Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems

doi:10.1109/TCDS.2020.3045574

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

	Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems
	Chen, Jiahao 2,3,4; Qiao, Hong 1,2,3,4
刊名	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
	2022-06-01
卷号	14 期号:2 页码:424-436
关键词	Muscles Musculoskeletal system Robots Statistics Sociology Recurrent neural networks Neurons Biologically inspired motor cortex muscle synergy musculoskeletal system neuromuscular control recurrent neural network (RNN)
ISSN号	2379-8920
DOI	10.1109/TCDS.2020.3045574
通讯作者	Qiao, Hong(hong.qiao@ia.ac.cn)
英文摘要	The musculoskeletal robot is a promising direction of the next-generation robots. However, current control methods of musculoskeletal robots lack multitask learning ability, great generalization, and biological plausibility. In this article, a motor-cortex-like recurrent neural network (RNN) and a reward-modulated multitask learning method are proposed. First, inspired by the dynamic system hypothesis of motor cortex, the RNN is introduced to transform movement targets into muscle excitations. The condition that makes an RNN generate motor-cortex-like consistent population response is investigated. Second, a reward-modulated multitask learning method of such an RNN is proposed. In the experiments, the control of a musculoskeletal system is realized with multitask learning ability, great generalization, and robustness for noises. Furthermore, the RNN and muscle excitations demonstrate motor-cortex-like consistent population response and human-like muscle synergies, respectively. Therefore, the proposed method has better performance and biological plausibility, and verifies the neural mechanisms in the robotic research.
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关键词	MUSCLE SYNERGIES ; CORTICAL REPRESENTATION ; DYNAMIC SIMULATIONS ; DIRECTION ; ARM
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:000809402600019
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/173211/49216]
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.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 2.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, 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	Chen, Jiahao,Qiao, Hong. Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2022,14(2):424-436.
APA	Chen, Jiahao,&Qiao, Hong.(2022).Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,14(2),424-436.
MLA	Chen, Jiahao,et al."Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 14.2(2022):424-436.