A Survey of Language-Based Communication in Robotics

TL;DR

This survey reviews how large language models are integrated into robotics for multimodal communication, control, and interaction, highlighting current applications, challenges, and future development needs.

Contribution

It systematically categorizes existing work on language-based robotics, emphasizing the diverse control flow points where language is incorporated and discussing future research directions.

Findings

Language models enable multimodal communication in robots.

Language-based interactions improve human-robot teamwork.

Current limitations include understanding context and real-time processing.

Abstract

Embodied robots which can interact with their environment and neighbours are increasingly being used as a test case to develop Artificial Intelligence. This creates a need for multimodal robot controllers that can operate across different types of information, including text. Large Language Models are able to process and generate textual as well as audiovisual data and, more recently, robot actions. Language Models are increasingly being applied to robotic systems; these Language-Based robots leverage the power of language models in a variety of ways. Additionally, the use of language opens up multiple forms of information exchange between members of a human-robot team. This survey motivates the use of language models in robotics, and then delineates works based on the part of the overall control flow in which language is incorporated. Language can be used by human to task a robot, by a robot to inform a human, between robots as a human-like communication medium, and internally for a robot's planning and control. Applications of language-based robots are explored, and numerous limitations and challenges are discussed to provide a summary of the development needed for the future of language-based robotics.