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Add relative position UMI style
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Pepijn
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#!/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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OpenArms Policy Evaluation with UMI-style Relative Actions
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Evaluates a policy trained with relative actions (use_relative_actions=True).
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During inference, the policy outputs relative deltas which are added to the
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current robot position to get absolute targets.
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This follows the UMI paper's "relative trajectory" action representation:
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action_absolute[t] = action_relative[t] + current_position
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Example usage:
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python examples/openarms/evaluate_relative.py
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"""
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import time
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from pathlib import Path
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import torch
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from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
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from lerobot.configs.policies import PreTrainedConfig
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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.datasets.utils import build_dataset_frame, combine_feature_dicts
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from lerobot.policies.factory import make_policy, make_pre_post_processors
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from lerobot.policies.utils import predict_action
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from lerobot.processor import make_default_processors
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from lerobot.processor.core import RobotAction
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from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
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from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
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from lerobot.utils.constants import ACTION, OBS_STR
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from lerobot.utils.control_utils import init_keyboard_listener, precise_sleep
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from lerobot.utils.device_utils import get_safe_torch_device
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from lerobot.utils.relative_actions import convert_from_relative_actions_dict, convert_state_to_relative
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from lerobot.utils.utils import log_say
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from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
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# Configuration - Update these for your setup
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HF_MODEL_ID = "your-org/your-relative-policy" # Policy trained with use_relative_actions=True
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HF_EVAL_DATASET_ID = "your-org/your-eval-dataset"
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TASK_DESCRIPTION = "your task description"
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NUM_EPISODES = 1
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FPS = 30
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EPISODE_TIME_SEC = 300
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# Robot CAN interfaces
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FOLLOWER_LEFT_PORT = "can0"
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FOLLOWER_RIGHT_PORT = "can1"
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# Camera configuration
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CAMERA_CONFIG = {
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"left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
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"right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
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"base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
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}
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def make_robot_action(action_values: dict, features: dict) -> RobotAction:
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"""Convert action values to robot action dict, filtering by features."""
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robot_action = {}
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for key in features:
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if key.startswith(ACTION + "."):
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action_key = key.removeprefix(ACTION + ".")
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if action_key in action_values:
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robot_action[action_key] = action_values[action_key]
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return robot_action
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def inference_loop_relative(
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robot,
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policy,
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preprocessor,
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postprocessor,
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dataset,
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events,
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fps: int,
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control_time_s: float,
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single_task: str,
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display_data: bool = True,
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state_key: str = "observation.state",
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):
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"""
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Inference loop for policies trained with UMI-style relative actions and state.
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Key differences from standard inference:
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- Observation state is converted to relative (provides velocity info)
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- Policy outputs relative deltas (action_relative)
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- We add current robot position to get absolute targets:
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action_absolute = action_relative + current_position
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"""
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device = get_safe_torch_device(policy.config.device)
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timestamp = 0
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start_t = time.perf_counter()
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while timestamp < control_time_s:
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loop_start = time.perf_counter()
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if events["exit_early"] or events["stop_recording"]:
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break
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# Get current robot observation
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obs = robot.get_observation()
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observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
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# Get current joint positions (reference for relative conversion)
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current_pos = {k: v for k, v in obs.items() if k.endswith(".pos")}
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# Convert observation state to relative (UMI-style)
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# This gives velocity-like information to the policy
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if state_key in observation_frame:
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state_tensor = observation_frame[state_key]
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if isinstance(state_tensor, torch.Tensor):
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observation_frame[state_key] = convert_state_to_relative(state_tensor)
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# Run policy inference - outputs relative actions
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action_values = predict_action(
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observation=observation_frame,
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policy=policy,
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device=device,
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preprocessor=preprocessor,
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postprocessor=postprocessor,
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use_amp=policy.config.use_amp,
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task=single_task,
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robot_type=robot.robot_type,
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)
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# Convert relative actions to absolute
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# action_values contains relative deltas, current_pos has absolute positions
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relative_action = make_robot_action(action_values, dataset.features)
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absolute_action = convert_from_relative_actions_dict(relative_action, current_pos)
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# Send absolute action to robot
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robot.send_action(absolute_action)
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# Record to dataset (store the absolute action that was sent)
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if dataset is not None:
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action_frame = build_dataset_frame(dataset.features, absolute_action, prefix=ACTION)
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frame = {**observation_frame, **action_frame, "task": single_task}
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dataset.add_frame(frame)
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if display_data:
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log_rerun_data(observation=obs, action=absolute_action)
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dt = time.perf_counter() - loop_start
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precise_sleep(1 / fps - dt)
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timestamp = time.perf_counter() - start_t
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def main():
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"""Main evaluation function for relative action policies."""
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print("=" * 65)
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print(" OpenArms Evaluation - UMI-style Relative Actions")
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print("=" * 65)
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print(f"\nModel: {HF_MODEL_ID}")
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print(f"Evaluation Dataset: {HF_EVAL_DATASET_ID}")
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print(f"Task: {TASK_DESCRIPTION}")
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print(f"Episodes: {NUM_EPISODES}")
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print(f"Episode Duration: {EPISODE_TIME_SEC}s")
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print("\nNote: Policy outputs are relative deltas, converted to absolute at inference time")
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# Setup robot
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follower_config = OpenArmsFollowerConfig(
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port_left=FOLLOWER_LEFT_PORT,
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port_right=FOLLOWER_RIGHT_PORT,
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can_interface="socketcan",
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id="openarms_follower",
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disable_torque_on_disconnect=True,
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max_relative_target=10.0,
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cameras=CAMERA_CONFIG,
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)
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follower = OpenArmsFollower(follower_config)
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follower.connect(calibrate=False)
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if not follower.is_connected:
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raise RuntimeError("Follower robot failed to connect!")
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# Build processors
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teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
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# Build dataset features
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action_features_hw = {k: v for k, v in follower.action_features.items() if k.endswith(".pos")}
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dataset_features = combine_feature_dicts(
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aggregate_pipeline_dataset_features(
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pipeline=teleop_action_processor,
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initial_features=create_initial_features(action=action_features_hw),
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use_videos=True,
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),
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aggregate_pipeline_dataset_features(
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pipeline=robot_observation_processor,
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initial_features=create_initial_features(observation=follower.observation_features),
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use_videos=True,
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),
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)
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# Check existing dataset
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dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
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if dataset_path.exists():
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print(f"\nDataset already exists at: {dataset_path}")
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choice = input("Continue and append? (y/n): ").strip().lower()
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if choice != 'y':
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follower.disconnect()
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return
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# Create dataset
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dataset = LeRobotDataset.create(
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repo_id=HF_EVAL_DATASET_ID,
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fps=FPS,
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features=dataset_features,
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robot_type=follower.name,
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use_videos=True,
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image_writer_processes=0,
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image_writer_threads=12,
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)
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# Load policy
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policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
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policy_config.pretrained_path = HF_MODEL_ID
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policy = make_policy(policy_config, ds_meta=dataset.meta)
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=policy.config,
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pretrained_path=HF_MODEL_ID,
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dataset_stats=dataset.meta.stats,
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preprocessor_overrides={
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"device_processor": {"device": str(policy.config.device)}
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},
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)
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# Initialize controls
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listener, events = init_keyboard_listener()
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init_rerun(session_name="openarms_eval_relative")
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episode_idx = 0
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print("\nControls:")
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print(" ESC - Stop recording and save")
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print(" → - End current episode")
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print(" ← - Re-record episode")
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try:
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while episode_idx < NUM_EPISODES and not events["stop_recording"]:
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log_say(f"Evaluating episode {episode_idx + 1} of {NUM_EPISODES}")
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print(f"\nRunning relative action inference for episode {episode_idx + 1}...")
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# Run inference with relative action conversion
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inference_loop_relative(
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robot=follower,
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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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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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)
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# Handle re-recording
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if events.get("rerecord_episode", False):
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log_say("Re-recording 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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# Save episode
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if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
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print(f"Saving episode {episode_idx + 1} ({dataset.episode_buffer['size']} frames)...")
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dataset.save_episode()
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episode_idx += 1
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events["exit_early"] = False
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# Wait for manual reset between episodes
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if not events["stop_recording"] and episode_idx < NUM_EPISODES:
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log_say("Waiting for manual reset")
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input("Press ENTER when ready for next episode...")
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print(f"\nEvaluation complete! {episode_idx} episodes recorded")
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log_say("Evaluation complete", blocking=True)
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except KeyboardInterrupt:
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print("\n\nEvaluation interrupted by user")
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finally:
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follower.disconnect()
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if listener is not None:
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listener.stop()
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dataset.finalize()
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print("\nUploading to Hugging Face Hub...")
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dataset.push_to_hub(private=True)
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if __name__ == "__main__":
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main()
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