Fast Generative Grasping via Lie Group-Constrained MeanFlow
S. Talha Bukhari, Yi Wei, Ruiqi Ni, Zachary Kingston, Aniket Bera · 2026 arXiv · PDF
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.
TL;DR
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.