Fast Generative Grasping via Lie Group-Constrained MeanFlow

2026-08-26

AI Summary

本文提出了一种基于Lie群约束的MeanFlow方法，解决了生成式抓取中多步采样耗时的问题，实现了快速可靠的机器人抓取。

Abstract

Grasp synthesis is a core task in robotic manipulation, for which the solution typically forms a multimodal distribution rather than a point estimate. Generative robotic grasping aims to learn this distribution with deep generative models such as diffusion and flow-based approaches. The iterative nature of such generative models makes them flexible and generalizable; however, multi-step sampling impedes the time-critical operation required in robotics. We devise an approach to fast generative grasping based on MeanFlow on the product Lie group $\mathcal{G} = \mathrm{SO}(3) \times \mathbb{R}^3$. The training objective couples a purely algebraic semigroup consistency condition with Riemannian Conditional Flow Matching on $\mathcal{G}$ that anchors the average velocity to the data distribution. The resulting Lie Group-constrained MeanFlow formulation samples reliable grasps in $\leq 5$ network evaluations, matching the grasp generation performance of state-of-the-art diffusion and flow-based models on the ACRONYM dataset at millisecond-scale inference latency (up to $39\times$ speed-up). We further demonstrate that the approach directly translates to real-world robotic grasping without additional training or domain adaptation, exhibiting robust grasp synthesis under observation noise.

Problem

• Generative Grasping
• Sampling Efficiency
• Robotic Manipulation
• Time-Critical Operation

Innovation

• Lie Group-Constrained MeanFlow
• Riemannian Conditional Flow Matching
• fast generative grasping
• multimodal distribution
• robust grasp synthesis