PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots

doi:10.3390/machines10030185

KMS > 信息科学与技术学院 > PI研究组 > ANDRE LUIS MACEDO ROSENDO SILVA组

	PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots
	Zhu, Wangshu; Rosendo, Andre
	2022-03
发表期刊	MACHINES (IF:2.1[JCR-2023],2.2[5-Year])
EISSN	2075-1702
卷号	10 期号:3
发表状态	已发表
DOI	10.3390/machines10030185
摘要	Energy efficiency is critical for the locomotion of quadruped robots. However, energy efficiency values found in simulations do not transfer adequately to the real world. To address this issue, we present a novel method, named Policy Search Transfer Optimization (PSTO), which combines deep reinforcement learning and optimization to create energy-efficient locomotion for quadruped robots in the real world. The deep reinforcement learning and policy search process are performed by the TD3 algorithm and the policy is transferred to the open-loop control trajectory further optimized by numerical methods, and conducted on the robot in the real world. In order to ensure the high uniformity of the simulation results and the behavior of the hardware platform, we introduce and validate the accurate model in simulation including consistent size and fine-tuning parameters. We then validate those results with real-world experiments on the quadruped robot Ant by executing dynamic walking gaits with different leg lengths and numbers of amplifications. We analyze the results and show that our methods can outperform the control method provided by the state-of-the-art policy search algorithm TD3 and sinusoid function on both energy efficiency and speed.
关键词	machine learning robot locomotion energy efficiency deep reinforcement learning
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China[61850410527] ; Shanghai Young Oriental Scholars Grant[0830000081]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic ; Engineering, Mechanical
WOS记录号	WOS:000775037200001
出版者	MDPI
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/171471
专题	信息科学与技术学院_PI研究组_ANDRE LUIS MACEDO ROSENDO SILVA组
通讯作者	Rosendo, Andre
作者单位	ShanghaiTech Univ, Sch Informat Sci & Technol, Living Machines Lab, Shanghai 201210, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhu, Wangshu,Rosendo, Andre. PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots[J]. MACHINES,2022,10(3).
APA	Zhu, Wangshu,&Rosendo, Andre.(2022).PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots.MACHINES,10(3).
MLA	Zhu, Wangshu,et al."PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots".MACHINES 10.3(2022).