Perception Is All You Need: A Neuroscience Framework for Low Cost Sensorless Gaze in HRI

TL;DR

This paper introduces a sensorless gaze-following framework for child-robot interaction that leverages neuroscience principles to create low-cost, privacy-preserving robots using perceptual cues instead of sensors.

Contribution

It proposes a novel, sensorless gaze-following method based on neuroscience insights, enabling low-cost, privacy-preserving robots with a simple design.

Findings

A sub-dollar robot design implementing the brain's gaze computation pipeline.

The framework relies on the convexity prior and perceptual illusions to produce gaze perception.

Design constraints and boundary conditions for successful deployment are identified.

Abstract

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.