Context-aware LLM-based Safe Control Against Latent Risks

Understand

Autonomous control systems face significant challenges in performing complex tasks in the presence of latent risks.

• To address this, we propose an integrated framework that combines Large Language Models (LLMs), numerical optimization, and optimization-based control to facilitate efficient subtask learning while ensuring safety against latent risks.
• The framework decomposes complex tasks into a sequence of context-aware subtasks that account for latent risks.
• These subtasks and their parameters are then refined through a multi-time-scale process: high-layer multi-turn in-context learning, mid-layer LLM Chain-of-Thought reasoning and numerical optimization, and low-layer model predictive control.