机构

• 自动化研究所 [12]
• 数学与系统科学研究院 [1]

采集方式

• OAI收割 [13]

内容类型

• 期刊论文 [11]
• 会议论文 [2]

发表日期

• 2024 [2]
• 2022 [2]
• 2021 [5]
• 2020 [1]
• 2019 [1]
• 2018 [1]
• 更多

学科主题

• 模式识别与智能系统 [1]

筛选

浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot 期刊论文  OAI收割 JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2024, 卷号: 37, 期号: 1, 页码: 82-113 作者:  Zhang, Jinhan;  Chen, Jiahao;  Zhong, Shanlin;  Qiao, Hong   |  收藏  |  浏览/下载：19/0  |  提交时间：2024/07/03 Basal ganglia and cerebellum  bio-inspired integration model  motion learning  muscu-loskeletal robot  reinforcement learning   A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot 期刊论文  OAI收割 Journal of Systems Science and Complexity, 2024, 卷号: 37, 页码: 82-113 作者:  Jinhan Zhang;  Jiahao Chen;  Shanlin Zhong;  Hong Qiao   |  收藏  |  浏览/下载：7/0  |  提交时间：2024/06/04 Improving performance of robots using human-inspired approaches: a survey 期刊论文  OAI收割 SCIENCE CHINA-INFORMATION SCIENCES, 2022, 卷号: 65, 期号: 12, 页码: 31 作者:  Qiao, Hong;  Zhong, Shanlin;  Chen, Ziyu;  Wang, Hongze   |  收藏  |  浏览/下载：14/0  |  提交时间：2023/03/20 human-inspired intelligent robots  brain-inspired intelligence  decision making  visual cognition  musculoskeletal robots   Editorial: Human inspired robotic intelligence and structure in demanding environments 期刊论文  OAI收割 FRONTIERS IN NEUROROBOTICS, 2022, 卷号: 16, 页码: 2 作者:  Chen, Jiahao;  Su, Hang;  Sandoval, Juan;  Zhang, Longbin;  Zhong, Shanlin   |  收藏  |  浏览/下载：34/0  |  提交时间：2022/11/14 demanding environments  human-robot interaction  robotic intelligence  dexterous manipulation  sensorimotor coordination   Structure transforming for constructing constraint force field in musculoskeletal robot 期刊论文  OAI收割 ASSEMBLY AUTOMATION, 2021, 页码: 12 作者:  Zhong, Shanlin;  Chen, Ziyu;  Zhou, Junjie   |  收藏  |  浏览/下载：34/0  |  提交时间：2021/12/28 High precision  Attractive region in environment  Constraint force field  Musculoskeletal robot   Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System 期刊论文  OAI收割 IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 14 作者:  Zhou, Junjie;  Zhong, Shanlin;  Wu, Wei   |  收藏  |  浏览/下载：23/0  |  提交时间：2022/01/27 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)   Trajectory-based Split Hindsight Reverse Curriculum Learning 会议论文  OAI收割 Prague, Czech Republic, 2021-9 作者:  Wu, Jiaxi;  Zhang, Dianmin;  Zhong, Shanlin;  Qiao, Hong   |  收藏  |  浏览/下载：26/0  |  提交时间：2022/06/14 Reinforcement Learning  Curriculum Learning   Learning Smooth and Omnidirectional Locomotion for Quadruped Robots 会议论文  OAI收割 Chongqing, China, 2021-7 作者:  Wu, Jiaxi;  Wang, Chen'an;  Zhang, Dianmin;  Zhong, Shanlin;  Wang, Boxing   |  收藏  |  浏览/下载：22/0  |  提交时间：2022/06/14 Quadruped Robot  Reinforcement Learning   Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot 期刊论文  OAI收割 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：25/0  |  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)   Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection 期刊论文  OAI收割 IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020, 卷号: 12, 期号: 3, 页码: 601-617 作者:  Zhong, Shanlin;  Chen, Jiahao;  Niu, Xingyu;  Fu, Hang;  Qiao, Hong   |  收藏  |  浏览/下载：25/0  |  提交时间：2021/01/07 Muscles  Robots  Mathematical model  Hardware  Redundancy  Numerical models  Convex hull vertexes  human-like robot  motion experiment  musculoskeletal system  reduce redundancy