Virtual object categorisation methods: Towards a richer understanding of object grasping for virtual reality

Abstract

Object categorisation methods have been historically used in literature for understanding and collecting real objects together into meaningful groups and can be used to define human interaction patterns (i. e grasping). When investigating grasping patterns for Virtual Reality (VR), researchers used Zingg’s methodology which categorises objects based on shape and form. However, this methodology is limited and does not take into consideration other object attributes that might influence grasping interaction in VR. To address this, our work presents a study into three categorisation methods for virtual objects. We employ Zingg’s object categorisation as a benchmark against existing real and virtual object interaction work and introduce two new categorisation methods that focus on virtual object equilibrium and virtual object component parts. We evaluate these categorisation methods using a dataset of 1872 grasps from a VR docking task on 16 virtual representations of real objects and report findings on grasp patterns. We report on findings for each virtual object categorisation method showing differences in terms of grasp classes, grasp type and aperture. We conclude by detailing recommendations and future ideas on how these categorisation methods can be taken forward to inform a richer understanding of grasping in VR.