Demonstration Generalization

Demonstration Generalization is a formal methodological framework within Learning from Demonstration that enables the extension of task-specific policies derived from a limited set of human demonstrations to broader, unseen task instances by inferring invariant features and structural regularities across demonstration examples.

38

Publications

2.5K

Citations

113

Authors

40

Institutions

Publications per year

2017–2026

13

Authors

113

  1. DG
    Darwin G. Caldwell

    Italian Institute of Technology, Italy

    2 publications · 86 citations

    2

    h-index

  2. SS
    Silvio Savarese

    Stanford University, United States

    2 publications · 138 citations

    2

    h-index

  3. DH
    De-An Huang

    Stanford University, United States

    2 publications · 138 citations

    2

    h-index

  4. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    2 publications · 89 citations

    2

    h-index

  5. GM
    Guilherme Maeda

    Technische Universität Darmstadt, Germany

    2 publications · 89 citations

    2

    h-index

1–5 of 113

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 74 citations

    4

    h-index

  2. IEEE Robotics and Automation Letters

    2 publications · 47 citations

    2

    h-index

  3. Journal of Robotics

    JR · 1 publications · 20 citations

    1

    h-index

  4. Journal of Intelligent & Robotic Systems

    1 publications · 11 citations

    1

    h-index

  5. The International Journal of Robotics Research

    IJRR · 1 publications · 193 citations

    1

    h-index

1–5