ACT Policy (Action Chunking Transformer)

A transformer-based imitation-learning policy commonly trained in lerobot for robotic manipulation. ACT (Action Chunking Transformer) predicts chunks of future actions from observations, a standard starting point for training so-101 policies from teleoperated demonstration datasets.

Usage context

• Trained on demonstration datasets recorded via SO-101 teleoperation.
• In the source conversation, the user planned to train ACT on home DGX Spark GPUs after dataset collection.

What matters most for a working policy

Dataset quality — demonstration consistency and camera placement (wrist-camera-setup) — generally dominates over raw GPU power or hyperparameter tuning. Good data on a modest GPU typically beats poor data on a strong GPU.

See also

• lerobot
• so-101
• imitation-learning
• wrist-camera-setup
• cross-embodiment-learning