PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects
  and Environments
Kairui Ding Boyuan Chen Ruihai Wu Yuyang Li Zongzheng Zhang Huan-ang Gao Siqi Li Yixin Zhu Guyue Zhou Hao Dong Hao Zhao
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, including those previously
unseen. Demonstrated on the ShapeNet-v2 dataset, PreAfford significantly
improves grasping success rates by 69% and validates its practicality through
real-world experiments. This work offers a robust and adaptable solution for
manipulating ungraspable objects.