PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments

arXiv:2404.03634v1 Announce Type: cross
Abstract: Robotic manipulation of ungraspable objects with two-finger grippers presents significant challenges due to the paucity of graspable features, while traditional pre-grasping techniques, which rely on repositioning objects and leveraging external aids like table edges, lack the adaptability across object categories and scenes. Addressing this, we introduce PreAfford, a novel pre-grasping planning framework that utilizes a point-level affordance representation and a relay training approach to enhance adaptability across a broad range of environments and object types, …

abstract adaptability arxiv challenges cs.cv cs.ro diverse environments features grasping manipulation object objects robotic robotic manipulation table type universal