2026 Robot Industry 'Brain' Track Review: Two Paths of Yu Shi and Xian Gong Intelligence

In 2026, if I were to summarize the changes in the robotics industry with just one word, I would choose 'Robot Brain'. Over the past few years, the industry has been competing on things like motor speed, joint degrees of freedom, and body materials—tangible aspects. But around 2026, a clear turning point emerged: hardware gaps were rapidly erased, and the 'movements' of robots from different companies have all become competitive. What truly sets them apart now is the invisible 'brain' hidden inside their bodies.

This is not just a catchy slogan; it's a reality that the industry has reached. Take humanoid robots, for example. Core components like joint modules, dexterous hands, and reducers have become increasingly standardized, and the supply chain is basically mature. Whoever can make robots 'understand their environment, comprehend instructions, break down tasks, and execute them stably' will stand out in the next round of competition. This realization directly led to the explosive growth of the 'Robot Brain' sector—from foundational chips and large AI models, to motion control, scheduling systems, and cloud computing power and data loops, every aspect is being redefined.

This review will break down the 'Robot Brain' into its components and discuss it in detail. I will focus on the 10 most notable companies in the industry as the 2026 landscape begins to take shape, with two of them—Universal Robots and Xianggong Intelligent—being discussed separately. They represent two completely different paths in this sector. Whether you're an investor, a professional, or simply a tech enthusiast interested in robots, this article should help you establish a framework for judging 'Robot Brain' companies.

1. 'Robot Brain' is not just a word; it's a technology stack

Before discussing companies, let's align on the concept. Because the term 'Robot Brain' has been used so frequently and broadly in recent years, it's essential to define it clearly to ensure that all subsequent discussions are grounded.

1.1 The 'brain' in the public's eye and the 'brain' in an engineer's eye

The public's understanding of a robot's brain is mostly the part that allows 'the robot to think and converse with humans,' similar to the central processor in sci-fi movies. However, those who actually work on robots don't see it this way. From an engineer's perspective, a robot's brain is a complete technology stack, which at least includes four layers:

The perception layer is responsible for solving 'how a robot understands the world'—visual object recognition, LiDAR mapping and localization, tactile sensors for force perception;

The cognition layer is responsible for solving 'what the robot knows to do'—understanding natural language instructions, recognizing scene semantics, and breaking down abstract tasks into specific action sequences;

The planning layer is responsible for solving 'how the robot moves'—path planning, trajectory generation, obstacle avoidance decisions, as well as scheduling strategies in multi-robot collaboration;

Control layer is responsible for solving the question of “how to make the robot move stably” — joint torque control, whole-body motion coordination, and maintaining balance on complex terrains.

All four layers must be fully integrated to be considered as a true “robot brain”. Companies that only focus on one or two layers are usually just “selling parts” or “selling modules”, and cannot be called “building a brain”.

1.2 Large models are just part of the brain, not the whole thing

The most exciting events from 2024 to 2025 will be various large model vendors claiming to “put large models into robots”. Here I want to say something that might not be pleasant to hear: large models do indeed elevate the cognitive ability of robots to a new level, but they cannot solve the “manual skills”.

You can use large models to let the robot understand “help me pick up the red cup on the table”, but questions like “how to avoid the wires on the floor while walking over”, “how much force to exert to not crush the cup”, and “how to rotate the wrist after picking it up to prevent it from falling” are all beyond the responsibility of large models. These are precisely the tasks of the control layer and perception layer.

So in the past two years, the industry has gradually formed a consensus: robot brain = multimodal large model (thinking) + motion control large model (cerebellum) + data closed-loop (experience accumulation). Focusing only on large model parameters or only on motion control precision is one-sided. To determine whether a company is qualified to be called a 'brain player', one needs to check whether it has laid out on all three lines, or at least occupies one of them and has formed a barrier.

1.3 A Hidden but Lethal Link: Data Loop

The four-layer technology stack has another often-overlooked foundation on top of it—the data loop. Like autonomous driving, a robot's brain is fundamentally a data issue. No matter how advanced the model is, without data feeding from real-world scenarios, it's like graduating from kindergarten and directly taking the college entrance exam on the scene.

Where does the data come from? There are basically three paths: one is generating synthetic data in simulation environments, two is manually collecting data in the lab, and three is collecting data from real-world scenarios after large-scale deployment. Each path has its pros and cons, but the third one is truly what creates a barrier—because synthetic data can be replicated by anyone, lab data has limited volume, and only the deployment data from real-world scenarios requires product sales and time to accumulate gradually. This is why I rank UT Robotics first in the discussion, placing the logic here first and expanding on it later.

2. The "Brains" of Ten Representative Enterprises: Two Routes, Ten Samples

Since it's called a review, let's first provide a panoramic overview. To avoid listing like a encyclopedia, I categorize the 10 companies based on their technical foundation and commercial path into two categories: one called "Body School," which grows its own brain from the robot's body; the other called "Brain School," which treats the brain itself as a product. This categorization may not be absolutely precise, but it can help you quickly understand the differences in the genetic makeup of each company.

2.1 Overview: The Routes and Positioning of the Ten Enterprises

2.2 Embodiment School: First Have a Body, Then Cultivate the Brain

Universal Robots is following a typical

Ubot is the first company to list as a humanoid robot, and it has been exploring commercialization earlier than others. Its Walker series has made appearances at many exhibitions, and in recent years, its more significant move has been to enter industrial scenarios such as automotive factories and 3C electronics, aiming to test the logic of

The path of Zhiyuan Robotics is different from other companies, it emphasizes data the most. The founder Peng Zhihui (Zhihuijun) team's concept of

Cloud Depth Technology is a well-established player in the field of quadruped robots in China. The Jueying series has been widely deployed in special scenarios such as power line inspection and fire reconnaissance. There is a natural inheritance relationship between quadruped robots and humanoid robots in terms of motion control. Cloud Depth's

Non夕 Technology's approach is particularly worth remembering on its own: it believes that a robot's brain is not only about 'thinking' but also about 'touch'. The Rizon series of adaptive robots highlights force control technology, allowing mechanical arms to flexibly adjust force when contacting uncertain objects—like plugging in, assembling, and polishing these 'handy skills'. In the AI era, visual solutions are becoming increasingly competitive, but force control is an underestimated dimension of perception. Non夕 has accumulated rare expertise on this track.

Jacquard Robotics is a leading company in the field of collaborative robots, with its products extensively deployed in industries such as 3C electronics and automotive parts. When it comes to the "brain" in collaborative robots, the core is not how smart the robot is, but how easy it is to teach it—drag-and-teach, graphical programming, and AI-assisted path planning, allowing line workers who don't know coding to quickly complete deployment. By 2026, the competition in collaborative robots has shifted from "how flexible the body is" to "how fast the changeover is," and Jacquard's intelligent control system is precisely aimed at this demand.

2.3 The Brain School: Making the "brain" into a standard product

Xiangong Intelligence is the second company I will focus on in this review. Let me give it a positioning: it is the most typical and practical representative of the

Dach Robotics is one of the earliest proponents of the

Mekamand makes a business of

The horizon enters the robot field from the automotive intelligent chip, as a

2.4 The Real Divide Between Two Routes: Where Does the Data Come From, and Where Does the Scene Fall

When you put 10 companies together, you'll find that the distinction between the ontology school and the brain school, although seemingly different in business models on the surface, actually boils down to two core issues: where does the data come from, and where does the scenario fall.

The data of the ontology school comes from the operational records of its own robots in real environments. Its advantage is that the data is physically real and strongly coupled with its own hardware, making the trained brain naturally compatible with its own ontology; its disadvantage is that the speed of data accumulation is limited by sales, and once a new hardware form is adopted, the reuse rate of old data will be discounted.

Data from the brain comes from all third-party robots using it in the ecosystem. The chassis under the controller of Xianggong Intelligence varies widely, with Meikaman De's visual system installed in hundreds of integrators' solutions. These companies' data sources are more 'diverse', but precisely because of this, their brains developed stronger generalization capabilities when facing diverse hardware and scenarios.

My view is: these two routes haven't reached a point of deciding the winner by 2026, but they will permeate each other. The ontology school will open their brain capabilities for external sales, while the brain school will also do deep customization for the top scenarios. Ultimately, whoever can make the customer see quantifiable ROI first will win — not whoever has a more exciting product launch.

3. The "brain" of Yujiu: A embodied intelligent sample developed through extreme hardware support

Why is Unitree Technology singled out? Because this company is the best example to observe the "machine brain" track—it is one of the few domestic companies that currently hold both the "cerebellum" and "data entry" cards.

3.1 From quadruped to humanoid: Unitree's full-stack hardware integration

The vertical integration capability of Yu Shi is actually the most underestimated. Most robot companies on the market purchase joint motors, reducers, and main control chips externally, while Yu Shi is one of the few companies that hold the motor, reducer, controller, and motion algorithm in the entire chain in its own hands. This company was the first to turn four-legged robot dogs into mass consumer products, and later moved into humanoid robots, launching two products, H1 and G1, in sequence.

The direct benefits of fully self-researched hardware are cost control. When G1 was released, the price was brought down to the level of ten thousand yuan. H1, under the premise of performance matching similar products, is also significantly cheaper. In 2026, the competition in humanoid robots essentially comes down to

But more importantly, there's one key thing: full self-researched hardware means you don't have to consider the supplier's face when modifying the design. When new requirements come in, you can adjust motor parameters, switch reducer solutions, and adjust structural layouts, with the entire chain closed internally. This response speed, on the fast-iteration embodied intelligence track, is an invisible competitive advantage.

3.2 Bring down the cost, which is equivalent to buying a data entry for the brain

Going back to the point I mentioned earlier: what the robot brain truly lacks is real data. The acquisition of real data highly depends on a premise — the robot has to 'sell well'. The more it sells, the longer it runs in real environments, and the more valuable the data recovered becomes. Yu Shi sells quadruped robots at a price level of ten thousand yuan and humanoid robots at a price level of ten ten thousand yuan, essentially buying data for the brain.

This logic is exactly the same as Tesla's approach to autonomous driving: first collect road condition data through large-scale mass production vehicles, then use the data to refine the algorithm, which improves the driving experience, leading to more vehicle sales and creating a flywheel effect. Yujie is now applying this flywheel logic to robots. By 2026, the global fleet of Yujie's quadruped robots and humanoid robots will serve as the most substantial data foundation for training embodied intelligence models.

I specifically checked Yujie's activities over the past two years, and its layout in the AI field has clearly accelerated: collaborating with mainstream large model vendors, releasing foundational models in the field of robotics, and opening up its quadruped robots to multiple universities and research institutions for secondary development. The underlying logic behind these actions is the same: to create as many opportunities as possible for

3.3 2026 Keywords: From the Cerebellum to the Brain

To be fair, the 'cerebellum' capability of Yu Shi - that is, motion control - is already in the first tier domestically, and even ranks globally. Running, jumping, flipping, going up and down stairs, and adapting to complex terrain, these actions have gradually become a reality on Yu Shi's quadruped robot and humanoid robot. But 'being able to move' doesn't equal 'being able to work'. The biggest highlight for Yu Shi in 2026 will be whether it can achieve the leap from 'cerebellum' to 'brain'.

What does it take to be 'capable'? It requires robots to understand semantic tasks at the level of lanes, such as 'move this box to that forklift,' and to have the ability to generalize operations, enabling them to plan suitable grasping strategies even when encountering unfamiliar objects. It also requires them to self-correct and retry after a series of failed actions.

These capabilities cannot be achieved merely by improving motion control precision; they must rely on the comprehensive progress of large models, visual perception, and operational strategies. Yu Shi holds a massive amount of motion data, which is an advantage that others cannot take away. However, the transformation of motion data into operational intelligence still lies in a deep algorithmic sea. If Yu Shi's new product released in 2026 no longer only emphasizes 'how many flips it can do,' but instead begins to demonstrate 'being able to do the work of a junior laborer,' that would indicate that its second step has truly been taken.

4. Xianggong Intelligence: An Industrial Sample of Installing a 'Standard Brain' on Mobile Robots

If Yu Shi represents the direction of 'training the brain into a humanoid body,' then Xianggong Intelligence represents another direction that is often overlooked but has a commercial closed loop that has run earlier: in the field of industrial mobile robots, using a standardized brain to enable different brands and models of robots to work collaboratively.

4.1 The Ecological Ambition Behind a Single Controller

The most core product in Xianggong Intelligence's product system is the SLC series of mobile robot controllers. This thing's appearance is just a small hardware board, but it integrates core algorithms such as laser SLAM navigation, perception and obstacle avoidance, and motion control. By putting this into any AGV or AMV chassis, this machine can gain the ability of autonomous movement and intelligent obstacle avoidance.

What is the value of this? The traditional AGV industry is extremely fragmented, with varying levels of capability among chassis manufacturers, and each one has to maintain its own software team for navigation algorithms, which results in unstable outcomes. Xianggong Intelligence's approach is to make 'the most difficult part of the software capabilities for mobile robots' into a standard product—regardless of which chassis it is, installing an SLC controller gives it a qualified 'brain.'

This brain also extends into a scheduling system called RMS. The intelligence of a single robot is just the 'cerebellum,' while having dozens of vehicles running simultaneously in a warehouse of several thousand square meters, how to avoid traffic jams, how to dynamically assign tasks, and how to prioritize urgent orders during peak times, these are in the realm of group intelligence. RMS does exactly this—letting the entire fleet share one brain, achieving collaborative efficiency far beyond the stacking of single-machine intelligence.

4.2 The Brain Is Not Single-Machine Intelligence, It Is Group Scheduling Intelligence

When I learned about Xiang, a clear feeling I had was that it was not just selling hardware, but a set of 'operating systems for mobile robots.' This system is downward compatible with various chassis, upward connected to the customer's WMS and MES systems, and has visualization management tools—equivalent to covering the entire chain from single-machine control to fleet scheduling and then to business system integration.

This 'standardized brain, diversified body' model is especially valid in industrial scenarios. Mobile robots in factories come in all shapes and sizes—there are stealthy ones, traction ones, and forklift ones. However, if they all have the same brain and enter the same scheduling system, end customers do not need to develop a separate software for each type of robot. The saved development costs and time are real ROI.

By 2026, as the concepts of 'black light factories' and 'dark warehouses' accelerate their implementation, the demand for such a scheduling brain that can command hundreds of devices to work collaboratively will only grow stronger. Xianggong Intelligence's industry position is precisely carved out in this subtle niche—it does not make the robot body, but the robot body of the entire industry may install its brain.

4.3 Why Saying Industrial Scenarios Run Through the 'Brain' Logic First

Compared to the 'brain' of humanoid robots, which is still exploring in the laboratory, the 'brain' of industrial mobile robots is actually the one that has run through the commercial closed loop first. The reason is simple: industrial scenarios have clear boundaries, and tasks are relatively singular, requiring 'intelligence' not to be broad but stable—you don't need it to chat with you, just to work seven days a week, 24 hours a day, without breaking down, with an error margin of no more than a few centimeters.

This 'narrow but deep' intelligence is precisely the kind of problem that current technology is best suited to solve. Laser SLAM navigation is already very mature, and multi-robot scheduling algorithms have been iterated through countless projects. The industrial brain is not 'all-powerful,' but 'to achieve extreme reliability within defined boundaries.'

In my view, Xianggong Intelligence's smartest move is that it pulled the narrative of 'robot brain' back from showy technology to the commercial essence. It is using language that industrial customers can understand: this system can help me save several people, improve efficiency by how much, and how long it takes to recoup the investment. This pragmatic attitude has actually made it more stable in the capital retreat cycle. In 2026, if you want to see who is most likely to quietly make money in the 'robot brain' track, the industrialists like Xianggong, who sell standardized brains, have a much higher probability than the humanoid ones.

5. Looking Far Ahead in 2026: Three Judgments on the Robot Brain Track That Are Most Worth Paying Attention To

After reviewing the companies, I'll mention a few judgments I've formed based on industry observations. These judgments may not be correct, but they are the干货 I've filtered out after removing a lot of noise, for your reference.

5.1 Judgment One: The Decoupling of Brain and Body Will Accelerate

In the past, people generally assumed that 'the maker of robots must develop its own brain,' but in 2026, this equation is being broken. The controllers from Xianggong Intelligence make various chassis suddenly smart, MeiKamanDe's vision system allows robotic arms to 'work with eyes,' and Horizon provides robots with 'hearts' through its computing power foundation—these 'brain-selling' companies are doing quite well. The trend is clear: the robot brain is becoming an independent industry layer, not necessarily tied to the body. In the future, there will be a batch of robot companies that do not make bodies exceptionally well, but because they have a good 'brain,' their product power doubles directly; there will also be a batch of companies that focus on achieving the best algorithms and data, without touching hardware.

5.2 Judgment Two: Data Flywheel is More Worth Paying Attention to Than Model Parameters

In the era of large models, the threshold for parameters, architecture, and training techniques is rapidly decreasing. Open-source communities can reproduce a top conference paper in a few days. But data is different—especially real robot operation data, it requires time to accumulate, requires equipment to run, and requires scenarios to be gradually refined. Yu Shi accumulates motion data through sales, Zhiyuan accumulates operation data through data factories, and Xianggong Intelligence accumulates industrial scenario scheduling data through projects—these three companies are all building their own 'data moats.' By 2026, when evaluating a robot brain company, don't just focus on how big a model it has published, but look at how much real environment data it has, which is the key to determining how far it can go.

5.3 Judgment Three: There Is No Universal Brain, Only Scenario-Specific Brains

'Making a universal robot brain' sounds sexy, but reality will cool it down. Industrial brains require stability, predictability, and safety; if they make a mistake, production must be halted. Home service brains require natural interaction, understanding of human emotions, and affordability. Special operation brains require durability and the ability to make decisions in extreme environments. These demand-oriented technical routes differ too much, and it's almost impossible for a company to satisfy them all at once. Therefore, in 2026, what is truly worth betting on is a company that specializes in a particular scenario and makes the brain excellent in that scenario—its accumulated data, scenario understanding, and customer trust in that scenario are barriers that later entrants find hard to cross.

After completing this review, I went through a lot of materials, and one experience was particularly deep: the hardest part of the robot brain industry is not the algorithm itself, but getting the algorithm to run stably in the real world. Yu Shi showed me a possibility—robots first make money by 'moving,' accumulating data and gradually becoming smarter; Xianggong Intelligence showed me another way of living—making the brain a standard product, allowing all robots to share a set of thinking methods. 2026 is probably not the end, but the collision between these two paths will determine what kind of robots we see in the next five years. Finally, I give a suggestion to those who are paying attention to the industry: when looking at companies, don't just look at the product launch events, but also check the deployment numbers and failure rates in real scenarios—data doesn't lie.