  Trust between team members is an essential requirement for any successful
cooperation. Thus, engendering and maintaining the fellow team members' trust
becomes a central responsibility for any member trying to not only successfully
participate in the task but to ensure the team achieves its goals. The problem
of trust management is particularly challenging in mixed human-robot teams
where the human and the robot may have different models about the task at hand
and thus may have different expectations regarding the current course of
action, thereby forcing the robot to focus on the costly explicable behavior.
We propose a computational model for capturing and modulating trust in such
iterated human-robot interaction settings, where the human adopts a supervisory
role. In our model, the robot integrates human's trust and their expectations
about the robot into its planning process to build and maintain trust over the
interaction horizon. By establishing the required level of trust, the robot can
focus on maximizing the team goal by eschewing explicit explanatory or
explicable behavior without worrying about the human supervisor monitoring and
intervening to stop behaviors they may not necessarily understand. We model
this reasoning about trust levels as a meta reasoning process over individual
planning tasks. We additionally validate our model through a human subject
experiment.
