GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
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 diffus...
| Authors | Dongli Wu, Xiaobao Wei, Hao Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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