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
Volume13Issue:61Pages:14
Abstract

Redundant 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 ProjectStrategic Priority Research Program of Chinese Academy of Science[XDB32000000] ; National Key Research and Development Program of China[2017YFB1300203] ; National Key Research and Development Program of China[2017YFB1300200] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000]
WOS Research AreaComputer Science ; Robotics ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS IDWOS:000478024100002
PublisherFRONTIERS MEDIA SA
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27764
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorQiao, Hong
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
3.Beijing Key Laboratory of Research and Application for Robotic Intelligence of “Hand–Eye–Brain” Interaction, Beijing, China
4.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
5.CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 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(61):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(61),14.
MLA Zhou, Junjie,et al."From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems".FRONTIERS IN NEUROROBOTICS 13.61(2019):14.
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