论文标题
人类机器人互动期间，自然语言无监督的在线基础
Unsupervised Online Grounding of Natural Language during Human-Robot Interactions
论文作者
论文摘要
允许人类通过自然语言与机器人进行交流需要单词和知觉之间的联系。建立这些连接的过程称为符号接地，并且已经研究了近三十年。尽管已经进行了许多研究，但并没有多少人认为同义词的基础，而被使用的算法仅在线或以监督方式工作。在本文中，提出了一个基于交叉学习的基础接地框架，该框架允许在没有人类监督和在线的情况下通过相应的感知来接地，即它不需要任何明确的培训阶段，而是更新了每种新接口情况所获得的映射。通过人类教师和机器人之间的互动实验来评估所提出的框架，并与现有的无监督接地框架进行了比较。结果表明，所提出的框架能够通过其在线和无监督的方式通过其相应的感知来扎根，同时表现不佳。
Allowing humans to communicate through natural language with robots requires connections between words and percepts. The process of creating these connections is called symbol grounding and has been studied for nearly three decades. Although many studies have been conducted, not many considered grounding of synonyms and the employed algorithms either work only offline or in a supervised manner. In this paper, a cross-situational learning based grounding framework is proposed that allows grounding of words and phrases through corresponding percepts without human supervision and online, i.e. it does not require any explicit training phase, but instead updates the obtained mappings for every new encountered situation. The proposed framework is evaluated through an interaction experiment between a human tutor and a robot, and compared to an existing unsupervised grounding framework. The results show that the proposed framework is able to ground words through their corresponding percepts online and in an unsupervised manner, while outperforming the baseline framework.
