Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System
文献类型：期刊论文
| 作者 | Zhou, Junjie1,2,3 ; Zhong, Shanlin1,2,3; Wu, Wei1
|
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2021-09-13 |
| 页码 | 14 |
| 关键词 | Muscles Adaptation models Task analysis Arms Mathematical model Integrated circuit modeling Basal ganglia Brain-inspired decision making Fitts' law motion generation musculoskeletal system speed-accuracy tradeoff (SAT) |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2021.3109021 |
| 通讯作者 | Zhou, Junjie(zhoujunjie2017@ia.ac.cn) |
| 英文摘要 | Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed-accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts' Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model. |
| WOS关键词 | ANTICIPATORY POSTURAL ADJUSTMENTS ; INFORMATION CAPACITY ; BASAL GANGLIA ; FITTS LAW ; MUSCLE ; ALGORITHMS ; MECHANISMS ; SIMULATION ; PATHWAYS |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000732323900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46803]  |
| 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 | Zhou, Junjie |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Res & Applicat Robot Intelligence, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Junjie,Zhong, Shanlin,Wu, Wei. Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:14. |
| APA | Zhou, Junjie,Zhong, Shanlin,&Wu, Wei.(2021).Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System.IEEE TRANSACTIONS ON CYBERNETICS,14. |
| MLA | Zhou, Junjie,et al."Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System".IEEE TRANSACTIONS ON CYBERNETICS (2021):14. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。