Virtual Object Categorisation Methods: Towards a Richer Understanding of Object Grasping for ...
summary
Compares three categorisation methods — Zingg benchmark, a novel virtual-equilibrium stable/unstable split, and a component-based one-part vs multiple-part scheme — using 16 virtual object models, 15 raters and 1872 grasps from a VR grasping/docking task. Reports no overall significance for equilibrium but finds stable objects bias toward large-diameter grasps and unstable toward power-sphere grips; Zingg-plus yields significant differences across every subcategory, driven largely by reduced grasp aperture on multipart objects. Recommendations target automated classification and adaptive VR grasping.
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