Yi Wei

2026

1. arXiv

   

   Fast Generative Grasping via Lie Group-Constrained MeanFlow

   S. Talha Bukhari, Yi Wei, Ruiqi Ni, Zachary Kingston, and Aniket Bera

   Under Review

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   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 SO(3) x R^3. The training objective couples a purely algebraic semigroup consistency condition with Riemannian Conditional Flow Matching on the product Lie group that anchors the average velocity to the data distribution. The resulting Lie Group-constrained MeanFlow formulation samples reliable grasps in at most 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.

   @misc{bukhari2026meanflow,
     archiveprefix = {arXiv},
     author = {Bukhari, S. Talha and Wei, Yi and Ni, Ruiqi and Kingston, Zachary and Bera, Aniket},
     eprint = {2608.26076},
     note = {Under Review},
     primaryclass = {cs.RO},
     title = {Fast Generative Grasping via {L}ie Group-Constrained {MeanFlow}},
     year = {2026}
   }