For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.

翻译：对于服务机器人而言，尽早感知到接近的人是否有互动意图至关重要：在这种情况下，机器人可以主动表现出友好的行为，从而提升用户体验。我们通过一个序列到序列分类器来解决这一感知任务，该分类器能够以自监督方式训练，对潜在用户的互动意图进行分类。我们的主要贡献在于研究了在此场景中代表用户注视特征的优势。基于新数据集的广泛实验表明，加入注视线索显著提升了分类器性能（AUROC从84.5%提升至91.2%）；实现准确分类的距离从2.4米提升至3.2米。我们还量化了系统在无外部监督下适应新环境的能力。定性实验展示了与侍者机器人交互的实际应用场景。