Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery-Reference-Cited by-同舟云学术

Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

Published:2023-02 Issue:2 Volume:8 Page:560-567
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Scheikl Paul Maria¹^ORCID,Tagliabue Eleonora²^ORCID,Gyenes Balazs¹^ORCID,Wagner Martin³^ORCID,Dall'Alba Diego²^ORCID,Fiorini Paolo²^ORCID,Mathis-Ullrich Franziska¹^ORCID

Affiliation:

1. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany

2. Department of Computer Science, University of Verona, Verona, Italy

3. Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany

Funder

Karlsruhe House of Young Scientists

Helmholtz Association

European Research Council

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/ielx7/7083369/9990971/09976185.pdf?arnumber=9976185

Reference30 articles.

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2. Learning dexterous in-hand manipulation

3. Sim-to-real reinforcement learning for deformable object manipulation;matas;Proc Conf Robot Learn,0

4. RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer

5. RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real

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