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

翻译：在执行与布料相关的任务（如衣物悬挂）时，识别并抓取特定结构区域（例如衬衫的衣领而非袖子）通常至关重要。然而，由于布料的可变形性，这些在家居、医疗及工业环境中至关重要的操作活动对机器人而言仍具挑战性。本文以悬挂任务为案例研究，聚焦于如何分割并抓取衣物的结构区域以实现操控任务。为此，我们提出了一种基于神经网络的感知系统，用于从深度图像中从代表场景其余部分的区域中分割出衬衫的衣领。通过使用一段10分钟的人类操作衬衫视频进行训练，该感知系统能够泛化至其他不同纹理的衬衫及带领衣物。随后，基于分割结果提出了一种新型抓取策略以确定抓取姿态。实验表明，所提出的抓取策略在单件折叠衣物、单件皱褶衣物及三件皱褶衣物上的抓取成功率分别达到92%、80%和50%。该抓取策略显著优于未考虑衣物结构特性的基线方法。借助所提出的区域分割与抓取策略，我们通过开环控制策略成功实现了具有挑战性的衣物悬挂任务。补充材料可访问 https://sites.google.com/view/garment-hanging。