Model-based Robot Learning
Model-Based Robot Learning is a subfield of robot learning that employs learned or predefined environmental models to predict system dynamics and plan optimal control policies, distinguishing itself from model-free approaches by explicitly representing and utilizing the underlying physical or stochastic structure of the robotic task.
552
Publications
31.6K
Citations
1.8K
Authors
401
Institutions
Publications per year
2017–2026
292
Parents
Children
Active Data Collection For Model LearningAdaptive Model Predictive ControlBayesian Dynamics ModelsContact-aware Dynamics ModelingEnsemble Dynamics ModelsForward Dynamics ModelingHierarchical Model-based ControlHybrid Model-based LearningImitation Learning With Dynamics ModelsInverse Dynamics Modeling
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Mastering Atari, Go, chess and shogi by planning with a learned model
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University of California, Berkeley
Berkeley, United States
23 h-index
146 publications
12.8K citations
Google (United States)
Mountain View, United States
17 h-index
172 publications
38.1K citations

Technische Universität Darmstadt
Darmstadt, Germany
17 h-index
44 publications
6.6K citations
Massachusetts Institute of Technology
Cambridge, United States
16 h-index
37 publications
2.2K citations
Carnegie Mellon University
Pittsburgh, United States
15 h-index
47 publications
6.4K citations

Italian Institute of Technology
Genoa, Italy
14 h-index
35 publications
2K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
13 h-index
30 publications
1.9K citations
Max Planck Institute for Intelligent Systems
Stuttgart, Germany
13 h-index
20 publications
4.1K citations
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Repository · 77 publications · 4K citations
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40 publications · 1.6K citations
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9 publications · 609 citations
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Conference · 8 publications · 335 citations
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8 publications · 692 citations
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