TACOS: Task Agnostic Coordinator of a Multi-Drone System

When a single pilot is responsible for managing a multi-drone system, the task may demand varying levels of autonomy, from direct control of individual UAVs to group-level coordination to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such interaction requires a framework that supports multiple modes of shared autonomy. As language models continue to improve in reasoning and planning, they provide a natural foundation for such systems. In this paper, we present TACOS (Task-Agnostic COordinator of a multi-drone System), a unified framework that enables high-level natural language control of multi-UAV systems through a Large Language Model (LLM). TACOS integrates three key capabilities into a single architecture: a one-to-many natural language interface for intuitive user interaction, an intelligent coordinator for translating user intent into structured task plans, and an autonomous agent that executes plans and interacts with the real world. TACOS allows an LLM to interact with a library of executable APIs, bridging semantic reasoning with real-time multi-robot coordination. We demonstrate the system on a real-world multi-drone system and conduct an ablation study to assess the contribution of each module.