Teaching robots to look where they step

Humanoid robots struggle with agile locomotion across complex terrain because processing full visual input is computationally expensive and imprecise. TAGA trains a policy using reinforcement learning to automatically learn where to look—fusing vision, body position, and motion commands to focus on informative terrain patches. The model emerges with human-like attention patterns without being told what to attend to. On real hardware, this enables reliable foothold selection, platform crossing, and the largest reported gap traversal (1.2m) for perceptive humanoid systems.