We present OPTIMo: an Online Probabilistic Trust Infer-
ence Model for quantifying the degree of trust that a human
supervisor has in an autonomous robot “worker”. Repre-
sented as a Dynamic Bayesian Network (DBN), OPTIMo
infers beliefs over the human’s moment-to-moment latent
trust states, based on the history of observed interaction
experiences. A separate model instance is trained on each
user’s experiences, leading to an interpretable and person-
alized characterization of that operator’s behaviors and at-
titudes. Using datasets collected from an interaction study
with a large group of roboticists, we empirically assess OP-
TIMo’s performance under a broad range of configurations.
These evaluation results highlight OPTIMo’s advances in
both prediction accuracy and responsiveness over several
existing trust models. This accurate and near real-time
human-robot trust measure makes possible the development
of autonomous robots that can adapt their behaviors dynam-
ically, to actively seek greater trust and greater efficiency
within future human-robot collaborations.