GraspMeanFlow：用于少步6自由度抓取生成的SE(3)等变MeanFlow
GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation
AI总结：
该研究提出SE(3)等变MeanFlow框架GraspMeanFlow，解决现有6自由度抓取生成方法采样效率低的问题，在ACRONYM数据集上实现少步高效抓取生成并提升成功率，且抓取分布可随物体精确变换。
AI中文摘要：
近期用于合成6自由度抓取位姿的数据驱动方法采用生成模型学习复杂的抓取位姿分布并生成多样化的候选位姿。其中，SE(3)等变流模型生成的抓取位姿可随物体的旋转和平移发生一致变换，但这些方法通过迭代数值积分采样，每个抓取需要数十次函数评估，限制了其在实时操作中的应用。我们提出GraspMeanFlow，这是一种用于少步6自由度抓取生成的SE(3)等变MeanFlow框架。我们的方法学习有限时间区间内的平均速度，该速度通过时间有序指数定义，以精确复现该区间内累积的刚体位移。我们证明，由点云条件分布经等变平均速度流图传输后仍保持等变性，因此少步采样下等变性得以保留；我们通过将两个时间提升为等变向量来对场进行条件约束，其余部分保持不变。为实现稳定训练，我们将流匹配边界项与两个一致性项中的任意一个配对：需要雅可比-向量乘积的微分MeanFlow恒等式，或避免该乘积的等价半群损失。在ACRONYM数据集上的实验表明，GraspMeanFlow的单次函数评估即可达到迭代SE(3)流模型需5步才能接近的EMD值，同一框架的另一种实例化在少步 regime 中使抓取成功率最多提升24.3个百分点，且两种方法生成的抓取分布均随物体精确变换。
英文摘要：
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.
