Perception

Spatial Attention

Spatial attention is a neural network mechanism that weights features by image or spatial location, letting a model focus computation on task-relevant regions of its visual input. In transformer architectures, self- and cross-attention over patch tokens implement it natively; robotic policy networks also use learned keypoint or attention maps over camera views to localize objects and grasp points. Attention maps double as an interpretability signal showing where a policy is looking.

Why it matters for physical AI

Manipulation success often hinges on a few pixels around the gripper and target; architectures that attend spatially generalize better across cluttered scenes and camera perturbations.