Title: From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
Primary area: applications to robotics, autonomy, planning
Keywords: Robot Learning, Robotics

Abstract: Existing vision-language-action (VLA) models operate in 3D environments but rely mainly on 2D encoders, creating a spatial reasoning gap that limits generalization. Recent attempts to integrate 3D cues either depend on specialized sensors—hindering modality transfer—or inject weak geometric hints that disrupt vision-language alignment. 
We introduce FALCON (From Spatial to Action), a new paradigm that directly injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to provide strong geometric priors from RGB alone, while an optional Embodied Spatial Model can fuse depth or pose information when available—without retraining or architectural changes. To preserve language reasoning, spatial tokens are processed by a Spatial-Enhanced Action Head rather than forced into the vision-language backbone. 
With these designs, FALCON effectively addresses the challenges of spatial representation, modality transferability, and alignment. Extensive evaluations on three simulation benchmarks and eleven real-world tasks show that FALCON achieves state-of-the-art performance, consistently outperforming strong baselines and maintaining robustness under clutter and spatial-prompt conditioning, and variations in object scale or height. Code will be released publicly.