Title
Diegetic Graphical User Interfaces for Robot Control via Eye-gazexmlui.dri2xhtml.METS-1.0.item-contributorDepartment
Robótica y AutomatizaciónVersion
Published versionDocument type
Conference ObjectEmbargo end date
2145-01-01Language
EnglishRights
© 2025 IEEEAccess
Embargoed accessPublisher’s version
https://doi.org/10.1109/IROS60139.2025.11247404Published at
International Conference on Intelligent Robots and Systems 2025 IEEE/RSJ IIROS. Hangzhou, ChinaPublisher
IEEEKeywords
Training
Protocols
Symbols
Switches ... [+]
Protocols
Symbols
Switches ... [+]
Training
Protocols
Symbols
Switches
Benchmark testing
Manipulators
User experience
Usability
Robots
Graphical user interfaces [-]
Protocols
Symbols
Switches
Benchmark testing
Manipulators
User experience
Usability
Robots
Graphical user interfaces [-]
Subject (UNESCO Thesaurus)
Automatic controlRobotics
Abstract
Eye-gaze stands out as an intuitive interface for hands-free control of robotic devices due to its brief training time, fast calibration, low invasiveness, and reduced complexity and cost. However, cu ... [+]
Eye-gaze stands out as an intuitive interface for hands-free control of robotic devices due to its brief training time, fast calibration, low invasiveness, and reduced complexity and cost. However, current approaches are limited by available screen space, excessive wait times, frequent context switching, inconsistent gaze tracker accuracy, and the trade-off between feature-richness and usability. This article presents Diegetic Graphical User Interfaces, a novel, intuitive, and computationally inexpensive approach for gaze-controlled interfaces applied to a robotic arm for precision pick-and-place tasks. By using customizable symbols paired with fiducial markers, interactive buttons are defined and embedded into the robot, which users can trigger via gaze. Twenty-one participants completed the Yale-CMU-Berkeley (YCB) Block Pick and Place Protocol, reporting good usability and user experience, while achieving comparable workload to similar systems. The resulting system is fast to learn, does not restrain the user’s head, and mitigates context switching, while demonstrating intuitive control continuous Cartesian control of a robot arm in precision tasks. [-]


















