  Trust-aware human-robot interaction (HRI) has received increasing research
attention, as trust has been shown to be a crucial factor for effective HRI.
Research in trust-aware HRI discovered a dilemma -- maximizing task rewards
often leads to decreased human trust, while maximizing human trust would
compromise task performance. In this work, we address this dilemma by
formulating the HRI process as a two-player Markov game and utilizing the
reward-shaping technique to improve human trust while limiting performance
loss. Specifically, we show that when the shaping reward is potential-based,
the performance loss can be bounded by the potential functions evaluated at the
final states of the Markov game. We apply the proposed framework to the
experience-based trust model, resulting in a linear program that can be
efficiently solved and deployed in real-world applications. We evaluate the
proposed framework in a simulation scenario where a human-robot team performs a
search-and-rescue mission. The results demonstrate that the proposed framework
successfully modifies the robot's optimal policy, enabling it to increase human
trust at a minimal task performance cost.
