Parallel Chefs via Agentic Intelligence:From AI Agents to Smart Digital Robotic Cuisine Systems
LI Bai1,2, SONG Zihan1, LI Xinyuan1, HUANG Jun3, TIAN Yonglin4, YIN Zhuyan1, WANG Fei-Yue3,5,6
1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082; 2. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082; 3. Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078; 4. State Key Laboratory of Multi-modal Artificial Intelligence Sys-tems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190; 5. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190; 6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
Abstract：Progress is made by conversation-based AI due to the rapid development of large language models(LLMs). However, limitations still remain in performing more complex tasks and decision-making. Therefore, agentic intelligence is increasingly emphasized to address the bottleneck that LLMs are confined to information processing. In this paper, a parallel chef cooking system based on agentic intelligence is presented to offer an end-to-end intelligent approach from dish planning to cooking execution. User health data, medical history, and dietary preferences are incorporated to enable personalized recipe design and cooking control. In addition, multi-agent structures are built upon the DeepSeek framework, and specialized Q&A pairs are extracted from culinary literature to offline fine-tune the large language model, thereby imparting cooking reasoning capabilities. Simulation experiments show that compared to GPT o1 pro, a static large model with strong reasoning ability, the agentic approach integrates more extensive professional knowledge and better meets user requirements, showcasing its potential in dietary health and personalized cooking services.
[1] RAIAAN M A K, MUKTA M S H, FATEMA K, et al. A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges. IEEE Access, 2024, 12: 26839-26874. [2] THIRUNAVUKARASU A J, TING D S J, ELANGOVAN K, et al. Large Language Models in Medicine. Nature Medicine, 2023, 29(8): 1930-1940. [3] LI Y H, WANG S F, DING H, et al. Large Language Models in Finance: A Survey // Proc of the 4th ACM International Conference on AI in Finance. New York, USA: ACM, 2023: 374-382. [4] KASNECI E, SESSLER K, KÜCHEMANN S, et al. ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 2023, 103. DOI: 10.1016/j.lindif.2023.102274. [5] UNSAL S, ATAS H, ALBAYRAK M,et al. Learning Functional Properties of Proteins with Language Models. Nature Machine Inte-lligence, 2022, 4(3): 227-245. [6] QIU J N, LAM K, LI G H, et al. LLM-Based Agentic Systems in Medicine and Healthcare. Nature Machine Intelligence, 2024, 6(12): 1418-1420. [7] WANG L, MA C, FENG X Y, et al. A Survey on Large Language Model Based Autonomous Agents. Frontiers of Computer Science, 2024, 18(6). DOI: 10.1007/s11704-024-40231-1. [8] 周涛,李鑫,周俊临,等.大模型智能体:概念、前沿和产业实践.电子科技大学学报(社科版), 2024, 26(4): 57-62. (ZHOU T, LI X, ZHOU J L, et al. Large-Model-Based Agents: Concepts, Frontiers and Industrial Practices. Journal of UESTC(Social Sciences Edition), 2024, 26(4): 57-62.) [9] AI Agents Market by Agent Role(Productivity & Personal Assistants, Sales, Marketing, Customer Service, Code Generation), Agent Systems(Single Agent, Multi Agent), Product Type(Ready to Deploy Agents, Build Your Own Agents)-Global Forecast to 2030[EB/OL]. [2025-02-02] https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html. [10] 仇星月,陈向东,陈鹏,等.大语言模型支持的元综合研究:基于智能体的方法.现代教育技术, 2025, 35(1): 63-72. (QIU X Y, CHEN X D, CHEN P, et al. Meta-Synthesis Research Supported by Large Language Model: An Agent-Based Approach. Modern Educational Technology, 2025, 35(1): 63-72.) [11] 孙毅,郑雨,黄海燕,等.多循环嵌套的大语言模型多智能体指挥控制过程.指挥与控制学报, 2024, 10(6): 732-739. (SUN Y, ZHENG Y, HUANG H Y, et al. Multi-loop Nested LLM-Based Multi-agent Command and Control Processes. Journal of Command and Control, 2024, 10(6): 732-739.) [12] 鲁越,郭超,倪清桦,等.人工智能代理驱动的平行博物馆智慧管控.智能科学与技术学报, 2024, 6(2): 134-149. (LU Y, GUO C, NI Q H, et al. AI Agent-Driven Intelligent Ma-nagement and Control of Chinese Parallel Museums. Journal of Intelligent Science and Technology, 2024, 6(2): 134-149.) [13] XU J, SUN Q Y, HAN Q L, et al. When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing. IEEE/CAA Journal of Automatica Sinica, 2025, 12(3): 485-501. [14] TIAN Y L, LIN F, LI Y D.UAVs Meet LLMs: Overviews and Perspectives towards Agentic Low-Altitude Mobility. Information Fusion, 2025, 122. DOI: 10.1016/j.inffus.2025.103158. [15] 王越芸,丁玫,温建萍,等.以大模型智能体为核心的智能审计范式构建.审计月刊, 2024(5): 8-11. (WANG Y Y, DING M, WEN J P, et al. Constructing an Intelli-gent Audit Paradigm Centered on Large-Model Agents. Audit Monthly, 2024(5): 8-11.) [16] 郭超,戴星原,廖玮珑,等.智能艺术工厂:基于平行艺术智能体的创作自动化.智能科学与技术学报, 2024, 6(2): 179-188. (GUO C, DAI X Y, LIAO W L, et al. Intelligent Art Factory: Achieving Creative Automation through Parallel Art Agents. Chinese Journal of Intelligent Science and Technology, 2024, 6(2): 179-188.) [17] RYU K, HAN H.Influence of the Quality of Food, Service, and Physical Environment on Customer Satisfaction and Behavioral Intention in Quick-Casual Restaurants: Moderating Role of Perceived Price. Journal of Hospitality & Tourism Research, 2010, 34(3): 310-329. [18] 薛斌. 烹饪培训课堂互动式教学策略研究.中国教育技术装备, 2021(23): 62-63. (XUE B.Study on Interactive Teaching Strategies in Cooking Training Classrooms. China Educational Technology & Equipment, 2021(23): 62-63.) [19] 王蓓. 烹饪专业现代学徒制精英人才培养模式探索.四川烹饪高等专科学校学报, 2013(4): 78-80. (WANG B.On Modern Apprenticeship for Cuisine Students in Colleges. Journal of Sichuan Higher Institute of Cuisine, 2013(4): 78-80.) [20] 滕丽菊. 数字化转型背景下烹饪专业创新人才培养模式探究.现代食品, 2023, 29(17): 141-144. (TENG L J.An Exploration of Innovative Talent Cultivation Mo-dels for Culinary Majors in the Context of Digital Transformation. Modern Food, 2023, 29(17): 141-144.) [21] 高志刚,张金英,唐林达,等.行会驻校·名店订单·企业教室·项目管理——中职烹饪专业人才培养模式的探索与实践.中国职业技术教育, 2015(31): 69-74. (GAO Z G, ZHANG J Y, TANG L D, et al. Guild Presence on Campus, Brand-Name Restaurant Orders, Enterprise Classrooms, and Project Management: Exploration and Practice of Talent Trai-ning Models for Secondary Vocational Culinary Majors. Chinese Vocational and Technical Education, 2015(31): 69-74.) [22] 杨铭铎. 我国现代烹饪教育体系的构建.中国职业技术教育, 2017(33): 65-71. (YANG M D.Construction of China's Modern Culinary Education System. Chinese Vocational and Technical Education, 2017(33): 65-71.) [23] 吕慧. 烹饪专业人才培养模式改革与产教深度融合机制构建研究——以南京旅游职业学院为例.湖北开放职业学院学报, 2021, 34(17): 50-51. (LÜ H.Research on the Reform of Culinary Professional Training Mode and the Construction of the Deep Integration Mechanism of Production and Education:Take Nanjing Vocational College of Tourism as an Example. Journal of Hubei Open Vocational Co-llege, 2021, 34(17): 50-51.) [24] 胡凯杰. 职业院校烹饪专业技能型人才培养模式的实践与创新.中国食品工业, 2023(1): 99-101, 128. (HU K J.Practice and Innovation in the Training Model for Skilled Culinary Professionals in Vocational Colleges. China Food Industry, 2023(1): 99-101, 128.) [25] 赵兴,闫维新,赵言正,等.基于DSP和CPLD的自动烹饪机器人设计.机器人, 2007, 29(5): 469-473. (ZHAO X, YAN W X, ZHAO Y Z, et al. Design of an Automatic Cooking Robot Based on DSP and CPLD. Robot, 2007, 29(5): 469-473.) [26] 彭放. 家用炒菜机的关键技术研究.硕士学位论文.武汉:华中科技大学, 2016. (PENG F.Study on the Key Technology of Domestic Cooking Machine. Master Dissertation. Wuhan, China: Huazhong University of Science and Technology, 2016.) [27] 张帆,宁萌,邓倩.全自动炒菜机结构设计与试验.食品与机械, 2023, 39(6): 92-98. (ZHANG F, NING M, DENG Q.Structural Design and Experiment of Automatic Cooking Machine. Food & Machinery, 2023, 39(6): 92-98.) [28] 马文涛,闫维新,付庄,等.一种用于中国烹饪机器人的火候模糊控制系统.哈尔滨工业大学学报, 2012, 44(1): 120-125. (MA W T, YAN W X, FU Z, et al. A Heating Fuzzy Control System for Chinese Cooking Robots. Journal of Harbin Institute of Technology, 2012, 44(1): 120-125.) [29] 余仕毅,汪涵,饶楚楚,等.一种自动炒菜机的研制.价值工程, 2023, 42(35): 50-52. (YU S Y, WANG H, RAO C C, et al. Development of an Automatic Vegetable Fryer. Value Engineering, 2023, 42(35): 50-52.) [30] 林泽聪,王玉山,甘嘉颖,等.基于协同过滤的个性化食材推荐算法研究.中国管理信息化, 2017, 20(7): 150-152. (LIN Z C, WANG Y S, GAN J Y, et al. A Study on a Collaborative Filtering-Based Personalized Ingredient Recommendation Algorithm. China Management Informationization, 2017, 20(7): 150-152.) [31] 樊祥超,於志文,耿会东,等.智能空间中的用户饮食偏好获取与推荐系统.计算机工程, 2011, 37(22): 237-239. (FAN X C, YU Z W, GENG H D, et al. System of User Diet Preferences Acquisition and Recommendation in Smart Space. Computer Engineering, 2011, 37(22): 237-239.) [32] 曾子文,袁操.基于图神经网络的个性化食谱推荐算法.武汉轻工大学学报, 2024, 43(3): 82-89, 105. (ZENG Z W, YUAN C.Personalized Diet Recommendation Algorithm Based on Graph Neural Network. Journal of Wuhan Polytechnic University, 2024, 43(3): 82-89, 105.) [33] 刘庆. 面向慢病管理的个性化饮食推荐研究与实现.硕士学位论文.哈尔滨:哈尔滨工业大学, 2022. (LIU Q.Research and Implementation of Personalized Diet Reco-mmendation for Chronic Disease Management. Master Dissertation. Harbin, China: Harbin Institute of Technology, 2022.) [34] 李鑫源,李柏,孙跃硕,等.基于多模态大语言模型的数字厨师与智能烹饪系统.智能科学与技术学报, 2024, 6(4): 429-444. (LI X Y, LI B, SUN Y S, et al. Digital Chefs and Intelligent Cooking Systems Based on Multimodal Large Language Model. Chinese Journal of Intelligent Science and Technology, 2024, 6(4): 429-444.) [35] DeepSeek-AI. Deepseek-v3 Technical Report[EB/OL].[2025-02-20]. https://arxiv.org/pdf/2412.19437. [36] DeepSeek-AI. Deepseek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning[EB/OL].[2025-02-20]. https://arxiv.org/pdf/2501.12948.
2025, Vol. 38 
