We present the effect of adapting to human preferences on trust in a human-robot teaming task. The team performs a task in which the robot acts as an action recommender to the human. It is assumed that the behavior of the human and the robot is based on some reward function they try to optimize. We use a new human trust-behavior model that enables the robot to learn and adapt to the human's preferences in real-time during their interaction using Bayesian Inverse Reinforcement Learning. We present three strategies for the robot to interact with a human: a non-learner strategy, in which the robot assumes that the human's reward function is the same as the robot's, a non-adaptive learner strategy that learns the human's reward function for performance estimation, but still optimizes its own reward function, and an adaptive-learner strategy that learns the human's reward function for performance estimation and also optimizes this learned reward function. Results show that adapting to the human's reward function results in the highest trust in the robot.

翻译：我们研究了在人机团队协作任务中，适应人类偏好对信任的影响。该团队执行的任务中，机器人作为行动推荐者向人类提供建议。假设人类和机器人的行为基于他们试图优化的某种奖励函数。我们采用了一种新的人类信任行为模型，使机器人能够通过贝叶斯逆强化学习在交互过程中实时学习并适应人类的偏好。我们提出了机器人与人互动的三种策略：非学习策略（机器人假设人类的奖励函数与自身相同）、非自适应学习策略（机器人学习人类奖励函数以进行性能估计，但仍优化自身奖励函数）以及自适应学习策略（机器人学习人类奖励函数以进行性能估计，并同时优化该学习到的奖励函数）。结果表明，适应人类奖励函数可带来对机器人的最高信任度。