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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：110/1  |  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)  
Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 16
作者:  Zhong, Shanlin;  Zhou, Junjie;  Qiao, Hong
收藏  |  浏览/下载：230/0  |  提交时间：2022/01/27
Robots  Modulation  Robot kinematics  Neurons  Brain modeling  Recurrent neural networks  Encoding  Biologically inspired control  gain modulation  motor primitives  musculoskeletal robot  recurrent neural network (RNN)  
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：269/46  |  提交时间：2022/01/27
Brain-inspired decision making  Fitts' law  Motion generation  Musculoskeletal system  Speed-accuracy tradeoff (SAT)  
Structure transforming for constructing constraint force field in musculoskeletal robot 期刊论文
ASSEMBLY AUTOMATION, 2021, 卷号: 42, 期号: 2, 页码: 169-180
作者:  Zhong, Shanlin;  Chen, Ziyu;  Zhou, Junjie
Adobe PDF(2152Kb)  |  收藏  |  浏览/下载：334/60  |  提交时间：2021/12/28
High precision  Attractive region in environment  Constraint force field  Musculoskeletal robot  
受情绪调控机制启发的机器人运动决策方法研究 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2020
作者:  黄销
Adobe PDF(20542Kb)  |  收藏  |  浏览/下载：333/8  |  提交时间：2020/06/09
脑启发式计算  情绪生成与调节  情绪调控决策  基于模型动态规划  无模型学习  强化学习  
Computational modeling of Emotion-motivated Decisions for Continuous Control of Mobile Robots 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2020, 卷号: 13, 期号: 2020, 页码: 1-14
作者:  Huang, Xiao;  Wu, Wei;  Qiao, Hong
Adobe PDF(5970Kb)  |  收藏  |  浏览/下载：257/89  |  提交时间：2020/06/09
Brain-inspired Computing  Emotion-motivated Learning  Emotion-memory Interactions  Decision-making  Reinforcement Learning  
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
Adobe PDF(1614Kb)  |  收藏  |  浏览/下载：269/89  |  提交时间：2020/06/09
Brain-inspired computing  decision-making  emotion modulation  emotion-cognition interactions  reinforcement learning  
From Rough to Precise: Human-Inspired Phased Target Learning Framework for Redundant Musculoskeletal Systems 期刊论文
FRONTIERS IN NEUROROBOTICS, 2019, 卷号: 13, 期号: 61, 页码: 14
作者:  Zhou, Junjie;  Chen, Jiahao;  Deng, Hu;  Qiao, Hong
Adobe PDF(3654Kb)  |  收藏  |  浏览/下载：355/41  |  提交时间：2019/12/16
musculoskeletal system  human-inspired motion learning  noise in nervous system  reinforcement learning  phased target learning  
Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning 期刊论文
NEUROCOMPUTING, 2019, 卷号: 334, 期号: 2019, 页码: 11-24
作者:  Li, Yinlin;  Jia, Lihao;  Wang, Zidong;  Qian, Yang;  Qiao, Hong
浏览  |  Adobe PDF(4423Kb)  |  收藏  |  浏览/下载：488/95  |  提交时间：2019/07/12
Hand segmentation  Un-supervised  Semi-supervised  Deep convolutional neural network  Noisy label  
A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2018, 卷号: 10, 期号: 2, 页码: 420-431
作者:  Yin, Peijie;  Qiao, Hong;  Wu, Wei;  Qi, Lu;  Li, Yinlin;  Zhong, Shanlin;  Zhang, Bo
Adobe PDF(3887Kb)  |  收藏  |  浏览/下载：338/78  |  提交时间：2018/10/09
Biologically inspired model  object recognition  semantic learning  structural learning