AI 中文总结
针对自动化烹饪机器人的个性化与可信性问题，提出智能体框架，通过多智能体分解需求、分阶段生成执行代码，实验验证其在真实场景中可靠且实用。
AI 中文摘要
传统自动化烹饪机器人依赖预定义流程和基于规则的控制，虽能保证稳定执行，但个性化程度有限；而近期的大模型方法支持自然语言交互，却存在决策不透明、在真实厨房中执行不可靠的问题。为应对这一挑战，本文提出一种智能体框架，将个性化烹饪需求系统分解为可验证的结构化控制程序，而非直接将语言映射到动作。多个AI智能体协同将用户意图转化为规范食谱、带有显式流程控制的工作流程序，以及基于原子动作库的可执行Python代码。该系统包含三个紧密耦合的阶段：通过多个智能体进行离线食谱转代码生成、利用多模态感知实现监督干预的在线闭环执行、更新用户偏好模型以实现长期个性化的运行后适配。在实体烹饪平台上开展的真实实验表明，该框架在各类个性化场景中实现了可靠的任务完成、透明的执行逻辑及有效的异常处理，验证了其在真实环境中实现可信自动化烹饪的实用性。
英文摘要
Automated cooking robots have traditionally relied on predefined procedures and rule-based control, ensuring stable execution but offering limited personalization, whereas recent large-model approaches support natural language interaction but often suffer from opaque decision making and unreliable execution in real kitchens. To address this challenge, this paper proposes an agentic framework that systematically decomposes personalized cooking requirements into structured and verifiable control programs rather than directly mapping language to actions. Multiple AI agents collaboratively transform user intents into canonical recipes, workflow programs with explicit flow control, and executable Python code grounded in an atomic action library. The system consists of three tightly coupled stages: offline recipe-to-code generation through multiple agents, online closed-loop execution with supervisory intervention enabled by multimodal perception, and post-run adaptation that updates user preference models for long-term personalization. Real-world experiments on a physical cooking platform demonstrate that the proposed framework achieves reliable task completion, transparent execution logic, and effective anomaly handling across diverse personalized scenarios, validating its practicality for trustworthy automated cooking in real environments.
