CASIA OpenIR
From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems
Zhou, Junjie1,2,3; Chen, Jiahao2,3,4; Deng, Hu1,3; Qiao, Hong1,2,5
Source PublicationFRONTIERS IN NEUROROBOTICS
ISSN1662-5218
2019-07-31
Volume13Pages:14
Corresponding AuthorQiao, Hong(hong.qiao@ia.ac.cn)
AbstractRedundant muscles in human-like musculoskeletal robots provide additional dimensions to the solution space. Consequently, the computation of muscle excitations remains an open question. Conventional methods like dynamic optimization and reinforcement learning usually have high computational costs or unstable learning processes when applied to a complex musculoskeletal system. Inspired by human learning, we propose a phased target learning framework that provides different targets to learners at varying levels, to guide their training process and to avoid local optima. By introducing an extra layer of neurons reflecting a preference, we improve the Q-network method to generate continuous excitations. In addition, based on information transmission in the human nervous system, two kinds of biological noise sources are introduced into our framework to enhance exploration over the solution space. Tracking experiments based on a simplified musculoskeletal arm model indicate that under guidance of phased targets, the proposed framework prevents divergence of excitations, thus stabilizing training. Moreover, the enhanced exploration of solutions results in smaller motion errors. The phased target learning framework can be expanded for general-purpose reinforcement learning, and it provides a preliminary interpretation for modeling the mechanisms of human motion learning.
Keywordmusculoskeletal system human-inspired motion learning noise in nervous system reinforcement learning phased target learning
DOI10.3389/fnbot.2019.00061
WOS KeywordPHYSICAL LIMITS ; MUSCLE ; MODEL ; MOVEMENT ; CONTRACTION ; PREDICTION ; CRITERION ; TENDON ; NOISE
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000] ; development of science and technology of Guangdong Province special fund project[2016B090910001]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; development of science and technology of Guangdong Province special fund project
WOS Research AreaComputer Science ; Robotics ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS IDWOS:000478024100002
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27764
Collection中国科学院自动化研究所
Corresponding AuthorQiao, Hong
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China
5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Junjie,Chen, Jiahao,Deng, Hu,et al. From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems[J]. FRONTIERS IN NEUROROBOTICS,2019,13:14.
APA Zhou, Junjie,Chen, Jiahao,Deng, Hu,&Qiao, Hong.(2019).From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems.FRONTIERS IN NEUROROBOTICS,13,14.
MLA Zhou, Junjie,et al."From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems".FRONTIERS IN NEUROROBOTICS 13(2019):14.
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