How can LLMs generate robot control policies?
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
April 1, 2025
https://arxiv.org/pdf/2503.23875This paper introduces GenSwarm, a system that uses large language models (LLMs) to automatically generate and deploy code-based control policies for groups of robots. Users describe desired swarm behavior in natural language, which GenSwarm translates into executable code for individual robots.
Key points for LLM-based multi-agent systems:
- End-to-end code generation: GenSwarm handles the entire process from natural language input to robot deployment, including task analysis, code generation, simulation, and real-world deployment.
- Multi-agent architecture: The system utilizes multiple LLM agents with specialized roles (constraint analysis, code generation, code review, feedback analysis).
- Scalability: GenSwarm's software framework using Ansible and Docker allows for automated deployment across large numbers of robots.
- Adaptability: The system can regenerate code for new or changed tasks on demand.
- Zero-shot learning: GenSwarm generates code without needing prior examples.
- Multi-modal feedback: Video analysis via VLMs and human feedback inform policy refinement.
- Code-as-policy: This approach promotes reproducibility, interpretability, and efficient execution on resource-constrained robots.