Mirror Eyes: Explainable Human-Robot Interaction at a Glance

The gaze of a person tends to reflect their interest. This work explores what happens when this statement is taken literally and applied to robots. Here we present a robot system that employs a moving robot head with a screen-based eye model that can direct the robot’s gaze to points in physical space and present a reflection-like mirror image of the attended region on top of each eye. We conducted a user study with 33 participants, who were asked to instruct the robot to perform pick-and-place tasks, monitor the robot’s task execution, and interrupt it in case of erroneous actions. Despite a deliberate lack of instructions about the role of the eyes and a very brief system exposure, participants felt more aware about the robot’s information processing, detected erroneous actions earlier, and rated the user experience higher when eye-based mirroring was enabled compared to non-reflective eyes. These results suggest a beneficial and intuitive utilization of the introduced method in cooperative human-robot interaction.

Authors

Institutions

Publication Details

Published
2025-08-25
DOI
https://doi.org/10.1109/ro-man63969.2025.11217810
Citations
1
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
1.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mirror Eyes: Explainable Human-Robot Interaction at a Glance

Michael Krüger, Stephan Hasler, Michael Gienger, Chao Wang et al.
1 citations
Gaze Tracking and Assistive Technology
1.94
article

Mirror Eyes: Explainable Human-Robot Interaction at a Glance

Michael Krüger, Stephan Hasler, Michael Gienger, Chao Wang, Daniel Tanneberg
article en
1 citations

Abstract

The gaze of a person tends to reflect their interest. This work explores what happens when this statement is taken literally and applied to robots. Here we present a robot system that employs a moving robot head with a screen-based eye model that can direct the robot’s gaze to points in physical space and present a reflection-like mirror image of the attended region on top of each eye. We conducted a user study with 33 participants, who were asked to instruct the robot to perform pick-and-place tasks, monitor the robot’s task execution, and interrupt it in case of erroneous actions. Despite a deliberate lack of instructions about the role of the eyes and a very brief system exposure, participants felt more aware about the robot’s information processing, detected erroneous actions earlier, and rated the user experience higher when eye-based mirroring was enabled compared to non-reflective eyes. These results suggest a beneficial and intuitive utilization of the introduced method in cooperative human-robot interaction.

Honda (Japan) (JP)
Openalex Percentile: Top 11%
Gaze Tracking and Assistive Technology
1.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.