Improving Human–Robot Collaboration through Augmented Reality and Eye Gaze

When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration depends on how these individual models overlap to form a shared mental model among team members; shared models are also important for collaborative processes in human–robot teams. The development and maintenance of an accurate shared mental model requires bidirectional communication of individual intent and the ability to interpret the intent of other team members. To enable effective human–robot collaboration, this article investigates the use of augmented reality (AR) technology and user eye gaze to enable bidirectional communication of intent in a joint action task. We tested this approach through a user study with 37 participants and found that this communication improves task efficiency, trust, as well as task fluency. We conclude that using AR and eye gaze to enable bidirectional communication and support shared mental models is a promising means for improving collaboration between humans and robots.

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Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2025-02-06
DOI
https://doi.org/10.1145/3716175
Citations
5
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
9.68
Controls
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article

Improving Human–Robot Collaboration through Augmented Reality and Eye Gaze

Wesley P. Chan, Elizabeth A. Croft, Nicole Robinson, Khoa C. Hoang et al.
5 citations
ACM Transactions on Human-Robot Interaction
Gaze Tracking and Assistive Technology
9.68
article

Improving Human–Robot Collaboration through Augmented Reality and Eye Gaze

Wesley P. Chan, Elizabeth A. Croft, Nicole Robinson, Khoa C. Hoang, Morgan Crouch, Charlie Chen
article en
5 citations

Abstract

When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration depends on how these individual models overlap to form a shared mental model among team members; shared models are also important for collaborative processes in human–robot teams. The development and maintenance of an accurate shared mental model requires bidirectional communication of individual intent and the ability to interpret the intent of other team members. To enable effective human–robot collaboration, this article investigates the use of augmented reality (AR) technology and user eye gaze to enable bidirectional communication of intent in a joint action task. We tested this approach through a user study with 37 participants and found that this communication improves task efficiency, trust, as well as task fluency. We conclude that using AR and eye gaze to enable bidirectional communication and support shared mental models is a promising means for improving collaboration between humans and robots.

ACM Transactions on Human-Robot InteractionVol. 14(3)
University of Victoria (CA), Monash University (AU)
Openalex Percentile: Top 2%
Gaze Tracking and Assistive Technology
9.68
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