Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from tele-operated 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:

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.