Sample-efficient Robot Learning

Sample-Efficient Robot Learning is a model-based robot learning paradigm that belongs to the class of data-driven robotic control methods, distinguished by its focus on minimizing the number of interaction samples required to achieve competent task performance through informed use of models, priors, or structured exploration.

72

Publications

3.2K

Citations

249

Authors

71

Institutions

Publications per year

2017–2026

66

Authors

249

  1. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    9 publications · 641 citations

    9

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    6 publications · 756 citations

    6

    h-index

  3. AR
    Akshara Rai

    Meta (Israel), Israel

    4 publications · 100 citations

    4

    h-index

  4. FM
    Franziska Meier

    University of Southern California, United States

    4 publications · 100 citations

    4

    h-index

  5. CF
    Chelsea Finn

    University of California, Berkeley, United States

    3 publications · 445 citations

    3

    h-index

1–5 of 249

Venues

  1. arXiv (Cornell University)

    Repository · 29 publications · 1.2K citations

    17

    h-index

  2. IEEE Robotics and Automation Letters

    10 publications · 496 citations

    10

    h-index

  3. The International Journal of Robotics Research

    IJRR · 2 publications · 84 citations

    2

    h-index

  4. 2022 International Conference on Robotics and Automation (ICRA)

    Conference · 2 publications · 49 citations

    2

    h-index

  5. Robotics and Autonomous Systems

    1 publications · 179 citations

    1

    h-index

1–5