A cup of coffee, the new foothold of AI + embodied intelligence

On August 16, a crowd of people queued up in front of a newly opened coffee shop at Beijing Galaxy SOHO. 

There is no bar counter in the shop, nor can baristas be seen. After a drink is prepared, it is grabbed by an embodied robot and delivered to the pickup cabinet. After getting their drinks, people hold their phones through the glass to film the robot working.

This shop is called 7Fresh Coffee, which operates 24 hours a day. On its opening day, it served 202 cups of coffee within one hour, successfully breaking the Guinness World Record for "the most cups of coffee served by a robot coffee shop in one hour".

Soon, the buzz spread from Beijing to overseas. Media outlets including US CBS and Australia's 7NEWS reported on this unmanned shop, with roughly similar focuses: a drink prepared within 30 seconds, 24-hour operation, and how the robot delivers coffee to consumers. The embodied robot is only the most visible part of this shop.

What actually supports the operation of the store is a complete system covering AI demand insight, product R&D, digital formula, automated production, embodied delivery, and consumer feedback. The fact that 7Fresh Coffee has quickly drawn attention at home and abroad is precisely because AI and embodied intelligence are implemented in real catering scenarios and tested through a complete commercial chain.

A cup of coffee has thus become a small yet fully complete AI sample in the physical world.

The System Behind a Cup of Coffee

The store operation of 7Fresh Coffee is jointly supported by AI, intelligent automated production and embodied intelligence.

Before a drink enters the production process, market hotspots, user reviews and consumption trends have been analyzed by AI, helping the team judge which products and flavors are favored by users and worth developing. R&D personnel and the supply chain then complete the raw material preparation, formula design and offline debugging, converting these judgments into digital formulas that can be executed by equipment to start stable production.

For a specific order, after the user selects the drink on the JD App or the 7Fresh Coffee mini-program, the system will complete cup placement, ice placement according to the cup size, temperature preference and ice amount, inject coffee liquid, fresh milk, fruit juice and other ingredients according to the formula, and then sequentially complete cup labeling, lid placement, capping, and weighing quality inspection. The qualified finished product is grabbed by the embodied robot and placed in the designated compartment of the self-pickup cabinet.

According to the data provided by 7Fresh Coffee, the average production time of a single drink is within 30 seconds, and the entire production line can make 7 to 8 cups at most at the same time.

Speed determines the production capacity ceiling of coffee and tea stores.

Take coffee as an example, its consumption is concentrated in several periods such as morning peak, noon peak and afternoon tea time. The carrying capacity at peak hours directly determines the queuing time, and also determines whether users are willing to continue waiting. Traditional manual stores usually only have two ways to cope with peak hours: adding more staff or adding more equipment, and both have upper limits.

"Reducing the time for a single cup to 30 seconds is the margin reserved by the automated production line for peak hours," the technical director of 7Fresh Coffee told 36Kr. The store's production capacity can also adjust the number of equipment according to demand to adapt to the production needs of different periods.

Such a speed leaves very little room for the equipment, so there are naturally no shortage of difficulties.

For example, fresh milk. 7Fresh Coffee only uses high-quality fresh milk. Fresh milk has strict storage conditions, and the storage temperature needs to be maintained at 0-5℃; for the quality requirement of finished drinks, the fresh milk needs to be heated to about 60℃.

This means that the equipment needs to complete the whole process from heating to serving within about 30 seconds without damaging the ingredients and taste. The delicate balance between reaching the required temperature and ensuring good taste is a specific problem that the R&D team needs to solve.

According to the technical director, at present, 7Fresh Coffee has realized that nearly 100% of the drinks are produced in line with digital standards, and the ratio error is controlled within 1%-3%. The difference in this range will hardly affect consumers' taste experience.

After the error is narrowed, an old problem in the catering industry is gradually eased. It is very common that the same brand and the same drink taste different in two different stores. The traditional model basically relies on repeated calibration through staff training and process supervision. The digital formula fixes the ratio and process into repeatable parameters, which lays a foundation for maintaining the same taste across different stores. More importantly, whether in peak or off-peak hours, the quality of a single cup will not change with the number of orders.

Of course, digital formula does not mean that everything is done after inputting numbers into the machine. There are many details in the ratio that need to be handled. The viscosity, dosage, particle size and temperature of raw materials will all affect the final result.

Beyond production, there is the on-site delivery of embodied robots.

In the real store scenario, there are far more variables than in the experimental site. The robot needs to judge the state of the cup, grab it stably, and then find the right position to place it accurately. The outer wall of the cold drink cup will have condensed water droplets and become slippery; the occupancy status of nearly 100 compartments of the pickup cabinet changes every minute, and some orders contain more than one cup.

In a real commercial environment, embodied intelligence can "try again", but every attempt costs money. Grabbing the wrong position leads to wrong orders, spilling drinks causes loss, and system shutdown means the store stops operating. To solve this problem, 7Fresh Coffee's solution is to make the system identify and handle exceptions by itself as much as possible, and manual intervention is only carried out in extreme cases. In the production process, weighing and quality inspection are also arranged, and unqualified drinks will be automatically remade.

These automatic error correction mechanisms also constitute part of the stable operation of the system.

Judging from the results, this store of 7Fresh Coffee has withstood the test. The achievement of 202 cups per hour is actually a public test of the overall scheduling capability of the system, and also provides an intuitive result for the collaboration between AI and embodied intelligence in real catering scenarios.

We have seen too many seemingly excellent DEMO products on exhibition stands and in venues, which are sufficient to prove that "AI + embodied robot is feasible". But a store that opens and operates every day needs to answer the question of "whether it can operate continuously" and finally form sustainable operational efficiency.

This point demonstrated by 7Fresh Coffee may be of greater value to the industry.

The AI Closed Loop of a Cup of Coffee

The implementation of AI in 7Fresh Coffee is not limited to the store site.

"At 7Fresh Coffee, 'AI + embodied intelligence' forms a complete chain," said the business director of 7Fresh Coffee to 36Kr. According to his disclosure, the overall business flow of 7Fresh Coffee is: after AI identifies market opportunities, users will form their own exclusive formulas with the assistance of AI, and then the store will complete accurate production and service delivery. If users give feedback, the feedback will be reintroduced into the judgment logic of the AI R&D system.

Along this chain, AI participates in the very early stage of drink R&D.

After the whole network hotspots, user reviews and consumption trends are imported into the system, AI will help the team judge which flavors are worth developing, and undertake part of the information sorting and direction judgment work in the early stage of the drink product manager.

Take the recently popular "little butter" latte in the industry as an example. The name itself is a flavor concept, and most of the ingredients in the formula have nothing to do with real butter. It is necessary to further figure out what consumers really like: the velvety and smooth taste, or the flavor close to butter and popcorn. The same popular keyword corresponds to different demands, and AI needs to distinguish these preferences from real reviews first.

Then comes the work of human beings. The supply chain looks for raw materials, R&D personnel communicate with suppliers to customize and adjust formulas, and then carry out offline debugging and on-machine testing. AI shortens the distance from massive information to product direction, and the final taste is actually produced and tasted through the automated production line.

However, a standard formula that has passed the taste test may not satisfy everyone.

The team once conducted an internal test for this. They took an existing drink, adjusted the flavor richness, sweetness-sour balance and caffeine concentration, made more than a dozen versions for the team to taste, and the distribution of preferences was more scattered than expected: the richness that some people liked in the same drink was exactly the part that others found too sweet and greasy.

The internal judgment of 7Fresh Coffee is that taste preferences vary from person to person, but consumers have long lacked channels to express their demands and cannot find more suitable choices.

Over the years, chain beverage stores have maintained standardization by relying on fixed SKUs and limited options. The combination of AI and embodied capabilities brings new possibilities to 7Fresh Coffee on the basis of standardization. It is understood that the store opens parameters such as sugar content, coffee liquid concentration and fruit juice concentration to consumers, supporting 1%-100% adjustment.

It is not easy to implement this operation. This means that every drink may be an independent formula. The combination of multiple parameters may form tens of thousands of formulas. The differences between formulas are so subtle that people cannot remember them, nor can they execute them stably.

But it is quite natural for machines to handle this.

After consumers finish DIY, they can also name their own drinks and generate custom cards.

This experience is still being iterated continuously. The business director of 7Fresh Coffee admitted that how to let users find the taste that suits them within one or two choices is the problem to be solved next. Opening parameters is the first step, and reducing consumers' exploration also requires design at the product and interaction levels.

After the drink is sold, the chain will continue. The user's selection, saving and repurchase behaviors, as well as the sales volume and user reviews after new products are launched, will be reintroduced into the R&D system. 7Fresh Coffee always maintains a continuously updated drink library, with AI assisting in judging which products need to be adjusted and what is worth developing next.

After the Galaxy SOHO store opened, there was such a product adjustment. The team found that there were training institutions and schools nearby, and there were many child customers. So combining these scenario information, market trends and AI analysis, they developed a lactic acid bacteria drink. According to the director, the product performed well in sales after its launch.

The links of stores identifying demands, AI forming product definitions, R&D landing, launch and sales, and new data backflow are connected. Following this idea, stores in different locations may eventually develop different product structures.

True 24-hour operation also opens up broader demand scenarios. Under the premise of permitting the site, automated stores can extend the service time to the fullest, and the marginal cost of adding new time periods is very low, so there is no need to require high sales volume every hour. For ordinary stores, adding business hours usually requires considering shift arrangement and corresponding investment.

This fills the business hour gap of traditional coffee shops. The demand for a freshly made drink at night and early in the morning can also be met. The supply includes coffee, tea, sparkling water and lactic acid bacteria drinks, and the store can serve different groups of people in a longer period of time.

For the R&D team, all-day operation also means that they can more clearly observe what drinks people choose at different times. Consumer data will continue to affect product selection and formula design, and continuous tasks and environmental changes on site also provide continuous testing opportunities for technology iteration.

These are exactly the innovations that make 7Fresh Coffee widely concerned. AI + embodied intelligence is no longer limited to the laboratory, but has truly entered daily consumption, forming a store model that can actually operate, and gradually verifying the feasibility of AI in commercial scenarios. The data generated in these real scenarios is precisely the more powerful fuel for training AI based on the real physical world, enabling AI to combine with business models and be truly applied to industries.

JD is Building More Such Physical Worlds

The physical world does not accept drafts. The real key to the success of physical AI lies in who owns the scenarios and data that allow AI to "work for real". The complete operation of real scenarios is a watershed for AI to enter the physical world.

From this perspective, 7Fresh Coffee has initially completed the full closed loop of AI and embodied intelligence in the catering consumption end. One end is the real supply chain of coffee beans, fresh milk and other raw materials, and the other end directly faces consumers. From AI making judgments, to machines executing, to real feedback promoting adjustments, a cup of coffee has gone through the whole process of this cycle.

That's why in the words of the business director of 7Fresh Coffee, this is a "real, closed-loop and complex commercial scenario". When 7Fresh Coffee is placed in JD's overall AI layout, its significance is more like a pilot, a specific attempt to enter the catering scenario.

It is necessary to distinguish that having physical world scenarios and enabling AI to form a closed loop in the physical world are two different stages.

Still take the delivery of embodied robots in 7Fresh Coffee as an example. Spilled coffee needs to be remade, placing the cup in the wrong compartment will affect the pickup, hot cups and cold cups are in different states, and the weight of drinks varies... In the laboratory stage, these may only be a successful grab and placement at a fixed position and path. But in the real consumption scenario, every time the robot picks up a cup of coffee, the state of the object it faces may be different. Paths that have not been verified by real scenario demands are not sufficient to cope with these changes in daily operation.

"This kind of end-to-end long task needs to be completed continuously in a changing environment." The system not only needs to know what to do next, but also needs to perceive what has been done