Learning With Dual Demonstration Domains: Random Domain-Adaptive Meta-Learning-Reference-Cited by-同舟云学术

Learning With Dual Demonstration Domains: Random Domain-Adaptive Meta-Learning

Published:2022-04 Issue:2 Volume:7 Page:3523-3530
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Hu Ziye^ORCID,Li Wei^ORCID,Gan Zhongxue,Guo Weikun^ORCID,Zhu Jiwei,Gao Xiang^ORCID,Yang Xuyun,Peng Yueyan,Zuo Zhihao,Wen James Zhiqing,Zhou Decheng

Funder

Ji Hua Laboratory

Shanghai Municipal Science and Technology

Science and Technology Commission of Shanghai Municipality

Shanghai Engineering Research Center of AI and Robotics

Engineering Research Center of AI and Robotics

Ministry of Education, China

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/ielx7/7083369/9647862/09691780.pdf?arnumber=9691780

Reference49 articles.

1. Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments

2. Transformers for one-shot visual imitation;dasari,2020

3. Robot Learning from Demonstration: A Task-level Planning Approach

4. Using dialog and human observations to dictate tasks to a learning robot assistant

5. On first-order meta-learning algorithms;nichol,2018

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