Learning From Human Demonstration

Learning From Human Demonstration is a subfield of Learning From Demonstration that specifically involves the acquisition of behavioral policies or task knowledge by machines through the observation and interpretation of human-generated action sequences, distinguished by its exclusive focus on human agents as demonstrators rather than synthetic or simulated sources.

486

Publications

25.7K

Citations

1.3K

Authors

400

Institutions

Publications per year

2017–2026

200

Variants

Learning From Human Demonstrations

Authors

1.3K

  1. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    19 publications · 2.8K citations

    18

    h-index

  2. SC
    Sonia Chernova

    Georgia Institute of Technology, United States

    14 publications · 1.5K citations

    12

    h-index

  3. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    11 publications · 1.6K citations

    11

    h-index

  4. RD
    Rüdiger Dillmann

    Karlsruhe Institute of Technology, Germany

    9 publications · 560 citations

    9

    h-index

  5. AU
    Aleš Ude

    Jožef Stefan Institute, Slovenia

    7 publications · 238 citations

    7

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 30 publications · 1.2K citations

    21

    h-index

  2. IEEE Robotics and Automation Letters

    13 publications · 370 citations

    12

    h-index

  3. Robotics and Autonomous Systems

    10 publications · 559 citations

    10

    h-index

  4. Journal of comparative psychology

    7 publications · 488 citations

    7

    h-index

  5. The International Journal of Robotics Research

    IJRR · 6 publications · 662 citations

    6

    h-index

1–5