is not None or cfg.root is not None:
-        logging.warning(
-            "merge uses --new_repo_id and --new_root for the merged dataset. The --repo_id and --root parameters are ignored."
-        )
+    if not cfg.repo_id:
+        raise ValueError("repo_id must be specified as the output repository for merged dataset")

-    if cfg.operation.roots:
-        if len(cfg.operation.roots) != len(cfg.operation.repo_ids):
-            raise ValueError("repo_ids and roots must have the same length for merge operation")
-        logging.info(f"Loading {len(cfg.operation.roots)} datasets to merge")
-        datasets = [
-            LeRobotDataset(repo_id=repo_id, root=root)
-            for repo_id, root in zip(cfg.operation.repo_ids, cfg.operation.roots, strict=True)
-        ]
-    else:
-        logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
-        datasets = [LeRobotDataset(repo_id) for repo_id in cfg.operation.repo_ids]
+    logging.info(f"Loading {len(cfg.operation.repo_ids)} datasets to merge")
+    datasets = [LeRobotDataset(repo_id, root=cfg.root) for repo_id in cfg.operation.repo_ids]

-    output_dir = Path(cfg.new_root) if cfg.new_root else HF_LEROBOT_HOME / cfg.new_repo_id
+    output_dir = Path(cfg.root) / cfg.repo_id if cfg.root else HF_LEROBOT_HOME / cfg.repo_id

-    logging.info(f"Merging datasets into {cfg.new_repo_id}")
+    logging.info(f"Merging datasets into {cfg.repo_id}")
    merged_dataset = merge_datasets(
        datasets,
-        output_repo_id=cfg.new_repo_id,
+        output_repo_id=cfg.repo_id,
        output_dir=output_dir,
    )

@@ -382,7 +316,7 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
    )

    if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {cfg.new_repo_id}")
+        logging.info(f"Pushing to hub as {cfg.repo_id}")
        LeRobotDataset(merged_dataset.repo_id, root=output_dir).push_to_hub()


@@ -395,15 +329,11 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    output_repo_id, output_dir = get_output_path(
-        cfg.repo_id,
-        new_repo_id=cfg.new_repo_id,
-        root=cfg.root,
-        new_root=cfg.new_root,
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
    )

-    # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if cfg.new_repo_id is None:
+        dataset.root = Path(str(dataset.root) + "_old")

    logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
    new_dataset = remove_feature(
@@ -431,10 +361,9 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
    if new_task is None and episode_tasks_raw is None:
        raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation")

-    if cfg.new_repo_id is not None or cfg.new_root is not None:
-        logging.warning(
-            "modify_tasks modifies datasets in-place. The --new_repo_id and --new_root parameters are ignored."
-        )
+    # Warn about in-place modification behavior
+    if cfg.new_repo_id is not None:
+        logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.")

    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
    logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.")
@@ -470,30 +399,32 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)

    # Determine output directory and repo_id
-    # Priority: 1) new_root, 2) new_repo_id, 3) operation.output_dir, 4) auto-generated name
+    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
    output_dir_config = getattr(cfg.operation, "output_dir", None)
-    if output_dir_config:
-        logging.warning(
-            "--operation.output_dir is deprecated and will be removed in future versions. "
-            "Please use --new_root instead."
-        )

-    if cfg.new_root:
-        output_dir = Path(cfg.new_root)
-        output_repo_id = cfg.new_repo_id or f"{cfg.repo_id}_video"
-        logging.info(f"Saving to new_root: {output_dir} as {output_repo_id}")
-    elif cfg.new_repo_id:
+    if cfg.new_repo_id:
+        # Use new_repo_id for both local storage and hub push
        output_repo_id = cfg.new_repo_id
-        output_dir = HF_LEROBOT_HOME / cfg.new_repo_id
+        # Place new dataset as a sibling to the original dataset
+        # Get the parent of the actual dataset root (not cfg.root which might be the lerobot cache dir)
+        # Extract just the dataset name (after last slash) for the local directory
+        local_dir_name = cfg.new_repo_id.split("/")[-1]
+        output_dir = dataset.root.parent / local_dir_name
        logging.info(f"Saving to new dataset: {cfg.new_repo_id} at {output_dir}")
    elif output_dir_config:
+        # Use custom output directory for local-only storage
        output_dir = Path(output_dir_config)
+        # Extract repo name from output_dir for the dataset
        output_repo_id = output_dir.name
-        logging.info(f"Saving to local directory: {output_dir} as {output_repo_id}")
+        logging.info(f"Saving to local directory: {output_dir}")
    else:
+        # Auto-generate name: append "_video" to original repo_id
        output_repo_id = f"{cfg.repo_id}_video"
-        output_dir = HF_LEROBOT_HOME / output_repo_id
-        logging.info(f"Saving to auto-generated location: {output_dir} as {output_repo_id}")
+        # Place new dataset as a sibling to the original dataset
+        # Extract just the dataset name (after last slash) for the local directory
+        local_dir_name = output_repo_id.split("/")[-1]
+        output_dir = dataset.root.parent / local_dir_name
+        logging.info(f"Saving to auto-generated location: {output_dir}")

    logging.info(f"Converting dataset {cfg.repo_id} to video format")

@@ -568,20 +499,8 @@ def handle_info(cfg: EditDatasetConfig):
        sys.stdout.write(f"{feature_dump_str}\n")


-def _validate_config(cfg: EditDatasetConfig) -> None:
-    if isinstance(cfg.operation, MergeConfig):
-        if not cfg.new_repo_id:
-            raise ValueError("--new_repo_id is required for merge operation (the merged dataset identifier)")
-    else:
-        if not cfg.repo_id:
-            raise ValueError(
-                f"--repo_id is required for {cfg.operation.type} operation (the input dataset identifier)"
-            )
-
-
@parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
-    _validate_config(cfg)
    operation_type = cfg.operation.type

    if operation_type == "delete_episodes":
@@ -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
@@ -140,6 +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,
@@ -155,7 +159,7 @@ class DatasetRecordConfig:
    repo_id: str
    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
    single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
    root: str | Path | None = None
    # Limit the frames per second.
    fps: int = 30
@@ -226,6 +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.
@@ -249,28 +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 )
 """
@@ -281,15 +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,
@@ -298,8 +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}).")

@@ -334,6 +349,16 @@ 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()
@@ -344,43 +369,75 @@ 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:
            no_action_count += 1
            if no_action_count == 1 or no_action_count % 10 == 0:
@@ -391,20 +448,6 @@ def record_loop(
                )
            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)
@@ -413,12 +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"
@@ -444,22 +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
@@ -505,6 +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,
@@ -515,11 +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:
@@ -535,9 +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,
@@ -546,6 +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,
                )

@@ -564,9 +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,
@@ -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,
@@ -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))
@@ -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,
@@ -100,6 +94,7 @@ from lerobot.teleoperators import (  # noqa: F401
    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
@@ -127,28 +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)
@@ -157,40 +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))
@@ -216,11 +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,
@@ -228,9 +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:
@@ -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))]
@@ -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

@@ -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

@@ -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
@@ -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
@@ -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
@@ -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.

@@ -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

@@ -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

@@ -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
@@ -31,9 +31,17 @@ from .config_openarm_mini import OpenArmMiniConfig

 logger = logging.getLogger(__name__)

-# Motors whose direction is inverted during readout
-RIGHT_MOTORS_TO_FLIP = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5"]
-LEFT_MOTORS_TO_FLIP = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
+# 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):
@@ -93,8 +101,28 @@ class OpenArmMini(Teleoperator):
            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 action_features(self) -> dict[str, type]:
+    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
@@ -103,7 +131,7 @@ class OpenArmMini(Teleoperator):
        return features

    @property
-    def feedback_features(self) -> dict[str, type]:
+    def raw_feedback_features(self) -> dict[str, type]:
        return {}

    @property
@@ -269,7 +297,7 @@ class OpenArmMini(Teleoperator):
            print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")

    @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
        """Get current action from both arms (read positions from all motors)."""
        start = time.perf_counter()

@@ -278,16 +306,64 @@ class OpenArmMini(Teleoperator):

        action: dict[str, Any] = {}
        for motor, val in right_positions.items():
-            action[f"right_{motor}.pos"] = -val if motor in RIGHT_MOTORS_TO_FLIP else val
+            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():
-            action[f"left_{motor}.pos"] = -val if motor in LEFT_MOTORS_TO_FLIP else val
+            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

-    def send_feedback(self, feedback: dict[str, float]) -> None:
-        raise NotImplementedError("Feedback is not yet implemented for OpenArm Mini.")
+    @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:
@@ -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()
@@ -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:
@@ -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"]
@@ -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,
+    )
@@ -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

@@ -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

@@ -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:
@@ -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.")
@@ -222,7 +222,7 @@ def tasks_factory():
    def _create_tasks(total_tasks: int = 3) -> pd.DataFrame:
        ids = list(range(total_tasks))
        tasks = [f"Perform action {i}." for i in ids]
-        df = pd.DataFrame({"task_index": ids}, index=pd.Index(tasks, name="task"))
+        df = pd.DataFrame({"task_index": ids}, index=tasks)
        return df

    return _create_tasks
@@ -87,7 +87,7 @@ class MockRobot(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
@@ -116,7 +116,7 @@ class MockRobot(Robot):
        pass

    @check_if_not_connected
-    def get_observation(self) -> RobotObservation:
+    def _get_observation(self) -> RobotObservation:
        if self.config.random_values:
            return {f"{motor}.pos": random.uniform(-100, 100) for motor in self.motors}
        else:
@@ -125,7 +125,7 @@ class MockRobot(Robot):
            }

    @check_if_not_connected
-    def send_action(self, action: RobotAction) -> RobotAction:
+    def _send_action(self, action: RobotAction) -> RobotAction:
        return action

    @check_if_not_connected
@@ -57,7 +57,7 @@ class MockTeleop(Teleoperator):
        self.motors = [f"motor_{i + 1}" for i in range(config.n_motors)]

    @cached_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.motors}

    @cached_property
@@ -86,7 +86,7 @@ class MockTeleop(Teleoperator):
        pass

    @check_if_not_connected
-    def get_action(self) -> RobotAction:
+    def _get_action(self) -> RobotAction:
        if self.config.random_values:
            return {f"{motor}.pos": random.uniform(-100, 100) for motor in self.motors}
        else:
@@ -95,7 +95,7 @@ class MockTeleop(Teleoperator):
            }

    @check_if_not_connected
-    def send_feedback(self, feedback: dict[str, Any]) -> None: ...
+    def _send_feedback(self, feedback: dict[str, Any]) -> None: ...

    @check_if_not_connected
    def disconnect(self) -> None:
@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-import pytest
 import torch

 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
@@ -38,9 +37,6 @@ def test_classifier_output():


@require_package("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_binary_classifier_with_default_params():
    from lerobot.policies.sac.reward_model.modeling_classifier import Classifier

@@ -82,9 +78,6 @@ def test_binary_classifier_with_default_params():


@require_package("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_multiclass_classifier():
    from lerobot.policies.sac.reward_model.modeling_classifier import Classifier

@@ -124,9 +117,6 @@ def test_multiclass_classifier():


@require_package("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_default_device():
    from lerobot.policies.sac.reward_model.modeling_classifier import Classifier

@@ -139,9 +129,6 @@ def test_default_device():


@require_package("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_explicit_device_setup():
    from lerobot.policies.sac.reward_model.modeling_classifier import Classifier

@@ -17,6 +17,7 @@
 """Test script to verify PI0Fast policy integration with LeRobot vs the original implementation"""
 # ruff: noqa: E402

+import os
 import random
 from copy import deepcopy
 from typing import Any
@@ -27,6 +28,10 @@ import torch

 pytest.importorskip("transformers")
 pytest.importorskip("scipy")
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="This test requires accepting the model license",
+)

 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
 from lerobot.policies.pi0_fast.modeling_pi0_fast import PI0FastPolicy
@@ -48,23 +53,22 @@ DUMMY_STATE_DIM = 20
 IMAGE_HEIGHT = 224
 IMAGE_WIDTH = 224
 NUM_VIEWS = 2  # Number of camera views
-DEVICE = "cuda"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 MODEL_PATH_LEROBOT = "lerobot/pi0fast-base"

 # Expected action token shape: (batch_size, max_decoding_steps)
 EXPECTED_ACTION_TOKENS_SHAPE = (1, 2)

 # Expected first 5 action tokens (for reproducibility check)
-EXPECTED_ACTION_TOKENS_FIRST_5 = torch.tensor([255020, 255589])
+EXPECTED_ACTION_TOKENS_FIRST_5 = torch.tensor([255657, 255362])

 # Expected actions after detokenization
 EXPECTED_ACTIONS_SHAPE = (1, 2, 32)  # (batch_size, n_action_steps, action_dim)
-EXPECTED_ACTIONS_MEAN = 0.046403881162405014
-EXPECTED_ACTIONS_STD = 0.2607129216194153
-EXPECTED_ACTIONS_FIRST_5 = torch.tensor([0.0000, 0.3536, 0.0707, 0.0000, 0.0000])
+EXPECTED_ACTIONS_MEAN = 0.04419417306780815
+EXPECTED_ACTIONS_STD = 0.26231569051742554
+EXPECTED_ACTIONS_FIRST_5 = torch.tensor([0.0000, 1.4849, 0.0000, 0.0000, 0.0000])


-@require_cuda
 def set_seed_all(seed: int):
    """Set random seed for all RNG sources to ensure reproducibility."""
    random.seed(seed)
@@ -81,7 +85,6 @@ def set_seed_all(seed: int):
    torch.use_deterministic_algorithms(True, warn_only=True)


-@require_cuda
 def instantiate_lerobot_pi0_fast(
    from_pretrained: bool = False,
    model_path: str = MODEL_PATH_LEROBOT,
@@ -124,7 +127,6 @@ def instantiate_lerobot_pi0_fast(
    return policy, preprocessor, postprocessor


-@require_cuda
 def create_dummy_data(device=DEVICE):
    """Create dummy data for testing both implementations."""
    batch_size = 1
@@ -156,25 +158,22 @@ def create_dummy_data(device=DEVICE):

 # Pytest fixtures
@pytest.fixture(scope="module")
-@require_cuda
 def pi0_fast_components():
    """Fixture to instantiate and provide all PI0Fast components for tests."""
    print(f"\nTesting with DEVICE='{DEVICE}'")
    print("\n[Setup] Instantiating LeRobot PI0Fast policy...")
    policy_obj, preprocessor_obj, postprocessor_obj = instantiate_lerobot_pi0_fast(from_pretrained=True)
    print("Model loaded successfully")
-    return policy_obj, preprocessor_obj, postprocessor_obj
+    yield policy_obj, preprocessor_obj, postprocessor_obj


@pytest.fixture(scope="module")
-@require_cuda
 def policy(pi0_fast_components):
    """Fixture to provide the PI0Fast policy for tests."""
    return pi0_fast_components[0]


@pytest.fixture(scope="module")
-@require_cuda
 def preprocessor(pi0_fast_components):
    """Fixture to provide the PI0Fast preprocessor for tests."""
    return pi0_fast_components[1]
@@ -16,8 +16,17 @@

 """Test script to verify PI0 policy integration with LeRobot, only meant to be run locally!"""

+import os
+
+import pytest
 import torch

+# Skip this entire module in CI
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="This test requires local OpenPI installation and is not meant for CI",
+)
+
 from lerobot.policies.factory import make_policy_config  # noqa: E402
 from lerobot.policies.pi0 import (  # noqa: E402
    PI0Config,
@@ -16,15 +16,25 @@

 """Test script to verify PI0.5 (pi05) support in PI0 policy, only meant to be run locally!"""

+import os
+
+import pytest
 import torch

+from lerobot.utils.random_utils import set_seed
+
+# Skip this entire module in CI
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="This test requires local OpenPI installation and is not meant for CI",
+)
+
 from lerobot.policies.factory import make_policy_config  # noqa: E402
 from lerobot.policies.pi05 import (  # noqa: E402
    PI05Config,
    PI05Policy,
    make_pi05_pre_post_processors,  # noqa: E402
 )
-from lerobot.utils.random_utils import set_seed
 from tests.utils import require_cuda  # noqa: E402


@@ -24,10 +24,9 @@ import torch
 # Skip this entire module in CI
 pytestmark = pytest.mark.skipif(
    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="TODO: This test seems to hang the CI",
+    reason="This test requires local OpenPI installation and is not meant for CI",
 )

-
 from lerobot.configs.types import FeatureType, PolicyFeature, RTCAttentionSchedule  # noqa: E402
 from lerobot.policies.pi05 import PI05Config, PI05Policy, make_pi05_pre_post_processors  # noqa: E402
 from lerobot.policies.rtc.configuration_rtc import RTCConfig  # noqa: E402
@@ -24,10 +24,9 @@ import torch
 # Skip this entire module in CI
 pytestmark = pytest.mark.skipif(
    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="TODO: This test seems to hang the CI",
+    reason="This test requires local OpenPI installation and is not meant for CI",
 )

-
 from lerobot.configs.types import FeatureType, PolicyFeature, RTCAttentionSchedule  # noqa: E402
 from lerobot.policies.pi0 import PI0Config, PI0Policy, make_pi0_pre_post_processors  # noqa: E402
 from lerobot.policies.rtc.configuration_rtc import RTCConfig  # noqa: E402
@@ -89,7 +88,6 @@ def test_pi0_rtc_initialization_without_rtc_config():
    print("✓ PI0 RTC initialization without RTC config: Test passed")


-@require_cuda
 def test_pi0_rtc_inference_with_prev_chunk():
    """Test PI0 policy inference with RTC and previous chunk."""
    set_seed(42)
@@ -305,9 +305,6 @@ def test_sac_policy_with_visual_input(batch_size: int, state_dim: int, action_di
    [(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
 )
@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_sac_policy_with_pretrained_encoder(
    batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
 ):
@@ -16,6 +16,8 @@

 """Test script to verify Wall-X policy integration with LeRobot, only meant to be run locally!"""

+import os
+
 import pytest
 import torch

@@ -24,15 +26,19 @@ pytest.importorskip("peft")
 pytest.importorskip("transformers")
 pytest.importorskip("torchdiffeq")

+# Skip this entire module in CI
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="This test requires local Wall-X installation and is not meant for CI",
+)
+
 from lerobot.policies.factory import make_policy_config  # noqa: E402
 from lerobot.policies.wall_x import WallXConfig  # noqa: E402
 from lerobot.policies.wall_x.modeling_wall_x import WallXPolicy  # noqa: E402
 from lerobot.policies.wall_x.processor_wall_x import make_wall_x_pre_post_processors  # noqa: E402
 from lerobot.utils.random_utils import set_seed  # noqa: E402
-from tests.utils import require_cuda  # noqa: E402


-@require_cuda
 def test_policy_instantiation():
    # Create config
    set_seed(42)
@@ -117,7 +123,6 @@ def test_policy_instantiation():
        raise


-@require_cuda
 def test_config_creation():
    """Test policy config creation through factory."""
    try:
@@ -129,3 +134,8 @@ def test_config_creation():
    except Exception as e:
        print(f"Config creation failed: {e}")
        raise
+
+
+if __name__ == "__main__":
+    test_policy_instantiation()
+    test_config_creation()
@@ -26,7 +26,7 @@ import torch
 import torch.nn as nn

 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features
+from lerobot.utils.pipeline_utils import _features_to_dataset_spec
 from lerobot.processor import (
    DataProcessorPipeline,
    EnvTransition,
@@ -2040,102 +2040,68 @@ def test_features_remove_from_initial(policy_feature_factory):
    )


-@dataclass
-class AddActionEEAndJointFeatures(ProcessorStep):
-    """Adds both EE and JOINT action features."""
-
-    def __call__(self, tr):
-        return tr
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        # EE features
-        features[PipelineFeatureType.ACTION]["action.ee.x"] = float
-        features[PipelineFeatureType.ACTION]["action.ee.y"] = float
-        # JOINT features
-        features[PipelineFeatureType.ACTION]["action.j1.pos"] = float
-        features[PipelineFeatureType.ACTION]["action.j2.pos"] = float
-        return features
+# ── Tests for _features_to_dataset_spec ──────────────────────────────────────────────────────────
+# These replace the old aggregate_pipeline_dataset_features tests, covering the same categorisation
+# / filtering / prefix-stripping / HF-format logic via the private helper directly.


-@dataclass
-class AddObservationStateFeatures(ProcessorStep):
-    """Adds state features (and optionally an image spec to test precedence)."""
-
-    add_front_image: bool = False
-    front_image_shape: tuple = (240, 320, 3)
-
-    def __call__(self, tr):
-        return tr
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        # State features (mix EE and a joint state)
-        features[PipelineFeatureType.OBSERVATION][f"{OBS_STATE}.ee.x"] = float
-        features[PipelineFeatureType.OBSERVATION][f"{OBS_STATE}.j1.pos"] = float
-        if self.add_front_image:
-            features[PipelineFeatureType.OBSERVATION][f"{OBS_IMAGES}.front"] = self.front_image_shape
-        return features
-
-
-def test_aggregate_joint_action_only():
-    rp = DataProcessorPipeline([AddActionEEAndJointFeatures()])
-    initial = {PipelineFeatureType.OBSERVATION: {"front": (480, 640, 3)}, PipelineFeatureType.ACTION: {}}
-
-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features=initial,
-        use_videos=True,
-        patterns=["action.j1.pos", "action.j2.pos"],
+def test_dataset_spec_action_with_patterns():
+    """Action features are filtered by pattern; unmatched keys are excluded."""
+    features = {
+        "action.ee.x": float,
+        "action.ee.y": float,
+        "action.j1.pos": float,
+        "action.j2.pos": float,
+    }
+    out = _features_to_dataset_spec(
+        features, is_action=True, use_videos=True, patterns=["action.j1.pos", "action.j2.pos"]
    )

-    # Expect only ACTION with joint names
-    assert ACTION in out and OBS_STATE not in out
+    assert ACTION in out
    assert out[ACTION]["dtype"] == "float32"
    assert set(out[ACTION]["names"]) == {"j1.pos", "j2.pos"}
    assert out[ACTION]["shape"] == (len(out[ACTION]["names"]),)
+    assert OBS_STATE not in out


-def test_aggregate_ee_action_and_observation_with_videos():
-    rp = DataProcessorPipeline([AddActionEEAndJointFeatures(), AddObservationStateFeatures()])
-    initial = {"front": (480, 640, 3), "side": (720, 1280, 3)}
+def test_dataset_spec_action_and_observation_with_videos():
+    """EE action + state obs + image obs; all appear with correct dtypes."""
+    action_features = {"action.ee.x": float, "action.ee.y": float}
+    obs_features = {
+        f"{OBS_STATE}.ee.x": float,
+        f"{OBS_STATE}.j1.pos": float,
+        "front": (480, 640, 3),
+        "side": (720, 1280, 3),
+    }

-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features={PipelineFeatureType.OBSERVATION: initial, PipelineFeatureType.ACTION: {}},
-        use_videos=True,
-        patterns=["action.ee", OBS_STATE],
-    )
+    act_out = _features_to_dataset_spec(action_features, is_action=True, use_videos=False)
+    obs_out = _features_to_dataset_spec(obs_features, is_action=False, use_videos=True)

-    # Action should pack only EE names
-    assert ACTION in out
-    assert set(out[ACTION]["names"]) == {"ee.x", "ee.y"}
-    assert out[ACTION]["dtype"] == "float32"
+    assert ACTION in act_out
+    assert set(act_out[ACTION]["names"]) == {"ee.x", "ee.y"}
+    assert act_out[ACTION]["dtype"] == "float32"

-    # Observation state should pack both ee.x and j1.pos as a vector
-    assert OBS_STATE in out
-    assert set(out[OBS_STATE]["names"]) == {"ee.x", "j1.pos"}
-    assert out[OBS_STATE]["dtype"] == "float32"
+    assert OBS_STATE in obs_out
+    assert set(obs_out[OBS_STATE]["names"]) == {"ee.x", "j1.pos"}
+    assert obs_out[OBS_STATE]["dtype"] == "float32"

-    # Cameras from initial_features appear as videos
-    for cam in ("front", "side"):
+    for cam, shape in [("front", (480, 640, 3)), ("side", (720, 1280, 3))]:
        key = f"{OBS_IMAGES}.{cam}"
-        assert key in out
-        assert out[key]["dtype"] == "video"
-        assert out[key]["shape"] == initial[cam]
-        assert out[key]["names"] == ["height", "width", "channels"]
+        assert key in obs_out, f"missing camera key {key}"
+        assert obs_out[key]["dtype"] == "video"
+        assert obs_out[key]["shape"] == shape
+        assert obs_out[key]["names"] == ["height", "width", "channels"]


-def test_aggregate_both_action_types():
-    rp = DataProcessorPipeline([AddActionEEAndJointFeatures()])
-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}},
-        use_videos=True,
-        patterns=["action.ee", "action.j1", "action.j2.pos"],
-    )
+def test_dataset_spec_all_action_types():
+    """EE and joint action features are both included when no pattern filter."""
+    features = {
+        "action.ee.x": float,
+        "action.ee.y": float,
+        "action.j1.pos": float,
+        "action.j2.pos": float,
+    }
+    out = _features_to_dataset_spec(features, is_action=True, use_videos=True, patterns=None)

    assert ACTION in out
    expected = {"ee.x", "ee.y", "j1.pos", "j2.pos"}
@@ -2143,58 +2109,40 @@ def test_aggregate_both_action_types():
    assert out[ACTION]["shape"] == (len(expected),)


-def test_aggregate_images_when_use_videos_false():
-    rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
-    initial = {"back": (480, 640, 3)}
+def test_dataset_spec_images_excluded_when_no_videos():
+    """Image observation features are dropped entirely when use_videos=False."""
+    obs_features = {
+        f"{OBS_STATE}.j1.pos": float,
+        "back": (480, 640, 3),
+        f"{OBS_IMAGES}.front": (240, 320, 3),
+    }
+    out = _features_to_dataset_spec(obs_features, is_action=False, use_videos=False)

-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
-        use_videos=False,  # expect "image" dtype
-        patterns=None,
-    )
-
-    key = f"{OBS_IMAGES}.back"
-    key_front = f"{OBS_IMAGES}.front"
-    assert key not in out
-    assert key_front not in out
+    assert f"{OBS_IMAGES}.back" not in out
+    assert f"{OBS_IMAGES}.front" not in out
+    # Non-image state feature is still present
+    assert OBS_STATE in out
+    assert "j1.pos" in out[OBS_STATE]["names"]


-def test_aggregate_images_when_use_videos_true():
-    rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
-    initial = {"back": (480, 640, 3)}
+def test_dataset_spec_images_included_when_use_videos():
+    """Image features appear as video entries when use_videos=True."""
+    obs_features = {
+        "back": (480, 640, 3),
+        f"{OBS_IMAGES}.front": (240, 320, 3),
+    }
+    out = _features_to_dataset_spec(obs_features, is_action=False, use_videos=True)

-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features={PipelineFeatureType.OBSERVATION: initial, PipelineFeatureType.ACTION: {}},
-        use_videos=True,
-        patterns=None,
-    )
+    assert f"{OBS_IMAGES}.back" in out
+    assert out[f"{OBS_IMAGES}.back"]["dtype"] == "video"
+    assert out[f"{OBS_IMAGES}.back"]["shape"] == (480, 640, 3)

-    key = f"{OBS_IMAGES}.front"
-    key_back = f"{OBS_IMAGES}.back"
-    assert key in out
-    assert key_back in out
-    assert out[key]["dtype"] == "video"
-    assert out[key_back]["dtype"] == "video"
-    assert out[key_back]["shape"] == initial["back"]
+    assert f"{OBS_IMAGES}.front" in out
+    assert out[f"{OBS_IMAGES}.front"]["dtype"] == "video"
+    assert out[f"{OBS_IMAGES}.front"]["shape"] == (240, 320, 3)


-def test_initial_camera_not_overridden_by_step_image():
-    # Step explicitly sets a different front image shape; initial has another shape.
-    # aggregate_pipeline_dataset_features should keep the step's value (setdefault behavior on initial cams).
-    rp = DataProcessorPipeline(
-        [AddObservationStateFeatures(add_front_image=True, front_image_shape=(240, 320, 3))]
-    )
-    initial = {"front": (480, 640, 3)}  # should NOT override the step-provided (240, 320, 3)
-
-    out = aggregate_pipeline_dataset_features(
-        pipeline=rp,
-        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
-        use_videos=True,
-        patterns=[f"{OBS_IMAGES}.front"],
-    )
-
-    key = f"{OBS_IMAGES}.front"
-    assert key in out
-    assert out[key]["shape"] == (240, 320, 3)  # from the step, not from initial
+def test_dataset_spec_empty_features_returns_empty():
+    """Empty feature dict returns an empty output dict."""
+    assert _features_to_dataset_spec({}, is_action=True, use_videos=True) == {}
+    assert _features_to_dataset_spec({}, is_action=False, use_videos=True) == {}
@@ -27,7 +27,6 @@ from lerobot.scripts.lerobot_edit_dataset import (
    OperationConfig,
    RemoveFeatureConfig,
    SplitConfig,
-    _validate_config,
 )


@@ -52,23 +51,11 @@ class TestOperationTypeParsing:
        ],
    )
    def test_operation_type_resolves_correct_class(self, type_name, expected_cls):
-        cfg = parse_cfg(
-            ["--repo_id", "test/repo", "--new_repo_id", "test/merged", "--operation.type", type_name]
-        )
+        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
        assert isinstance(cfg.operation, expected_cls), (
            f"Expected {expected_cls.__name__}, got {type(cfg.operation).__name__}"
        )

-    def test_merge_requires_new_repo_id(self):
-        cfg = parse_cfg(["--operation.type", "merge"])
-        with pytest.raises(ValueError, match="--new_repo_id is required for merge"):
-            _validate_config(cfg)
-
-    def test_non_merge_requires_repo_id(self):
-        cfg = parse_cfg(["--operation.type", "delete_episodes"])
-        with pytest.raises(ValueError, match="--repo_id is required for delete_episodes"):
-            _validate_config(cfg)
-
    @pytest.mark.parametrize(
        "type_name, expected_cls",
        [
@@ -82,8 +69,6 @@ class TestOperationTypeParsing:
        ],
    )
    def test_get_choice_name_roundtrips(self, type_name, expected_cls):
-        cfg = parse_cfg(
-            ["--repo_id", "test/repo", "--new_repo_id", "test/merged", "--operation.type", type_name]
-        )
+        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
        resolved_name = OperationConfig.get_choice_name(type(cfg.operation))
        assert resolved_name == type_name
@@ -0,0 +1,108 @@
+#!/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.
+
+"""
+Integration tests for loading robot/teleop pipelines from the Hugging Face Hub.
+
+These tests require network access and are marked with ``@pytest.mark.integration``.
+Run with::
+
+    pytest tests/test_pipeline_hub.py -m integration -v
+
+The tests verify the full end-to-end flow of:
+1. Loading a pipeline from the Hub via ``RobotProcessorPipeline.from_pretrained(...)``
+2. Attaching it to a robot or teleoperator via ``set_output_pipeline`` / ``set_input_pipeline``
+3. Verifying that ``observation_features`` / ``action_features`` differ from the raw versions
+
+Note: The Hub repos referenced below are placeholders. Update them once actual pipelines
+are published to the Hub.
+"""
+
+import pytest
+
+
+# ─── Shared mock infrastructure (mirrors test_robot_pipeline.py) ──────────────
+
+try:
+    from tests.test_robot_pipeline import MockRobot, MockTeleop  # type: ignore[import]
+except ImportError:
+    # Fallback if tests are run from a different working directory
+    import sys
+    from pathlib import Path
+
+    sys.path.insert(0, str(Path(__file__).parent))
+    from test_robot_pipeline import MockRobot, MockTeleop
+
+
+# ─── Integration tests ────────────────────────────────────────────────────────
+
+
+@pytest.mark.integration
+def test_load_robot_pipeline_from_hub(tmp_path):
+    """
+    Full end-to-end: load a FK observation pipeline for SO-101 from the Hub,
+    attach it to a robot, and verify that observation_features are transformed.
+
+    Prerequisites:
+    - A pipeline must be published at ``lerobot/so101-fk-observation-pipeline`` on the Hub.
+    - A URDF file must be available locally (update ``local_urdf_path`` to point to it).
+    """
+    pytest.importorskip("huggingface_hub")
+    from lerobot.processor.pipeline import RobotProcessorPipeline
+
+    local_urdf_path = tmp_path / "so101.urdf"
+    # NOTE: In a real test environment, provide an actual URDF or mock the kinematics.
+    # For now, this test validates the Hub loading mechanism only if a URDF is provided.
+    if not local_urdf_path.exists():
+        pytest.skip("URDF not available; skipping Hub loading test")
+
+    pipeline = RobotProcessorPipeline.from_pretrained(
+        "lerobot/so101-fk-observation-pipeline",
+        overrides={"step_0": {"urdf_path": str(local_urdf_path)}},
+    )
+    robot = MockRobot()
+    robot.set_output_pipeline(pipeline)
+
+    # Pipeline-transformed features should differ from raw features (EE vs joints)
+    assert robot.observation_features != robot.raw_observation_features
+
+
+@pytest.mark.integration
+def test_load_teleop_pipeline_from_hub(tmp_path):
+    """
+    Full end-to-end: load a FK action pipeline for SO-101 leader from the Hub,
+    attach it to a teleoperator, and verify that action_features are transformed.
+
+    Prerequisites:
+    - A pipeline must be published at ``lerobot/so101-leader-fk-action-pipeline`` on the Hub.
+    - A URDF file must be available locally (update ``local_urdf_path`` to point to it).
+    """
+    pytest.importorskip("huggingface_hub")
+    from lerobot.processor.pipeline import RobotProcessorPipeline
+
+    local_urdf_path = tmp_path / "so101.urdf"
+    if not local_urdf_path.exists():
+        pytest.skip("URDF not available; skipping Hub loading test")
+
+    pipeline = RobotProcessorPipeline.from_pretrained(
+        "lerobot/so101-leader-fk-action-pipeline",
+        overrides={"step_0": {"urdf_path": str(local_urdf_path)}},
+    )
+    teleop = MockTeleop()
+    teleop.set_output_pipeline(pipeline)
+
+    # Pipeline-transformed features should differ from raw features (EE vs joints)
+    assert teleop.action_features != teleop.raw_action_features
@@ -0,0 +1,433 @@
+#!/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.
+
+"""
+Unit tests for the robot/teleoperator pipeline interface.
+
+Tests cover:
+- Default (identity) pipeline behaviour
+- Custom pipeline attachment via set_output_pipeline / set_input_pipeline
+- Auto-derived observation_features / action_features via pipelines
+- Compatibility checks
+- build_dataset_features utility
+"""
+
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+
+from lerobot.configs.types import PipelineFeatureType
+from lerobot.processor import RobotAction, RobotObservation
+from lerobot.processor.converters import (
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    robot_action_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.processor.factory import (
+    _make_identity_feedback_pipeline,
+    _make_identity_observation_pipeline,
+    _make_identity_robot_action_pipeline,
+    _make_identity_teleop_action_pipeline,
+)
+from lerobot.processor.pipeline import (
+    IdentityProcessorStep,
+    ObservationProcessorStep,
+    RobotActionProcessorStep,
+    RobotProcessorPipeline,
+)
+from lerobot.robots.robot import Robot
+from lerobot.teleoperators.teleoperator import Teleoperator
+from lerobot.utils.pipeline_utils import (
+    build_dataset_features,
+    check_action_space_compatibility,
+    check_observation_space_compatibility,
+)
+
+
+# ─── Mock hardware classes ────────────────────────────────────────────────────
+
+
+@dataclass
+class MockRobotConfig:
+    id: str = "mock_robot"
+    calibration_dir: Path | None = None
+
+
+@dataclass
+class MockTeleopConfig:
+    id: str = "mock_teleop"
+    calibration_dir: Path | None = None
+
+
+_JOINT_NAMES = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
+_JOINT_FEATURES = {f"{j}.pos": float for j in _JOINT_NAMES}
+_EE_FEATURES = {"ee.x": float, "ee.y": float, "ee.z": float, "ee.wx": float, "ee.wy": float, "ee.wz": float, "ee.gripper_vel": float}
+
+
+class MockRobot(Robot):
+    """Minimal Robot that stores last action for assertion."""
+
+    config_class = MockRobotConfig
+    name = "mock_robot"
+
+    def __init__(self):
+        # bypass filesystem calibration setup; initialize with identity pipelines directly
+        self._output_pipeline = _make_identity_observation_pipeline()
+        self._input_pipeline = _make_identity_robot_action_pipeline()
+        self._last_raw_obs: RobotObservation = {}
+        self._last_sent: RobotAction = {}
+
+    @property
+    def raw_observation_features(self) -> dict:
+        return {**_JOINT_FEATURES, "camera": (480, 640, 3)}
+
+    @property
+    def raw_action_features(self) -> dict:
+        return _JOINT_FEATURES
+
+    @property
+    def is_connected(self) -> bool:
+        return True
+
+    def connect(self, calibrate=True):
+        pass
+
+    @property
+    def is_calibrated(self) -> bool:
+        return True
+
+    def calibrate(self):
+        pass
+
+    def configure(self):
+        pass
+
+    def _get_observation(self) -> RobotObservation:
+        return {f"{j}.pos": float(i) for i, j in enumerate(_JOINT_NAMES)} | {"camera": None}
+
+    def _send_action(self, action: RobotAction) -> RobotAction:
+        self._last_sent = action
+        return action
+
+    def disconnect(self):
+        pass
+
+
+class MockTeleop(Teleoperator):
+    """Minimal Teleoperator."""
+
+    config_class = MockTeleopConfig
+    name = "mock_teleop"
+
+    def __init__(self):
+        # bypass filesystem calibration setup; initialize with identity pipelines directly
+        self._output_pipeline = _make_identity_teleop_action_pipeline()
+        self._input_pipeline = _make_identity_feedback_pipeline()
+
+    @property
+    def raw_action_features(self) -> dict:
+        return _JOINT_FEATURES
+
+    @property
+    def raw_feedback_features(self) -> dict:
+        return {}
+
+    @property
+    def is_connected(self) -> bool:
+        return True
+
+    def connect(self, calibrate=True):
+        pass
+
+    @property
+    def is_calibrated(self) -> bool:
+        return True
+
+    def calibrate(self):
+        pass
+
+    def configure(self):
+        pass
+
+    def _get_action(self) -> RobotAction:
+        return {f"{j}.pos": float(i) for i, j in enumerate(_JOINT_NAMES)}
+
+    def _send_feedback(self, feedback):
+        pass
+
+    def disconnect(self):
+        pass
+
+
+# ─── Simple transform step (doubles all float values) ────────────────────────
+
+
+class DoubleActionStep(RobotActionProcessorStep):
+    """Doubles all float action values."""
+
+    def action(self, action: RobotAction) -> RobotAction:
+        return {k: v * 2 for k, v in action.items()}
+
+    def transform_features(self, features):
+        return features
+
+
+class RenameToEEObsStep(ObservationProcessorStep):
+    """Renames joint obs keys to EE-like keys for testing transform_features."""
+
+    def observation(self, obs: RobotObservation) -> RobotObservation:
+        return {f"ee.{i}": v for i, v in enumerate(obs.values()) if isinstance(v, float)}
+
+    def transform_features(self, features):
+        obs = features.get(PipelineFeatureType.OBSERVATION, {})
+        new_obs = {f"ee.{i}": float for i in range(len([v for v in obs.values() if v == float]))}
+        return {**features, PipelineFeatureType.OBSERVATION: new_obs}
+
+
+# ─── Tests: Robot pipeline interface ─────────────────────────────────────────
+
+
+def test_robot_default_pipeline_is_identity():
+    """With no custom pipeline, get_observation returns the same as _get_observation."""
+    robot = MockRobot()
+    raw = robot._get_observation()
+    obs = robot.get_observation()
+    assert obs == raw
+
+
+def test_robot_observation_caches_last_raw():
+    """get_observation caches raw result for IK use in send_action."""
+    robot = MockRobot()
+    robot.get_observation()
+    assert robot._last_raw_obs is not None
+    assert "shoulder_pan.pos" in robot._last_raw_obs
+
+
+def test_robot_default_send_action_is_identity():
+    """With no custom pipeline, send_action passes action unchanged to _send_action."""
+    robot = MockRobot()
+    robot.get_observation()  # populate _last_raw_obs
+    action = {f"{j}.pos": 1.0 for j in _JOINT_NAMES}
+    sent = robot.send_action(action)
+    assert sent == action
+    assert robot._last_sent == action
+
+
+def test_robot_custom_output_pipeline_applied():
+    """A custom action pipeline is applied to the action before _send_action."""
+    robot = MockRobot()
+    double_pipeline = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[DoubleActionStep()],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+    robot.set_input_pipeline(double_pipeline)
+    robot.get_observation()  # populate _last_raw_obs
+    action = {f"{j}.pos": 1.0 for j in _JOINT_NAMES}
+    robot.send_action(action)
+    assert all(v == 2.0 for v in robot._last_sent.values())
+
+
+def test_robot_observation_features_identity_matches_raw():
+    """observation_features equals raw_observation_features with identity pipeline."""
+    robot = MockRobot()
+    assert robot.observation_features == robot.raw_observation_features
+
+
+def test_robot_raw_observation_features_unchanged_after_pipeline():
+    """raw_observation_features is unaffected by the output pipeline."""
+    robot = MockRobot()
+    # Even with an FK-like renaming pipeline, raw_observation_features stays the same
+    transform_pipeline = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[RenameToEEObsStep()],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+    robot.set_output_pipeline(transform_pipeline)
+    # raw should still be joints + camera
+    raw = robot.raw_observation_features
+    assert "shoulder_pan.pos" in raw
+    assert "camera" in raw
+
+
+def test_robot_action_features_identity_matches_raw():
+    """action_features equals raw_action_features with identity input pipeline."""
+    robot = MockRobot()
+    assert robot.action_features == robot.raw_action_features
+
+
+def test_robot_raw_action_features_unchanged_after_pipeline():
+    """raw_action_features is unaffected by any pipeline."""
+    robot = MockRobot()
+    double_pipeline = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[DoubleActionStep()],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+    robot.set_input_pipeline(double_pipeline)
+    assert robot.raw_action_features == _JOINT_FEATURES
+
+
+def test_robot_set_output_pipeline_replaces_identity():
+    """set_output_pipeline replaces the default identity."""
+    robot = MockRobot()
+    p = _make_identity_observation_pipeline()
+    robot.set_output_pipeline(p)
+    assert robot._output_pipeline is p
+
+
+def test_robot_set_input_pipeline_replaces_identity():
+    robot = MockRobot()
+    p = _make_identity_robot_action_pipeline()
+    robot.set_input_pipeline(p)
+    assert robot._input_pipeline is p
+
+
+# ─── Tests: Teleoperator pipeline interface ───────────────────────────────────
+
+
+def test_teleop_default_get_action_is_identity():
+    """With no custom pipeline, get_action returns the same as _get_action."""
+    teleop = MockTeleop()
+    raw = teleop._get_action()
+    action = teleop.get_action()
+    assert action == raw
+
+
+def test_teleop_action_features_identity_matches_raw():
+    """action_features equals raw_action_features with identity pipeline."""
+    teleop = MockTeleop()
+    assert teleop.action_features == teleop.raw_action_features
+
+
+def test_teleop_feedback_features_identity_matches_raw():
+    """feedback_features equals raw_feedback_features with identity input pipeline."""
+    teleop = MockTeleop()
+    assert teleop.feedback_features == teleop.raw_feedback_features
+
+
+def test_teleop_feedback_features_empty_when_raw_empty():
+    """feedback_features returns empty dict when raw_feedback_features is empty."""
+    teleop = MockTeleop()
+    assert teleop.feedback_features == {}
+
+
+def test_teleop_set_output_pipeline():
+    teleop = MockTeleop()
+    p = _make_identity_teleop_action_pipeline()
+    teleop.set_output_pipeline(p)
+    assert teleop._output_pipeline is p
+
+
+def test_teleop_send_feedback_calls_send_feedback_impl():
+    """send_feedback applies identity pipeline and delegates to _send_feedback."""
+    teleop = MockTeleop()
+    received = {}
+
+    def capture(fb):
+        received.update(fb)
+
+    teleop._send_feedback = capture
+    teleop.send_feedback({"key": 1.0})
+    assert received == {"key": 1.0}
+
+
+# ─── Tests: Compatibility checks ─────────────────────────────────────────────
+
+
+def test_check_action_space_compatibility_matching():
+    """No warning when teleop output and robot action features match."""
+    teleop = MockTeleop()
+    robot = MockRobot()
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        check_action_space_compatibility(teleop, robot)  # should not warn
+
+
+def test_check_action_space_compatibility_mismatch_warns():
+    """Warning issued when teleop and robot action features differ."""
+
+    class EETeleop(MockTeleop):
+        @property
+        def raw_action_features(self):
+            return _EE_FEATURES
+
+    teleop = EETeleop()
+    robot = MockRobot()  # still returns joint features
+    with pytest.warns(UserWarning, match="Action space mismatch"):
+        check_action_space_compatibility(teleop, robot)
+
+
+def test_check_observation_space_compatibility_no_feedback():
+    """No warning when teleop has empty feedback_features."""
+    robot = MockRobot()
+    teleop = MockTeleop()
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        check_observation_space_compatibility(robot, teleop)  # empty feedback → no warning
+
+
+# ─── Tests: build_dataset_features ───────────────────────────────────────────
+
+
+def test_build_dataset_features_identity():
+    """With identity pipelines, dataset features contain joint keys."""
+    robot = MockRobot()
+    teleop = MockTeleop()
+    features = build_dataset_features(robot, teleop, use_videos=False)
+    # Should contain action features (joint names)
+    action_keys = {k for k in features if "action" in k or any(j in k for j in _JOINT_NAMES)}
+    assert len(action_keys) > 0
+
+
+def test_build_dataset_features_includes_images_when_use_videos_true():
+    """Image features are included when use_videos=True."""
+    robot = MockRobot()
+    teleop = MockTeleop()
+    feats_with = build_dataset_features(robot, teleop, use_videos=True)
+    feats_without = build_dataset_features(robot, teleop, use_videos=False)
+    # With videos should have more features (camera)
+    assert len(feats_with) >= len(feats_without)
+
+
+# ─── Tests: Factory identity pipeline helpers ─────────────────────────────────
+
+
+def test_make_identity_observation_pipeline_is_noop():
+    pipeline = _make_identity_observation_pipeline()
+    obs = {"shoulder_pan.pos": 1.0, "camera": None}
+    result = pipeline(obs)
+    assert result == obs
+
+
+def test_make_identity_robot_action_pipeline_is_noop():
+    pipeline = _make_identity_robot_action_pipeline()
+    action = {"shoulder_pan.pos": 1.0}
+    obs = {"shoulder_pan.pos": 0.0}
+    result = pipeline((action, obs))
+    assert result == action
+
+
+def test_make_identity_teleop_action_pipeline_is_noop():
+    pipeline = _make_identity_teleop_action_pipeline()
+    action = {"shoulder_pan.pos": 1.0}
+    result = pipeline(action)
+    assert result == action
Author	SHA1	Message	Date
Pepijn	bf03414b38	also have pipeline for feedback_features and action_features	2026-03-02 22:07:13 +01:00
Pepijn	9860f794cf	also refactor and remove use of `aggregate_pipeline_dataset_features()` as we already aggregate expected features on robot and teleop classes	2026-03-02 21:05:58 +01:00
Pepijn	7940bfad52	make lerobot record easier	2026-03-02 20:17:50 +01:00
Pepijn	a2246a650b	pipeline architecture changes	2026-03-02 13:09:35 +01:00