This paper explores the integration of linguistic inputs within robotic
navigation systems, drawing upon the symbol interdependency hypothesis to
bridge the divide between symbolic and embodied cognition. It examines previous
work incorporating language and semantics into Neural Network (NN) and
Simultaneous Localization and Mapping (SLAM) approaches, highlighting how these
integrations have advanced the field. By contrasting abstract symbol
manipulation with sensory-motor grounding, we propose a unified framework where
language functions both as an abstract communicative system and as a grounded
representation of perceptual experiences. Our review of cognitive models of
distributional semantics and their application to autonomous agents underscores
the transformative potential of language-integrated systems.

本文探讨了语言输入与机器人导航系统的整合，借助符号互操作假设来连接符号认知和体验性认知的鸿沟。我们研究了将语言和语义融入神经网络和同时定位与映射方法的先前工作，突出展示了这些整合如何推进了该领域的发展。通过将抽象符号处理与感知动作基础相对比，我们提出了一个统一的框架，其中语言既作为一个抽象的交际系统，又作为感知经验的基础性表示。我们对分布语义认知模型及其应用于自主代理的审查强调了语言整合系统的转变潜力。