  Robotic manipulation with two-finger grippers is challenged by objects
lacking distinct graspable features. Traditional pre-grasping methods, which
typically involve repositioning objects or utilizing external aids like table
edges, are limited in their adaptability across different object categories and
environments. To overcome these limitations, we introduce PreAfford, a novel
pre-grasping planning framework that incorporates a point-level affordance
representation and a relay training approach. Our method significantly improves
adaptability, allowing effective manipulation across a wide range of
environments and object types. When evaluated on the ShapeNet-v2 dataset,
PreAfford not only enhances grasping success rates by 69% but also demonstrates
its practicality through successful real-world experiments. These improvements
highlight PreAfford's potential to redefine standards for robotic handling of
complex manipulation tasks in diverse settings.
