Autonomous robots are nowadays successfully used in industrial environments, where tasks follow predetermined plans and the world is a known (and closed) set of objects. The context of social robotics brings new challenges to the robot. First of all, the world is no longer closed. New objects can be introduced at any time, and it is now impossible to build an exaustive list of them nor having a precomputed set of descriptors. Moreover, natural interactions with a human being don’t follow any precomputed graph of sequences or grammar. To deal with the complexity of such an open world, a robot can no longer solely rely on its sensors data: a compact representation to comprehend its surrounding is needed. Our approach focuses on task independent environment representation where human-robot interactions are involved. We propose a global architecture bridging the gap between perception and semantic modalities through instances (physical realizations of semantic concepts). In this article, we describe a method for automatic generation of object-related ontology. Based on it, a practical formalization of the ill-defined notion of “context” is discussed. We then tackle human-robot interactions in our system through the description of user request processing. Finally, we illustrate the flow of our model on two showcases which demonstrate the validity of the approach.

中文翻译：

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辅助机器人的基于多模式对象的环境表示

如今，自主机器人已成功用于工业环境中，在该环境中，任务遵循预定的计划，并且世界是一组已知（且封闭）的对象。社会机器人技术的环境给机器人带来了新的挑战。首先，世界不再封闭。可以随时引入新对象，现在无法建立它们的详尽列表，也无法拥有一组预先计算的描述符。此外，与人类的自然互动不会遵循任何预先计算的序列图或语法图。为了应对这种开放世界的复杂性，机器人不再能够仅依靠其传感器数据：需要一种紧凑的表示形式来理解其周围环境。我们的方法侧重于涉及人机交互的独立于任务的环境表示。我们提出了一个全球架构，通过实例（语义概念的物理实现）弥合了感知和语义模态之间的差距。在本文中，我们描述了一种自动生成与对象相关的本体的方法。在此基础上，讨论了对“上下文”的定义不清的概念的实际形式化。然后，我们通过描述用户请求处理来解决系统中的人机交互。最后，我们在两个展示柜上说明了模型的流程，这些展示柜展示了该方法的有效性。然后，我们通过描述用户请求处理来解决系统中的人机交互。最后，我们在两个展示柜上说明了模型的流程，这些展示柜展示了该方法的有效性。然后，我们通过描述用户请求处理来解决系统中的人机交互。最后，我们在两个展示柜上说明了模型的流程，这些展示柜展示了该方法的有效性。