A Robust and Compliant Robotic Assembly Control Strategy for Batch Precision Assembly Task

In many high-precision industrial applications, robots are deployed to perform precision peg-in-hole assembly on large batches of manufactured pegs and holes. When the nominal design adopts a transition fit, machining errors may cause each peg–hole pair to exhibit either a clearance fit or a slight interference fit, with an unknown and continuously varying fit amount. This paper addresses robotic batch precision assembly tasks under uncertain fit types and fit amounts, and proposes a novel multi-stage framework for systematically constructing a robust assembly control strategy. The overall batch precision assembly task is first decomposed into multiple deterministic subtasks characterized by different but fixed fit amounts. For these subtasks, a force-vision fusion controller-driven reinforcement learning method combined with a multi-task reinforcement learning training framework (FVFC-MTRL) is developed to jointly learn multiple compliance control strategies. Subsequently, a multi-teacher policy distillation scheme is designed to integrate multiple trained strategies into a single student strategy, yielding a robust control strategy. Real-world experiments demonstrate that the proposed method successfully constructs a robust control strategy for high-precision assembly tasks with varying fit types and fit amounts. Moreover, the MTRL framework significantly improves training efficiency by 50%, and the final control strategy achieves superior force compliance and a higher success rate compared with several existing methods.