Learning Smooth and Omnidirectional Locomotion for Quadruped Robots
文献类型:会议论文
作者 | Wu, Jiaxi2,3; Wang, Chen'an1; Zhang, Dianmin2,3; Zhong, Shanlin2,3; Wang, Boxing2,3; Qiao, Hong2,3 |
出版日期 | 2021-07 |
会议日期 | 2021-7 |
会议地点 | Chongqing, China |
关键词 | Quadruped Robot Reinforcement Learning |
英文摘要 | It often takes a lot of trial and error to get a quadruped robot to learn a proper and natural gait directly through reinforcement learning. Moreover, it requires plenty of attempts and clever reward settings to learn appropriate locomotion. However, the success rate of network convergence is still relatively low. In this paper, the referred trajectory, inverse kinematics, and transformation loss are integrated into the training process of reinforcement learning as prior knowledge. Therefore reinforcement learning only needs to search for the optimal solution around the referred trajectory, making it easier to find the appropriate locomotion and guarantee convergence. When testing, a PD controller is fused into the trained model to reduce the velocity following error. Based on the above ideas, we propose two control framework - single closed-loop and double closed-loop. And their effectiveness is proved through experiments. It can efficiently help quadruped robots learn appropriate gait and realize smooth and omnidirectional locomotion, which all learned in one model. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/48523] |
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 |
通讯作者 | Qiao, Hong |
作者单位 | 1.School of Mechanical Engineering, University of Science and Technology Beijing 2.the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences 3.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wu, Jiaxi,Wang, Chen'an,Zhang, Dianmin,et al. Learning Smooth and Omnidirectional Locomotion for Quadruped Robots[C]. 见:. Chongqing, China. 2021-7. |
入库方式: OAI收割
来源:自动化研究所
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