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Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning 期刊论文
Frontiers in Neurorobotics, 2021, 卷号: 15, 页码: 658280
作者:  Duan, Haonan;  Wang, Peng;  Huang, Yayu;  Xu, Guangyun;  Wei, Wei;  Shen, Xiaofei
Adobe PDF(3145Kb)  |  收藏  |  浏览/下载：42/14  |  提交时间：2024/05/29
Robotics  Dexterous grasping  Point Cloud  Deep learning  
Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 卷号: 14, 期号: 4, 页码: 1691-1704
作者:  Wang, Xiaona;  Chen, Jiahao;  Qiao, Hong
Adobe PDF(2080Kb)  |  收藏  |  浏览/下载：163/20  |  提交时间：2023/03/20
Muscles  Recurrent neural networks  Mathematical models  Bio-inspired control  Robot kinematics  Tendons  Musculoskeletal system  Biologically inspired control  motor cortex  movement preparation  musculoskeletal system  recurrent neural network (RNN)  
Constructing Constraint Force Field in Musculoskeletal Robot by Co-optimizing Muscle Arrangements and Constant Activations 会议论文
, PADUA，Italy, 2022-7-18
作者:  Zhong SL(钟汕林);  Zhou JJ(周俊杰);  Wu W(吴伟)
Adobe PDF(937Kb)  |  收藏  |  浏览/下载：343/94  |  提交时间：2022/06/14
Musculoskeletal robot  Constraint Force Field  High precision  Control Schemes of robot  
Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 14
作者:  Zhou, Junjie;  Zhong, Shanlin;  Wu, Wei
Adobe PDF(5440Kb)  |  收藏  |  浏览/下载：307/52  |  提交时间：2022/01/27
Brain-inspired decision making  Fitts' law  Motion generation  Musculoskeletal system  Speed-accuracy tradeoff (SAT)  
Connecting Model-Based and Model-Free Control With Emotion Modulation in Learning Systems 期刊论文
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 卷号: 10, 期号: 4, 页码: 1-15
作者:  Huang, Xiao;  Wu, Wei;  Qiao, Hong
浏览  |  Adobe PDF(1614Kb)  |  收藏  |  浏览/下载：302/100  |  提交时间：2020/06/09
Brain-inspired computing  decision-making  emotion modulation  emotion-cognition interactions  reinforcement learning