ReachVox: Clutter-Free Reachability Visualization for Robot Motion Planning in Virtual Reality

Human-Robot-Collaboration can enhance workflows by leveraging the mutual strengths of human operators and robots. Planning and understanding robot movements remain major challenges in this domain. This problem is prevalent in dynamic environments that might need constant robot motion path adaptation. In this paper, we investigate whether a minimalistic encoding of the reachability of a point near an object of interest, which we call ReachVox, can aid the collaboration between a remote operator and a robotic arm in VR. Through a user study ($\mathrm{n}=20$), we indicate the strength of the visualization relative to a point-based reachability check-up.

Authors

Publication Details

Journal
Apollo (University of Cambridge)
Published
2025-10-12
DOI
https://doi.org/10.17863/cam.124235
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
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ReachVox: Clutter-Free Reachability Visualization for Robot Motion Planning in Virtual Reality

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Apollo (University of Cambridge)
Human Motion and Animation
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ReachVox: Clutter-Free Reachability Visualization for Robot Motion Planning in Virtual Reality

Diar Abdlkarim, Per Ola Kristensson, Eyal Ofek, Steffen Hauck, Jens Grubert, John J Dudley
article en

Abstract

Human-Robot-Collaboration can enhance workflows by leveraging the mutual strengths of human operators and robots. Planning and understanding robot movements remain major challenges in this domain. This problem is prevalent in dynamic environments that might need constant robot motion path adaptation. In this paper, we investigate whether a minimalistic encoding of the reachability of a point near an object of interest, which we call ReachVox, can aid the collaboration between a remote operator and a robotic arm in VR. Through a user study ($\mathrm{n}=20$), we indicate the strength of the visualization relative to a point-based reachability check-up.

Apollo (University of Cambridge)
Openalex Percentile: Top 37%
Human Motion and Animation
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