Preference-based Robot Learning

Preference-Based Robot Learning is a robot learning paradigm that belongs to the class of data-efficient learning methods, distinguished by its use of human or agent preference comparisons over alternative behaviors as supervisory signals to guide policy optimization in the absence of explicit reward functions.

18

Publications

746

Citations

63

Authors

28

Institutions

Publications per year

2017–2026

14

Authors

63

  1. AD
    Anca D. Dragan

    University of California, Berkeley, United States

    2 publications · 46 citations

    2

    h-index

  2. AS
    Ashutosh Saxena

    Cornell University, United States

    2 publications · 156 citations

    2

    h-index

  3. AJ
    Ashesh Jain

    Cornell University, United States

    2 publications · 156 citations

    2

    h-index

  4. TJ
    Thorsten Joachims

    Cornell University, United States

    2 publications · 156 citations

    2

    h-index

  5. KO
    Kentaro Oguchi

    NTT (Japan), Japan

    1 publications · 12 citations

    1

    h-index

1–5 of 63

Venues

  1. IEEE Robotics and Automation Letters

    2 publications · 54 citations

    2

    h-index

  2. The International Journal of Robotics Research

    IJRR · 2 publications · 125 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 74 citations

    2

    h-index

  4. ACM Transactions on Human-Robot Interaction

    THRI · 1 publications · 12 citations

    1

    h-index

  5. Machine Learning for Healthcare Conference

    Conference · 1 publications · 23 citations

    1

    h-index

1–5