A Task-Oriented Framework for Autonomous Robotic Disassembly and Reassembly in Remanufacturing

Abstract

Remanufacturing involves contact-rich disassembly and assembly operations that remain challenging for robotic automation. While disassembly trajectories can be obtained reliably, collecting assembly demonstrations is often costly and impractical. This study proposes a framework that enables assembly skills to be learned from disassembly experience. A task-oriented remanufacturing model is introduced to integrate perception, planning, and execution in an autonomous workflow. Disassembly trajectories are used to construct an axis-based progress representation combining pose and force-torque feedback, which provides guidance for reinforcement learning without manually designed reward functions. The method is validated in simulation and on a physical robotic platform using three representative skills: direct removal, lateral extraction, and twist-pull. Results show improved training efficiency and higher assembly success rates compared with conventional reward design and inverse reinforcement learning methods. Physical experiments further demonstrate robustness under typical remanufacturing disturbances such as grasping deviation and axial misalignment, indicating that disassembly knowledge can effectively guide assembly learning.