Abstract
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.
Abstract (translated)
本文探讨了在机器人导航系统中集成语言输入的问题，并借鉴符号互依性假设来弥合符号和身体认知之间的分歧。它回顾了将语言和语义融入神经网络（NN）和同时定位与映射（SLAM）方法中的先驱工作，并强调了这些整合如何推动该领域的发展。通过将抽象符号操作与感知-运动 groundeding 相比较，我们提出了一个统一框架，其中语言既作为抽象交流系统，又作为感知经历的 grounded 表示。我们对分布式语义模型的认知模型及其应用于自主机器人的回顾强调了语言集成系统的变革潜力。
URL
https://arxiv.org/abs/2404.03049
