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https://github.com/huggingface/lerobot.git
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Pepijn
228 lines
8.3 KiB
Python
228 lines
8.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Inference script for a pi0 model trained with **relative EE actions**.
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This uses the built-in ``RelativeActionsProcessorStep`` and
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``AbsoluteActionsProcessorStep`` that are already wired into pi0's
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processor pipeline when ``use_relative_actions=True``.
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The inference loop:
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1. Reads joint positions from the robot.
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2. Converts to EE pose via forward kinematics (FK).
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This produces ``observation.state`` with the current EE pose.
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3. The pi0 preprocessor:
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a) ``RelativeActionsProcessorStep`` caches the raw state.
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b) ``NormalizerProcessorStep`` normalizes state and actions.
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4. pi0 predicts relative action chunk.
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5. The pi0 postprocessor:
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a) ``UnnormalizerProcessorStep`` unnormalizes.
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b) ``AbsoluteActionsProcessorStep`` adds cached state → absolute EE.
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6. IK converts absolute EE → joint targets → robot.
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Based on the so100_to_so100_EE/evaluate.py example.
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Usage:
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python evaluate.py
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"""
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from __future__ import annotations
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from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
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from lerobot.configs.types import FeatureType, PolicyFeature
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from lerobot.datasets.feature_utils import combine_feature_dicts
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
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from lerobot.model.kinematics import RobotKinematics
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from lerobot.policies.factory import make_pre_post_processors
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from lerobot.policies.pi0.modeling_pi0 import PI0Policy
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from lerobot.processor import (
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RobotProcessorPipeline,
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make_default_teleop_action_processor,
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)
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from lerobot.processor.converters import (
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observation_to_transition,
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robot_action_observation_to_transition,
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transition_to_observation,
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transition_to_robot_action,
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)
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from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
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from lerobot.robots.so_follower.robot_kinematic_processor import (
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ForwardKinematicsJointsToEE,
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InverseKinematicsEEToJoints,
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)
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from lerobot.scripts.lerobot_record import record_loop
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from lerobot.types import RobotAction, RobotObservation
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from lerobot.utils.control_utils import init_keyboard_listener
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from lerobot.utils.utils import log_say
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from lerobot.utils.visualization_utils import init_rerun
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NUM_EPISODES = 5
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FPS = 10
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EPISODE_TIME_SEC = 60
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TASK_DESCRIPTION = "manipulation task"
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HF_MODEL_ID = "<hf_username>/<model_repo_id>"
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HF_DATASET_ID = "<hf_username>/<dataset_repo_id>"
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# EE feature keys produced by ForwardKinematicsJointsToEE
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EE_KEYS = ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]
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def main():
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camera_config = {"wrist": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
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robot_config = SO100FollowerConfig(
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port="/dev/tty.usbmodem5A460814411",
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id="my_awesome_follower_arm",
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cameras=camera_config,
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use_degrees=True,
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)
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robot = SO100Follower(robot_config)
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policy = PI0Policy.from_pretrained(HF_MODEL_ID)
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kinematics_solver = RobotKinematics(
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urdf_path="./SO101/so101_new_calib.urdf",
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target_frame_name="gripper_frame_link",
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joint_names=list(robot.bus.motors.keys()),
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)
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# FK: joint observation → EE observation (produces observation.state)
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robot_joints_to_ee_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
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steps=[
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ForwardKinematicsJointsToEE(
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kinematics=kinematics_solver,
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motor_names=list(robot.bus.motors.keys()),
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)
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],
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to_transition=observation_to_transition,
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to_output=transition_to_observation,
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)
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# IK: EE action → joint targets
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robot_ee_to_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
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steps=[
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InverseKinematicsEEToJoints(
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kinematics=kinematics_solver,
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motor_names=list(robot.bus.motors.keys()),
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initial_guess_current_joints=True,
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),
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],
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to_transition=robot_action_observation_to_transition,
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to_output=transition_to_robot_action,
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)
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# Dataset handle for stats (used by preprocessor/postprocessor)
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dataset = LeRobotDataset.create(
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repo_id=HF_DATASET_ID,
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fps=FPS,
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features=combine_feature_dicts(
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aggregate_pipeline_dataset_features(
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pipeline=robot_joints_to_ee_processor,
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initial_features=create_initial_features(observation=robot.observation_features),
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use_videos=True,
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),
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aggregate_pipeline_dataset_features(
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pipeline=make_default_teleop_action_processor(),
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initial_features=create_initial_features(
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action={f"ee.{k}": PolicyFeature(type=FeatureType.ACTION, shape=(1,)) for k in EE_KEYS}
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),
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use_videos=True,
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),
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),
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robot_type=robot.name,
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use_videos=True,
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image_writer_threads=4,
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)
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# Build pre/post processors from the trained model.
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# The pi0 processor pipeline already includes:
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# pre: ... → RelativeActionsProcessorStep → NormalizerProcessorStep
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# post: UnnormalizerProcessorStep → AbsoluteActionsProcessorStep → ...
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# These handle the relative ↔ absolute conversion automatically.
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=policy,
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pretrained_path=HF_MODEL_ID,
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dataset_stats=dataset.meta.stats,
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preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
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)
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robot.connect()
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listener, events = init_keyboard_listener()
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init_rerun(session_name="umi_pi0_relative_ee_evaluate")
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try:
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if not robot.is_connected:
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raise ValueError("Robot is not connected!")
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print("Starting evaluate loop...")
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for episode_idx in range(NUM_EPISODES):
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log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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policy=policy,
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preprocessor=preprocessor,
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postprocessor=postprocessor,
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dataset=dataset,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=make_default_teleop_action_processor(),
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_processor,
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)
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if not events["stop_recording"] and (
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(episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
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):
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log_say("Reset the environment")
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record_loop(
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robot=robot,
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events=events,
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fps=FPS,
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control_time_s=EPISODE_TIME_SEC,
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single_task=TASK_DESCRIPTION,
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display_data=True,
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teleop_action_processor=make_default_teleop_action_processor(),
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robot_action_processor=robot_ee_to_joints_processor,
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robot_observation_processor=robot_joints_to_ee_processor,
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)
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if events["rerecord_episode"]:
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log_say("Re-record episode")
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events["rerecord_episode"] = False
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events["exit_early"] = False
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dataset.clear_episode_buffer()
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continue
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dataset.save_episode()
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finally:
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log_say("Stop recording")
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robot.disconnect()
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listener.stop()
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dataset.finalize()
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dataset.push_to_hub()
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if __name__ == "__main__":
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main()
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