Hierarchical Terrain-Aware Control for Quadrupedal Locomotion by Combining Deep Reinforcement Learning and Optimal Control
文献类型：会议论文
| 作者 | Yao QF(么庆丰)1,2; Wang, Jilong; Wang DL(王东林); Yang, Shuyu; Zhang, Hongyin; Wang, Yinuo; Wu, Zhengqing |
| 出版日期 | 2021 |
| 会议日期 | September 27 - October 1, 2021 |
| 会议地点 | Prague, Czech republic |
| 页码 | 4546-4551 |
| 英文摘要 | Quadruped robots possess advantages on different terrains over other types of mobile robots by virtue of their flexible choices of foothold points. It is crucial to integrate terrain perception with motion planning to exploit the potential of quadruped robots. We propose a novel hierarchical terrain-aware control (HTC) framework, which leverages deep reinforcement learning (DRL) for the high-level planner and optimal control for the low-level controller. In general, traditional control methods yield better stability by using an optimization algorithm. In addition, DRL is able to offer more adaptive behavior. Our approach makes full use of the advantages of these two methods and possesses better adaptability and stability in challenging natural environments. Furthermore, the global height map of the terrain serves as visual information for the DRL, which determines the desired footholds for the robot's leg swings and body postures. Optimal control calculates the torque of the joints on the standing legs to maintain body balance. Our method is tested on various terrains both simulated and real environments. The experimental results show that HTC can effectively enhance the adaptability of the quadruped robot by coordinating body posture. |
| 产权排序 | 1 |
| 会议录 | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2153-0858 |
| ISBN号 | 978-1-6654-1714-3 |
| WOS记录号 | WOS:000755125503086 |
| 源URL | [http://ir.sia.cn/handle/173321/30497]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Wang DL(王东林) |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China; University of California Santa Cruz, Santa Cruz, CA 95064, United States; 2.Machine Intelligence Lab (MiLAB), School of Engineering, Westlake University, Hangzhou 310024, China; Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China |
| 推荐引用方式 GB/T 7714 | Yao QF,Wang, Jilong,Wang DL,et al. Hierarchical Terrain-Aware Control for Quadrupedal Locomotion by Combining Deep Reinforcement Learning and Optimal Control[C]. 见:. Prague, Czech republic. September 27 - October 1, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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