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Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States

文献类型：期刊论文

作者Wang, Xiaona2,3,4; Chen, Jiahao2,3,4; Qiao, Hong1,2,3,4
刊名IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
出版日期2022-12-01
卷号14期号:4页码:1691-1704
ISSN号2379-8920
关键词Muscles Recurrent neural networks Mathematical models Bio-inspired control Robot kinematics Tendons Musculoskeletal system Biologically inspired control motor cortex movement preparation musculoskeletal system recurrent neural network (RNN)
DOI10.1109/TCDS.2021.3136854
通讯作者Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要Musculoskeletal robot with high precision and robustness is a promising direction for the next generation of robots. However, motion learning and rapid generalization of complex musculoskeletal systems are still challenging. Therefore, inspired by the movement preparation mechanism of the motor cortex, this article proposes a motion learning framework based on the recurrent neural network (RNN) modulated by initial states. First, two RNNs are introduced as a preparation network and an execution network to generate initial states of the execution network and time-varying motor commands of movement, respectively. The preparation network is trained by a reward-modulated learning rule, and the execution network is fixed. With the modulation of initial states, initial states can be explicitly expressed as knowledge of movements. By dividing the preparation and execution of movements into two RNNs, the motion learning is accelerated to converge under the application of the node-perturbation method. Second, with the utilization of learned initial states, a rapid generalization method for new movement targets is proposed. Initial states of unlearned movements can be computed by searching for low-dimensional ones in latent space constructed by learned initial states and then transforming them into the whole neural space. The proposed framework is verified in simulation with a musculoskeletal model. The results indicate that the proposed motion learning framework can realize goal-oriented movements of the musculoskeletal system with high precision and significantly improve the generalization efficiency for new movements.
WOS关键词MOVEMENT ; DYNAMICS ; NEUROSCIENCE ; SIMULATION ; MODEL
资助项目National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91948303] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100]
WOS研究方向Computer Science ; Robotics ; Neurosciences & Neurology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000916821100033
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/51344]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Qiao, Hong
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xiaona,Chen, Jiahao,Qiao, Hong. Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2022,14(4):1691-1704.
APA Wang, Xiaona,Chen, Jiahao,&Qiao, Hong.(2022).Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,14(4),1691-1704.
MLA Wang, Xiaona,et al."Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 14.4(2022):1691-1704.

入库方式： OAI收割

来源：自动化研究所

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