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Fast Generative Grasping via Lie Group-Constrained MeanFlow
arXiv:2608.26076v1 Announce Type: new Abstract: Grasp synthesis is a core task in robotic manipulation, for which the solution typically forms a multimodal distribution rather than a point estimate. G
arXiv:2608.26076v1 Announce Type: new 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 G = SO(3) imes R^3. The training objective couples a purely algebraic semigroup consistency condition with Riemannian Conditional Flow Matching on 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 39imes 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.
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Source: arXiv cs.RO | 2026-08-27