PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots
2022-03
Source PublicationMACHINES
EISSN2075-1702
Volume10Issue:3
Status已发表
DOI10.3390/machines10030185
AbstractEnergy 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.
Keywordmachine learning robot locomotion energy efficiency deep reinforcement learning
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Indexed BySCI ; SCIE
Language英语
Funding ProjectNational Natural Science Foundation of China[61850410527] ; Shanghai Young Oriental Scholars Grant[0830000081]
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic ; Engineering, Mechanical
WOS IDWOS:000775037200001
PublisherMDPI
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Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/171471
Collection信息科学与技术学院_PI研究组_ANDRE LUIS MACEDO ROSENDO SILVA组
Corresponding AuthorRosendo, Andre
Affiliation
ShanghaiTech Univ, Sch Informat Sci & Technol, Living Machines Lab, Shanghai 201210, Peoples R China
First Author AffilicationSchool of Information Science and Technology
Corresponding Author AffilicationSchool of Information Science and Technology
First Signature AffilicationSchool of Information Science and Technology
Recommended Citation
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).
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