The employment of personal robots or service robots has aroused much interest in recent years with an amazing growth of robotics in different domains. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities. Interactions with humans and/or with other agents are still limited and not considered satisfactory. So, the way we store and represent knowledge in a cognitive architecture is fundamental in order to overcome these limitations and improve the human–machine and machine–machine interactions. In this article, we propose an unsupervised approach for knowledge construction based on the robot’s perception. Our approach makes use of Kohonen maps as an unsupervised machine learning technique and allows the definition of semantic clusters from visual features perceived by the robot. Besides, a multimedia graph knowledge base using a pure formalism is presented, which can be actively used by personal robots in their classic activities, such as environment exploration or information gathering, to represent and share the acquired knowledge, linking it to abstract concepts gifted with semantic relations.

中文翻译：

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一种无监督的知识构建方法，应用于个人机器人

近年来，随着不同领域中机器人技术的惊人增长，个人机器人或服务机器人的使用引起了人们的极大兴趣。尽管已经开发了复杂的类人机器人，但仍需要付出更多的努力来提高其认知能力。与人和/或与其他试剂的相互作用仍然是有限的，并且不被认为是令人满意的。因此，我们在认知体系中存储和表示知识的方式对于克服这些局限性并改善人机交互和机机交互是至关重要的。在本文中，我们提出了一种基于机器人感知的无监督知识构建方法。我们的方法将Kohonen映射用作无监督的机器学习技术，并允许根据机器人感知的视觉特征来定义语义簇。此外，提出了使用纯形式主义的多媒体图形知识库，个人机器人可以在其经典活动（例如环境探索或信息收集）中积极使用它来表示和共享所获得的知识，并将其链接到具有天赋的抽象概念上。语义关系。