Active Learning For Robots

Active Learning for Robots is a machine learning paradigm within human-robot interaction in which a robot autonomously identifies and queries the most informative data samples from a human expert to improve its learning efficiency and performance with minimal labeled data.

59

Publications

2.7K

Citations

158

Authors

68

Institutions

Publications per year

2017–2026

23

Authors

158

  1. MÇ
    Maya Çakmak

    University of Washington, United States

    5 publications · 494 citations

    5

    h-index

  2. AL
    Andrea L. Thomaz

    Georgia Institute of Technology, United States

    5 publications · 494 citations

    5

    h-index

  3. PO
    Pierre‐Yves Oudeyer

    Institut national de recherche en sciences et technologies du numérique, France

    4 publications · 608 citations

    4

    h-index

  4. CC
    Crystal Chao

    Georgia Institute of Technology, United States

    4 publications · 277 citations

    4

    h-index

  5. AB
    Adrien Baranès

    Institut national de recherche en sciences et technologies du numérique, France

    3 publications · 587 citations

    3

    h-index

1–5 of 158

Venues

  1. IEEE Robotics and Automation Letters

    3 publications · 92 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 67 citations

    2

    h-index

  3. Robotics and Autonomous Systems

    2 publications · 605 citations

    2

    h-index

  4. IEEE Transactions on Autonomous Mental Development

    TAMD · 2 publications · 275 citations

    2

    h-index

  5. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 2 publications · 32 citations

    2

    h-index

1–5