FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models

Jul 17, 2026

FoMoVLA is a framework that augments Vision-Language-Action (VLA) models with explicit spatio-temporal supervision by jointly learning future feature foresight and sparse 2D point tracking. This approach enhances continuous action policy learning and achieves state-of-the-art performance on the LIBERO, RoboCasa, and LIBERO-Plus benchmarks, demonstrating strong zero-shot generalization.

Why it matters: By integrating visual foresight with motion guidance, FoMoVLA addresses a key limitation of reactive VLA models and enables more robust and generalizable robot manipulation policies.

Full story at: arXiv Computer Vision ↗