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
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Parents
13
In situ bidirectional human-robot value alignment
Luyao Yuan, Xiaofeng Gao, Zilong Zheng et al. · Science Robotics · 2022 · 74 citations
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29
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
7 publications
609 citations

University of Leeds
Leeds, United Kingdom
1 h-index
1 publications
14 citations
Centre Inria de l'Université Grenoble Alpes
Montbonnot-Saint-Martin, France
1 h-index
1 publications
14 citations
Institut polytechnique de Grenoble
Grenoble, France
1 h-index
1 publications
14 citations

Université Grenoble Alpes
Saint-Martin-d'Hères, France
1 h-index
2 publications
28 citations
Brown University
Providence, United States
1 h-index
2 publications
52 citations

University of Birmingham
Birmingham, United Kingdom
1 h-index
1 publications
40 citations
SA Technologies (United States)
Marietta, United States
1 h-index
1 publications
36 citations
1–8 of 29
1 publications · 40 citations
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1 publications · 14 citations
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1 publications · 23 citations
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1 publications · 36 citations
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1 publications · 17 citations
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