Empirical Study of Gaze Behavior in Children and Young Adults Using Deep Neural Networks and Robot Implementation: A Comparative Analysis of Social Situations

• URL: http://arxiv.org/abs/2603.00074v1
• Date: Thu, 12 Feb 2026 07:01:04 GMT
• Title: Empirical Study of Gaze Behavior in Children and Young Adults Using Deep Neural Networks and Robot Implementation: A Comparative Analysis of Social Situations
• Abstract summary: We train deep neural network models to mimic children's and/or adults' gaze behavior in certain social situations.<n>We assess the practical effectiveness of our models by deploying them on a Nao robot in real-life settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Human-Like Gaze Behavior in Social Robots: A Deep Learning Approach Integrating Human and Non-Human Stimuli [0.0]
  This study aims to increase the similarity between robot and human gaze behavior across various social situations.<n>A key innovation in this study is the investigation of gaze responses to non-human stimuli.<n>Data on gaze directions from 41 participants were collected via virtual reality (VR) glasses.
  arXiv  Detail & Related papers  (2026-02-12T07:01:17Z)
• Developing Neural Network-Based Gaze Control Systems for Social Robots [0.0]
  This study aims to develop an empirical motion-time pattern for human gaze behavior in various social situations.<n>Deep learning models, specifically Long Short-Term Memory (LSTM) and Transformers, were used to analyze and predict gaze patterns.
  arXiv  Detail & Related papers  (2026-02-11T15:30:39Z)
• Recognizing Actions from Robotic View for Natural Human-Robot Interaction [52.00935005918032]
  Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationary.<n>Existing benchmarks for N-HRI fail to address the unique complexities in N-HRI due to limited data, modalities, task categories, and diversity of subjects and environments.<n>We introduce (Action from Robotic View) a large-scale dataset for perception-centric robotic views prevalent in mobile service robots.
  arXiv  Detail & Related papers  (2025-07-30T09:48:34Z)
• Designing for Difference: How Human Characteristics Shape Perceptions of Collaborative Robots [8.095309110037315]
  Antisocial robot behavior was consistently rated as the lowest, while collaboration with aged individuals elicited more sensitive evaluations.<n>These findings suggest that both human characteristics and interaction paradigms influence the perceived acceptability of collaborative robots.
  arXiv  Detail & Related papers  (2025-07-22T11:36:08Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• Predicting Human Impressions of Robot Performance During Navigation Tasks [8.01980632893357]
  We investigate the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. Results suggest that facial expressions alone provide useful information about human impressions of robot performance. Supervised learning techniques showed promise because they outperformed humans' predictions of robot performance in most cases.
  arXiv  Detail & Related papers  (2023-10-17T21:12:32Z)
• Affordances from Human Videos as a Versatile Representation for Robotics [31.248842798600606]
  We train a visual affordance model that estimates where and how in the scene a human is likely to interact. The structure of these behavioral affordances directly enables the robot to perform many complex tasks. We show the efficacy of our approach, which we call VRB, across 4 real world environments, over 10 different tasks, and 2 robotic platforms operating in the wild.
  arXiv  Detail & Related papers  (2023-04-17T17:59:34Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• A trained humanoid robot can perform human-like crossmodal social attention conflict resolution [13.059378830912912]
  Our study adopted a neurorobotic paradigm of gaze-triggered audio-visual crossmodal integration to make an iCub robot express human-like social attention responses. Masks were used to cover all facial visual cues other than the avatars' eyes. We observed that the avatar's gaze could trigger crossmodal social attention with better human performance in the audio-visual congruent condition than in the incongruent condition.
  arXiv  Detail & Related papers  (2021-11-02T21:49:52Z)
• Modeling User Empathy Elicited by a Robot Storyteller [2.309914459672557]
  We present the first approach to modeling user empathy elicited during interactions with a robotic agent. We conducted experiments with 8 classical machine learning models and 2 deep learning models to detect empathy. Our highest-performing approach, based on XGBoost, achieved an accuracy of 69% and AUC of 72% when detecting empathy in videos.
  arXiv  Detail & Related papers  (2021-07-29T21:56:19Z)
• A robot that counts like a child: a developmental model of counting and pointing [69.26619423111092]
  A novel neuro-robotics model capable of counting real items is introduced. The model allows us to investigate the interaction between embodiment and numerical cognition. The trained model is able to count a set of items and at the same time points to them.
  arXiv  Detail & Related papers  (2020-08-05T21:06:27Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.