pac-act-90b33a2d·1 events·first seen Aliases: PAC-ACT
PAC-ACT is a reinforcement learning post-training framework that fine-tunes pretrained Action Chunking Transformer (ACT) policies for precision industrial contact manipulation tasks. The method reformulates policy optimization at the chunk level, introduces an actor-critic architecture adapted for ACT, and uses a hybrid behavior-prior constraint to prevent distribution shift during online RL fine-tuning. Experiments on industrial contact benchmarks show significant improvements in task success, contact stability, and force safety — including a 46x reduction in force readings above 60 N on a contour-following task — while preserving low latency and GPU memory usage.