Self-supervised Locomotion Learning
Self-Supervised Locomotion Learning is a machine learning framework within the domain of robotic or embodied agent control that leverages unlabeled sensorimotor data to autonomously generate supervisory signals for training locomotion policies, thereby distinguishing itself from supervised and reinforcement learning approaches by eliminating the need for external rewards or annotated trajectories.
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Publications
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Authors
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Institutions
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2017–2026
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Institute of Fundamental Technological Research
Warsaw, Poland
1 h-index
1 publications
38 citations

Polish Academy of Sciences
Warsaw, Poland
1 h-index
1 publications
38 citations
University of California, Berkeley
Berkeley, United States
1 h-index
6 publications
228 citations

Czech Technical University in Prague
Prague, Czechia
1 h-index
2 publications
26 citations
ETH Zurich
Zurich, Switzerland
1 publications
12 citations
Max Planck Institute for Intelligent Systems
Stuttgart, Germany
1 publications
12 citations
TH Bingen University of Applied Sciences
Bingen am Rhein, Germany
1 publications
12 citations
1–7 of 4
1 publications · 13 citations
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1 publications · 38 citations
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1 publications · 12 citations
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