Towards Modeling Real-Time Trust in Asymmetric Human-Robot Collaborations

We are interested in enhancing the efficiency of human-robot collaborations, especially in “supervisor-worker” settings where autonomous robots work under the supervision of a human operator. We believe that trust serves a critical role in modeling the interactions within these teams, and also in streamlining their efficiency. We propose an operational formulation of human-robot trust on a short interaction time scale, which is tailored to a practical tele-robotics setting. We also report on a controlled user study that collected interaction data from participants collaborating with an autonomous robot to perform visual navigation tasks. Our analyses quantify key correlations between real-time human-robot trust assessments and diverse factors, including properties of failure events reflecting causal trust attribution, as well as strong influences from each user’s personality. We further construct and optimize a predictive model of users’ trust responses to discrete events, which provides both insights on this fundamental aspect of real-time human-machine interaction, and also has pragmatic significance for designing trust-aware robot agents.