We are interested in asymmetric human-robot teams, where
a human supervisor occasionally takes over control to aid
an autonomous robot in a given task. Our research aims to
optimize team efficiency by improving the robot’s task per-
formance, decreasing the human’s workload, and building
trust in the team. We envision synergistic collaborations
where the robot adapts its behaviors dynamically to opti-
mize efficacy, reduce manual interventions, and actively seek
for greater trust. We describe recent works that study two
facets of this trust-seeking adaptive methodology: model-
ing human-robot trust dynamics, and developing interactive
behavior adaptation techniques. We also highlight ongo-
ing efforts to combine these works, which will enable future
human-robot teams to be maximally trusting and efficient.