Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer-Reference-Cited by-同舟云学术

Training Robots Without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

Published:2023-05 Issue:5 Volume:8 Page:2906-2913
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Kim Heecheol¹^ORCID,Ohmura Yoshiyuki¹^ORCID,Nagakubo Akihiko²^ORCID,Kuniyoshi Yasuo¹^ORCID

Affiliation:

1. Laboratory for Intelligent Systems and Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

2. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan

Funder

Grant-in-Aid for Scientific Research

Department of Social Cooperation Program Intelligent Mobility Society Design

Toyota Central R&D Labs., Inc.

Next Generation AI Research Center

University of Tokyo

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/ielx7/7083369/10073596/10083284.pdf?arnumber=10083284

Reference24 articles.

1. Eye movements in natural behavior

2. Third-party Evaluation of the Robotic Hand Design Using a Mechanical Glove

3. Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

4. Quantifying Attention Flow in Transformers

5. The coordination of eye, head, and hand movements in a natural task

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