Representation Learning For Robotics
Representation Learning for Robotics is a methodological framework within robotic machine learning that enables autonomous systems to automatically discover and structure salient features from raw sensory data to facilitate downstream tasks such as control, planning, and decision-making in real-world environments.
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19.2K
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1.5K
Authors
388
Institutions
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2017–2026
187
407
Convolutional networks and applications in vision
Yann LeCun, Koray Kavukcuoglu, Clément Farabet · 2010 · 2.2K citations · Full text
Rodney A. Brooks · Science · 1991 · 751 citations
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal et al. · 2023 · 520 citations · Full text
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Massachusetts Institute of Technology
Cambridge, United States
16 h-index
46 publications
2.8K citations
The University of Texas at Austin
Austin, United States
12 h-index
36 publications
2.6K citations
Stanford University
Stanford, United States
11 h-index
73 publications
8K citations
Google (United States)
Mountain View, United States
11 h-index
163 publications
35.4K citations
University of Washington
Seattle, United States
10 h-index
37 publications
3.8K citations
University of California, Berkeley
Berkeley, United States
10 h-index
65 publications
7K citations
University of Southern California
Los Angeles, United States
9 h-index
18 publications
1.2K citations
Technical University of Munich
Munich, Germany
9 h-index
22 publications
1.3K citations
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Repository · 47 publications · 1.9K citations
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15 publications · 361 citations
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IJRR · 8 publications · 542 citations
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8 publications · 242 citations
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