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This paper examines the effect of real-time, personalized alignment of arobot's reward function to the human's values on trust and team performance. Wepresent and compare three distinct robot interaction strategies: a non-learnerstrategy where the robot presumes the human's reward function mirrors its own,a non-adaptive-learner strategy in which the robot learns the human's rewardfunction for trust estimation and human behavior modeling, but still optimizesits own reward function, and an adaptive-learner strategy in which the robotlearns the human's reward function and adopts it as its own. Two human-subjectexperiments with a total number of 54 participants were conducted. In bothexperiments, the human-robot team searches for potential threats in a town. Theteam sequentially goes through search sites to look for threats. We model theinteraction between the human and the robot as a trust-aware Markov DecisionProcess (trust-aware MDP) and use Bayesian Inverse Reinforcement Learning (IRL)to estimate the reward weights of the human as they interact with the robot. InExperiment 1, we start our learning algorithm with an informed prior of thehuman's values/goals. In Experiment 2, we start the learning algorithm with anuninformed prior. Results indicate that when starting with a good informedprior, personalized value alignment does not seem to benefit trust or teamperformance. On the other hand, when an informed prior is unavailable,alignment to the human's values leads to high trust and higher perceivedperformance while maintaining the same objective team performance.

Evaluating the Impact of Personalized Value Alignment in Human-Robot Interaction: Insights into Trust and Team Performance Outcomes