Augmenting robotic disassembly skill: combining compliance control strategy with reinforcement learning for twist-pulling disassembly *

Abstract

Efficient robotic disassembly of end-of-life products is often impeded by inherent uncertainties in product condition and unknown internal structures. Conventional disassembly methods face challenges when adaptive exploration is required—particularly in cap-shaft disassembly, where connection mechanisms are frequently concealed. This paper proposes a novel robotic twist-and-pull disassembly strategy that integrates compliance control with reinforcement learning (RL). By enabling the robot to adapt to unknown connection geometries and systematic misalignments, the approach enhances the capabilities of robotic skill and reduces dependence on precisely pre-programmed trajectories. Experimental results confirm that the proposed strategy substantially improves robotic disassembly performance, improves RL training success rate, and demonstrates strong domain transferability, supporting its application across varied disassembly contexts.