中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
自动化研究所 [10]
数学与系统科学研究院 [1]
采集方式
OAI收割 [11]
内容类型
期刊论文 [9]
会议论文 [2]
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2022 [2]
2021 [5]
2020 [1]
2019 [1]
2018 [1]
2017 [1]
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学科主题
模式识别与智能系统 [1]
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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:9/0
  |  
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:18/0
  |  
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:21/0
  |  
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:15/0
  |  
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:15/0
  |  
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:11/0
  |  
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:12/0
  |  
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:17/0
  |  
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
Realizing human-like manipulation with a musculoskeletal system and biologically inspired control scheme
期刊论文
OAI收割
NEUROCOMPUTING, 2019, 卷号: 339, 页码: 116-129
作者:
Chen, Jiahao
;
Zhong, Shanlin
;
Kang, Erlong
;
Qiao, Hong
;
Hong Qiao
  |  
收藏
  |  
浏览/下载:66/0
  |  
提交时间:2019/07/12
Human-like manipulation
Musculoskeletal system
Biologically inspired
Muscle synergy
Attractive region in environment
A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity
期刊论文
OAI收割
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
  |  
收藏
  |  
浏览/下载:34/0
  |  
提交时间:2018/07/30
Biologically inspired model
object recognition
semantic learning
structural learning