Language, Environment, and Robotic Navigation
This is an incremental review and theoretical proposal for improving robotic navigation systems through language integration, primarily relevant to researchers in robotics and AI.
The paper tackles the problem of integrating linguistic inputs into robotic navigation by proposing a unified framework that bridges symbolic and embodied cognition, but it does not present new experimental results or concrete numbers.
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.