Abstract
Trust is the key determinant of human’s acceptance and hence willingness to utilize robots. Improper levels of trust may lead to performance degradation, human overload, and even catastrophic consequences. Therefore, in human-robot interaction (HRI) and robot control with humans-in-the-loop, it is crucial to quantify and analyze trust so as to develop robot motion plans, control algorithms, and autonomous decision aids to enable better human-robot collaboration (HRC). This entry provides an overview of the state-of-the-art trust models. In particular, the focus here is on computational trust models that quantify human trust in robots and capture its dynamic evolution. Four categories of computational trust models are introduced and their respective pros and cons summarized and compared. The utilization of computational trust models in robot motion planning, control, and decision-making is reviewed. Future research directions in trust modeling in HRI and control are also discussed.
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Wang, Y. (2021). Trust Models for Human-Robot Interaction and Control. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-44184-5_100121
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DOI: https://doi.org/10.1007/978-3-030-44184-5_100121
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