Representation of Pre-Grasp Strategies for Object Manipulation
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In this paper, we present a method for representing and re-targeting manipulations for object adjustment before final grasping. Such pre-grasp manipulation actions bring objects into better configurations for grasping through e.g. object rotation or object sliding. For this purpose, we propose a scaling-invariant and rotation-invariant representation of the hand poses, which is then automatically adapted to the target object to perform the selected pre-grasp manipulations. We show that pre-grasp strategies such as sliding manipulations not only enable more robust object grasping, but also significantly increase the success rate for grasping.