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Grounding spatial relations in natural language by fuzzy representation for human-robot interaction

机译:用人机交互的模糊表示法在自然语言中建立空间关系

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摘要

This paper addresses the issue of grounding spatial relations in natural language for human-robot interaction and robot control. The problem is approached by identifying two set of spatial relations, the image space-based and object-centered, and expressing them as fuzzy sets to capture the ambiguity inherent to the linguistic expressions for the relations. The sizes and shades of the scene objects have also been modeled as fuzzy sets for conditioning the spatial relations. To verify the validity of our approach and test its feasibility in a natural language-based interface, we have considered the typical scenarios of using the spatial relations in simple declarative and imperative sentences and designed simple grammars for parsing such sentences. Our experiment has shown that fuzzy spatial relation analysis provides a useful way for modeling the ambiguity or imprecision of the natural language in describing spatial relations and that it is possible to use the spatial relation models to support robot control and human-robot interaction in a natural language-based interface.
机译:本文探讨了自然语言中空间关系的基础,以实现人机交互和机器人控制。通过识别两组空间关系(基于图像的空间和以对象为中心的空间关系)并将它们表示为模糊集以捕获关系的语言表达所固有的歧义来解决该问题。场景对象的大小和阴影也已建模为模糊集,用于调节空间关系。为了验证我们方法的有效性并在基于自然语言的界面中测试其可行性,我们考虑了在简单的陈述式和命令式句子中使用空间关系的典型场景,并设计了用于解析此类句子的简单语法。我们的实验表明,模糊空间关系分析为描述自然语言在描述空间关系时的歧义或不精确性提供了一种有用的方法,并且有可能使用空间关系模型来支持自然状态下的机器人控制和人机交互。基于语言的界面。

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