  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 work 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.
