Personalized Robot Behavior Learning

Personalized Robot Behavior Learning is a computational framework within human-robot interaction that enables a robot to adapt its behavioral responses to individual users through learning mechanisms informed by user-specific interaction data, thereby differentiating it from generalized robotic learning approaches by its focus on individualized, context-sensitive behavioral adaptation.

13

Publications

370

Citations

44

Authors

20

Institutions

Publications per year

2017–2026

9

Authors

44

  1. AT
    Adriana Tapus

    École Nationale Supérieure de Techniques Avancées, France

    2 publications · 37 citations

    2

    h-index

  2. RA
    Roxana Agrigoroaie

    École Nationale Supérieure de Techniques Avancées, France

    2 publications · 37 citations

    2

    h-index

  3. AS
    Alessandra Sciutti

    Italian Institute of Technology, Italy

    1 publications · 15 citations

    1

    h-index

  4. PC
    Praminda Caleb-Solly

    University of the West of England, United Kingdom

    1 publications · 21 citations

    1

    h-index

  5. DF
    Dylan F. Glas

    Advanced Telecommunications Research Institute International, Japan

    1 publications · 32 citations

    1

    h-index

1–5 of 44

Venues

  1. International Journal of Social Robotics

    1 publications · 32 citations

    1

    h-index

  2. Frontiers in Robotics and AI

    1 publications · 58 citations

    1

    h-index

  3. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 1 publications · 15 citations

    1

    h-index

  4. Human-Robot Interaction

    Conference · 1 publications · 14 citations

    1

    h-index

  5. 1

    h-index

1–5