In this work, we grant robot agents the capacity
to sense and react to their human supervisor.s changing
trust state, as a means to maintain the efficiency of their
collaboration. We propose the novel formulation of Trust-Aware
Conservative Control (TACtiC), in which the agent alters its
behaviors momentarily whenever the human loses trust. This
trust-seeking robot framework builds upon an online trust
inference engine and also incorporates an interactive behavior
adaptation technique. We present end-to-end instantiations
of trust-seeking robots for distinct task domains of aerial
terrain coverage and interactive autonomous driving. Empirical
assessments comprise a large-scale controlled interaction study
and its extension into field evaluations with an autonomous car.
These assessments substantiate the efficiency gains that trust-
seeking agents bring to asymmetric human-robot teams.