  When performing cloth-related tasks, such as garment hanging, it is often
important to identify and grasp certain structural regions -- a shirt's collar
as opposed to its sleeve, for instance. However, due to cloth deformability,
these manipulation activities, which are essential in domestic, health care,
and industrial contexts, remain challenging for robots. In this paper, we focus
on how to segment and grasp structural regions of clothes to enable
manipulation tasks, using hanging tasks as case study. To this end, a neural
network-based perception system is proposed to segment a shirt's collar from
areas that represent the rest of the scene in a depth image. With a 10-minute
video of a human manipulating shirts to train it, our perception system is
capable of generalizing to other shirts regardless of texture as well as to
other types of collared garments. A novel grasping strategy is then proposed
based on the segmentation to determine grasping pose. Experiments demonstrate
that our proposed grasping strategy achieves 92\%, 80\%, and 50\% grasping
success rates with one folded garment, one crumpled garment and three crumpled
garments, respectively.
  Our grasping strategy performs considerably better than tested baselines that
do not take into account the structural nature of the garments. With the
proposed region segmentation and grasping strategy, challenging garment hanging
tasks are successfully implemented using an open-loop control policy.
Supplementary material is available at
https://sites.google.com/view/garment-hanging
