Abstract
In a preliminary exploratory study, our goal was to train deep neural network models to mimic children's and/or adults' gaze behavior in certain social situations to reach this objective. Additionally, we aim to identify potential differences in gaze behavior between these two age groups based on our participants' gaze data. Furthermore, we aimed to assess the practical effectiveness of our adult and children models by deploying them on a Nao robot in real-life settings. To achieve this, we first created two video clips, one animation and one live-action, to depict some social situations. Using an eye-tracking device, we collected eye-tracking data from 24 participants, including 12 children and 12 adults. Then, we utilized deep neural networks, specifically LSTM and Transformer Networks, to analyze and model the gaze patterns of each group of participants. Our results indicate that when the models attempted to predict people's locations (in the next frame), they had an accuracy in the range of 62%-70% with one attempt, which increased by ~20% when attempted twice (i.e. the two highest-ranked predicted labels as outputs). As expected, the result underscores that gaze behavior is not a wholly unique phenomenon. We obtained feedback from 57 new participants to evaluate the robot's functionality. These participants were asked to watch two videos of the robot's performance in each mode and then complete a comprehensive questionnaire. The questionnaire results indicate that the participants expressed satisfaction with the robot's interaction, including its attention, intelligence, and responsiveness to human actions. However, they did not perceive the robot as a social companion comparable to a human. This exploratory study tries to address/show potentials of the social acceptance of robots based on human nonverbal behavioral cues for future research.
Abstract (translated)
在初步探索性研究中，我们的目标是训练深度神经网络模型来模仿儿童和/或成人特定社交情景中的注视行为。此外，我们还旨在基于参与者的眼动数据识别这两个年龄段之间潜在的注视行为差异。另外，为了评估我们的成人和儿童模型的实际效果，我们将它们部署在一个Nao机器人上进行实地测试。为此，我们首先创建了两个视频片段，一个是动画，另一个是实拍镜头，以描绘一些社交情景。使用眼动追踪设备，我们从24名参与者（包括12名儿童和12名成人）收集了眼动数据。然后，我们利用深度神经网络（特别是LSTM和Transformer Networks），来分析并建模每个组参与者的注视模式。 我们的结果显示，在模型尝试预测人们的视线位置（下一个帧中的目标对象）时，一次尝试的准确率在62%-70%之间，而两次尝试（即输出两个最高排名的预测标签）后准确率提高了约20%。如预期的那样，这些结果表明注视行为并不是一种完全独特的现象。 我们从57名新参与者那里获得了反馈以评估机器人的功能。这些参与者的任务是观看机器人在每种模式下表演的两段视频，并完成一份详细的问卷调查。问卷结果显示，参与者对机器人的互动表示满意，包括其注意力、智能性和对人类行为的响应性。然而，他们并没有将机器人视为与人相当的社会伙伴。 这项探索性研究试图基于人类非语言行为线索来展示未来研究中关于机器人社会接受潜力的可能性。
URL
https://arxiv.org/abs/2603.00074
