Action Chunking with Transformers (ACT) 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. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
Independent publisher
Kim Min Jung
wego-mink
Models in Library1
Datasets in Library0
Models on Hugging Face2
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Published tasks: Robotics.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
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