用眼动追踪预测人机互动舒适距离，发现瞳孔最小直径最关键。
SensHRPS: Sensing Comfortable Human-Robot Proxemics and Personal Space With Eye-Tracking
- 通过眼动数据建模人类与机器人互动的舒适距离
- 决策树模型准确率达F1=0.73，瞳孔最小直径最有效
- 结果揭示人机舒适区不同于人际互动，模型可解释性强
社交机器人需适应人类的近身空间规范以保障用户舒适与参与感。尽管先前研究证实眼动特征能可靠估计人际互动中的舒适度，但其在人形机器人交互中的适用性尚未探索。本研究通过移动眼动追踪与主观报告，在四个实验控制距离（0.5米至2.0米）下评估用户与机器人Ameca的舒适度（N=19）。我们比较了多种机器学习与深度学习模型，基于注视特征预测舒适度。与以往人际研究中Transformer表现优异不同，决策树分类器达到最高性能（F1-score = 0.73），其中最小瞳孔直径被识别为最关键预测因子。结果表明，人机交互中的生理舒适阈值不同于人际动态，且可用可解释逻辑模型有效建模。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。