Since 2026, robotics has been infiltrating practical applications across major industries at an unprecedented pace. Within the traditional food service sector, cooking robots and coffee machines have evolved from concepts into fiercely competitive niche markets, serving as a microcosm of the deep integration between advanced manufacturing and modern services. Yet, as capital floods in rapidly, these culinary-focused robots are confronting significant trials. For instance, can cooking robots truly transition from "blind stir-frying" to "vision-based decision making," and how can the industry surmount the hurdles of generalization and commercialization?
Recently, Yang Jiancheng, founder and chairman of Oak Deer Robot (Beijing) Co., Ltd. (hereafter "Oak Deer Robot"), sat down for an exclusive discussion. He stated that AI-powered cooking robots are no longer conventional "stir-fry machines" in the traditional sense. A single unit drives a manufacturing chain involving over two thousand components while embedding itself into the operational fabric of restaurant kitchens and home cooking spaces, bridging the "hard" side of manufacturing with the "soft" side of services. Capital and industry are jointly seeking the "GPT moment" for culinary robots—the critical threshold for a phenomenon-level breakthrough akin to ChatGPT.
Collaboratively Tackling Embodied Intelligence Generalization Challenges
Over the past few years, the market response to cooking robots has been notably tepid. The root cause lies in the fact that most traditional cooking robots are instruction-based automated devices lacking feedback systems. When inputs fluctuate, the quality of the output dish deteriorates significantly. This mirrors a common difficulty in deploying embodied intelligence in niche scenarios: the dynamic nature of real-world conditions renders preset programs frequently ineffective.
"With the addition of vision, the AI cooking robot now has a feedback system that makes intelligent adjustments through computational power," Yang explained. He outlined that cooking robots have undergone two iterative phases: standardized procedure automation and intelligent decision-making. Pointing to a next-generation model equipped with cameras, he noted, "On this unit, the AI foundation model converts recipe text comprehension into an end-to-end closed loop." The robot captures pan imagery at 30 frames per second, processing visual data through five steps: recognition, segmentation, modeling, tracking, and intervention.
Behind this lies an accelerated manufacturing ecosystem. Culinary robots not only serve as a proving ground for AI technology but also act as a powerful engine pulling the supply chain forward. A single cooking robot incorporates over 2,000 components—including magnetic sensors, matrix temperature sensors, positioning sensors, angle sensors, and olfactory sensors—spanning precision machining, AI chips, motion control, and sensing technologies. Simultaneously, when these robots enter kitchens, integrate with ingredient supply chains, and serve diners, they become deeply woven into the operations of modern services. The "hard" strength of manufacturing underpins the "soft" agility of services, while service data feeds back to refine manufacturing iterations.
However, the difficulty of technological deployment cannot be underestimated. Yang conceded that in cooking scenarios, ingredients constantly deform and discolor, with taste and doneness containing substantial information not directly visible to vision systems. "When a mapo tofu sauce-reduction model is transferred to braised pork, accuracy drops to around 60%. If we factor in both sauce reduction and undercooking into the judgment, the model requires retraining from scratch." The "generalization" challenge in culinary AI stands as the industry's core bottleneck to overcome. "The demands for generalization across material variations and specific applications are extremely high, which is why we focus on dataset optimization and model architecture refinement based on open-source foundational models," Yang added.
Accelerating the Leap Across the Commercial-Home Divide
From a market perspective, the "China Commercial AI Cooking Robot Report" indicates that in 2025, the domestic market size for commercial AI cooking robots reached RMB 7.72 billion, projected to surpass RMB 109.6 billion by 2030. With an estimated 7 million restaurant locations nationwide capable of deploying such robots, the combined potential market could total RMB 472.2 billion. Capital is swiftly moving in response.
In April 2026, Oak Deer Robot announced a RMB 300 million financing round, led by an industrial upgrade fund managed by Yizhuang State Investment, with participation from Amber and Jiuan. Guojin Securities released a research note stating that as restaurant chains develop their own cooking robots and more funds flow into niche segments of the supply chain, industry investment is forming a virtuous cycle between commercial implementation and capital infusion.
Meanwhile, the globalization of Chinese cuisine is generating explosive incremental demand for culinary robots. Yang revealed that Oak Deer Robot's penetration rate among chain restaurants using cooking equipment in Singapore has already reached 70% to 80%. He noted that these robots address a long-standing pain point overseas: the inability to standardize and replicate Chinese recipes. Previously, Chinese food exports relied on packaged goods; now it's about brand expansion. Through recipe distribution and localized execution, order volumes for the company's cooking robots in overseas markets have surged substantially.
When discussing the "GPT moment" for the AI cooking robot sector, Yang outlined two conditions that could trigger this inflection point. First, regardless of ingredient variations, a chef's developed recipe could be "dropped in" and the machine would automatically cook it to master-level standards. Second, the entire supply system achieves full automation, freeing humans from the necessity of prolonged stovetop attendance.