Imitation Learning

Imitation Learning is a robot learning paradigm in which an agent learns a control policy by inferring how to reproduce expert behavior from demonstration data, rather than by relying primarily on explicit reward signals or hand-crafted task-specific programming.

3K

Publications

197.5K

Citations

7.6K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.5K

Authors

7.6K

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    54 publications · 5.3K citations

    34

    h-index

  2. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    46 publications · 6K citations

    30

    h-index

  3. AN
    Andrew N. Meltzoff

    University of Washington, United States

    33 publications · 5.5K citations

    29

    h-index

  4. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    32 publications · 4.7K citations

    27

    h-index

  5. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    30 publications · 4.3K citations

    26

    h-index

1–5 of 7.6K

Venues

  1. arXiv (Cornell University)

    Repository · 302 publications · 15.5K citations

    56

    h-index

  2. IEEE Robotics and Automation Letters

    71 publications · 3.5K citations

    33

    h-index

  3. Journal of Experimental Child Psychology

    JECP · 51 publications · 2.8K citations

    28

    h-index

  4. Child Development

    39 publications · 4K citations

    27

    h-index

  5. Robotics and Autonomous Systems

    44 publications · 5.2K citations

    23

    h-index

1–5