SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking

TL;DR

This study explores how eye-tracking can predict user comfort in human-robot interactions, revealing that simple models and physiological cues like pupil size are effective, and that comfort thresholds differ from human-human interactions.

Contribution

It demonstrates the applicability of eye-tracking features for estimating comfort in human-robot proxemics, highlighting the effectiveness of decision trees and physiological indicators.

Findings

Decision Tree achieved F1-score of 0.73 in comfort prediction.

Minimum pupil diameter is the most important predictor.

Comfort thresholds in human-robot interactions differ from human-human dynamics.

Abstract

Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in human-human interactions, their applicability to interactions with humanoid robots remains unexplored. In this study, we investigate user comfort with the robot "Ameca" across four experimentally controlled distances (0.5 m to 2.0 m) using mobile eye-tracking and subjective reporting (N=19). We evaluate multiple machine learning and deep learning models to estimate comfort based on gaze features. Contrary to previous human-human studies where Transformer models excelled, a Decision Tree classifier achieved the highest performance (F1-score = 0.73), with minimum pupil diameter identified as the most critical predictor. These findings suggest that physiological comfort thresholds in human-robot interaction differ from human-human dynamics and can be effectively modeled using interpretable logic.