GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models

Abstract

The development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks. This has motivated research on methods to automatically create control policies. However, these methods require iterative processes of manually crafting and refining objective functions, thereby prolonging the development cycle. This work introduces GenSwarm, an end-to-end system that leverages large language models to automatically generate and deploy control policies for real-world multi-robot systems based on user instructions in natural language. As a multi-language-agent system, GenSwarm achieves zero-shot learning, enabling rapid adaptation to altered or unseen tasks. The white-box nature of the code policies ensures strong reproducibility and interpretability. With its scalable software and hardware architectures, GenSwarm supports efficient and automated policy deployment on both simulated and real-world multi-robot systems, realizing an instruction-to-execution end-to-end functionality that may transform the development paradigm of multi-robot systems in the future.