The robots were no longer experiments. They were becoming infrastructure.
In the city of Wuhu, China, two companies crossed a quiet but consequential threshold: robots designed for public service moved from demonstration into deployment. EXEED and AiMOGA Robotics, drawing on the same perceptual intelligence that guides autonomous vehicles, have secured orders for one thousand intelligent police units and begun delivering them to real streets in real cities. The moment speaks to a broader human pattern — the long, unglamorous distance between invention and infrastructure, and what it takes to finally close it.
- One thousand robot units ordered and 110 already working in live urban environments signals that the prototype era is over — this is now a supply chain problem, not a research problem.
- A Vietnamese partner's commitment to an experience center and industrial park suggests the deal is less a transaction than the seed of a regional robotics ecosystem.
- The gap between ambition and adoption is being attacked directly: a new leasing platform lets organizations access robots without the capital burden of outright purchase.
- Partnerships with 100 universities reveal a calculated bet that the industry's real bottleneck is not hardware but the trained human talent to design, deploy, and sustain it.
- A three-phase roadmap — children's companions, public service robots, home assistants — charts a deliberate path from high-visibility use cases toward the intimacy of everyday domestic life.
On a spring afternoon in Wuhu, Anhui, EXEED and AiMOGA Robotics stood before an audience and signed contracts for one thousand intelligent police robots — 110 of them already built, tested, and ready to work. The moment was less a launch than a crossing: these machines were no longer experiments. They were becoming infrastructure.
The partnership grew from a practical insight. The perception and navigation systems that make a self-driving car functional are, at their core, the same systems a walking robot needs. EXEED had spent years refining that intelligence in vehicles; now it was transferring that expertise to machines capable of directing traffic, supporting hospital staff, and moving through crowded streets with the situational awareness of an autonomous car.
The thousand-unit order originated with a Vietnamese partner who envisioned deploying the robots across traffic management, healthcare, and education — and who also committed to building an experience center and a dedicated industrial park. The 110 units already delivered had been operating for months in school zones during peak hours, managing marathon crowds, and detecting illegal parking. They were not in laboratories. They were on streets.
AiMOGA's strategy, unveiled at the conference, follows three deliberate phases: affordable companion robots for children, robots tailored to public and enterprise services, and finally home-based intelligent assistants. Each phase is grounded in real-world validation rather than speculation. To support this arc, the company has established 31 innovation laboratories, is building a specialist training academy, and is developing a components industrial park to reduce manufacturing costs at scale.
A new leasing platform lowers the barrier further — organizations can access robots through financing and operational support rather than outright purchase, spreading cost and risk across time. The simultaneous partnership with 100 universities signals that EXEED and AiMOGA are investing not just in machines, but in the generations of people who will build and improve them. What remains is the harder, slower work: scaling production, proving reliability city by city, and making robots as unremarkable a fixture of urban life as the traffic lights they now help manage.
In a convention center in Wuhu, Anhui, on a spring afternoon in late April, two companies announced they had moved past the prototype phase. EXEED and AiMOGA Robotics stood before an audience and signed contracts for one thousand intelligent police robots. One hundred ten of those units were already built, tested, and ready to deploy. The moment marked a threshold: the robots were no longer experiments. They were becoming infrastructure.
The partnership between EXEED, a premium electric vehicle manufacturer, and AiMOGA Robotics emerged from a simple observation about technology. The systems that allow a self-driving car to perceive its environment, plan a route, and execute movement are fundamentally the same systems a robot needs. EXEED had spent years refining those capabilities in vehicles. Now the company was applying that expertise to machines that walk on two legs or four, that can direct traffic or assist in hospitals, that can think through a crowded street the way an autonomous car does.
The thousand-unit order came from a Vietnamese partner who saw potential in deploying these robots across traffic management, healthcare, education, and other sectors. The partner also committed to building an experience center and an industrial park dedicated to robotics. This was not a single customer buying a batch of machines. It was the beginning of an ecosystem. The 110 units already delivered had been working in real cities for months—guiding traffic during peak hours at school zones, managing crowds at marathons, detecting illegal parking, supporting urban events. They were not performing in laboratories. They were on streets, doing jobs that previously required human officers.
AiMOGA's new strategy, unveiled at the conference, laid out a three-phase vision. First, the company would develop affordable robots designed to be companions for children. Second, it would create robots tailored to public services and enterprise needs. Third, it would bring robots into homes as everyday intelligent assistants. The path was deliberate, scenario-driven—each phase building on real-world validation rather than speculation about what robots should do.
To make this vision real, AiMOGA recognized that no single product could sustain an industry. The company needed a full ecosystem: clear use cases, teams with both robotics and domain expertise, reliable supply chains, sales channels, financing options, service networks, and systems to collect data from deployed robots so they could improve continuously. The company had already established thirty-one innovation laboratories across six fields and was building an academy to train specialists. It was also developing a core components industrial park to drive down manufacturing costs and increase production capacity.
A new robot leasing platform launched at the conference was designed to lower the barrier to entry. Instead of purchasing a robot outright, organizations could lease one, with financial services and operational support included. This model had worked in other industries—it could accelerate adoption here by spreading the cost and risk across time.
The partnership with one hundred universities signaled a long-term commitment to the field itself. EXEED and AiMOGA were not just building robots; they were investing in the people who would design, deploy, and improve them for decades to come. The companies had moved from announcing technology to announcing orders, from showing prototypes to delivering working units, from imagining scenarios to operating in them. What remained was the harder work: scaling production, refining reliability, expanding into new cities and countries, and proving that robots could become as ordinary in urban life as traffic lights.
Citas Notables
From single-city pilots to multi-city, multi-scenario deployment, and from isolated application trials to thousand-unit agreements, AiMOGA intelligent police units are rapidly integrating into the daily operations of urban traffic management.— AiMOGA Robotics, 2026 Global Release Conference
La Conversación del Hearth Otra perspectiva de la historia
Why does a car company think it can build robots better than a robotics company?
Because the hard part isn't the robot itself—it's the brain inside it. EXEED spent years teaching cars to see the world, plan a path, and move safely through it. A robot needs exactly those same skills. The company is translating expertise, not starting from scratch.
But one hundred ten units is still a small number. How does that become a thousand?
The thousand-unit order is the proof that someone believes in the product enough to commit. Those 110 units are already working in real cities, doing real jobs. A partner in Vietnam saw that and said: we want a thousand. That's how you move from pilot to scale.
What's the leasing platform actually solving?
Money. A city or a company might want a robot, but buying one is expensive and risky. Leasing spreads the cost over time and transfers some of the risk to the lessor. It's the same reason companies lease trucks instead of buying them. It makes adoption faster.
Why partner with a hundred universities?
Because robots are still new. There aren't enough people trained to design them, deploy them, maintain them. Universities are where you build that talent pipeline. In five years, you need engineers and technicians who understand both robotics and the specific fields—traffic management, healthcare, education. You can't wait for universities to figure it out on their own.
The three-phase strategy—children's robots, then public service, then home robots. Why that order?
Each phase is easier than the last in some ways, harder in others. Children's robots are lower stakes—if something goes wrong, the consequences are contained. Public service robots have to work reliably in unpredictable environments, but the use case is narrow and clear. Home robots are the hardest because they have to adapt to infinite scenarios. You build the muscle on easier problems first.
What happens if the robots fail at scale?
Then the whole thing collapses. That's why those 110 units matter so much. They're not just sales—they're proof of concept in the real world. If they break down, if they make mistakes, if they can't handle the job, the thousand-unit order never happens. The companies are betting their credibility on reliability.