Task Assignment Learning

Task Assignment Learning is a multi-robot learning framework that specifically addresses the acquisition of optimal task-to-robot allocation policies through experience-driven adaptation, distinguishing itself from general coordination mechanisms by its focus on learning assignment strategies that maximize collective performance in dynamic and uncertain environments.

39

Publications

1.6K

Citations

163

Authors

71

Institutions

Publications per year

2017–2026

28

Authors

163

  1. KZ
    Kai Zheng

    University of Electronic Science and Technology of China, China

    2 publications · 56 citations

    2

    h-index

  2. CS
    Christian S. Jensen

    Aalborg University, Denmark

    2 publications · 56 citations

    2

    h-index

  3. GS
    Gabriel Synnaeve

    Meta (Israel), Israel

    2 publications · 26 citations

    2

    h-index

  4. AL
    Alessandro Lazaric

    Meta (Israel), Israel

    2 publications · 26 citations

    2

    h-index

  5. NU
    Nicolas Usunier

    Meta (Israel), Israel

    2 publications · 26 citations

    2

    h-index

1–5 of 163

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 48 citations

    3

    h-index

  2. IEEE Internet of Things Journal

    2 publications · 130 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 102 citations

    2

    h-index

  4. 2

    h-index

  5. Future Generation Computer Systems

    FGCS · 1 publications · 106 citations

    1

    h-index

1–5