Context-aware LLM-based Safe Control Against Latent Risks

2024-03-18

arXiv.org

AI Summary

To address safety and efficiency challenges in autonomous systems executing complex tasks under latent risks, this paper proposes a multi-timescale safety-critical control framework integrating large language models (LLMs), numerical optimization, and model predictive control (MPC). The method features a three-layer synergistic architecture: (i) a high-level semantic-driven subtask decomposition module leveraging in-context learning; (ii) a mid-level risk-adaptive parameter synthesis module guided by chain-of-thought reasoning to steer numerical optimization; and (iii) a low-level MPC-based closed-loop controller augmented with physics-informed simulation for enhanced dynamic robustness. Evaluated on robotic manipulation and autonomous driving simulations, the framework significantly improves task completion rates and behavioral safety in risk-sensitive scenarios, while enabling efficient online learning and adaptive behavior refinement.

Technology Category

• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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. The framework iteratively improves decisions by leveraging qualitative feedback and optimized trajectory data from lower-layer optimization processes and a physics simulator. We validate the proposed framework through simulated case studies involving robot arm and autonomous vehicle scenarios. The experiments demonstrate that the proposed framework can mediate actions based on the context and latent risks and learn complex behaviors efficiently.

Problem

• Addressing latent risks in autonomous control systems
• Integrating LLMs, optimization, and control for safe subtask learning
• Decomposing complex tasks into context-aware subtasks with risk mitigation

Innovation

• Combines LLMs, optimization, and control for safety
• Decomposes tasks into context-aware subtasks
• Uses multi-time-scale learning and optimization