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feat(scripts): lerobot-rollout

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This commit is contained in:
Steven Palma
2026-04-14 15:42:04 +02:00
committed by Steven Palma
parent 5c43fa1cce
commit bc06cb44ca
54 changed files with 5204 additions and 2816 deletions
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examples/lekiwi/evaluate.py
+62 -31
View File
@@ -14,17 +14,21 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from lerobot.common.control_utils import init_keyboard_listener
import logging
import time
from lerobot.common.control_utils import init_keyboard_listener, predict_action
from lerobot.datasets import LeRobotDataset
from lerobot.policies import make_pre_post_processors
from lerobot.policies.act import ACTPolicy
from lerobot.policies.utils import make_robot_action
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
from lerobot.utils.feature_utils import hw_to_dataset_features
from lerobot.utils.feature_utils import build_dataset_frame, hw_to_dataset_features
from lerobot.utils.robot_utils import precise_sleep
from lerobot.utils.utils import log_say
from lerobot.utils.visualization_utils import init_rerun
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
NUM_EPISODES = 2
FPS = 30
@@ -35,6 +39,9 @@ HF_DATASET_ID = "<hf_username>/<eval_dataset_repo_id>"
def main():
# NOTE: For production policy deployment, use `lerobot-rollout` CLI instead.
# This script provides a self-contained example for educational purposes.
# Create the robot configuration & robot
robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
@@ -83,43 +90,67 @@ def main():
raise ValueError("Robot is not connected!")
print("Starting evaluate loop...")
control_interval = 1 / FPS
recorded_episodes = 0
while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
# Main record loop
record_loop(
robot=robot,
events=events,
fps=FPS,
policy=policy,
preprocessor=preprocessor, # Pass the pre and post policy processors
postprocessor=postprocessor,
dataset=dataset,
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,
)
# Inline evaluation loop: predict actions and send to robot
timestamp = 0
start_episode_t = time.perf_counter()
while timestamp < EPISODE_TIME_SEC:
start_loop_t = time.perf_counter()
if events["exit_early"]:
events["exit_early"] = False
break
# Get robot observation
obs = robot.get_observation()
obs_processed = robot_observation_processor(obs)
observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
# Predict action using the policy
action_tensor = predict_action(
observation=observation_frame,
policy=policy,
device=policy.config.device,
preprocessor=preprocessor,
postprocessor=postprocessor,
use_amp=policy.config.device.type == "cuda",
task=TASK_DESCRIPTION,
robot_type=robot.name,
)
# Convert policy output to robot action dict
action_values = make_robot_action(action_tensor, dataset.features)
# Process and send action to robot
robot_action_to_send = robot_action_processor((action_values, obs))
robot.send_action(robot_action_to_send)
# Write to dataset
action_frame = build_dataset_frame(dataset.features, action_values, prefix=ACTION)
frame = {**observation_frame, **action_frame, "task": TASK_DESCRIPTION}
dataset.add_frame(frame)
log_rerun_data(observation=obs_processed, action=action_values)
dt_s = time.perf_counter() - start_loop_t
sleep_time_s = control_interval - dt_s
if sleep_time_s < 0:
logging.warning(
f"Evaluate loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({FPS} Hz)."
)
precise_sleep(max(sleep_time_s, 0.0))
timestamp = time.perf_counter() - start_episode_t
# Reset the environment if not stopping or re-recording
if not events["stop_recording"] and (
(recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
):
log_say("Reset the environment")
record_loop(
robot=robot,
events=events,
fps=FPS,
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,
)
log_say("Waiting for environment reset, press right arrow key when ready...")
if events["rerecord_episode"]:
log_say("Re-record episode")
examples/lekiwi/record.py
+10 -9
View File
@@ -45,9 +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)
@@ -77,6 +74,10 @@ def main():
if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
raise ValueError("Robot or teleop is not connected!")
teleop_action_processor, robot_action_processor, robot_observation_processor = (
make_default_processors()
)
print("Starting record loop...")
recorded_episodes = 0
while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
@@ -87,14 +88,14 @@ def main():
robot=robot,
events=events,
fps=FPS,
teleop_action_processor=teleop_action_processor,
robot_action_processor=robot_action_processor,
robot_observation_processor=robot_observation_processor,
dataset=dataset,
teleop=[leader_arm, keyboard],
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
@@ -106,13 +107,13 @@ def main():
robot=robot,
events=events,
fps=FPS,
teleop_action_processor=teleop_action_processor,
robot_action_processor=robot_action_processor,
robot_observation_processor=robot_observation_processor,
teleop=[leader_arm, keyboard],
control_time_s=RESET_TIME_SEC,
single_task=TASK_DESCRIPTION,
display_data=True,
teleop_action_processor=teleop_action_processor,
robot_action_processor=robot_action_processor,
robot_observation_processor=robot_observation_processor,
)
if events["rerecord_episode"]:
examples/lekiwi/rollout.py
+77
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@@ -0,0 +1,77 @@
# !/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run a trained policy on LeKiwi without recording (base rollout).
Uses the rollout engine's :class:`BaseStrategy` (autonomous execution,
no dataset) with :class:`SyncInferenceConfig` (inline policy call per
control tick). For a CLI entry point with the same capabilities plus
recording, upload, and human-in-the-loop variants, see ``lerobot-rollout``.
"""
from lerobot.configs import PreTrainedConfig
from lerobot.robots.lekiwi import LeKiwiClientConfig
from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
from lerobot.rollout.inference import SyncInferenceConfig
from lerobot.rollout.strategies import BaseStrategy
from lerobot.utils.process import ProcessSignalHandler
from lerobot.utils.utils import init_logging
FPS = 30
DURATION_SEC = 60
TASK_DESCRIPTION = "My task description"
HF_MODEL_ID = "<hf_username>/<model_repo_id>"
def main():
init_logging()
# Robot: LeKiwi client — make sure lekiwi_host is already running on the robot.
robot_config = LeKiwiClientConfig(remote_ip="172.18.134.136", id="lekiwi")
# Policy: load the pretrained config. ``pretrained_path`` is read downstream
# by ``build_rollout_context`` to reload the full model.
policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
policy_config.pretrained_path = HF_MODEL_ID
# Assemble the rollout config: base strategy (no recording) + sync inference.
cfg = RolloutConfig(
robot=robot_config,
policy=policy_config,
strategy=BaseStrategyConfig(),
inference=SyncInferenceConfig(),
fps=FPS,
duration=DURATION_SEC,
task=TASK_DESCRIPTION,
)
# Graceful Ctrl-C: the strategy loop exits when shutdown_event is set.
signal_handler = ProcessSignalHandler(use_threads=True)
# Build the context (connects robot, loads policy, wires the inference strategy).
# No custom processors here — LeKiwi runs on raw joint features.
ctx = build_rollout_context(cfg, signal_handler.shutdown_event)
strategy = BaseStrategy(cfg.strategy)
try:
strategy.setup(ctx)
strategy.run(ctx)
finally:
strategy.teardown(ctx)
if __name__ == "__main__":
main()