Meta-learning For Robotics
Meta-Learning for Robotics is a formal framework within data-efficient robot learning that enables robotic systems to rapidly adapt to new tasks by acquiring inductive biases from prior experience across related tasks, thereby distinguishing itself from general learning approaches through its focus on learning-to-learn mechanisms specifically tailored for robotic domains.
65
Publications
3.8K
Citations
203
Authors
70
Institutions
Publications per year
2017–2026
56
65
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie et al. · Neural Information Processing Systems · 2017 · 279 citations
One-Shot Visual Imitation Learning via Meta-Learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang et al. · arXiv (Cornell University) · 2017 · 270 citations · Full text
Neural-Fly enables rapid learning for agile flight in strong winds
Michael O’Connell, Guanya Shi, Xichen Shi et al. · Science Robotics · 2022 · 239 citations · Full text
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie et al. · arXiv (Cornell University) · 2017 · 229 citations · Full text
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Massachusetts Institute of Technology
Cambridge, United States
6 h-index
13 publications
466 citations
Google (United States)
Mountain View, United States
4 h-index
18 publications
917 citations

University of Toronto
Toronto, Canada
3 h-index
23 publications
528 citations
Stanford University
Stanford, United States
3 h-index
7 publications
152 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
4 publications
136 citations
Vector Institute
Toronto, Canada
3 h-index
9 publications
192 citations
American Institute of Aeronautics and Astronautics
Reston, United States
2 h-index
2 publications
144 citations
Technion – Israel Institute of Technology
Haifa, Israel
2 h-index
4 publications
98 citations
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Repository · 15 publications · 913 citations
12
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7 publications · 342 citations
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Conference · 2 publications · 37 citations
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Conference · 2 publications · 304 citations
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Conference · 2 publications · 135 citations
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