Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This article proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation.

中文翻译：

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以抓取为中心的布料操作分析

在过去的十年中，柔顺柔软的手因其适应物体形状的能力而受到广泛关注，从而提高了抓握的效率。然而，在抓取纺织品等高度灵活的物体时，我们面临双重问题：物体会适应手或抓手的形状。在这种情况下，经典的抓握分析或抓握分类不适合描述纺织物体的抓握。本文提出了纺织物体抓取的新定义，该定义从机器人体现或手形中抽象出来，并从早期神经科学文献中关于手抓取技能的概念中恢复过来。该框架使我们能够识别迄今为止文献中使用的抓手来执行机器人布料操作，并允许根据基于重新抓取的操作原语对所有已解决的任务进行精确定义。此外，我们还审查了使用的夹具。我们的分析表明，绝大多数布料操作仅依赖于一种类型的抓取，同时我们确定了一些需要更多抓取类型才能成功执行的任务。我们的框架是通用的，提供了布料操作基元的分类，可以激发抓手设计和布料操作的基准构建。同时，我们确定了几个需要更多抓取类型才能成功执行的任务。我们的框架是通用的，提供了布料操作基元的分类，可以激发抓手设计和布料操作的基准构建。同时，我们确定了几个需要更多抓取类型才能成功执行的任务。我们的框架是通用的，提供了布料操作基元的分类，可以激发抓手设计和布料操作的基准构建。