HiFOR: A Hierarchical Foresight Framework with Action-Guided World Retrieval for Vision-Language-Action Models

HiFOR proposes a hierarchical foresight framework with action-guided world retrieval for vision-language-action (VLA) models. Its hierarchical foresight jointly predicts short-term state evolution, semantic milestones, and the final goal state through a Hierarchical Dependency Mask (HDM), while structure-aware latent world modeling organizes future representations along the temporal, view, and spatial dimensions, and action-guided factorized world retrieval retrieves relevant future information by action representation during denoising. The authors report that the method's effectiveness was validated on two simulation benchmarks and six real-world robotic manipulation tasks.