Optimising Human Trust in Robots: A Reinforcement Learning Approach

Abstract

This study explores optimising human-robot trust using reinforcement learning (RL) in simulated environments. Establishing trust in human-robot interaction (HRI) is crucial for effective collaboration, but misaligned trust levels can re-strict successful task completion. Current RL approaches mainly prioritise performance metrics without directly addressing trust management. To bridge this gap, we integrated a validated mathematical trust model into an RL framework and conducted experiments in two simulated environments: Frozen Lake and Battleship. The results showed that the RL model facilitated trust by dynamically adjusting it based on task outcomes, enhancing task performance and reducing the risks of insufficient or extreme trust. Our findings highlight the potential of RL to enhance human-robot collaboration (HRC) and trust calibration in different experimental HRI settings.