Human-Robot Variable Impedance Skills Transfer Learning Based on Dynamic Movement Primitives-Reference-Cited by-同舟云学术

Human-Robot Variable Impedance Skills Transfer Learning Based on Dynamic Movement Primitives

Published:2022-07 Issue:3 Volume:7 Page:6463-6470
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Yu Xinbo¹,Liu Peisen²^ORCID,He Wei²^ORCID,Liu Yu³^ORCID,Chen Qi²^ORCID,Ding Liang⁴^ORCID

Affiliation:

1. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China

2. Institute of Artificial Intelligence, School of Automation and Electrical Engineering, and Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China

3. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China

4. State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Beijing Top Discipline for Artificial Intelligent Science and Engineering

University of Science and Technology Beijing

Basic and Applied Basic Research Foundation of Guangdong Province

Technological Innovation Foundation of Shunde Graduate School of University of Science and Technology Beijing

State Key Laboratory of Advanced Metallurgy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9750005/09721664.pdf?arnumber=9721664

Reference60 articles.

1. Conservative Congruence Transformation for Joint and Cartesian Stiffness Matrices of Robotic Hands and Fingers

2. Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

3. Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

4. Deep amended COPERT model for regional vehicle emission prediction

5. Skill transfer learning for autonomous robots and human–robot cooperation: A survey

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