EquiGraspFlow: SE(3)-Equivariant 6-DoF Grasp Pose Generative Flows

[ Information ]

International Conference on Robot Learning, 2024.

[ Authors ]

Byeongdo Lim, Jongmin Kim, Jihwan Kim, Yonghyeon Lee, Frank C. Park

[ Abstract ]

Traditional methods for synthesizing 6-DoF grasp poses from 3D observations often rely on geometric heuristics, resulting in poor generalizability, limited grasp options, and higher failure rates. Recently, data-driven methods have been proposed that use generative models to learn the distribution of grasp poses and generate diverse candidate poses. The main drawback of these methods is that they fail to achieve SE(3)-equivariance, meaning that the generated grasp poses do not transform correctly with object rotations and translations. In this paper, we propose \textit{EquiGraspFlow}, a flow-based SE(3)-equivariant 6-DoF grasp pose generative model that can learn complex conditional distributions on the SE(3) manifold while guaranteeing SE(3)-equivariance. Our model achieves the equivariance without relying on data augmentation, by using network architectures that guarantee the equivariance by construction. Extensive experiments show that \textit{EquiGraspFlow} accurately learns grasp pose distribution, achieves the SE(3)-equivariance, and significantly outperforms existing grasp pose generative models. Code is available at https://github.com/bdlim99/EquiGraspFlow.