Human-robot Team Learning

Human-Robot Team Learning is a multi-agent learning framework in which humans and robots jointly adapt their behaviors through iterative interaction, distinguished by bidirectional learning processes that integrate human cognitive and social cues into the robot’s decision-making models to achieve shared goals.

191

Publications

8.9K

Citations

545

Authors

201

Institutions

Publications per year

2017–2026

80

Authors

545

  1. SN
    Stefanos Nikolaidis

    University of Southern California, United States

    7 publications · 497 citations

    7

    h-index

  2. MV
    Manuela Veloso

    Carnegie Mellon University, United States

    6 publications · 232 citations

    6

    h-index

  3. JS
    Julie Shah

    Massachusetts Institute of Technology, United States

    6 publications · 584 citations

    6

    h-index

  4. PS
    Peter Stone

    The University of Texas at Austin, United States

    6 publications · 270 citations

    6

    h-index

  5. MA
    Minoru Asada

    The University of Osaka, Japan

    6 publications · 271 citations

    6

    h-index

1–5 of 545

Venues

  1. Robotics and Autonomous Systems

    8 publications · 632 citations

    8

    h-index

  2. Robotics and Computer-Integrated Manufacturing

    6 publications · 629 citations

    6

    h-index

  3. Autonomous Robots

    5 publications · 163 citations

    5

    h-index

  4. Neurocomputing

    4 publications · 112 citations

    4

    h-index

  5. Journal of Intelligent & Robotic Systems

    4 publications · 122 citations

    4

    h-index

1–5