Abstract
This paper evaluates the current gaze estimation methods within an HRI context of a shared workspace scenario. We introduce a new, annotated dataset collected with the NICO robotic platform. We evaluate four state-of-the-art gaze estimation models. The evaluation shows that the angular errors are close to those reported on general-purpose benchmarks. However, when expressed in terms of distance in the shared workspace the best median error is 16.48 cm quantifying the practical limitations of current methods. We conclude by discussing these limitations and offering recommendations on how to best integrate gaze estimation as a modality in HRI systems.
Abstract (translated)
本文评估了在人机交互（HRI）环境中共享工作空间场景下的当前凝视估计方法。我们引入了一个新的、带有标注的数据集，该数据集是使用NICO机器人平台收集的。我们评估了四种最先进的凝视估计模型。评估结果显示，在通用基准测试中报告的角度误差与此相近，但在表示为共享工作空间内的距离时，最佳的中位误差为16.48厘米，这量化了当前方法的实际局限性。最后，我们讨论了这些局限性，并提出了如何在HRI系统中最好地集成凝视估计这一模态的建议。
URL
https://arxiv.org/abs/2509.24001
