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

TL;DR

PreAfford is a new pre-grasping planning framework that uses affordance representations and relay training to improve robotic manipulation across diverse objects and environments, achieving a 69% success rate increase.

Contribution

It introduces a novel affordance-based pre-grasping method with relay training, enhancing adaptability for complex manipulation tasks across various settings.

Findings

69% increase in grasping success rate on ShapeNet-v2

Effective real-world manipulation demonstrated

Significant improvement over traditional methods

Abstract

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.