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

Authors

1.2K

  1. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    12 publications · 1.4K citations

    12

    h-index

  2. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    9 publications · 364 citations

    9

    h-index

  3. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    8 publications · 260 citations

    8

    h-index

  4. SL
    Sergey Levine

    University of California, Berkeley, United States

    8 publications · 538 citations

    8

    h-index

  5. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    7 publications · 502 citations

    7

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 2K citations

    24

    h-index

  2. IEEE Robotics and Automation Letters

    17 publications · 579 citations

    15

    h-index

  3. Robotics and Autonomous Systems

    7 publications · 231 citations

    7

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 120 citations

    6

    h-index

  5. Autonomous Robots

    6 publications · 261 citations

    6

    h-index

1–5