Hierarchical Terrain-Aware Control for Quadrupedal Locomotion by Combining Deep Reinforcement Learning and Optimal Control

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 数字工厂研究室

	Hierarchical Terrain-Aware Control for Quadrupedal Locomotion by Combining Deep Reinforcement Learning and Optimal Control
	Yao QF(么庆丰)1,2; Wang, Jilong ; Wang DL(王东林); Yang, Shuyu ; Zhang, Hongyin ; Wang, Yinuo ; Wu, Zhengqing
	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
内容类型	会议论文
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.