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.