Reinforcement Learning Based Variable Impedance Control for High Precision Human-robot Collaboration Tasks | |
Meng, Yan1,2; Su, Jianhua1; Wu, Jiaxi1,2 | |
2021-09 | |
会议日期 | July 3-5, 2021 |
会议地点 | 线上 |
英文摘要 | Human-robot collaboration is an important area with great potential in intelligent manufacturing. Due to the diversity of collaboration tasks, robot collaboration skills should have the ability to adapt to different skills. However, problems such as skill expression and generalization are challenging. Meanwhile, the differences in the skills of various operators bring difficulties to collaborative robots. This work develops a variable impedance learning method for human-robot collab oration assembly. Unlike most previous work that mainly dis cussed a special human collaborator with the fixed impedance parameters, this work learns a robot impedance by reinforce ment learning. We aim to make the inertia, damping, and stiffness parameters adaptive by Proximal Policy Optimization (PPO) algorithm. Hence, we can let the robot collaborate with various human collaborators to accomplish a high-precision assembly task. Two experiment results illustrate the validity of the proposed method. The detailed experimental videos are available at https://youtu.be/AJyjW2NwA74. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48677] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
作者单位 | 1.the State Key Laboratory for Manage ment and Control of Complex System, Institute of Automation, Chinese Academy of Science, Beijing 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing |
推荐引用方式 GB/T 7714 | Meng, Yan,Su, Jianhua,Wu, Jiaxi. Reinforcement Learning Based Variable Impedance Control for High Precision Human-robot Collaboration Tasks[C]. 见:. 线上. July 3-5, 2021. |
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