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

doi:10.1109/TCDS.2020.3045574

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

	Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the Control of Musculoskeletal Systems
	Chen, Jiahao1,2,3 ; Qiao, Hong1,2,3,4
发表期刊	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
ISSN	2379-8920
	2022-06-01
卷号	14 期号:2 页码:424-436
通讯作者	Qiao, Hong(hong.qiao@ia.ac.cn)
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.
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)
DOI	10.1109/TCDS.2020.3045574
关键词[WOS]	MUSCLE SYNERGIES ; CORTICAL REPRESENTATION ; DYNAMIC SIMULATIONS ; DIRECTION ; ARM
收录类别	SCI
语种	英语
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]
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 ; Robotics ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS记录号	WOS:000809402600019
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：35[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/173211/49217
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.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 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	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.