Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot

doi:10.1109/TNNLS.2021.3071196

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot
	Zhong, Shanlin1,2 ; Zhou, Junjie 1,3; Qiao, Hong1,4,5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-04-15
页码	16
通讯作者	Qiao, Hong(hong.qiao@ia.ac.cn)
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.
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)
DOI	10.1109/TNNLS.2021.3071196
关键词[WOS]	DYNAMIC SIMULATIONS ; TRACKING CONTROL ; MOVEMENT ; MODEL ; SEROTONIN ; MECHANISM ; NEURONS ; TIME
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000732076900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46993
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Qiao, Hong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Inst Neurosci, Beijing 200031, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.