  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.
