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Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot

文献类型：期刊论文

作者Zhong, Shanlin2,3; Zhou, Junjie1,3; Qiao, Hong3,4,5
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-04-15
页码16
关键词Robots Modulation Robot kinematics Neurons Brain modeling Recurrent neural networks Encoding Biologically inspired control gain modulation motor primitives musculoskeletal robot recurrent neural network (RNN)
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3071196
通讯作者Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要The motor cortex can arouse abundant transient responses to generate complex movements with the regulation of neuromodulators, while its architecture remains unchanged. This characteristic endows humans with flexible and robust abilities in adapting to dynamic environments, which is exactly the bottleneck in the control of complex robots. In this article, inspired by the mechanisms of the motor cortex in encoding information and modulating motor commands, a biologically plausible gain-modulated recurrent neural network is proposed to control a highly redundant, coupled, and nonlinear musculoskeletal robot. As the characteristics observed in the motor cortex, this network is able to learn gain patterns for arousing transient responses to complete the desired movements, while the connections of synapses keep unchanged, and the dynamic stability of the network is maintained. A novel learning rule that mimics the mechanism of neuromodulators in regulating the learning process of the brain is put forward to learn gain patterns effectively. Meanwhile, inspired by error-based movement correction mechanism in the cerebellum, gain patterns learned from demonstration samples are leveraged as prior knowledge to improve calculation efficiency of the network in controlling novel movements. Experiments were conducted on an upper extremity musculoskeletal model with 11 muscles and a general articulated robot to perform goal-directed tasks. The results indicate that the gain-modulated neural network can effectively control a complex robot to complete various movements with high accuracy, and the proposed algorithms make it possible to realize fast generalization and incremental learning ability.
WOS关键词DYNAMIC SIMULATIONS ; TRACKING CONTROL ; MOVEMENT ; MODEL ; SEROTONIN ; MECHANISM ; NEURONS ; TIME
资助项目National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[91948303] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732076900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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/173211/46992]  
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.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Neurosci, Beijing 200031, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Shanlin,Zhou, Junjie,Qiao, Hong. Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:16.
APA Zhong, Shanlin,Zhou, Junjie,&Qiao, Hong.(2021).Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,16.
MLA Zhong, Shanlin,et al."Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):16.

入库方式： OAI收割

来源：自动化研究所

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