EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
This addresses the need for more efficient and robust robotic manipulation systems by reducing data requirements and enhancing adaptability to novel poses, representing a domain-specific advancement.
The paper tackles the problem of robotic manipulation in diverse object poses by introducing the EqvAfford framework, which ensures SE(3) equivariance in point-level affordance learning, resulting in improved performance and generalization ability on representative tasks.
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.