Neuro-symbolic AI for reliable human-swarm collaboration in complex environments

We propose a formal method and LLM framework for coordinating large fleets of heterogeneous robots in open and dynamic environments. Our approach integrates model-checking-based task planning with LLM-powered reasoning and interaction, ensuring adaptability, explainability, and optimal mission execution. Validated through simulations and real-world deployments, it proves effective for disaster response, infrastructure inspection, and dynamic surveillance.

In Science Robotics