Industrial robots performing precision contact tasks need policies robust to pose errors and force constraints, but common vision-action models trained via behavior cloning suffer from distribution shift in contact-rich scenarios. This paper introduces a reinforcement-learning post-training method for Action Chunking Transformers, optimizing at the action-chunk level while preserving pretrained behavior through a hybrid constraint. Tested on industrial benchmarks, it improved task success and contact-force safety, notably reducing high-force incidents by 46-fold on a contour-following task. Applications include safer, more reliable industrial robotic manipulation, especially for delicate or contact-sensitive assembly and finishing operations.
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