Learning From Demonstrations Via Multi-Level and Multi-Attention Domain-Adaptive Meta-Learning-Reference-Cited by-同舟云学术

Learning From Demonstrations Via Multi-Level and Multi-Attention Domain-Adaptive Meta-Learning

Published:2022-10 Issue:4 Volume:7 Page:11910-11917
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Hu Ziye¹^ORCID,Gan Zhongxue¹^ORCID,Li Wei¹^ORCID,Guo Weikun¹^ORCID,Gao Xiang²^ORCID,Zhu Jiwei¹

Affiliation:

1. Academy for Engineering and Technology, Fudan University, Shanghai, China

2. Department of Engineering Research Center for Intelligent Robotics, Ji Hua Laboratory, Guangdong, China

Funder

Ji Hua Laboratory of Foshan

Science and Technology Commission of Shanghai Municipality

Shanghai Municipal Science and Technology Major Project

Shanghai Engineering Research Center of Artificial Intelligence and Robotics

Engineering Research Center of Artificial Intelligence 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/ielx7/7083369/9831196/09894675.pdf?arnumber=9894675

Reference29 articles.

1. One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning

2. Vision-Based One-Shot Imitation Learning Supplemented with Target Recognition via Meta Learning

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

4. Learning From Visual Demonstrations via Replayed Task-Contrastive Model-Agnostic Meta-Learning

5. Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition

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