An Estimation of Three-Layered Human’s Trust in Robots

Trust is an essential aspect of human-robot interaction (HRI) and plays an important role in decision-making. Currently, measuring trust in real-time is challenging, especially in repeated interaction. Consequently, we see limited work on calibrating humans' trust in robots in HRI. In this work, we describe a mathematical model that attempts to emulate the three-layered (initial, situational, learned) framework of trust capable of potentially estimating humans' trust in robots in real-time. We evaluated the trust model in two different HRI user studies. The results showed that the model is valid based on the linear regression analysis. We look to design a robotic system that adapts to optimize human's trust in robots, using our validated model's metrics.