GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation

• URL: http://arxiv.org/abs/2608.03295v1
• Date: Tue, 04 Aug 2026 08:06:34 GMT
• Title: GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation
• Abstract summary: We propose GraspMeanFlow, an SE(3)-equivariant MeanFlow framework for few-step 6-DoF grasp generation.<n>Our method learns the average velocity over a finite time interval, defined through the time-ordered exponential so that it reproduces exactly the rigid-body displacement accumulated over that interval.<n>Experiments on ACRONYM show that a single function evaluation of GraspMeanFlow reaches the distributions that an iterative SE(3) flow model needs five steps to approach.
• Score: 12.564090284474863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent data-driven methods for synthesizing 6-DoF grasp poses use generative models to learn complex grasp pose distributions and generate diverse candidate poses. In particular, SE(3)-equivariant flow-based models generate grasp poses that transform consistently with object rotations and translations. However, these methods sample by iterative numerical integration, requiring tens of function evaluations per grasp and limiting their use in real-time manipulation. We propose GraspMeanFlow, an SE(3)-equivariant MeanFlow framework for few-step 6-DoF grasp generation. Our method learns the average velocity over a finite time interval, defined through the time-ordered exponential so that it reproduces exactly the rigid-body displacement accumulated over that interval. We prove that a point-cloud-conditioned distribution transported by an equivariant average-velocity flow map remains invariant, so equivariance is retained under few-step sampling, and we condition the field on a pair of times by lifting both to equivariant vectors, leaving the backbone otherwise unchanged. For stable training, we pair a flow-matching boundary term with either of two consistency terms: the differential MeanFlow identity, whose target requires a Jacobian-vector product, or an equivalent semigroup loss that avoids it. Experiments on ACRONYM show that a single function evaluation of GraspMeanFlow reaches the EMD that an iterative SE(3) flow model needs five steps to approach, that a second instantiation of the same framework improves grasp success by up to 24.3 points in the few-step regime, and that both generate grasp distributions transforming exactly with the object.

Related papers

• SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups [33.76281571675535]
  We introduce SE(3)-MeanFlow, a few-step generative framework that extends MeanFlow from Euclidean space to the Lie group geometry of protein frames.<n>We derive closed-form average-velocity identities for rotations and translations, giving simulation-free training targets.
  arXiv  Detail & Related papers  (2026-07-29T19:58:00Z)
• SurF: A Generative Model for Multivariate Irregular Time Series Forecasting [9.605729240308543]
  We propose SurF, a generative model that learns between event sequences and i.i.d. unit-rate exponential noise.<n>On six real-world benchmarks, SurF achieves the best reported time RMSE on Earthquake, Retweet, and Taobao.<n>Under a strict leave-one-out protocol, SurF beats every classical and neural-autoregressive baseline on 5/6 datasets.
  arXiv  Detail & Related papers  (2026-05-13T19:46:48Z)
• AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation [70.80207977907908]
  We introduce AnyFlow, the first any-step video diffusion distillation framework based on flow maps.<n>We show that AnyFlow achieves performance matches or surpasses consistency-based counterparts in the few-step regime.
  arXiv  Detail & Related papers  (2026-05-13T16:06:34Z)
• Efficient Hybrid SE(3)-Equivariant Visuomotor Flow Policy via Spherical Harmonics for Robot Manipulation [51.48086451600577]
  E3Flow is a novel framework that addresses the limitations of equivariant diffusion policies.<n>It unifies efficient rectified flow with stable, multi-modal equivariant learning for the first time.<n>E3Flow achieves a 3.12% improvement in average success rate over the state-of-the-art Spherical Diffusion Policy.
  arXiv  Detail & Related papers  (2026-03-24T14:00:36Z)
• Riemannian MeanFlow for One-Step Generation on Manifolds [54.09734511705173]
  Flow Matching enables simulation-free training of generative models.<n>MeanFlow can be extended to manifold-valued generation where velocities lie in location-dependent tangent spaces.<n>Experiments on spheres, tori, and SO(3) demonstrate competitive one-step sampling with improved quality-efficiency trade-offs and substantially reduced sampling cost.
  arXiv  Detail & Related papers  (2026-03-11T12:41:46Z)
• MeanFlowSE: one-step generative speech enhancement via conditional mean flow [13.437825847370442]
  MeanFlowSE is a conditional generative model that learns the average velocity over finite intervals along a trajectory.<n>On VoiceBank-DEMAND, the single-step model achieves strong intelligibility, fidelity, and perceptual quality with substantially lower computational cost than multistep baselines.
  arXiv  Detail & Related papers  (2025-09-18T11:24:47Z)
• Contrastive Flow Matching [61.60002028726023]
  We introduce Contrastive Flow Matching, an extension to the flow matching objective that explicitly enforces uniqueness across all conditional flows.<n>Our approach adds a contrastive objective that maximizes dissimilarities between predicted flows from arbitrary sample pairs.<n>We find that training models with Contrastive Flow Matching (1) improves training speed by a factor of up to 9x, (2) requires up to 5x fewer de-noising steps and (3) lowers FID by up to 8.9 compared to training the same models with flow matching.
  arXiv  Detail & Related papers  (2025-06-05T17:59:58Z)
• Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts [64.34482582690927]
  We provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models.<n>We propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality.
  arXiv  Detail & Related papers  (2025-03-04T17:46:51Z)
• SE(3) Equivariant Augmented Coupling Flows [16.65770540017618]
  Coupling normalizing flows allow for fast sampling and density evaluation. Standard coupling architecture precludes endowing flows that operate on the Cartesian coordinates of atoms.
  arXiv  Detail & Related papers  (2023-08-20T20:49:15Z)
• Fast Sampling of Diffusion Models via Operator Learning [74.37531458470086]
  We use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
  arXiv  Detail & Related papers  (2022-11-24T07:30:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.