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Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning
2022-10-01
发表期刊IEEE ROBOTICS AND AUTOMATION LETTERS (IF:4.6[JCR-2023],5.5[5-Year])
ISSN2377-3766
EISSN2377-3766
卷号7期号:4页码:1-8
发表状态已发表
DOI10.1109/LRA.2022.3191071
摘要

While quadruped robots usually have good stability and load capacity, bipedal robots offer a higher level of flexibility / adaptability to different tasks and environments. A multi-modal legged robot can take the best of both worlds. In this paper, we propose a multi-modal locomotion framework that is composed of a hand-crafted transition motion and a learning-based bipedal controllerlearnt by a novel algorithm called Automated Residual Reinforcement Learning. This framework aims to endow arbitrary quadruped robots with the ability to walk bipedally. In particular, we 1) design an additional supporting structure for a quadruped robot and a sequential multi-modal transition strategy; 2) propose a novel class of Reinforcement Learning algorithms for bipedal control and evaluate their performances in both simulation and the real world. Experimental results show that our proposed algorithms have the best performance in simulation and maintain a good performance in a real-world robot. Overall, our multi-modal robot could successfully switch between biped and quadruped, and walk in both modes. Experiment videos and code are available at https://chenaah.github.io/multimodal/. IEEE

关键词Anthropomorphic robots Biped locomotion Learning algorithms Machine design Multipurpose robots Evolutionary robotics Hip Humanoid robot Knee Legged locomotion Legged robots Multi-modal locomotion Quadrupedal robot Reinforcement learnings
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收录类别SCI ; SCIE ; EI
语种英语
WOS研究方向Robotics
WOS类目Robotics
WOS记录号WOS:000835813000063
出版者Institute of Electrical and Electronics Engineers Inc.
EI入藏号20223112529851
EI主题词Reinforcement learning
EI分类号461.3 Biomechanics, Bionics and Biomimetics ; 601 Mechanical Design ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 731.5 Robotics ; 731.6 Robot Applications
原始文献类型Article in Press
来源库IEEE
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文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/211747
专题信息科学与技术学院
信息科学与技术学院_PI研究组_ANDRE LUIS MACEDO ROSENDO SILVA组
信息科学与技术学院_硕士生
作者单位
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一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
Chen Yu,Andre Rosendo. Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2022,7(4):1-8.
APA Chen Yu,&Andre Rosendo.(2022).Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning.IEEE ROBOTICS AND AUTOMATION LETTERS,7(4),1-8.
MLA Chen Yu,et al."Multi-Modal Legged Locomotion Framework with Automated Residual Reinforcement Learning".IEEE ROBOTICS AND AUTOMATION LETTERS 7.4(2022):1-8.
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