让自动控制懂人话，能理解上下文并动态调整行为。
Agentic MPC for Semantic Control System Resynthesis
- 用大模型代理解析自然语言和环境信息，重生成控制指令
- 在自动驾驶中实现按用户偏好或应急场景自主响应
- 适合需要灵活适应复杂场景的智能控制系统研究者
虽然模型预测控制（MPC）能有效处理结构化、多样且低层级的规范，但难以动态融入社会规范、用户意图或自然语言指令等高层上下文信息。为解决这一问题，本文提出一种代理式MPC框架，通过集成基于大语言模型的智能体，实现上下文感知与语义自适应的控制合成。该智能体可解读自然语言消息、环境观测及外部知识，重新生成控制规范。在自动驾驶场景中验证了该框架的有效性，系统能根据个人偏好调整行为，或响应如紧急车辆避让等社会情境。
原文摘要 · Abstract (English)
While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions. To address this limitation, this manuscript introduces an agentic MPC framework that enables context-aware, semantically adaptive control synthesis by integrating with large language model-based agents. The agent interprets heterogeneous inputs, including natural language messages, environmental observations, and external knowledge, to resynthesize the control specifications. The effectiveness of the framework is demonstrated in an autonomous driving scenario, where the system aligns with personal preferences or responds to social situations such as emergency vehicle yielding.
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