Exploring Spontaneous Social Interaction Swarm Robotics Powered by Large Language Models

Abstract

Traditional swarm robots rely on specific communication and planning strategies to coordinate particular tasks. Human swarms exhibit distinctive characteristics due to their capacity for language-based communication and active reasoning. This paper presents an exploratory approach to robotic swarm intelligence that leverages Large Language Models (LLMs) to emulate human-like active problem-solving behaviors. We introduce a decentralized multi-robot system where each robot initially only has its local information and does not know of the existence of the other robots. The robots utilize LLMs for reasoning and natural language for inter-robot communication, enabling them to discover peers, share information, and coordinate actions dynamically. In a series of experiments in zero-shot settings, we observed human-like social behaviors, including mutual discovery, identification, information exchange, collaboration, negotiation, and error correction. While the technical approach is straightforward, the main contribution lies in exploring the interactive societies that LLM-driven robots form – a form of robot social dynamics (or robotic social behavior analysis), examining how human-like communication protocols and collaborative structures emerge among robots through language-based interaction. In this context, we use the term "robot social dynamics" to describe the interaction patterns that arise within robot collectives, inspired by, but distinct from traditional human anthropology.