ChatHSI: Reliable LLM-Powered Human-Swarm Interaction Framework

Human-swarm interaction (HSI) is critical for scalable control of UAV swarm systems. Traditional interfaces struggle with generalization and user workload, especially in immersive environments. Hence, we present ChatHSI, a framework leveraging large language models (LLMs) for swarm task planning. ChatHSI integrates prompt engineering, action validation, and a human-in-the-loop mechanism to improve planning feasibility and executability. We implement ChatHSI in an immersive simulation to improve users’ spatial and situational awareness. Our method shows improved task efficiency, reduced workload, and higher usability in user studies. Ablation study proves the effectiveness of prompt context and action validation. The results show the feasibility of LLM-driven interaction for immersive swarm control and point toward adaptive, intuitive, and scalable HSI systems.