Self-supervised Policy Learning

Self-Supervised Policy Learning is a machine learning framework within robotics that enables policy acquisition through structuring learning objectives from unlabeled environmental interactions, distinguishing itself by eliminating the need for externally provided rewards or supervision.

95

Publications

5.2K

Citations

441

Authors

156

Institutions

Publications per year

2017–2026

85

Authors

441

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    7 publications · 506 citations

    7

    h-index

  2. CF
    Chelsea Finn

    University of California, Berkeley, United States

    5 publications · 386 citations

    5

    h-index

  3. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    4 publications · 148 citations

    4

    h-index

  4. FE
    Frederik Ebert

    University of California, Berkeley, United States

    4 publications · 361 citations

    4

    h-index

  5. AX
    Annie Xie

    University of California, Berkeley, United States

    3 publications · 344 citations

    3

    h-index

1–5 of 441

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 832 citations

    15

    h-index

  2. Medical Image Analysis

    4 publications · 720 citations

    4

    h-index

  3. Neurocomputing

    2 publications · 44 citations

    2

    h-index

  4. Information Processing & Management

    2 publications · 37 citations

    2

    h-index

  5. Robotics and Autonomous Systems

    2 publications · 53 citations

    2

    h-index

1–5