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Optimising Human Trust in Robots: A Reinforcement Learning Approach

Abdullah Alzahrani, Muneeb Ahmad Orcid Logo

Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction, Pages: 1202 - 1206

Swansea University Authors: Abdullah Alzahrani, Muneeb Ahmad Orcid Logo

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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 restrict successful task completion. Current RL approaches mainlyp...

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Published in: Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction
ISBN: 979-8-3503-7893-1
Published: IEEE 2025
Online Access: https://dl.acm.org/doi/10.5555/3721488.3721647
URI: https://cronfa.swan.ac.uk/Record/cronfa68696
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 restrict successful task completion. Current RL approaches mainlyprioritise 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.
College: Faculty of Science and Engineering
Start Page: 1202
End Page: 1206

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