Multi-modal Demonstration Learning

Multi-Modal Demonstration Learning is a learning framework within the domain of Learning from Demonstration that integrates multiple sensory or data modalities—such as visual, auditory, tactile, or kinesthetic signals—to capture, represent, and generalize demonstrated behaviors more robustly than unimodal approaches.

3

Publications

220

Citations

13

Authors

5

Institutions

Publications per year

2017–2026

2

Authors

13

  1. RB
    Rachel Barr

    Georgetown University, United States

    1 publications · 169 citations

    1

    h-index

  2. PG
    Peter Gerhardstein

    Binghamton University, United States

    1 publications · 169 citations

    1

    h-index

  3. KD
    Kelly Dickerson

    Binghamton University, United States

    1 publications · 169 citations

    1

    h-index

  4. AN
    Andrew N. Meltzoff

    University of Washington, United States

    1 publications · 169 citations

    1

    h-index

  5. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    1 publications · 20 citations

    1

    h-index

1–5 of 13

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 20 citations

    1

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 31 citations

    1

    h-index

  3. British Journal of Developmental Psychology

    1 publications · 169 citations

    1

    h-index

1–3