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feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)
* feat(scripts): lerobot-rollout * fix(rollout) require dataset in dagger + use duration too * fix(docs): dagger num_episodes * test(rollout): fix expectations * fix(rollout): features check * fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config * docs(rollout): edit rename_map instructions * chore(rollout): multiple minor improvements * chore(rollout): address coments + minor improvements * fix(rollout): enable default * fix(tests): default value RTCConfig * fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): prevent relativeactions with sync inference engine Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): rtc reanchor to non normalized state Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): fixing the episode length to use hwc (#3469) also reducing default length to 5 minutes * feat(rollout): go back to initial position is now a config * fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470) * chore(rollout): note about dagger correction stage * chore(docs): update comments and docstring * fix(test): move rtc relative out of rollout module * fix(rollout): address the review comments --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co>
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@@ -34,7 +34,7 @@ pip install -e ".[smolvla]"
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### Using RTC with Pi0
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You can find a complete reference implementation in [eval_with_real_robot.py](examples/rtc/eval_with_real_robot.py).
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You can use `lerobot-rollout --strategy.type=base --inference.type=rtc` for RTC deployment on real robots.
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The snippet below provides a simplified pseudo-example of how RTC operates with Pi0 in your pipeline:
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```python
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@@ -137,8 +137,12 @@ The script generates a visualization of the denoising process, comparing standar
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## Testing RTC with a Real Robot
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```bash
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python examples/rtc/eval_with_real_robot.py \
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lerobot-rollout \
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--strategy.type=base \
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--policy.path=${HF_USERNAME}/policy_repo_id \
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--inference.type=rtc \
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--inference.rtc.execution_horizon=10 \
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--inference.rtc.max_guidance_weight=10.0 \
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--robot.type=so100_follower \
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--robot.port=/dev/tty.usbmodem58FA0834591 \
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--robot.cameras="{ gripper: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
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@@ -178,7 +182,7 @@ visualizer = RTCDebugVisualizer()
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# ... create plots
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```
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See `examples/rtc/eval_dataset.py` for a complete example of visualization.
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See `examples/rtc/eval_dataset.py` for a complete example of offline RTC visualization.
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## References
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