Robots are already present in people’s lives as receptionists, caregivers, and tutors. In human–robot interaction, social behavior is not only expected but often associated with users’ confidence. Although several studies have been researching in this direction, the robot adaptation and the existing gap between the system and nonprogramming designers still need more effort to achieve success. In this article, a cognitive architecture is proposed and implemented into a humanoid robot. The aim is to offer a framework programmable for controlling the robot’s resources, approaching previous knowledge, and new content in educational interactive activities. Furthermore, the system adapts the robot’s behavior according to objective measures of users, attention and engagement during the activity. After the interactive sessions, these measures are provided in a graphical interface for students, skills evaluation. Functions of visual classification, speech processing, autonomous web search for new content, and attention detectors were tested and analyzed separately. This approach shows effectiveness in basic and medium condition levels from a set of sceneries for each module.

中文翻译：

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R-CASTLE 项目：用于教学和学习的机器人认知自适应系统

机器人已经作为接待员、护理员和家庭教师出现在人们的生活中。在人机交互中，社交行为不仅是预期的，而且通常与用户的信心有关。虽然已经有几项研究在这个方向上进行研究，但机器人的适应性以及系统与非编程设计者之间存在的差距仍然需要更多的努力才能取得成功。在本文中，提出了一种认知架构并将其实施到类人机器人中。目的是提供一个可编程的框架，用于控制机器人的资源，接近以前的知识，以及教育互动活动中的新内容。此外，系统会根据用户在活动期间的注意力、注意力和参与度的客观测量来调整机器人的行为。互动环节结束后，这些措施都在图形界面中提供给学生进行技能评估。分别测试和分析了视觉分类、语音处理、新内容的自主网络搜索和注意力检测器的功能。这种方法从每个模块的一组风景中显示了基本和中等条件级别的有效性。