Learning-based Locomotion
Learning-Based Locomotion is a computational approach to robotic movement control that, as a subset of locomotion strategies in legged robotics, employs machine learning methodologies to enable adaptive and autonomous generation of dynamic walking, running, or climbing behaviors through experience-driven optimization rather than explicit programming.
119
Publications
6.2K
Citations
343
Authors
129
Institutions
Publications per year
2017–2026
62
Parents
119
Sergey Levine, Vladlen Koltun · 2013 · 503 citations
Learning to coordinate behaviors
Pattie Maes, Rodney A. Brooks · 1990 · 364 citations
ANYmal parkour: Learning agile navigation for quadrupedal robots
David Hoeller, Nikita Rudin, Dhionis Sako et al. · Science Robotics · 2024 · 220 citations
Multi-expert learning of adaptive legged locomotion
Science Robotics · 2020 · 193 citations · Full text
1–5 of 119
343
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Advanced Telecommunications Research Institute International, Japan
6 publications · 251 citations
6
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4
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4
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1–5 of 343
129
Georgia Institute of Technology
Atlanta, United States
6 h-index
12 publications
922 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
9 publications
489 citations
Stanford University
Stanford, United States
5 h-index
8 publications
1.3K citations
Japan Science and Technology Agency
Tokyo, Japan
4 h-index
7 publications
220 citations
ETH Zurich
Zurich, Switzerland
4 h-index
16 publications
1.6K citations
University of California, Berkeley
Berkeley, United States
4 h-index
15 publications
549 citations
The University of Texas at Austin
Austin, United States
4 h-index
10 publications
766 citations

University of British Columbia
Vancouver, Canada
4 h-index
8 publications
350 citations
1–8 of 129
7 publications · 462 citations
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Repository · 6 publications · 169 citations
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5 publications · 84 citations
5
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IJRR · 4 publications · 267 citations
4
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TOG · 3 publications · 273 citations
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