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This study investigates how augmented reality interfaces affect the programming of physical robot arms, specifically examining whether AR mitigates cognitive load disparities among users with varying spatial abilities. The findings suggest that while AR does not universally improve usability metrics, it effectively compensates for lower spatial ability, potentially democratizing access to human-robot interaction tools. These insights are crucial for designing personalized interfaces that optimize human-robot collaboration without relying on advanced spatial reasoning skills.
Sources coveringarXiv, LessWrong
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Robot Programming with Augmented Reality: The Role of Spatial Ability

arXiv2 min read

arXiv:2602.03544v2 Announce Type: replace Abstract: Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented reality (AR) can make these spatial relations visible, but its benefits may depend on users' spatial ability. We conducted a randomized between-subjects experiment ($N=71$) in which participants learned to program a physical UR5e robot using either conventional teach-pendant controls with PDF instructions or a head-mounted AR interface that displayed joints, coordinate frames, and waypoints. We measured users' spatial ability with the Mental Rotation Test and assessed subjective cognitive load and system usability. Overall, AR did not significantly improve cognitive load or usability compared with conventional instruction. However, exploratory analyses revealed a compensatory effect: spatial ability predicted higher usability and lower extraneous cognitive load in the control group, but not in the AR condition, suggesting AR mitigated the disadvantage typically faced by users with lower spatial ability. These findings point to a compensatory function of AR that should be explored to guide the design of personalized AR interfaces for human-robot interaction.

Abstract:Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented reality (AR) can make these spatial relations visible, but its benefits may depend on users' spatial ability. We conducted a randomized between-subjects experiment ($N=71$) in which participants learned to program a physical UR5e robot using either conventional teach-pendant controls with PDF instructions or a head-mounted AR interface that displayed joints, coordinate frames, and waypoints. We measured users' spatial ability with the Mental Rotation Test and assessed subjective cognitive load and system usability. Overall, AR did not significantly improve cognitive load or usability compared with conventional instruction. However, exploratory analyses revealed a compensatory effect: spatial ability predicted higher usability and lower extraneous cognitive load in the control group, but not in the AR condition, suggesting AR mitigated the disadvantage typically faced by users with lower spatial ability. These findings point to a compensatory function of AR that should be explored to guide the design of personalized AR interfaces for human-robot interaction.

Robotics (cs.RO); Human-Computer Interaction (cs.HC)

arXiv:2602.03544 [cs.RO]

(or arXiv:2602.03544v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.03544 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 264 words

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