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

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 incorporating 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.

I. Introduction

Consider a robotic arm equipped with a two-finger gripper attempting to grasp a mobile phone lying flat on a surface, as illustrated in Fig. 2(a). The close contact between the phone and the table results in a lack of graspable features, creating an ungraspable scenario [4]. Inspired by human object manipulation strategies, an effective approach involves repositioning the object towards a table edge, allowing the phone to hang off slightly and thus become graspable. This technique, termed pre-grasping manipulation, is a critical preliminary step in enabling successful grasping in challenging scenarios [2],[5],[8],[9]. As depicted in Fig. 2(b-e), various environmental features, also known as extrinsic dexterity [10],[11], can be leveraged to reconfigure the object into a more favorable position for grasping.