Attention from Action, for Action: Emergent Visual Bottlenecks for Policy Learning
Seeker is an action-supervised module that learns where visual evidence is needed for control by turning observation-action data into a progression-aware Region of Interest (ROI). It achieves this by iteratively updating a query based on gathered visual evidence, exposing spatial bottlenecks without needing semantic labels or gaze information. This method improves policy learning and robustness.