Language, Environment, and Robotic Navigation

TL;DR

The paper addresses the problem of integrating linguistic inputs into robotic navigation to bridge symbolic and embodied cognition. It advocates a symbol interdependency framework that combines semantic SLAM, distributional semantics, and NN-based language grounding, moving from language as a command interface toward language as a representational space tied to perception. Key contributions include contrasting S-SLAM with a distributional-semantic approach, outlining an encoder–decoder architecture with convergence zones, and proposing concrete testing strategies to validate cross-domain learning. The work aims to enhance navigational reasoning, adaptability, and human–robot interaction by grounding language in sensorimotor experiences within dynamic environments.

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.