An integrated 3D eye-gaze tracking framework for assessing trust in human–robot interaction
File(s)
Published version
OA Location
Author(s)
Chacón Quesada, Rodrigo
Estévez Casado, Fernando
Demiris, Yiannis
Type
Journal Article
Abstract
We introduce a comprehensive approach to examining the complexities of trust during Human–Robot Interactions (HRIs) through an innovative 3D eye-gaze tracking framework. Trust is a fundamental psychological factor in HRI studies, influencing how humans perceive and interact with robots. Although researchers have previously highlighted eye-tracking as a promising tool for capturing behavioural manifestations of trust continuously and non-intrusively, traditional approaches have been limited to 2D setups, leaving their applicability to real-world HRI largely unexplored. Thus, there still is limited evidence for the feasibility and validity of using eye-tracking to assess human–robot trust in more realistic settings. To this end, our framework employs Head-Mounted Displays with 3D eye-gaze and spatial tracking capabilities to gather continuous eye-gaze data alongside real-time user and robot positions. In addition to 3D eye-gaze tracking capabilities, we designed and incorporated a Bayesian model to evaluate experimental treatments’ effectiveness while identifying eye-gaze features correlating with participants’ subjective trust scores. The latter are measured using Likert-type instruments, widely used in HRI research. We applied our framework to a user study involving 25 participants performing an inspection task with a robot under two reliability conditions—high versus low. Our results revealed significant differences in subjective trust between conditions. Moreover, the results show that participants exposed to the low-reliability condition fixate for longer and have higher fixation and saccade amplitudes when compared to those in the high-reliability condition. Additionally, the group with low reliability had a greater rate of transitions between fixations. These findings are consistent with previous research on 2D settings. However, we observed differences in scan-path length and total fixation count compared to previous studies. Lastly, our results show that incorporating multiple eye-gaze feature categories simultaneously into our Bayesian model can lead to a more nuanced comprehension of the intricate connections between eye-gaze patterns and subjective trust in HRI. A supplementary video providing additional details is available online as supplementary material and can also be accessed at https://www.imperial.ac.uk/personal-robotics/videos/.
Date Issued
2025-06-09
Date Acceptance
2025-03-11
Citation
ACM Transactions on Human-Robot Interaction, 2025, 14 (3), pp.1-28
ISSN
2573-9522
Publisher
Association for Computing Machinery (ACM)
Start Page
1
End Page
28
Journal / Book Title
ACM Transactions on Human-Robot Interaction
Volume
14
Issue
3
Copyright Statement
© 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 (https://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
10.1145/3725861
Subjects
CCS Concepts: • Human-centered computing → User studies
• Computing methodologies → Mixed / augmented reality
• Computer systems organization → Robotics
Human-Robot Interaction, User Studies, Trust, Eye-Gaze, Augmented Reality, Legged Robot, Bayesian Data Analysis This work is licensed under Creative Commons Attribution International 4.0
Publication Status
Published
Date Publish Online
2025-03-28