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Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system | |
Jiahao Chen1,2,3![]() ![]() | |
发表期刊 | IEEE Transactions on Systems, Man, and Cybernetics: Systems
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2021 | |
卷号 | 51期号:6页码:3993 - 4006 |
摘要 | Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics. |
关键词 | Motion generalization motion learning muscle synergy musculoskeletal system neuromuscular control |
收录类别 | SCI |
WOS记录号 | WOS:000652103000058 |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 受人机理启发的类脑控制和类肌肉骨骼系统理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44405 |
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 | Hong Qiao |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.Research Center for Brain Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, 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 | Jiahao Chen,Hong Qiao. Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2021,51(6):3993 - 4006. |
APA | Jiahao Chen,&Hong Qiao.(2021).Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system.IEEE Transactions on Systems, Man, and Cybernetics: Systems,51(6),3993 - 4006. |
MLA | Jiahao Chen,et al."Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system".IEEE Transactions on Systems, Man, and Cybernetics: Systems 51.6(2021):3993 - 4006. |
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