Learning From Corrective Feedback

Learning From Corrective Feedback is a machine learning framework within human-robot interaction that enables a robot to iteratively improve its behavior by incorporating human-provided corrective signals that indicate deviations from desired actions.

29

Publications

977

Citations

100

Authors

47

Institutions

Publications per year

2017–2026

14

Authors

100

  1. CC
    Carlos Celemin

    Delft University of Technology, Netherlands

    3 publications · 105 citations

    3

    h-index

  2. JR
    Javier Ruíz-del-Solar

    University of Chile, Chile

    3 publications · 105 citations

    3

    h-index

  3. DP
    Dylan P. Losey

    Virginia Tech, United States

    2 publications · 97 citations

    2

    h-index

  4. HL
    H. Levent Akın

    Boğaziçi University, Türkiye

    2 publications · 31 citations

    2

    h-index

  5. ÇM
    Çetin Meriçli

    Carnegie Mellon University, United States

    2 publications · 31 citations

    2

    h-index

1–5 of 100

Venues

  1. International Journal of Educational Research

    1 publications · 115 citations

    1

    h-index

  2. Autonomous Robots

    1 publications · 25 citations

    1

    h-index

  3. IEEE Robotics and Automation Letters

    1 publications · 15 citations

    1

    h-index

  4. Frontiers in Neurorobotics

    1 publications · 17 citations

    1

    h-index

  5. 1

    h-index

1–5