Transfer Learning For Robotics
Transfer Learning for Robotics is a machine learning paradigm that enables a robotic system to leverage knowledge acquired from a source domain, such as simulation or a related task, to improve learning efficiency and performance in a target domain with limited data, thereby reducing the need for extensive real-world training.
138
Publications
8.7K
Citations
686
Authors
160
Institutions
Publications per year
2017–2026
113
138
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray et al. · 2017 · 2.8K citations
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal et al. · 2023 · 520 citations · Full text
Successor Features for Transfer in Reinforcement Learning
André Barreto, Will Dabney, Rémi Munos et al. · UCL Discovery (University College London) · 2019 · 208 citations · Full text
Successor Features for Transfer in Reinforcement Learning
André Sales Barreto, Will Dabney, Rémi Munos et al. · arXiv (Cornell University) · 2016 · 184 citations · Full text
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160
Carnegie Mellon University
Pittsburgh, United States
9 h-index
25 publications
1.4K citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
19 publications
559 citations
University of California, Berkeley
Berkeley, United States
6 h-index
14 publications
3.7K citations
Tsinghua University
Beijing, China
5 h-index
18 publications
534 citations
Google (United States)
Mountain View, United States
5 h-index
69 publications
27.5K citations
The University of Texas at Austin
Austin, United States
5 h-index
9 publications
667 citations
Stanford University
Stanford, United States
4 h-index
5 publications
386 citations
Technical University of Munich
Munich, Germany
4 h-index
8 publications
468 citations
1–8 of 160
Repository · 32 publications · 1.4K citations
21
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9 publications · 315 citations
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4 publications · 162 citations
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TCDS · 3 publications · 63 citations
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2 publications · 92 citations
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