An Integrated 3D Eye-Gaze Tracking Framework for Assessing Trust in Human–Robot Interaction

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/ .