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Active learning based on computer vision and human-robot interaction for the user profiling and behavior personalization of an autonomous social robot
参考中译:基于计算机视觉和人机交互的自主社交机器人用户特征和行为个性化主动学习


          

刊名：Engineering Applications of Artificial Intelligence
作者：Marcos Maroto-Gomez（Systems Engineering and Automation, University Carlos III of Madrid.）
Sara Marques-Villaroya（Systems Engineering and Automation, University Carlos III of Madrid.）
Jose Carlos Castillo（Systems Engineering and Automation, University Carlos III of Madrid.）
Alvaro Castro-Gonzalez（Systems Engineering and Automation, University Carlos III of Madrid.）
Maria Malfaz（Systems Engineering and Automation, University Carlos III of Madrid.）
刊号：738C0174
ISSN：0952-1976
出版年：2023
年卷期：2023, vol.117, no.Pt.B
页码：105631-1--105631-16
总页数：16
分类号：TP18; TP3
关键词：Active learning；Human-robot interaction；Social robots；User profiling；User recognition
参考中译：主动学习；人机交互；社交机器人；用户特征描述；用户识别
语种：eng
文摘：Social robots coexist with humans in situations where they have to exhibit proper communication skills. Since users may have different features and communicative procedures, personalizing human-robot interactions is essential for the success of these interactions. This manuscript presents Active Learning based on computer vision and human-robot interaction for user recognition and profiling to personalize robot behavior. The system identifies people using Intel-face-detection-retail-004 and FaceNet for face recognition and obtains users' information through interaction. The system aims to improve human-robot interaction by (i) using online learning to allow the robot to identify the users and (ii) retrieving users' information to fill out their profiles and adapt the robot's behavior. Since user information is necessary for adapting the robot for each interaction, we hypothesized that users would consider creating their profile by interacting with the robot more entertaining and easier than taking a survey. We validated our hypothesis with three scenarios: the participants completed their profiles using an online survey, by interacting with a dull robot, or with a cheerful robot. The results show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles with the cheerful robot than in the other scenarios. Statistically significant differences in the usability were found between the scenarios using the robot and the scenario that involved the online survey. Finally, we show two scenarios in which the robot interacts with a known user and an unknown user to demonstrate how it adapts to the situation.
参考中译：在社交机器人必须表现出适当的沟通技能的情况下，它们与人类共存。由于用户可能具有不同的功能和交流过程，个性化的人-机器人交互对于这些交互的成功至关重要。这篇手稿介绍了基于计算机视觉和人-机器人交互的主动学习，用于用户识别和剖析，以个性化机器人行为。该系统使用Intel-Face-Detect-Retail-004和FaceNet进行人脸识别，并通过交互获取用户信息。该系统旨在通过(I)使用在线学习来允许机器人识别用户和(Ii)检索用户的信息来填写他们的个人资料并调整机器人的S行为，从而改善人与机器人的交互。由于用户信息是使机器人适应每次交互所必需的，因此我们假设用户会考虑通过与机器人交互来创建他们的个人资料，这比进行调查更有趣、更容易。我们用三种情景验证了我们的假设：参与者通过在线调查、通过与枯燥的机器人互动或与快乐的机器人互动来完成他们的个人资料。结果显示，参与者给快乐机器人的可用性评分更高(82.14/100分)，与其他场景相比，他们使用快乐机器人创建个人资料时更具娱乐性。在使用机器人的场景和涉及在线调查的场景之间，在可用性方面发现了统计上的显著差异。最后，我们展示了机器人与已知用户和未知用户交互的两个场景，以演示它如何适应这种情况。



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