Learning From Demonstration

Learning From Demonstration is a robot learning paradigm in which a robot acquires task-specific behaviors by observing and generalizing from expert demonstrations, distinguishing itself from other learning methods by relying on observational data rather than explicit reward signals or trial-and-error exploration.

3K

Publications

162.6K

Citations

7.3K

Authors

1.4K

Institutions

Publications per year

2017–2026

1.6K

Authors

7.3K

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    85 publications · 8.5K citations

    44

    h-index

  2. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    63 publications · 10.5K citations

    40

    h-index

  3. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    60 publications · 6.5K citations

    34

    h-index

  4. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    49 publications · 5K citations

    33

    h-index

  5. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    52 publications · 2.8K citations

    27

    h-index

1–5 of 7.3K

Venues

  1. arXiv (Cornell University)

    Repository · 316 publications · 14.5K citations

    59

    h-index

  2. IEEE Robotics and Automation Letters

    106 publications · 4.4K citations

    35

    h-index

  3. Robotics and Autonomous Systems

    57 publications · 6.1K citations

    28

    h-index

  4. IEEE Transactions on Robotics

    30 publications · 2.7K citations

    22

    h-index

  5. The International Journal of Robotics Research

    IJRR · 29 publications · 2.5K citations

    22

    h-index

1–5