  To facilitate effective human-robot interaction (HRI), trust-aware HRI has
been proposed, wherein the robotic agent explicitly considers the human's trust
during its planning and decision making. The success of trust-aware HRI depends
on the specification of a trust dynamics model and a trust-behavior model. In
this study, we proposed one novel trust-behavior model, namely the reverse
psychology model, and compared it against the commonly used disuse model. We
examined how the two models affect the robot's optimal policy and the
human-robot team performance. Results indicate that the robot will deliberately
"manipulate" the human's trust under the reverse psychology model. To correct
this "manipulative" behavior, we proposed a trust-seeking reward function that
facilitates trust establishment without significantly sacrificing the team
performance.
