Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives-Reference-Cited by-同舟云学术

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Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives

Published:2022-07-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
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Author:

Chang Chunyang¹,Haninger Kevin²,Shi Yunlei³,Yuan Chengjie⁴,Chen Zhaopeng¹,Zhang Jianwei³

Affiliation:

1. Agile Robots AG,Munich,Germany

2. Fraunhofer IPK,Department of Automation,Berlin,Germany

3. Universität Hamburg,TAMS (Technical Aspects of Multimodal Systems),Department of Informatics,Hamburg,Germany

4. Intel Asia-Pacific Research & Development Ltd.,Shanghai,China

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9863218/9863240/09863416.pdf?arnumber=9863416

Reference28 articles.

1. Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach

2. High-accuracy robotized industrial assembly task control schema with force overshoots avoidance

3. Reinforcement learning of variable admittance control for human-robot co-manipulation

4. A Learning-Based Framework for Robot Peg-Hole-Insertion

5. Force-based variable impedance learning for robotic manipulation

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4. Force-Position Hybrid Control for Robot Assisted Thoracic-Abdominal Puncture With Respiratory Movement;IEEE Robotics and Automation Letters;2024-06

5. Adaptive Tuning of Robotic Polishing Skills based on Force Feedback Model;2023 IEEE International Conference on Robotics and Biomimetics (ROBIO);2023-12-04

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