用大脑看东西的原理，让纸板机器人实现不需传感器的对视。

Gaze-following in child-robot interaction improves attention, recall, and learning, but requires expensive platforms (\$30,000+), sensors, algorithms, and raises privacy concerns. We propose a framework that avoids sensors and computation entirely, instead relying on the human visual system's assumption of convexity to produce perceptual gaze-following between a robot and its viewer. Specifically, we motivate sub-dollar cardboard robot design that directly implements the brain's own gaze computation pipeline in reverse, making the viewer's perceptual system the robot's "actuator", with no sensors, no power, and no privacy concerns. We ground this framework in three converging lines of theoretical and empirical neuroscience evidence. Namely, the distributed face processing network that computes gaze direction via the superior temporal sulcus, the high-precision convexity prior that causes the brain to perceive concave faces as convex, and the predictive processing hierarchy in which top-down face knowledge overrides bottom-up depth signals. These mechanisms explain why a concave eye socket with a painted pupil produces the perception of mutual gaze from any viewing angle. We derive design constraints from perceptual science, present a sub-dollar open-template robot with parameterized interchangeable eye inserts, and identify boundary conditions (developmental, clinical, and geometric) that predict where the framework will succeed and where it will fail. If leveraged, two decades of HRI gaze findings become deliverable at population scale.