Active Learning For Robot Learning

Active Learning for Robot Learning is a data-efficient robot learning strategy that belongs to the class of active learning methods, distinguished by its specific application to robotic systems, where the learner actively selects the most informative data points to query for labeling in order to optimize learning performance with minimal interaction cost.

67

Publications

3.7K

Citations

188

Authors

76

Institutions

Publications per year

2017–2026

28

Authors

188

  1. MÇ
    Maya Çakmak

    University of Washington, United States

    5 publications · 494 citations

    5

    h-index

  2. PO
    Pierre‐Yves Oudeyer

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

    5 publications · 627 citations

    5

    h-index

  3. AL
    Andrea L. Thomaz

    Georgia Institute of Technology, United States

    5 publications · 494 citations

    5

    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

    4 publications · 606 citations

    4

    h-index

1–5 of 188

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 49 citations

    3

    h-index

  2. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 2 publications · 32 citations

    2

    h-index

  3. IEEE Robotics and Automation Letters

    2 publications · 29 citations

    2

    h-index

  4. IEEE Transactions on Autonomous Mental Development

    TAMD · 2 publications · 275 citations

    2

    h-index

  5. 2

    h-index

1–5