Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models

Recent work explores new opportunities at the intersectionof vision-language-action models (VLAs) and geometric foundation mod-els (GFMs) for 3D reconstruction, such as VGGT. While the resultinggeometric VLAs often show improved performance, it remains unclear(i) if modern VLAs already have sufficient geometric understanding tostart with, (ii) what is the best architecture to inject geometric under-standing into a VLA, and (iii) what is the effect of other design choicesthat affect geometric VLAs. In this paper we provide a rigorous exper-imental analysis to shed light on these questions, for a specific choiceof VLA (GR00T-N1.5) and GFM (VGGT). Our first contribution is toformalize prior work’s intuition that current VLAs lack geometric un-derstanding, by providing a rigorous analysis based on linear probing.The analysis quantifies, for the first time, the “geometric gap” betweenVLAs and GFMs. Our second contribution is to identify and comparedifferent strategies to bridge GFMs with VLAs. We implement threedifferent architectures, which differ in the way they inject geometry inthe VLA, while keeping low-level implementation details as similar aspossible, to ensure a fair comparison. Finally, we analyze the impact ofnon-architectural choices (e.g., training data, number of cameras, recon-struction quality) on the performance of the geometric VLAs.