Robot skill learning for flexible assembly based on deep reinforcement learning
基于深度强化学习的柔性装配机器人技能学习

摘要

本研究提出一种基于深度强化学习的柔性装配机器人技能学习方法，旨在提升机器人在复杂、多变工业环境中的装配适应性与任务完成精度。通过构建状态-动作映射策略网络，实现机器人对装配流程的自主学习与策略优化。结合仿真训练与现实迁移，验证了所提方法在多种典型装配任务中的有效性与鲁棒性。实验结果显示，该方法在装配精度、策略泛化与动态响应方面具有显著优势，可满足智能制造系统中对高柔性与智能化装配的技术需求。

Abstract

This study proposes a robot skill learning method for flexible assembly based on deep reinforcement learning, aiming to improve the robot's assembly adaptability and task completion accuracy in complex and variable industrial environments. By constructing a state-action mapping strategy network, the robot's autonomous learning and strategy optimization of the assembly process are realized. Combining simulation training and real-world transfer, the effectiveness and robustness of the proposed method in various typical assembly tasks are verified. Experimental results show that this method has significant advantages in assembly accuracy, strategy generalization, and dynamic response, which can meet the technical requirements for high flexibility and intelligent assembly in intelligent manufacturing systems.