The Teenager's Problem: Efficient Garment Decluttering With Grasp Optimization

This paper addresses the ''Teenager's Problem'': efficiently removing scattered garments from a planar surface. As grasping and transporting individual garments is highly inefficient, we propose analytical policies to select grasp locations for multiple garments using an overhead camera. Two classes of methods are considered: depth-based, which use overhead depth data to find efficient grasps, and segment-based, which use segmentation on the RGB overhead image (without requiring any depth data); grasp efficiency is measured by Objects per Transport, which denotes the average number of objects removed per trip to the laundry basket. Experiments suggest that both depth- and segment-based methods easily reduce Objects per Transport (OpT) by $20\%$; furthermore, these approaches complement each other, with combined hybrid methods yielding improvements of $34\%$. Finally, a method employing consolidation (with segmentation) is considered, which manipulates the garments on the work surface to increase OpT; this yields an improvement of $67\%$ over the baseline, though at a cost of additional physical actions.