Reproduce “GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation” on OpenResearch
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About this paper
Jiyong Kwon, Yikun Bai, Amirhossein Mollaali, Guang Lin
GraspMeanFlow introduces an SE(3)-equivariant MeanFlow framework for 6-DoF grasp generation, extending the concept of few-step generative modeling to complex Lie groups. The method enables high-quality grasp pose generation using significantly fewer network function evaluations, achieving competitive distributional fidelity and grasp success rates for robotic manipulation.
Key insights
- Correctly defining average angular velocity on SO(3) requires the time-ordered exponential, as a naive integral does not reproduce the endpoint rotation of a trajectory.
- SE(3)-equivariance can be preserved in a few-step generative model, ensuring generated grasps are robust to object transformations and reducing the need for extensive data augmentation.
- The MeanFlow concept can be effectively extended to non-commutative Lie groups like SE(3), enabling a significant reduction in network function evaluations during inference for generative tasks.
Summary by alphaXiv.
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