Task Learning From Demonstration

Task Learning From Demonstration is a subfield of Learning From Demonstration that focuses on acquiring task-specific knowledge or representations through expert demonstrations, distinguishing itself by emphasizing the structure, decomposition, and generalization of discrete tasks rather than generic motor or policy learning.

336

Publications

16.5K

Citations

940

Authors

260

Institutions

Publications per year

2017–2026

158

Authors

940

  1. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    12 publications · 1.2K citations

    12

    h-index

  2. AL
    Andrea L. Thomaz

    Georgia Institute of Technology, United States

    10 publications · 840 citations

    10

    h-index

  3. SC
    Sonia Chernova

    Georgia Institute of Technology, United States

    10 publications · 1.3K citations

    10

    h-index

  4. SN
    Scott Niekum

    The University of Texas at Austin, United States

    9 publications · 592 citations

    9

    h-index

  5. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    9 publications · 1.2K citations

    9

    h-index

1–5 of 940

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 1.2K citations

    20

    h-index

  2. IEEE Robotics and Automation Letters

    12 publications · 290 citations

    12

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 225 citations

    8

    h-index

  4. Journal of comparative psychology

    4 publications · 295 citations

    4

    h-index

  5. Robotics and Autonomous Systems

    4 publications · 528 citations

    4

    h-index

1–5