Model Learning From Demonstrations
Model Learning From Demonstrations is a machine learning framework within model-based robot learning that acquires dynamic models of environments or tasks through observed expert demonstrations, distinguishing itself by leveraging demonstration data to infer system dynamics rather than relying solely on active interaction or exploration.
331
Publications
16.2K
Citations
1.2K
Authors
301
Institutions
Publications per year
2017–2026
179
Parents
331
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Sewon Min, Xinxi Lyu, Ari Holtzman et al. · 2022 · 644 citations · Full text
Champion-level drone racing using deep reinforcement learning
Elia Kaufmann, Leonard Bauersfeld, Antonio Loquercio et al. · Nature · 2023 · 575 citations · Full text
DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang et al. · Nature · 2025 · 568 citations · Full text
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Carnegie Mellon University
Pittsburgh, United States
13 h-index
48 publications
2.6K citations
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
43 publications
3K citations

Technische Universität Darmstadt
Darmstadt, Germany
12 h-index
42 publications
2K citations
University of California, Berkeley
Berkeley, United States
11 h-index
81 publications
8.3K citations
The University of Texas at Austin
Austin, United States
11 h-index
30 publications
1.9K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
11 h-index
25 publications
3K citations
Georgia Institute of Technology
Atlanta, United States
10 h-index
23 publications
859 citations

Imperial College London
London, United Kingdom
10 h-index
20 publications
1.3K citations
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Repository · 40 publications · 2K citations
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17 publications · 579 citations
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7 publications · 231 citations
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Conference · 6 publications · 120 citations
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6 publications · 261 citations
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