EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning

TL;DR

EqvAfford introduces an equivariance-aware framework for point-level affordance learning, enhancing robotic manipulation by improving generalization across diverse object poses and reducing training data needs.

Contribution

The paper presents a novel equivariance-preserving design for point-level affordance learning, improving generalization in robotic manipulation tasks.

Findings

Achieves superior performance on diverse object poses

Reduces data requirements for training

Demonstrates strong generalization in manipulation tasks

Abstract

Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses. For robotic manipulation, such equivariance also exists in many scenarios. For example, no matter what the pose of a drawer is (translation, rotation and tilt), the manipulation strategy is consistent (grasp the handle and pull in a line). While traditional models usually do not have the awareness of equivariance for robotic manipulation, which might result in more data for training and poor performance in novel object poses, we propose our EqvAfford framework, with novel designs to guarantee the equivariance in point-level affordance learning for downstream robotic manipulation, with great performance and generalization ability on representative tasks on objects in diverse poses.