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.
Fund:National Natural Science Foundation of China(No.62103139), Hibiscus Mutabilis Youth Talent Program of Hunan Province(No.2023RC3115)
Corresponding Authors:WANG Fei-Yue, Ph.D., professor. His research interests include methods and applications of parallel systems, social computing, parallel intelligence and knowledge automation.
About author:: LI Bai, Ph.D., associate professor. His research interests include motion planning and optimal control for autonomous unmanned systems. SONG Zihan, Master student. Her research interests include decision planning and control for autonomous unmanned systems based on agent intelligence. LI Xinyuan, Master student. His research interests include large language models, embodied intelligence, and modeling and control of flexible robotic arms. HUANG Jun, Ph.D. candidate. His research interests include parallel intelligence, trajectory prediction and planning for autonomous driving, prompt engineering, and large language models. TIAN Yonglin, Ph.D., assistant resear-cher. His research interests include parallel systems, autonomous driving and scenario engineering. YIN Zhuyan, Bachelor, assistant research. Her research interests include operational optimization, intelligent control and multimodal large language models.
LI Bai,SONG Zihan,LI Xinyuan等. Parallel Chefs via Agentic Intelligence:From AI Agents to Smart Digital Robotic Cuisine Systems[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(3): 252-267.
[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.
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