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--- |
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datasets: |
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- shuohsuan/grasp_data_000_009 |
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- shuohsuan/grasp_data_010_019 |
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- shuohsuan/grasp_data_020_029 |
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- shuohsuan/grasp_data_030_039 |
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- shuohsuan/grasp_data_040_049 |
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- shuohsuan/grasp_data_050_059 |
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- shuohsuan/grasp_data_060_069 |
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- shuohsuan/grasp_data_070_079 |
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- shuohsuan/grasp_data_080_089 |
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- shuohsuan/grasp_data_090_099 |
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- shuohsuan/grasp_data_100_109 |
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- shuohsuan/grasp_data_110_119 |
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- shuohsuan/grasp_data_120_129 |
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- shuohsuan/grasp_data_130_139 |
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- shuohsuan/grasp_data_140_149 |
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- shuohsuan/grasp_data_150_159 |
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- shuohsuan/grasp_data_160_169 |
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- shuohsuan/grasp_data_170_179 |
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- shuohsuan/grasp_data_180_189 |
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- shuohsuan/grasp_data_190_199 |
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- shuohsuan/grasp_data_200_209 |
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- shuohsuan/grasp_data_210_219 |
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- shuohsuan/grasp_data_220_229 |
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- shuohsuan/grasp_data_230_239 |
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- shuohsuan/grasp_data_240_249 |
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- shuohsuan/grasp_data_250_259 |
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- shuohsuan/grasp_data_260_269 |
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- shuohsuan/grasp_data_270_279 |
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- shuohsuan/grasp_data_280_289 |
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- shuohsuan/grasp_data_290_299 |
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- shuohsuan/grasp_data_300_309 |
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- shuohsuan/grasp_data_310_319 |
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- shuohsuan/grasp_data_320_329 |
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- shuohsuan/grasp_data_330_339 |
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- shuohsuan/grasp_data_340_349 |
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- shuohsuan/grasp_data_350_359 |
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- shuohsuan/grasp_data_360_369 |
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- shuohsuan/grasp_data_370_379 |
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- shuohsuan/grasp_data_380_389 |
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- shuohsuan/grasp_data_390_399 |
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- shuohsuan/grasp_data_400_409 |
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- shuohsuan/grasp_data_410_419 |
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- shuohsuan/grasp_data_420_429 |
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- shuohsuan/grasp_data_430_439 |
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- shuohsuan/grasp_data_440_449 |
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- shuohsuan/grasp_data_450_459 |
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- shuohsuan/grasp_data_460_469 |
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- shuohsuan/grasp_data_470_479 |
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- shuohsuan/grasp_data_480_489 |
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- shuohsuan/grasp_data_490_499 |
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- shuohsuan/grasp_data_500_509 |
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- shuohsuan/grasp_data_510_519 |
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- shuohsuan/grasp_data_520_529 |
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- shuohsuan/grasp_data_530_539 |
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- shuohsuan/grasp_data_540_549 |
|
|
- shuohsuan/grasp_data_550_559 |
|
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- shuohsuan/grasp_data_560_569 |
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- shuohsuan/grasp_data_570_579 |
|
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- shuohsuan/grasp_data_580_589 |
|
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- shuohsuan/grasp_data_590_599 |
|
|
- shuohsuan/grasp_data_600_609 |
|
|
- shuohsuan/grasp_data_610_619 |
|
|
- shuohsuan/grasp_data_620_629 |
|
|
- shuohsuan/grasp_data_630_639 |
|
|
- shuohsuan/grasp_data_640_649 |
|
|
- shuohsuan/grasp_data_650_659 |
|
|
- shuohsuan/grasp_data_660_669 |
|
|
- shuohsuan/grasp_data_670_679 |
|
|
- shuohsuan/grasp_data_680_689 |
|
|
- shuohsuan/grasp_data_690_699 |
|
|
- shuohsuan/grasp_data_700_709 |
|
|
- shuohsuan/grasp_data_710_719 |
|
|
- shuohsuan/grasp_data_720_729 |
|
|
- shuohsuan/grasp_data_730_739 |
|
|
- shuohsuan/grasp_data_740_749 |
|
|
- shuohsuan/grasp_data_750_759 |
|
|
- shuohsuan/grasp_data_760_769 |
|
|
- shuohsuan/grasp_data_770_779 |
|
|
- shuohsuan/grasp_data_780_789 |
|
|
- shuohsuan/grasp_data_790_799 |
|
|
- shuohsuan/grasp_data_800_809 |
|
|
- shuohsuan/grasp_data_810_819 |
|
|
- shuohsuan/grasp_data_820_829 |
|
|
- shuohsuan/grasp_data_830_839 |
|
|
- shuohsuan/grasp_data_840_849 |
|
|
- shuohsuan/grasp_data_850_859 |
|
|
- shuohsuan/grasp_data_860_869 |
|
|
- shuohsuan/grasp_data_870_879 |
|
|
- shuohsuan/grasp_data_880_889 |
|
|
- shuohsuan/grasp_data_890_899 |
|
|
- shuohsuan/grasp_data_900_909 |
|
|
- shuohsuan/grasp_data_910_919 |
|
|
- shuohsuan/grasp_data_920_929 |
|
|
- shuohsuan/grasp_data_930_939 |
|
|
- shuohsuan/grasp_data_940_949 |
|
|
- shuohsuan/grasp_data_950_959 |
|
|
- shuohsuan/grasp_data_960_969 |
|
|
- shuohsuan/grasp_data_970_979 |
|
|
- shuohsuan/grasp_data_980_989 |
|
|
- shuohsuan/grasp_data_990_999 |
|
|
library_name: lerobot |
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license: apache-2.0 |
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model_name: act |
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pipeline_tag: robotics |
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tags: |
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- lerobot |
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- robotics |
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- act |
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--- |
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# Model Card for act |
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<!-- Provide a quick summary of what the model is/does. --> |
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[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. |
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). |
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). |
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--- |
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## How to Get Started with the Model |
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). |
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Below is the short version on how to train and run inference/eval: |
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### Train from scratch |
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```bash |
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python -m lerobot.scripts.train \ |
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--dataset.repo_id=${HF_USER}/<dataset> \ |
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--policy.type=act \ |
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--output_dir=outputs/train/<desired_policy_repo_id> \ |
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--job_name=lerobot_training \ |
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--policy.device=cuda \ |
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id> |
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--wandb.enable=true |
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``` |
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*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.* |
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### Evaluate the policy/run inference |
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```bash |
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python -m lerobot.record \ |
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--robot.type=so100_follower \ |
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--dataset.repo_id=<hf_user>/eval_<dataset> \ |
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--policy.path=<hf_user>/<desired_policy_repo_id> \ |
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--episodes=10 |
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``` |
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. |
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--- |
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## Model Details |
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* **License:** apache-2.0 |
