You can boost industrial robot performance by fine-tuning pretrained vision-action models with RL while constraining them to stay near their original behavior—this keeps inference fast while improving contact stability and force safety.
PAC-ACT improves robot policies for precise industrial tasks by adding reinforcement learning to pretrained action-chunking models. It uses a behavior-prior constraint to keep learned policies close to their training distribution while optimizing for contact safety and task success, maintaining the speed and memory efficiency needed for real-time control.