A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration

We introduce a novel capabilities-based bi-directional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-07, Vol.6 (3), p.5913-5920
Main Authors: Azevedo-Sa, Hebert, Yang, X. Jessie, Robert, Lionel P., Tilbury, Dawn M.
Format: Article
Language:English
Subjects:
Acceptability and trust
Adaptation models
Bidirectional control
Collaboration
Computational modeling
human-robot collaboration
Predictive models
Robots
social HRI
Task analysis
Trustworthiness
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Summary:We introduce a novel capabilities-based bi-directional multi-task trust model that can be used for trust prediction from either a human or a robotic trustor agent. Tasks are represented in terms of their capability requirements, while trustee agents are characterized by their individual capabilities. Trustee agents' capabilities are not deterministic; they are represented by belief distributions. For each task to be executed, a higher level of trust is assigned to trustee agents who have demonstrated that their capabilities exceed the task's requirements. We report results of an online experiment with 284 participants, revealing that our model outperforms existing models for multi-task trust prediction from a human trustor. We also present simulations of the model for determining trust from a robotic trustor. Our model is useful for control authority allocation applications that involve human-robot teams.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3088082