To some extent, this also captures the most immediate challenge facing the embodied AI industry today: what will determine whether the sector can keep moving forward is no longer technological breakthroughs alone, but whether robots can truly enter real-world scenarios, operate continuously, and form a closed data loop.
At the start of 2026, the embodied AI sector has clearly heated up. According to EqualOcean’s incomplete statistics, more than 127 financing events took place in the industry during January and February alone, covering around 120 companies and drawing participation from over 310 investment institutions. From Shenzhen to Beijing, from humanoid hardware to AI brains, and from early-stage validation to batch orders, both capital and industry players are accelerating in tandem. At the same time, Unitree Robotics has launched its IPO process, marking an important signal that the sector is moving toward capitalization.
As the industry heats up, the narrative around embodied AI is also shifting. Early discussions were largely centered on technological breakthroughs; today, the focus is gradually moving toward scenarios, commercialization, and global deployment pathways.
In this transition, a clear gap remains: there is still a lack of content that can explain the technological trajectories and industrial logic of Chinese embodied AI companies in a global context.
To address this gap, EqualOcean is continuing to expand its English-language coverage and research on embodied AI. From technology roadmaps and scenario-based deployment to overseas expansion models, the goal is to interpret this new wave of technological and industrial change for international audiences from a perspective that is closer to the front lines of the industry.
1. Semi-Structured Scenarios Are the Real Starting Point for Embodied AI
From current industry practice, the deployment landscape for embodied AI has already taken on a relatively clear structure. Major application areas now include industrial settings, commercial services, public services, scientific research and extreme environments, as well as home and personal use. Among them, industrial manufacturing and warehousing/logistics have been the first to achieve deployment at scale; commercial and public service scenarios serve as a transitional layer; research functions mainly as a test bed for technical validation; and the home remains a long-term frontier rather than a near-term commercial destination.
AI² Robotics offers a representative example. Its path is not about simply expanding across as many scenarios as possible, but about following the same underlying logic: first entering semi-structured scenarios such as high-end industrial manufacturing, then gradually expanding into semi-open environments such as public services and new retail once system stability has been validated. Highly open-ended settings such as the home are being approached at a later stage. Research-related exploration, meanwhile, is used more for capability validation than for near-term commercial deployment.
This is not an isolated choice, but a common pattern across the industry. For almost every company, industry is the first stop. That leads to a more fundamental question: why does embodied AI begin with industry?
The answer is straightforward. Under current technological conditions, the greatest challenge for robots is not simply whether they can complete a task, but whether they can operate reliably and continuously in real-world environments over time. That means the scenario itself must have sufficiently low uncertainty in order to support sustained system performance.
As AI² Robotics told EqualOcean, “Factory environments are relatively the most robot-friendly: density, temperature, and humidity are all controllable, the ground is flat, and for humanoid robots, they are essentially unobstructed.” At the same time, general-purpose robots are not intended to replace traditional industrial equipment, but to complement it by taking on flexible tasks that fixed workstations are not well suited to handle.On that basis, public services and new retail form an important transitional layer. In public service settings, airports are a good example: robots are already able to perform standardized tasks such as luggage cart collection and operate stably in real-world, high-traffic environments. In new retail, robots are beginning to participate in services such as coffee preparation, turning processes once dependent on human labor into replicable automated operating units. These scenarios combine real demand with scalability, while still remaining within the manageable range of current technical capabilities, making them important windows for deployment at this stage.
By contrast, the home is a much more difficult environment. Although demand is clear, the household setting is highly unstructured, tasks vary widely, and issues such as safety and privacy add further complexity. As a result, large-scale deployment in the home remains difficult in the near term.
That said, the home has not been abandoned by the industry. Some consumer-facing robots are still moving in that direction. The reason is that industrial scenarios may be easier to commercialize first, but they often require a high degree of customization, making it difficult to spread R&D costs across large volumes. The home, by contrast, is more complex—but once the necessary capabilities are truly in place, it is far more likely to support large-scale replication through standardized products. In that sense, the home may not be the most realistic deployment scenario today, but it could ultimately become one of the most important pathways through which embodied AI achieves scale.
2. At Its Core, Embodied AI Is About Delivery
If we return to commercial reality, embodied AI remains, at this stage, fundamentally a B2B business. AI² Robotics told EqualOcean that each robot is currently priced in the several-hundred-thousand-RMB range, with overseas prices expected to be even higher. At that price point, it is unlikely to enter the consumer market anytime soon in the way consumer electronics do. Instead, it must first target enterprise scenarios that can generate clear value and have the willingness and ability to pay.
But what determines whether commercialization can truly work is not price alone. More importantly, it is the logic of delivery. For enterprise customers, a robot is not a display product, but a solution that must be integrated into actual workflows and operate reliably over the long term. As AI² Robotics told EqualOcean, its products were designed from the outset to meet industrial-grade standards: key components, battery capacity, and the overall mechanical structure are all configured around real task requirements, with a lifecycle of up to 50,000 hours. What matters here is not whether the specifications look best on paper, but whether the machine can keep working, reduce labor costs, and generate verifiable value in real operations.
In that sense, the core challenge of B2B commercialization today is not simply entering a given scenario, but proving that robots can be continuously purchased by customers, reliably delivered, and meaningfully integrated into real workflows. Only then can embodied AI move beyond technical demonstrations and begin to take shape as a sustainable commercial system.
3. The Real Differentiator Is the Data Loop
If large models solve the question of whether robots can possess general intelligence, the real challenge of embodied AI is how to make those capabilities operate stably in the physical world.
In this respect, AI² Robotics follows a distinctly engineering-driven path. As founder Dr. Guo Yandong has argued, a robot does not necessarily need to “look like a human,” but it must be able to “work like a human.” Based on this logic, the company has adopted a wheeled base with dual arms, prioritizing entry into executable real-world scenarios rather than pursuing more complex humanoid structures for their own sake.
From the perspective of capability architecture, embodied AI can be broken down into three layers. The first is the algorithmic model—the “general brain”—which is responsible for understanding tasks and environments. The second is the data system, which provides the experience needed for learning and generalization. The third is the hardware and control system—the “body and cerebellum”—which is responsible for physical execution and real-time response. In this framework, the model determines the upper bound of capability, data defines the boundary of generalization, and hardware and control determine whether the system can truly function in the real world.
Under AI² Robotics’ approach, data does not come from a single source, but from a dynamically evolving structure. In the early stage, the system resembles an upright triangle. At the base is large-scale internet data, which supports cold start and general training. In the middle sits simulation and synthetic data, used to amplify training efficiency. At the top is real-world robot data collected from actual scenarios—smaller in volume, but decisive in determining whether specific tasks can truly be executed.
As products are gradually deployed, this structure shifts into an inverted triangle. In other words, internet data is critical in the early stage for building foundational capabilities, but once robots enter real-world environments, the system increasingly comes to rely on operational data. The share of real-world data continues to rise and becomes central to model optimization, while the relative importance of internet data declines.
On the data openness front, AI² Robotics has jointly released the open-source embodied AI dataset RoboCOIN with BAAI (Beijing Academy of Artificial Intelligence). Of its roughly 180,000 data entries, more than 35% consist of real-world semi-humanoid data contributed by AI² Robotics, reflecting the company’s strength in accumulating real deployment data.
At the same time, embodied AI data is difficult to transfer across different robot embodiments. Differences in structure and control systems mean that data and models are tightly coupled to the hardware itself. As a result, companies must build their own closed-loop system linking data, models, and embodiment. This is also why competition in embodied AI is gradually shifting away from model capability alone and toward competition at the level of the overall system.
4. What Goes Global Is Not the Robot, but the Scenario Solution
Unlike consumer electronics, the globalization of embodied AI is not fundamentally about selling a robot overseas. It is about replicating a scenario solution that has already been validated domestically and deploying it in overseas markets. As AI² Robotics told EqualOcean, its current mainstream path is “domestic validation + overseas replication”: first refining the scenario and validating the solution in China, then expanding into overseas industry settings with similar needs, such as airports, logistics, and retail. The core value of this approach is not just market expansion, but uncertainty reduction. Compared with starting from scratch, replicating a proven solution makes delivery, communication, and commercial execution far more controllable.
That said, replication does not mean simple transplantation. As AI² Robotics noted, even within the same scenario category, such as airports—different countries vary in operational workflows, efficiency priorities, and partnership models. As a result, companies still need to rebuild the business case around each specific customer. In some more localized demand scenarios, such as public canteen cleaning in Singapore, AI² Robotics typically works with local technology partners or service providers and supports secondary development through open interfaces in order to complete local adaptation.
So how should embodied AI companies choose their first overseas market?
AI² Robotics’ view is that companies should usually prioritize countries where labor shortages are more acute, demand for automation is more real, and government support is more visible. In terms of market entry strategy, it also tends to favor a “landmark customer first” approach—building a small number of sustainable reference cases before scaling up, rather than rolling out aggressively from the start. For example, through its collaboration with Singapore’s A*STAR, the company is already providing embodied AI solutions to Fortune 500 clients. In the Middle East, universities have also begun introducing its robots for application validation in aerospace-related scenarios.
Still, based on AI² Robotics’ current overseas experience, embodied AI remains at a very early stage in international markets. Most deployments today are still small-scale POCs, typically involving one or two units for validation before any gradual move toward scale. At this stage, the company generally adopts a “product first, compliance in parallel” approach. This is especially true in markets such as Southeast Asia, where early-stage compliance requirements are relatively more flexible. Only once shipment volumes reach a certain threshold does the company begin systematically completing the certification framework needed to enter Europe and the United States.
5. The Real Watershed Is Not the Demo, but Long-Term Operation
Over a longer time horizon, competition in embodied AI will not ultimately be defined by model parameters, robot form factors, or single-point performance metrics. It will gradually shift toward competition at the level of system capability. In AI² Robotics’ view, the companies most likely to build real defensibility will be those that can place robots into real-world scenarios first, keep them running stably, continue collecting data, and keep iterating over time.
From this perspective, the advantage of Chinese companies lies not only in their more complete supply chains or faster hardware iteration cycles, but more importantly in their access to denser application scenarios and shorter validation loops. This allows companies to complete the loop from product definition and scenario validation to delivery optimization earlier, pushing embodied AI beyond simply “being built” toward “being deployed, replicated, and scaled.” For the same reason, Chinese embodied AI companies may be among the first to establish a set of replicable global expansion pathways.
In the end, the industry’s real watershed will not be who can produce the most impressive demo first, but who can make robots run sustainably in specific real-world scenarios over the long term—and build a self-reinforcing data loop around that operation.