Explainability For Collaborative Robots

Explainability for Collaborative Robots is a computational framework designed to render transparent the decision-making processes of collaborative robots during human-robot interaction, distinguishing itself by enabling real-time, context-sensitive, and human-interpretable explanations of robotic actions to foster trust, safety, and effective teamwork in shared workspaces.

13

Publications

628

Citations

53

Authors

29

Institutions

Publications per year

2017–2026

13

Authors

53

  1. JS
    Julie Shah

    Massachusetts Institute of Technology, United States

    3 publications · 286 citations

    3

    h-index

  2. BH
    Bradley Hayes

    University of Colorado Boulder, United States

    3 publications · 331 citations

    3

    h-index

  3. XG
    Xiaofeng Gao

    University of California, Los Angeles, United States

    2 publications · 111 citations

    2

    h-index

  4. SZ
    Song‐Chun Zhu

    University of California, Los Angeles, United States

    2 publications · 111 citations

    2

    h-index

  5. MA
    Mark A. Neerincx

    Delft University of Technology, Netherlands

    1 publications · 23 citations

    1

    h-index

1–5 of 53

Venues

  1. KI - Künstliche Intelligenz

    1 publications · 40 citations

    1

    h-index

  2. IEEE Pervasive Computing

    1 publications · 14 citations

    1

    h-index

  3. Frontiers in Robotics and AI

    1 publications · 23 citations

    1

    h-index

  4. International Journal of Social Robotics

    1 publications · 36 citations

    1

    h-index

  5. IEEE Robotics and Automation Letters

    1 publications · 17 citations

    1

    h-index

1–5