Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot

doi:10.1109/TNNLS.2021.3071196

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot
	Zhong, Shanlin 2,3; Zhou, Junjie 1,3; Qiao, Hong 3,4,5
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	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
DOI	10.1109/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.
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关键词	DYNAMIC SIMULATIONS ; TRACKING CONTROL ; MOVEMENT ; MODEL ; SEROTONIN ; MECHANISM ; NEURONS ; TIME
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000732076900001
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
内容类型	期刊论文
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.