GraspFoM：基于3D基础先验的重建驱动机器人抓取
GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
AI总结：
提出GraspFoM框架，利用3D基础先验（SAM3D）构建共享3D物体潜变量，联合优化重建与抓取姿态预测，通过锚点初始化的截断姿态推理扩散器生成连续多模态抓取，实现高保真重建与最优抓取。
摘要：
机器人抓取是机器人操作中的基本能力。然而，在部分观测下抓取仍然具有挑战性。可靠的抓取依赖于局部接触线索和物体级3D结构。现有的几何感知抓取方法认识到重建的价值，但通常将几何视为中间预测，而不是可重用的抓取物体先验。在本文中，我们提出了GraspFoM，一个统一的框架，利用3D基础先验（SAM3D）为重建和抓取姿态预测构建共享的3D物体潜变量。基于这个共享的物体潜变量，我们引入了一个锚点初始化的截断姿态推理扩散器，它预测连续且多模态的抓取姿态，而不直接依赖离散的抓取候选。我们进一步通过一个重建感知评分器和残差潜变量更新器来研究重建与抓取之间的相互作用。重建提供基于几何的线索，而抓取监督则使共享的物体潜变量向与抓取相关的可操作性区域细化。GraspFoM联合预测抓取姿态并以网格和3DGS形式重建高保真3D资产。综合实验表明，GraspFoM在重建和抓取上都达到了最先进的结果。值得注意的是，这些改进只需要少量额外的可训练参数。组件消融研究也证明了每个组件的贡献。
英文摘要：
Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping depends on both local contact cues and object-level 3D structure. Existing geometry-aware grasping methods recognize the value of reconstruction, but they typically treat geometry as an intermediate prediction rather than a reusable object prior for grasping. In this paper, we present GraspFoM, a unified framework that leverages 3D foundation priors (SAM3D) to build a shared 3D object latent for both reconstruction and grasp pose prediction. Built on this shared object latent, we introduce an anchor-initialized truncated pose-reasoning diffuser that predicts continuous and multimodal grasp poses without directly relying on discrete grasp candidates. We further investigate the interaction between reconstruction and grasping through a reconstruction-aware scorer and a residual latent updater. Reconstruction provides grounded geometric cues, while grasp supervision refines the shared object latent toward grasp-relevant affordances. GraspFoM jointly predicts grasp poses and reconstructs high-fidelity 3D assets in mesh and 3DGS forms. Comprehensive experiments demonstrate that GraspFoM achieves state-of-the-art results on both reconstruction and grasping. Notably, these improvements require only a small number of additional trainable parameters. Component-wise ablation studies also demonstrate the contribution of each component.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
