Bliek A, Cansev M, Beckerle P (2026)
Publication Type: Journal article
Publication year: 2026
Book Volume: 15
Article Number: 68
Journal Issue: 3
DOI: 10.1145/3802818
Trust is a critical factor in human-robot interaction. This study investigates how using human data to train reinforcement learning (RL) agents affects perceived performance and trust. We conducted two experiments: an online study using the video game Seaquest, and a physical robot study involving a wire loop task. In both, RL agents were trained using demonstrations from human users with varying skill levels, as well as without human data. Participants then rated the agents' performance and how much they trusted them.Our results for the online study indicate that RL agents trained with expert human data received significantly higher trust ratings than other agents and that trust was higher in replay and long training conditions compared to short and no training. Videos showing beginner players were also trusted, despite lower performance, although these differences did not always reach statistical significance. This highlights that trust is influenced by factors beyond mere performance, suggesting a role for cues such as perceived human involvement.In the physical robot study, our findings indicate that trust was significantly influenced by the type of training data, with data from an experienced human having a positive effect under certain training conditions. Performance ratings were influenced primarily by the training approach, with replay receiving higher ratings than short training. Although trust was notably higher under certain conditions, performance ratings did not always align with trust, indicating a partial dissociation between the two measures.Together, these results suggest that training with high-quality human data can enhance the trustworthiness of RL agents under appropriate training conditions. However, trust depends not only on performance but also on how human-like an agent's behavior appears. Designing agents for trust-sensitive applications may therefore benefit from aligning behavior with user expectations rather than optimizing performance alone.
APA:
Bliek, A., Cansev, M., & Beckerle, P. (2026). Human or Agent: Whom to Trust? Investigating Trust in Human-Robot Interaction Using Human Data to Train Reinforcement Agents. ACM Transactions on Human-Robot Interaction, 15(3). https://doi.org/10.1145/3802818
MLA:
Bliek, Adna, Mehmet Cansev, and Philipp Beckerle. "Human or Agent: Whom to Trust? Investigating Trust in Human-Robot Interaction Using Human Data to Train Reinforcement Agents." ACM Transactions on Human-Robot Interaction 15.3 (2026).
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