  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.
