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A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot

文献类型：期刊论文

作者Zhang, Jinhan1,2; Chen, Jiahao1,2; Zhong, Shanlin1,2; Qiao, Hong1,2
刊名JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
出版日期2024-02-01
卷号37期号:1页码:82-113
关键词Basal ganglia and cerebellum bio-inspired integration model motion learning muscu-loskeletal robot reinforcement learning
ISSN号1009-6124
DOI10.1007/s11424-024-3414-7
通讯作者Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and generalization. The cooperations of multiple brain regions are crucial to improving motion performance. Inspired by the neural mechanisms of structures, functions, and interconnections of basal ganglia and cerebellum, a biologically inspired integration model for motor learning of musculoskeletal robots is proposed. Based on the neural characteristics of the basal ganglia, the basal ganglia actor network, which mainly simulates the dorsal striatum, outputs motion commands, and the basal ganglia critic network, which simulates the ventral striatum, estimates action-state values. Their network parameters are updated using the soft actor-critic method. Based on the sensorimotor prediction mechanism of the cerebellum, the cerebellum network evaluates the state feature extraction quality of the basal ganglia actor network and then updates the weights of its feature layer. This learning method is proven to converge to the optimal policy. Furthermore, drawing on the mechanism of dopaminergic dynamic regulation in the basal ganglia, the adaptive adjustment of target entropy and the dopaminergic experience replay are proposed to further improve the integration model, which contributes to the exploration-exploitation trade-off of motor learning. The bio-inspired integration model is validated on a musculoskeletal system. Experimental results indicate that this model can effectively control the musculoskeletal robot to accomplish the motion task from random starting locations to random target positions with high precision and robustness.
WOS关键词ENCODE ; MODULATION ; SIMULATION ; ATTITUDE ; UAV
资助项目Major Project of Science and Technology Innovation 2030 Brain Science and Brain-Inspired Intelligence[2021ZD0200408] ; National Natural Science Foundation of China[62203439] ; National Natural Science Foundation of China[62203443] ; Major program of the National Natural Science Foundation of China[T2293720] ; Major program of the National Natural Science Foundation of China[T2293723] ; Major program of the National Natural Science Foundation of China[T2293724]
WOS研究方向Mathematics
语种英语
WOS记录号WOS:001173126600007
出版者SPRINGER HEIDELBERG
资助机构Major Project of Science and Technology Innovation 2030 Brain Science and Brain-Inspired Intelligence ; National Natural Science Foundation of China ; Major program of the National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/57969]  
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, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jinhan,Chen, Jiahao,Zhong, Shanlin,et al. A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,2024,37(1):82-113.
APA Zhang, Jinhan,Chen, Jiahao,Zhong, Shanlin,&Qiao, Hong.(2024).A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot.JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,37(1),82-113.
MLA Zhang, Jinhan,et al."A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot".JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 37.1(2024):82-113.

入库方式： OAI收割

来源：自动化研究所

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