pipeline architecture changes

tests/mocks/mock_robot.py

@@ -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

tests/mocks/mock_teleop.py

@@ -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:

tests/test_pipeline_hub.py

@@ -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

tests/test_robot_pipeline.py

@@ -0,0 +1,403 @@
+#!/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 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 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_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_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