Robotic Disassembly Skill Acquisition Based on Reinforcement Learning With External Knowledge Injection

Abstract

Reinforcement learning (RL) has great potential for skill acquisition in robotic disassembly, but the process in real-world scenarios remains inefficient. In response, we present an efficient RL-based method for acquiring disassembly skills, incorporating relevant external knowledge to enhance learning performance. This method aims at a rapid and stable acquisition of disassembly skills which cable with transferability. The successful disassembly of various types of clearance-fit components highlights the practical applicability of the method. In this content, we design both control policies and reward functions according to the disassembly knowledge to guide and optimize the RL process. Our findings indicate that augmenting RL with external knowledge not only enhances learning efficiency, but also significantly improves skill performance. Comparative evaluations of disassembly skills obtained by different methods on the same tasks demonstrate that the proposed method yields skills with better transferability. Additionally, the method outlined is adaptable and can be implemented with various RL algorithms, making it a robust solution for enhancing robotic disassembly operations.