用AI模仿儿童与成人的目光行为，测试机器人社交互动的可行性。
Empirical Study of Gaze Behavior in Children and Young Adults Using Deep Neural Networks and Robot Implementation: A Comparative Analysis of Social Situations
- 用LSTM和Transformer建模儿童与成人的眼动模式。
- 预测下帧人物位置准确率达62%-70%，两次尝试提升至约80%。
- 机器人表现获认可，但未被视作真正社交伙伴。
本研究旨在训练深度神经网络模型以模拟儿童与成人于特定社交情境下的眼动行为，并基于参与者的眼动数据识别两组间的潜在差异。研究采集了24名参与者（12名儿童、12名成人）在观看两个视频片段（一个动画、一个实拍）时的眼动数据，使用LSTM与Transformer网络分析其眼动模式。结果显示，模型在单次预测下对人物位置的准确率为62%-70%，若采用两次尝试（取前两名预测结果），准确率提升约20%。此外，将模型部署于Nao机器人后，57名新参与者评估了其表现，问卷显示他们对机器人的注意力、智能性和响应能力表示满意，但未将其视为可比人类的社交伙伴。本探索性研究展示了基于人类非语言行为线索实现机器人社会接受度的潜力，为后续研究提供参考。
原文摘要 · Abstract (English)
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.
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