ShanghaiTech University Knowledge Management System
| PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots | |
| 2022-03 | |
| Source Publication | MACHINES
 |
| EISSN | 2075-1702 |
| Volume | 10Issue:3 |
| Status | 已发表 |
| DOI | 10.3390/machines10030185 |
| Abstract | 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. |
| Keyword | machine learning robot locomotion energy efficiency deep reinforcement learning |
| URL | 查看原文 |
| Indexed By | SCI ; SCIE |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61850410527] ; Shanghai Young Oriental Scholars Grant[0830000081] |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic ; Engineering, Mechanical |
| WOS ID | WOS:000775037200001 |
| Publisher | MDPI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://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 Author | Rosendo, Andre |
| Affiliation | ShanghaiTech Univ, Sch Informat Sci & Technol, Living Machines Lab, Shanghai 201210, Peoples R China |
| First Author Affilication | School of Information Science and Technology |
| Corresponding Author Affilication | School of Information Science and Technology |
| First Signature Affilication | School 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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