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.

翻译：信任感知的人机交互因信任被证实是高效人机交互的关键因素而受到日益增长的研究关注。信任感知人机交互研究发现了一个困境——最大化任务奖励往往导致人类信任度下降，而最大化人类信任则会损害任务性能。本研究通过将人机交互过程建模为双人马尔可夫博弈，并利用奖励塑形技术在限制性能损失的同时提升人类信任，从而解决这一困境。具体而言，我们证明当塑形奖励基于势能函数时，性能损失可被马尔可夫博弈终态评估的势能函数所界定。我们将所提框架应用于基于经验的信任模型，最终形成可在实际应用中高效求解与部署的线性规划问题。我们在人机协作执行搜索与救援任务的仿真场景中评估了该框架，结果表明该框架成功修正了机器人的最优策略，使其能以极小任务性能代价提升人类信任度。