2026-09-11 06:40:39
Tuesday, September 8 will go down as a milestone in artificial intelligence alongside IBM’s Deep Blue defeating world chess champion Gary Kasparov in 1997 and AlexNet winning the 2012 ImageNet competition. For the first time, an AI model was used to solve one of math’s most famous problems — a Millennium Problem. OpenAI announced that a swarm of 10,000 agents had produced a solution to a problem known as Navier-Stokes.
Unlike those earlier AI milestones, however, no one was happy on Tuesday. Not even OpenAI.
The night before OpenAI’s announcement, the NYU mathematician Tristan Buckmaster announced that he and collaborator Levent Alpöge had solved three problems closely related to Navier-Stokes. Alongside rough drafts of three papers — some 245 pages in total — Buckmaster released a statement excoriating OpenAI.
“I had planned to say on announcing our work that the results are not the important thing. Rather the important thing is instead the significance that a mathematician and an LLM model can now do all this work in a month,” Buckmaster wrote on Monday. But “instead of these incredibly important developments, I find myself writing about something else.”
Buckmaster wrote that he initially reached out to a mathematician at OpenAI on Thursday, September 3. Rumors were swirling that Anthropic had solved two Millennium Problems. Buckmaster wanted OpenAI to know that those rumors likely referred to his effort, which wasn’t officially supported by Anthropic. His collaborator Alpöge was an Anthropic employee, Buckmaster said, but was working on the project in his spare time.
Three days later, Buckmaster spoke with that mathematician and Sébastien Bubeck, who led the OpenAI effort to solve Millennium Problems. OpenAI’s approach to Navier-Stokes used the same broad approach as Alpöge and Buckmaster’s, which Buckmaster wrote “almost nobody” was working on. OpenAI began work after the rumours about an Anthropic effort reached OpenAI.
Through a massive computational effort, OpenAI had beaten Buckmaster and Alpöge to a full Navier-Stokes solution. According to Buckmaster, Bubeck offered to merge their efforts and let Buckmaster write a paper announcing the full Navier-Stokes result, as long as the paper acknowledged that an OpenAI model had solved it. But under this offer, Alpöge, who works for Anthropic, would not be a co-author.
Buckmaster was furious and went public with the story.
Predictably, the drama overshadowed the mathematics. People debated whether Buckmaster was right to be outraged — or whether Bubeck’s response exonerated OpenAI.
But I think that focusing on the details of the drama risks missing the larger point.
Mathematics is as much about cultivating a community of experts as it is about solving individual problems. Some of OpenAI’s behavior might have been reasonable in the context of competing with another well-resourced company like Anthropic. But spending millions of dollars on compute and thereby scooping an academic researcher breaks the norms of the academic math community.
If everyone behaved like OpenAI, mathematicians would have to keep their work secret until it was ready for publication. And mathematicians value openness and collaboration. So it’s considered bad form for someone to learn that another mathematician has had some promising early results and then sprint to complete the work first.
OpenAI sought to gain prestige by solving a famous math problem. But the way it went about that has arguably undermined the community that made solving the problem prestigious in the first place.
The Navier-Stokes equations are a way of describing how a fluid — such as water in a stream or air in the atmosphere — moves through space.
Fluid dynamics is very complicated, and in most cases there’s no explicit formula that can calculate where the fluid will be at every point in the future. Instead, Navier-Stokes equations describe how the fluid’s motion is changing by calculating the direction and speed at which each point in the fluid is moving at a specific point in time.
In the illustration below, every point has an arrow describing its direction — the direction and length of these arrows are described by Navier-Stokes equations.
To model a fluid’s movement over time, scientists can move forward through time in small steps. At each step, they use the Navier-Stokes equations to estimate how the velocity field changes, move the fluid forward slightly, and repeat. The result looks something like this:
This is a useful way to simulate the movement of a liquid. But it gives rise to an interesting theoretical question: are there situations where fluid movement predicted by the Navier-Stokes equations leads to absurd outcomes?
OpenAI found a fluid arrangement in 3D where the Navier-Stokes equations lead to absurd outcomes, but the company’s solution is hard to draw. So to help give readers an intuition for what this means, here’s an example of where a simpler model of fluid motion breaks down:
At first, we have a normal water wave. But as the simulation progresses, the leading edge gets so steep that water particles basically have to teleport in order to reach their positions in time. A real wave wouldn’t behave like this.
OpenAI found a situation where the Navier-Stokes equations give rise to another type of mathematical breakdown — called a singularity — in a scenario where the liquid is acted on by a specially chosen smooth force.1 As the company wrote in the announcement: “The solution is a vortex, a spinning swirl of fluid, that spirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics.”
Eventually, in the central region, “the velocity of the fluid grows without bound.” Again, this would never happen in a real fluid.
Alpöge and Buckmaster found a similarly implausible outcome for three related and somewhat simpler models of how liquids flow.2
While this is a significant breakthrough for mathematicians, it probably won’t have much practical significance. On September 3, the mathematician Terence Tao wrote a Mastodon thread about the implications of an AI model solving Navier-Stokes. He wrote:
While the equations do come from a very natural physical motivation - the study of incompressible fluids - the regularity problem is not important for its direct physical application. Computational fluid dynamics is already a mature subject, deployed extensively in the atmospheric sciences, for instance, and its empirical capabilities and limitations are already well understood. A theoretical guarantee of regularity, or conversely a pathological instance of blowup, for these equations would be intellectually interesting for such applications, but would not radically transform the way we would, for instance, model weather prediction or climate change.
If it’s not useful for physical applications, why should anyone care?
2026-09-05 00:04:09
This is the final article in our Robot Week series. This post (like the last two) is for paid subscribers only.
It’s also the last day to take advantage of our Robot Week sale! Click here to get 25% off an annual subscription.
The last few years have seen an explosion in the robotics industry. In the first half of 2026, at least 621 robotics companies (not including Waymo) received funding — some $31.8 billion in total.1 With all that froth, it can be hard to know which companies to pay attention to.
Below you’ll find my list of 19 companies that I expect to have a big impact on the robotics industry over the next few years. This list is the fruit of months of research — including interviews with the CEOs of several companies.
To keep things manageable, I’ll focus on companies that either directly make robots or make generalist AI models to control robots. I won’t include companies that only collect data, make components, or build infrastructure for robotics.
The current wave of robotics is very new — over half of the companies on my list were founded in the past four years — so it’s unclear which of these companies (if any) will come out on top. Regardless, the next few years are going to be very interesting for the industry.
2026-09-04 02:16:36
It’s day four of Robot Week! You can click here to get 25% off an annual subscription.
On May 28, the startup Shift announced that it would clean any New York City apartment for free. In a launch video, cheerful young men scrubbed toilets, vacuumed floors, and wiped down counters.
The catch? Cleaners wore baseball caps with cameras mounted under the brims. The company planned to record the workers’ actions and sell the data to robotics companies.
While the whole deal might have been a gimmick — the scheduling website notes in an FAQ that the offer is only available for a “limited time” — it’s still a perfect encapsulation of one of the most important trends in robotics today.
Early LLMs were famously trained to “predict the next word” across billions of tokens of text scraped from the Internet. Most roboticists expect we’ll need something similar to train general-purpose robots: an Internet-scale database of everyday tasks that robots can learn from.
But right now, humanity doesn’t have anything like that. The largest openly available dataset of robots performing tasks, ABC-130K, only has 3,500 hours of task demonstrations.
Over the last few months, I’ve talked to dozens of founders, engineers, and robotics researchers about the need for demonstration data and the ways people are trying to get more of it. I visited a robotics lab at the University of Pennsylvania to try my hand at collecting robot data. During my spring trip to China, I watched men wearing virtual reality headsets puppet humanoid robots to open fridges, sweep trash, and move pillows around.
When I attended the Actuate conference in San Francisco in August, I was surprised by how many people there were working at data-collections startups.
A plethora of startups like Shift are trying to solve the data shortage by recording the actions of humans and converting the videos into training data for robots. Other companies are hiring humans to directly operate robots in labs, factories, and even people’s homes. Still others are hiring humans to perform everyday tasks while wearing gloves or exoskeletons that force the human to move in a robot-like way and capture rich data on the worker’s actions. Some large data-collection companies like Scale AI are experimenting with all of these strategies.
The ultimate goal is to develop robots that are good enough to operate (mostly) autonomously in the real world. Once that happens, robots could generate additional training data while doing useful work. This could lead to a flywheel where the companies with the best robots are able to generate the most high-quality data, allowing them to improve their robots even more.
But Deepak Pathak, the CEO of robotics startup Skild, told me that there’s a “chicken-and-egg problem” here. In order to generate high-quality data from deployments, robots need to be able to do some amount of useful work. And getting there will probably take a fair amount of data. So companies first need to figure out a scalable way to get robotics data without deploying robots commercially. The first company to figure this out could have a big advantage.
Before we explore the strategies companies use to generate real-world training data, it’s worth asking why we need real-world data at all. Nearly a decade ago, Google DeepMind trained an AI to play Go entirely by self-play. After playing millions of games against itself, the model became better at Go than the top humans.
Could we do something similar for robots? Instead of training physical robots in the real world, maybe we could have virtual robots “teach themselves” to perform tasks through trial and error in a simulated environment. This approach actually does work for certain robotics tasks.
In March 2025, the humanoid robotics company Figure posted a high-level description of how it trains its robots to walk. Figure programmed a digital twin of its Figure 02 robot in a physics simulator and had that virtual robot try to walk over and over for millions of attempts. Each time, the robot received programmatic feedback — in a process called reinforcement learning — until the robot could walk in simulation. When Figure installed the resulting model on a physical robot, it could walk in the real world too.
When this process works, it’s the ideal way to train a robot.
It can be very fast: Figure said it was able to obtain “years of simulated demonstrations in a few hours.” This method can also result in a very robust model: after training for the equivalent of 1,000 years in a simulator, the foundation model company Skild produced a model that could control a quadruped robot even when engineers sawed its legs in half.
Basically every company today making a humanoid robot uses reinforcement learning in a simulated environment to teach it how to walk.1 Unfortunately, while this approach works well for locomotion tasks like walking and dancing, it doesn’t work as well for manipulation tasks, which involve more complex interactions with the environment.
Imagine trying to train a robot to hammer a nail. If a robot starts out acting entirely at random, it might go through millions of iterations without a single success. Reinforcement learning works by “rewarding” the model when it succeeds, but if the model never succeeds, there’s nothing to reinforce.
Developers can help the virtual robot by giving it fine-grained feedback that acts as a trail of breadcrumbs along the path to success. The robot might earn points for touching the hammer, more points for picking it up, still more for touching the nail with the hammer, and so forth. But this technique, known as “reward shaping,” is labor-intensive, doesn’t transfer well between tasks, and still may not produce good results.
In 2017, when prominent researchers tried to use reinforcement learning to teach a robot to hammer a nail in simulation, they couldn’t get it to work with just a “sparse” reward that judged whether the robot succeeded at the overall task. With help from shaped rewards, it took 50 hours of training for the robot model to learn — but the robot’s technique was still awkward:
However, if the researchers provided 25 demonstrations of a human completing the task, the robot model learned how to do the task in about six hours — almost 10 times faster. And the robot wound up with better hammering technique.
While this paper is almost a decade old now, the basic observation is still true: in order to learn from trial and error, it’s helpful for the model to start with a certain level of basic competence so it succeeds at least some of the time. And one of the best ways to achieve basic competence is to have it first learn from human examples.
There’s another problem with trying to train a robot entirely in simulation: many aspects of the world are so complex that we don’t know how to simulate them with enough fidelity.
Take the hammer example again: one of the reasons the model learned to use an awkward grip that probably wouldn’t work in real life is that the simulator couldn’t model friction perfectly. This discrepancy between simulation and the real world — the sim-to-real gap — is one of the central challenges developers face in training robots in simulation.
Some research groups are optimistic about the sim-to-real gap. At the GTC conference in March, I talked to Ranjay Krishna, who recently co-supervised a research project at the Allen Institute for AI (Ai2). “Our bet was that the sim-to-real gap is something we can overcome with large amounts of diversity in simulation,” Krishna told me.
The idea is to use large-scale randomization to make robotic models more robust. Randomization is already a standard technique — when teaching a robot to walk, companies will simulate thousands of different terrains for the robot to walk over. The Ai2 research group scaled it up for manipulation tasks: the researchers generated 5,704 hours of programmatically generated simulation trajectories across 94,200 distinct simulated environments. They also randomized other parts of the scene, like what cameras the robot had access to.
The results were promising, albeit somewhat narrow. When deployed on a real-world robot, the model was able to complete several tasks that involved rigid objects, like putting an apple on a plate. However, the researchers did not attempt more difficult tasks. As they explained in their paper:
We focus on rigid body and articulated object manipulation — tasks where modern simulators provide sufficient fidelity for transfer. Extending to contact-rich manipulation (e.g., insertion, peg-in-hole), deformable objects (cloth, rope, food), or tasks requiring accurate fluid or granular dynamics remains an open challenge. We believe that coupled with advances in physics-based and generative world model simulators, our recipe of massive-scale procedural generation may extend to these more challenging tasks requiring contact-rich dexterity and deformables.
Deepak Pathak, the CEO of Skild, has a similar view. When we talked in August, he argued that if a model is trained to adapt to a large enough variety of simulated environments and robotic embodiments, then it will be able to adapt to varied real-world situations as well. He hinted that future Skild releases would demonstrate such a capability. Later in the month, Skild released S1, which showed an impressive ability to pick up new tasks from humans.
Robot Week special: Click here to get 25% off an annual subscription.
However, most of the experts I talked with don’t share Krishna and Pathak’s optimism about simulation.
2026-09-03 00:24:29
It’s day three of Robot Week! You can click here to get 25% off an annual subscription.
Most people first heard about large language models after OpenAI introduced ChatGPT in 2022. But in the minds of many AI researchers, the key breakthrough came two years earlier with the release of GPT-3.
With 175 billion parameters, the OpenAI model was more than 100 times larger than its predecessor, GPT-2. It was trained on a massive 300 billion tokens. And as a result, it generalized far better than previous models. For the first time, a single model could perform a wide variety of tasks — from translating between languages to answering trivia questions — without task-specific training.
It took OpenAI two more years to develop the techniques that transformed this raw “base model” into a user-friendly chatbot like ChatGPT. And then it took a couple more years to develop the techniques — like long-context reasoning, tool use, and context management — that transformed those early chatbots into the powerful agents we have today.
In short, there was a long road from GPT-3 in 2020 to Claude Code in 2025. But for those who knew where to look, the potential of LLMs was already clear in 2020.
The robotics world is now traveling a similar path. Its “GPT-3 moment” came in July 2023, when Google announced a model called RT-2. To create it, Google trained a multimodal LLM to directly generate robot actions. RT-2 wasn’t Google’s first transformer-based robotics model — the company released a predecessor called RT-1 a few months earlier, for example — but RT-2 was massively larger than earlier models. RT-1 had 35 million parameters. The RT-2 models had billions of parameters.
And as with GPT-3, size mattered. The RT-2 team reported its model showed “significant improvements to generalization over objects, scenes, and instructions.” They added that the new model exhibited “a breadth of emergent capabilities inherited from web-scale vision-language pretraining.”
For example, researchers placed a can of Coca-Cola on a counter alongside framed photos of Snoop Dogg, Tom Cruise, and Taylor Swift. They then prompted the robot to “move coke can to Taylor Swift.” The robot grabbed the can and moved it toward Swift’s photo.
At the time, Karol Hausman was a member of the RT-2 team. In a March interview, he described this as a moment of “huge, huge excitement” because “the robot models had never had any of Taylor Swift in their data. It had to understand the concept of Taylor Swift, connect it to the image of Taylor Swift, and then connect it to the right motion that would move the Coke can to the picture of Taylor Swift, all from Internet data.”
“That was the moment where it clicked for us that it could actually work — where you could bring in a lot of prior knowledge from LLMs, from the Internet, and connect it to robot motions,” Hausman said.
Google dubbed RT-2 a vision-language-action (VLA) model. Both Google’s approach and the term VLA quickly became industry standards. But as impressive as RT-2 was, it also had significant shortcomings — shortcomings the industry has been working to remedy over the last three years.
The RT-2 breakthrough kicked off a robotics boom that’s been underway ever since. Big companies in both the US and China have poured resources into robotics. Numerous robot startups have been created, and several have raised hundreds of millions of dollars in venture capital. And the models powering most of these robots are based on the basic architecture Google pioneered back in 2023.
Google invented the transformer in 2017 and had been experimenting with large language models ever since. The company had also been working on robotics for many years. So combining LLMs and robots was an obvious research direction.
In March 2023, Google announced PaLM-E, a 12-billion-parameter model that was optimized for robotics (the “E” stood for “embodied”). PaLM-E was a vision-language model (VLM) — meaning an LLM trained to understand images as well as text. It had been trained to generate natural-language robot commands like “move the blue block to the left.”
But PaLM-E couldn’t control a robot directly. Google’s robots didn’t have enough onboard computing power to run a VLM as large as PaLM-E. So PaLM-E ran in the cloud, and it was designed to work with a second, smaller model that would run on the robot. This second model would translate PaLM-E’s English instructions into low-level robot commands.
The RT-2 team’s plan was simple: delete the smaller model and instead train PaLM-E to directly control the robot.1 RT-2 — like PaLM-E — was too big to run directly on a robot. So the team ran the model in a Google data center and had it send commands to the robot over the network.
Like any LLM, RT-2 worked by prompting. Google would send RT-2 a prompt like “What action should the robot take to move coke can to Taylor Swift?” along with an image from the robot’s camera.
RT-2 would respond with a sequence of numbers like “1 128 91 241 5 101 127 217.” The robot would interpret this as a command to move the robot’s gripper to certain x-y-z coordinates (like x=128, y=91, and z=241), rotate the gripper to a certain angle (roll=5, yaw=101, pitch=127), and open (or close) the gripper to a certain position (217).
Then RT-2 would get the same prompt again, but with a fresh image. The model would generate another sequence of numbers representing a new target position for the robot arm. The robot would move its arm another few inches. Then the whole cycle would repeat again. It might take dozens of iterations to complete a task like “move coke can to Taylor Swift.”
To transform PaLM-E into RT-2, Google had to teach the model how to generate low-level robot instructions. That required a different kind of training data.
To collect that data, Google built three test kitchens and purchased 13 robots. Over the course of 17 months, human workers teleoperated the robots as they performed tasks — picking up objects, opening drawers, placing objects in the drawers, and so forth — more than 130,000 times.
Training PaLM-E on this data gave RT-2 surprisingly broad capabilities. Robots could manipulate objects they hadn’t seen before. They could operate in new kitchens. And they could complete tasks on counters that were cluttered with “distractor objects” that weren’t needed for the assigned task.
Karol Hausman was excited by the RT-2 breakthrough, but he also concluded that Google wasn’t the right place to develop the technology.
“It became clear that the way to accomplish this is to create an organization whose sole purpose is to solve physical intelligence,” Hausman said in March. “It can’t be solved as priority number 20 in another organization.”
So Hausman became the CEO of a startup called Physical Intelligence. He was joined by four other members of Google’s RT-2 team and two others from outside Google.
According to Hausman, the team sought out “investors that are fully aligned with this starting as a research company and not being oriented around short-term revenue.”
“If we do this right, this is going to completely change the world and it’s going to be the most valuable business of all time,” Hausman said. “But you need to have the patience to let us do it the right way.”
There was a lot to do. RT-2 was a big improvement over previous robotic models, but it was still far less capable than the average human. Over the last two years, the Physical Intelligence (PI) team has been working hard to close that gap. The company has been remarkably transparent, publishing at least 10 papers describing their work. For this story, I read all the PI papers I could find — along with 20 more from other companies and academic labs.
I’ll use PI’s research as a lens to explain the evolution of VLA models over the last three years. During that time period, VLA-controlled robots achieved much better fine motor control. They gained the ability to perform complex tasks that take several minutes. And companies are exploring new ways to have models reason using images as well as text — which could unlock the ability to learn from videos of humans performing tasks.
At the end, I’ll discuss the view that VLA models are on the verge of being eclipsed by a new architecture called world models. PI co-founder Sergey Levine has a perspective on this that I find pretty persuasive.
Robot Week special: Click here to get 25% off an annual subscription.
Hausman was impressed that RT-2 was able to move a Coke can to Taylor Swift. But later in the same interview, he described it as “totally unimpressive” and a “pretty pathetic demonstration of what robots could do.” That sounds like a contradiction, but you can see what he meant if you watch the video:
2026-09-01 20:52:39
Today’s Robot Week article is sponsored by 80,000 Hours, a non-profit that helps early-career professionals make the most of their careers.
If you’ve been paying any attention to the robotics world in the last couple of years, you’ve probably noticed that humanoid robots are getting better at an impressive pace.
In October 2024, Elon Musk had several of Tesla’s Optimus robots serving drinks at an event unveiling Tesla’s new Cybercab.
“Optimus is not a canned video. It’s not walled off. The Optimus robots will walk among you,” Musk said at the event.
Then in February 2026, the Chinese company Unitree staged a stunning martial arts performance at the Spring Festival Gala in Beijing. A mixed cast of humans and humanoid robots carried out a perfectly synchronized, fluid dance routine. Robots performed spins, jumps, and even backflips.
It was a big improvement over the 2025 show, which featured robots walking stiffly across the stage while waving handkerchiefs.
Just last week, at the 2026 World Humanoid Robot Games in Beijing, a robot ran 100 meters in 8.86 seconds, crushing Usain Bolt’s human world record of 9.59 seconds. At last year’s competition, the fastest robot took more than 20 seconds to run 100 meters.
Demonstrations like these have impressed a lot of casual observers — and created a lot of anxiety about future job losses. If humanoid robots can already serve people drinks, perform elaborate dance routines, and outrun humans, how long will it be before they put millions of people out of work?
But if you talk to robotics experts — and I’ve talked to many in recent months — a more nuanced picture emerges.
As Physical Intelligence co-founder Karol Hausman put it, people (including himself) “are not very good at judging progress in robotics or judging what is impressive and what isn’t.” Sure, robots can do acrobatic maneuvers that are “very difficult for a human to do,” he said. But then “something as simple as picking up a Coke can turns out to be very, very difficult.”
Some of the most impressive demos of humanoid robots involve someone controlling the robot remotely — a process known as teleoperation. It seems pretty clear this was the case with those Optimus robots in 2024, for example. Tesla’s hardware was sufficient to act as a bartender, but its software wasn’t up to the task. So Tesla apparently hired human operators to control the robots remotely.
And while those Unitree robots’ dance moves were not teleoperated, they don’t tell us all that much about the robots’ capacity to do useful work. Most physical labor involves manipulating objects in the real world — packing boxes, hammering nails, flipping hamburgers, and so forth. As we’ll see, training a robot on physical manipulation tasks like these is much harder than training a robot to dance.
There are also broader challenges that transcend individual tasks. For example, human workers are extremely flexible — they can perform a wide variety of tasks, and they can learn easily while on the job. So far, nobody has figured out how to give AI robotics models the same capacity for generalization.
Today’s most impressive robotics demos involve tasks that take humans several minutes at most. But human workers also perform tasks that take hours — things like “rebuild this car’s engine” or “assemble those kitchen cabinets.” Training a robot to complete longer projects requires building skills unnecessary in short tasks, like the ability to keep track of what’s already been done.
Then there are a lot of practical economic and safety concerns that will become obvious once we try to deploy robots in the real world. Robots will need to work for hours without breaking down. They can’t be too expensive to manufacture, train, or repair. They need to be extremely safe to operate in proximity to human beings.
It will take many years — maybe even decades — to overcome all of these challenges. So yes, humanoid robots have made a lot of progress in the last few years. But there’s still a long road ahead.
A key challenge in robotics is predicting how the outside world will react to a potential robot action. In this respect, dancing is simpler than most other tasks because (as Bracket Bot CEO Brian Machado told me) “the floor doesn’t do anything.”
But while acrobatic robots are impressive to watch, it’s not actually that useful for a robot to dance or do backflips. Most useful work involves interacting with objects that move and change in response to a robot’s actions.
“The really, really core unsolved problem in robotics that unlocks 90% plus of the value is manipulation,” Theophile Gervet, president of the robotics startup Genesis AI, told me.
Picking up an object doesn’t just change its location, it can also change its shape. And different objects respond in different ways that are hard to model in a general way. Think about the different ways that a pillow, a bag of chips, and a glass of water behave when they are picked up.
In September 2025, the roboticist Benjie Holson (formerly Google X, currently OpenAI) announced the Humanoid Olympics, a list of 15 manipulation tasks that he believed would require researchers to “push the state of the art” for a robot to be able to solve.
Most of them would be trivial for an eight-year-old child to perform. Three of the tasks involved opening doors. Another was to make a peanut butter sandwich given bread and a closed jar of peanut butter. Perhaps the hardest task on the list for a human to perform would be to peel an orange.
Even with tasks this easy for humans, it was an impressive accomplishment when — three and a half months later — the startup Physical Intelligence announced that it had successfully demonstrated 10 of the tasks.
Having a robot company “do basically almost all of them in the first three months is wild,” Holson told Scientific American.
But Physical Intelligence’s performance came with caveats.
The researchers taught the robot how to do these tasks by puppeting a robot over and over until they could fine-tune a model to complete the task. To turn a sock inside out, they trained on 176 successful examples, or around eight hours of data. They peeled so many oranges that the researchers told Holson that the “corner grocery probably noticed the increase in orange sales and the one guy at the company who really liked mandarins was getting pretty tired of them.”
The robot took four to 10 times longer than a human to complete almost all of these tasks — while only succeeding 52% of the time!
None of this is meant to dismiss Physical Intelligence: its result was a genuine accomplishment. But even on these fairly simple tasks, robots are still far from human-level performance.
And it’s still easy to find tasks that are straightforward for humans but entirely beyond the abilities of robots. In January, Holson released a new set of manipulation challenges. While these tasks are more difficult, they are still straightforward for most adults: make a bed, hammer a nail, catch an egg without breaking it.
One category of manipulation task in Holson’s new list is worth noting: those that take a long time. Many humans are able to complete physical tasks which take hours — such as putting a bed together or painting a room. But like current LLMs, robots today struggle to complete longer, many-part tasks.
Holson included two tasks he dubbed “long horizon”: taking out the trash from a home and making an egg sunny-side up. Neither task took longer than five minutes.
I imagine it will take a lot of work to extend the capabilities of robotics models past these several-minute tasks. Current robotics models only have a limited capacity to remember what actions they’ve previously taken — for instance, Physical Intelligence’s most recent model can remember up to 15 minutes using a method to compress its previous observations into text.1 Models also aren’t yet reliable enough to string tens or hundreds of diverse subtasks together.
When I asked Gervet about long-horizon tasks, he said he wasn’t worried about it. “I think the job of a robot foundation model company, whether it’s full-stack or not, is to build more low-level five- to 10-minute horizon tasks” rather than to completely solve robotic reasoning. He expects that general-purpose AI companies will solve long-time planning and execution.
But it’s not obvious that it will be possible to cleanly separate short-term tasks from longer-term planning. Often, as humans work on individual subtasks, we learn things that cause us to change our overall plan. A system where different models are responsible for long-term planning and short-term task completion may lack our capacity for real-time adaptation.
Robot Week special: get 25% off an annual subscription.
Holson announced his second batch of challenges more than seven months ago. As far as I can tell, no one has announced a solution to any of them. Maybe that will change in the coming months. But even if manipulation capabilities increase dramatically, there is an important interlocking challenge: generalization.
Robot companies have figured out how to train a robot on a specific task by pouring tremendous effort into that one task. But most individual tasks where this type of high-effort approach makes economic sense have already been solved using conventional automation techniques. So a key bottleneck is whether robot capabilities can generalize to new environments without significant training data.
For instance, when I visited China in May, I saw a Galbot robot working in a pharmaceutical warehouse. Its task was to take boxes off the shelf one at a time and place them in a chute for delivery workers.
Despite the simplicity of the task, it took the robot around 40 seconds to put each item into the chute. (Galbot said that newer deployments are twice as fast.) Every time a new item was added to the warehouse, Galbot had to train the robot to be able to pick up that specific new item as well — though the company said it only takes five minutes of training.
Basically every humanoid deployment today takes a similar approach. Companies choose a task with enough variability that traditional automation methods won’t work. But there can’t be too much variability, or else contemporary AI methods won’t work either.2 And each deployment typically requires a ton of setup and training effort.
Some startups expect that AI will make it easier to deploy robots across a broad variety of tasks. Jagdeep Singh, the CEO of Rhoda AI, told me that the company has “literally over 100 different use cases” in manufacturing and warehousing they’re working on bringing to deployment.
When I went to Nvidia GTC in March, several roboticists told me that the most impressive demo they saw was from a company called Generalist.
Generalist showed a robot inserting and removing a phone from its box using two robotic arms. While the task itself is pretty difficult for current robots, what impressed people the most was how little time and effort Generalist put into the demo. The company claimed that it only had a handful of days to prepare the demo and, notably, did not train the robot to do the task in the exhibit hall itself.
The fact that this impressed roboticists underscores how weak past models have been. Historically, many robotics demos have been extraordinarily brittle — for example, a change in lighting could cause them to fail.
While Generalist CEO Pete Florence told me that generalizing to a different background environment is basically “solved” today — at least for his company — there are other types of generalization that are still difficult. He gave me a hypothetical example: “Let’s say that you were trained to take one type of backpack that had a zipped opening and put a lunchbox into it, and then somebody gives you a different backpack and it has buckles.”
It’s unclear if current robots would be able to generalize to this new setting.
The state of the art in robot generalization is rapidly advancing. On August 20, Generalist announced that its latest model was capable of completing a new task from just one human demonstration. Last week, the startup Skild AI said that its model S1 could also complete a new task from a single video demonstration — on tasks up to ten minutes long.
A couple of companies have also publicly announced they will start deploying robots into homes — which are extremely diverse — by the end of 2026. But I expect that these deployments will be quite limited. And some of them are likely to rely heavily on teleoperation.
I’ve focused thus far on challenges with general manipulation because it’s the biggest barrier to building useful robots, no matter the form factor. But humanoids aren’t just two hands manipulating objects: they also have legs to maneuver around.
Legs have engineering challenges of their own. The biggest one is safety.
Most current legged humanoids have to actively maintain balance. If, for whatever reason, the motors in the legs stop running, the robot will fall down, potentially injuring people in the process.3
And sometimes, the motors will stop running.
In 2025, researchers from Stanford and Simon Fraser University developed a system called TWIST that allows a human operator to control the full body of a humanoid by having the robot mimic the operator’s body position. The system worked well, but had a major limitation: overheating. The researchers wrote “our robot’s motors tend to overheat after 5 to 10 minutes of continuous operation, especially during tasks that require crouching, which necessitates cooling periods between tests.”
If the researchers ran the robot too long, it would shut down and collapse.
Overheating is a major challenge for Unitree’s G1, one of the most common humanoids in the world today.4 A year or two ago, a G1 “could carry a box of a couple kilograms for maybe five minutes at most. Then it would overheat, and you’d have to let it sit in the corner for 30 minutes — sometimes a full hour — before doing another five minutes,” according to Reyk Knuhtsen, robotics lead at SemiAnalysis, on a podcast in July. Unitree has improved the G1’s design, but heat is still a challenge. Knuhtsen said that operators can now get five to 15 minutes of work with 10 minutes of rest.
But overheating isn’t the only reason a humanoid robot might fall over. A robot’s battery might unexpectedly die, as might have happened with Tim’s robot dog. Or there might be a subtle design flaw that only pops up deep into large-scale deployment.
The CTO of Agility Robotics, Pras Velagapudi, told me that at one point, Agility faced a perplexing failure. A few robots that had been out in the field for a while suddenly started having a problem. When a robot crouched, one of its legs would fail and the robot would fall over. If the robot stood back up, however, it would work fine.
It turned out that in the legs, “there was a particular printed circuit board which was flexing over time,” Velagapudi said. Eventually, crouching would disconnect one of the cables. Straightening the leg would push it back in. This was a very subtle issue that only arose after thousands of steps.
To prevent failures like these from endangering humans, companies have to make careful design and deployment decisions.
Agility currently keeps its deployed humanoids in safety enclosures away from human workers. The company plans to allow its humanoids to work around humans soon by having the robot slow or stop its movements whenever a human gets too close.
Other companies have instead restricted their robots’ designs. For instance, 1X, which aims to put legged humanoids into home environments by the end of the year, has designed its robot to be light and mechanically compliant to reduce the risk of injuries if the robot does fall. Nevertheless, 1X told the Wall Street Journal that families with young children won’t be able to participate in its testing program later this year.
Another popular choice is to use a wheeled base instead of legs. Wheels are less expensive to engineer and manufacture. They are also passively stable, meaning the robot won’t fall over if it loses power. But wheels are also a lot less versatile — they can’t go up stairs or move through uneven terrain.
Ultimately, I expect that companies will figure out how to make legged robots safe and useful at scale. But these are tricky engineering challenges that don’t necessarily benefit from quicker AI progress.
Agility’s experience with mysteriously failing robotic legs is a perfect illustration of a broader point: turning a working demo into a broadly deployed robot is incredibly difficult.
“I have rarely seen a new technology that is less than ten years out from a lab demo make it into a deployed robot,” legendary roboticist Rodney Brooks wrote in 2024. “It takes time to see how well the method works, and to characterize it well enough that it is unlikely to fail in a deployed robot that is working by itself in the real world.”
So it will probably take a while to turn today’s impressive demos into shipping products.
We’ve seen this story before. “Right now, in learning for robotic manipulation, it feels like it felt in 2015 in self-driving cars,” the roboticists Stefanie Tellex and David Watkins wrote in a recent blog post.
In the mid-2010s, dozens of startups flooded into self-driving cars and quickly achieved impressive demos and test deployments. It really seemed like companies might be able to “solve” self-driving within a few years.
In 2015, Chris Urmson, then head of Google’s self-driving project (which later became Waymo), gave a talk where he said, “My oldest son is 11, and that means in four and a half years, he’s going to be able to get his driver’s license. My team and I are committed to making sure that doesn’t happen.”
The next year, Ford announced it would mass-produce a car without a steering wheel by 2021. Lyft’s president John Zimmer predicted that “within five years a fully autonomous fleet of cars will provide the majority of Lyft rides across the country.” He added that by 2025, driverless taxis would become so cheap and ubiquitous that “owning a car will go the way of the DVD.”
A decade later, Waymo has active robotaxi deployments in 11 cities, but you still can’t buy a fully self-driving car or access one outside of a few urban environments.
It turned out that there is much more to scaling robotaxis than just making a car that drives itself most of the time. While Waymo has broadly succeeded at making a self-driving car that crashes less than human drivers (at least within its operational environment), there are still a huge number of barriers to actually scaling its deployment, from legal pushback to the challenge of actually procuring and maintaining a robotaxi fleet.
Perhaps the biggest challenge is that there are an enormous number of edge cases in the real world that are very difficult for autonomous vehicles to understand and deal with appropriately.
Humanoid robots won’t face exactly the same deployment challenges as autonomous vehicles. For example, a mistake by a humanoid robot may be less likely to kill someone. But a lot of the same bottlenecks apply to both types of robots. The real world is extraordinarily complicated, and it takes a huge amount of effort to go from demonstrating a capability to deploying it at scale.
Historically, a robot’s sticker price has been well under half the total cost of deploying it. While the AI methods we’ve covered above will help lower the cost of new deployments by making robots more general, they have their own challenges, especially around debugging neural network failures.
This doesn’t mean that the AI methods being developed today aren’t important. “There is a real breakthrough,” Tellex and Watkins wrote in their essay. Many problems that seemed basically impossible five years ago — like having a robot fold a shirt — are mundane today. The videos of Unitree’s dancing robots reflect massive progress in robotics hardware and training techniques.
But there’s still a long road ahead from impressive videos to ubiquitous, useful robots.
Robot Week special: get 25% off an annual subscription.
Unlike with LLMs, robotics models can’t just use their context window as a memory system. Robotic sensors create a lot of data — up to a terabyte a day — so developers need ways to compress past sensor observations into usable memories.
Indeed, as Chris Paxton notes, a substantial proportion of humanoid deployments fall into four categories: rigid or semi-rigid pick and place, package reorientation, box packing, and clothes folding.
I’m unsure whether overheating is specifically a Unitree issue, or whether other robot designs have this problem. On the one hand, Unitree is probably the most popular company for researchers buying humanoids, so we know much more about the limits of its hardware than of its competitors. (And other companies have occasionally referenced heating challenges.) But Unitree also optimizes heavily to make cheap robots, so the quality of components is lower, potentially exacerbating heating issues.
2026-08-31 21:57:26
This week we’re publishing a series of five articles about the state of robotics — we’re calling it Robot Week. It’s the most ambitious project we’ve ever undertaken. Over the last nine months, I’ve read dozens of research papers, Kai has gone on five reporting trips (including one to China), and we even purchased a robot dog from China!
Our goal is to help readers understand the pace of progress in robotics — and the implications for the economy. How close are today’s robots to human-level performance? What are the biggest problems remaining to be solved?
Our first two articles (including this one) will be free, but the final three will be exclusively for paying subscribers. This week we’re also offering 25% off an annual subscription. So it’s a great time to upgrade. Click here to get the 25% Robot Week discount!
On a sunny morning in June, I walked to work with a quadruped robot beside me. I’ve never gotten more attention from strangers.
A bunch of people snapped pictures of my robot dog. Several people asked me questions. Was it mine? (Yes.) Did I build it? (No.) Was it being used for surveillance? (No.)
Biological dogs kept a safe distance from my mechanical companion. Some growled or barked at it.
Children were fascinated. I paused and had it do tricks for several of them: “shake hands,” do a handstand, or leap into the air.
I live in a leafy Washington DC neighborhood called Mount Pleasant. The neighborhood lives up to its name, so my morning commute is mostly downhill. My robot companion, manufactured by the Chinese company Unitree, walked the two miles to my office near the White House with battery capacity to spare.
I recharged the battery during the workday, but it still struggled on the afternoon walk home. We were now mostly walking uphill, and the temperature had risen to 87°F (30°C). The robot’s steps seemed increasingly labored as the path got steeper.
As I entered my own neighborhood, I glanced at two indicators in the corner of my phone screen. One showed that the robot’s battery was at 5% — dangerously close to empty. The other showed the internal temperature was 84°C — 183°F.
We were within sight of my front door when the robot suddenly collapsed. I’m not sure if it ran out of power or overheated, but either way it didn’t shut down gracefully — it rolled onto its back with its legs in the air.
Several people have asked me what my robot is useful for, and the honest answer is not much.
Unitree quadrupeds are widely used for academic research and they are sometimes used for entertainment. But there don’t seem to be a ton of practical applications.
Wheeled robots are faster and more energy-efficient, making them better for deliveries. Flying drones are a better choice for a lot of surveying and inspection work. My robot has no arms or hands, making it mostly useless around the house.
But my robot did have one big thing going for it: it was astonishingly cheap.
I paid $4,017. That’s more money than I’ve ever spent to review a product. But it’s also far less than I would have had to pay for this type of robot a few years ago. And it’s cheap enough that people may find uses for it that wouldn’t have made sense at higher prices.
Sometimes what changes the world isn’t the invention of a new technology, it’s figuring out how to make it affordable enough for a mass market. Xerox built the first personal computer with a graphical user interface, but companies like Apple, IBM, and Microsoft made the technology mainstream. Perhaps Unitree will play a similar role for quadruped robots — though as I’ll discuss later, Unitree’s robots now face legal restrictions in the United States.
Unitree has also used quadruped robots as a stepping stone to another market that could be much more important — humanoids. Unitree launched its first humanoid robot in 2023. Leveraging its experience making cheap quadrupeds, Unitree is able to sell humanoid robots for as little as $13,500.
That’s still way out of my price range, which is why I bought a robot dog instead. But it’s dramatically cheaper than any humanoid you could buy a few years ago. And the low cost has made Unitree a global leader in humanoid robots.
Financial markets are bullish. Unitree debuted on the Shanghai stock market on August 19. On its first day of trading, shares shot up more than fivefold. It has lost some altitude since then, but at Monday’s closing price, the company was still valued at $34 billion — more than triple its valuation before the IPO.
Earlier in June, I visited Prof. Xuesu Xiao, a roboticist at George Mason University.
“Ten years ago, only a very small set of research groups were working on quadruped locomotion because they were the only people on the planet who could build a quadruped,” Xiao told me. “Then Unitree came onto the market. And it basically democratized the entire world of quadruped locomotion research.”
Xiao gave me a tour of his lab, which had four Unitree quadrupeds. Each cost around $15,000. The lab also had a quadruped robot called Spot. It was made by Boston Dynamics — widely seen as the industry leader before Unitree came along. But Spot is expensive, starting around $75,000.
Unitree founder Wang Xingxing developed a crude quadruped robot called XDog for his master’s thesis at Shanghai University.
“Marc Raibert from Boston Dynamics is my idol,” Wang told IEEE Spectrum in March 2016. “Their papers helped me to design XDog.”
Early Boston Dynamics prototypes — with names like BigDog and WildCat — had gasoline engines and hydraulic actuators. This made them too large, noisy, and polluting for practical use.
In that 2016 interview, Wang said that his goal was to “make quadruped robots simpler and smaller, so that they can help ordinary people with things like carrying objects or as companions.”
Boston Dynamics officially unveiled an electric quadruped called Spot in June 2016. But Spot didn’t become commercially available until 2020.
That created an opening for Unitree, which Wang founded in May 2016. The company’s first quadruped products — Laikago in 2017 and AlienGo in 2019 — were powered by electric motors. Designed for research labs, they cost tens of thousands of dollars.
In 2021, Unitree launched the Go1 line, which started at $2,700 for the “Air” model. In 2023, Unitree unveiled the Go2, which was even cheaper — $1,600 for the Air and $2,800 for the more capable Pro.
Today, Unitree’s cheapest robot — the Go2 Air — is more than 97% cheaper than Spot. But this is not an apples-to-apples comparison; Spot is larger than Unitree’s Go2 line and can carry heavier payloads.1
Still, the fact remains that Unitree made quadruped robots affordable to many people who could never afford Spot — including me! My wife never would have let me spend $75,000 or even $15,000 on a robot dog. But I convinced her to let me spend $4,017 (after tariffs and shipping costs) on a Go2 Pro.2
Last year the firm Simplexity tore a Go2 robot dog apart and made a video showing what they found.
Each of the robot’s four legs is powered by three motors. There’s a motor in the shoulder that controls the angle of the legs and a motor driving each of the two leg segments. “With three motors, you get a really dynamic range of motion,” said Luis Elenes, a mechanical engineer at Simplexity.
“They use all the same motors throughout,” Elenes added. “All four shoulders are identical in arrangement. There’s tons of benefit to that. There’s reduced cost, there’s reduced part count. The more you can reuse motors and reuse parts, the simpler your design gets, and the more robust overall your design will be.”
Jakub Bartoszek, an expert on motors and legged robots at the startup MAB Robotics, argues that Unitree also saved money by using low-quality components. The motors in Unitree robots — many of which are made in-house — tend to wear out more quickly than those in some other robot brands, he told me. But the cost savings make the robots affordable to more people.
Unitree seems to have designed its robots for easy repairs when they wear out.
“They want these legs to be serviceable in the field,” Elenes said. Thanks to the simple way the robot’s legs are attached to its body, “you can swap out a leg pretty easily. I would guess it would take you less than a minute.”
Unitree also gets significant support from the Chinese government, including tax credits and large-scale purchases by public institutions. It’s hard to quantify the scale of this support because the Chinese system blurs the line between public and private, military and civilian. But a supportive policy environment clearly helped Unitree to scale up its manufacturing operations and thereby drive down the cost of each robot.
Unitree has another counterintuitive cost-saving strategy: using large and powerful motors. More powerful motors aren’t inherently cheaper, of course. But they allow Unitree to save money on another component called the reducer that can be even more expensive
Suppose you have a lever where one side is five times longer than the other. If you push the long side down by five inches, the short side goes up by just one inch. But force is magnified: 10 pounds on the long side becomes 50 pounds on the short side.
Many robots use gear systems called reducers that serve the same function; they convert the fast, relatively weak movement of an electric motor into a slower but stronger movement of a robot’s body parts.
Traditional industrial robots require extreme precision, so they tend to have large reduction ratios.
A page on the Boston Dynamics website, for example, indicates that two of the motors in Spot’s legs are paired with reducers that have gear ratios of 51-to-1. In other words, the motor would need to do 51 full rotations in order to produce one full rotation of a leg joint.
This enables Spot’s movements to be very precise while magnifying the power of Spot’s motors. But it also has some disadvantages. One is cost. Reducers with 51-to-1 ratios are complex and — as a result — tend to be significantly more expensive than reducers with lower ratios.
Another is rigidity. When a robot’s arm encounters physical resistance, it should yield gracefully — a property called backdriveability. It’s also helpful if a robot can sense this kind of physical resistance electrically. Robots with higher gear ratios perform badly on both these fronts; their limbs feel stiffer and they are less sensitive to physical resistance.
In contrast, Unitree uses large motors combined with a simple 6.33-to-1 gearbox. This makes its robots more nimble and dynamic while reducing costs. The simpler design is also easier to simulate, making Unitree robots easier to train.
Unitree’s decision to use a lower gear ratio is part of an industry-wide trend. High gear ratios made sense for industrial robots that were programmed to perform simple, repetitive motions. In contrast, many modern robots perform complex actions in unpredictable environments. This requires sophisticated control software that can respond flexibly to changing conditions. The software is constantly making small corrections anyway, so it can compensate for less precise hardware.
One of the most impressive and crowd-pleasing capabilities of my Go2 Pro robot dog is handstands. The robot can balance on either its front or hind legs indefinitely, and can even move forwards, backwards, or side to side while doing so.
So it was natural for Unitree to expand into humanoids. Unitree first debuted a humanoid called the H1 in 2023. The next year, Unitree unveiled a cheaper and more capable robot called the G1.
I got to see one when I visited Prof. Xuesu Xiao’s lab in June. He paid around $50,000 for a research-grade EDU version. The consumer version starts at $13,500. This is shockingly cheap given the robot’s capabilities.
A humanoid robot is more than a quadruped standing on its hind legs. My Go2 quadruped robot has three motors per leg, for a total of 12. The cheapest G1 humanoid has five motors in each arm, six in each leg, and one in the torso, for a total of 23. The leg motors in a humanoid robot also need to be more powerful, since it’s more work to balance on two legs than to stand on four.
Still, I suspect that Unitree’s experience with quadrupeds gave it a leg up on humanoids. By the early 2020s, Unitree had been shipping quadruped robots for several years. The company had deep expertise in actuator design and an extensive network of suppliers.
According to filings connected to Unitree’s August stock offering, the company’s quadruped revenue tripled between 2024 and 2025 — from RMB 230 million ($34 million) to RMB 697 million ($104 million). The comparable figures for Unitree’s humanoids were RMB 107 million ($16 million) and RMB 867 million ($129 million) — an eight-fold increase.
Humanoids accounted for 51% of Unitree’s revenue in 2025. I expect this figure to be even higher for 2026.
“We are witnessing the birth of another Chinese hardware giant,” wrote a team at SemiAnalysis in June. “Three years ago, Unitree was a quadruped company. By last year, they parlayed quadruped dominance into creating and leading the humanoid market.”
The SemiAnalysis authors draw a parallel to DJI, the Chinese company that dominates the global drone market.
“DJI’s Phantom 1 shipped January 2013 at $679, and was not a fully-fledged product at the time,” the SemiAnalysis team writes. “It had no built-in camera, no gimbal (stabilizer), ten minutes of flight, no live video feed, but it was roughly half the cost of the build-it-yourself drone.”
Like DJI in 2013, Unitree’s robots today have a lot of rough edges. Certainly mine does.
The app to control the robot is buggy. Features Unitree showcased in its launch video — like climbing stairs and following a human owner — don’t work well in real life. If I turn the robot on with its legs slightly out of place, they will often thrash around wildly and the robot will wind up on its back.
Then there was the time my robot collapsed just feet from my house. A more polished product might have detected the low battery (or high temperature) and shut itself down gracefully.
But for bleeding-edge technologies, this kind of polish may not be very important. Far more important is getting costs down.
“DJI chose to inhouse the most expensive and technically difficult component first: the flight controller,” SemiAnalysis writes. “Later on, DJI brought inhouse the gimbals, motors, and ESCs.”
DJI figured out how to make these components more cheaply than they could be purchased from external suppliers, which in turn allowed it to undercut other drone makers. And that expanded the market for DJI’s drones.
Before DJI came along, “professional aerial photography was the domain of helicopters and Hollywood second-unit teams, but now, small businesses could perform this on their own. As such, whole new markets were unlocked for DJI, like real estate listings, wedding videos, local news, agricultural surveying.”
There’s a flywheel here: the more units a company sells, the better deals it can negotiate with suppliers and the more money it can spend optimizing its manufacturing process. Larger sales volumes also allow a company to bring more components in house. This allows the company to push costs even lower, which will mean more sales and even more money to invest in improving the production process.
Over a few years, this flywheel helped DJI to dominate the global drone market. SemiAnalysis argues that Unitree is on track to do the same thing for legged robots.
According to SemiAnalysis, Unitree has developed its own motors, gearboxes, lidar sensors, and cameras. “Unitree’s self-produced motors can run as low as 30-40% of equivalent Western motors,” SemiAnalysis reports. “They now make some of the cheapest humanoid gearboxes in the world.”
This could enable Unitree to take over the global market for quadruped and humanoid robots in much the same way that DJI took over the global drone market.
Of course that’s not guaranteed to happen. Unitree has a number of Chinese rivals. Unitree is the global leader in quadrupeds, but a Chinese rival, AgiBot, has sold more humanoid robots than Unitree in recent months. There are also many smaller robot companies in China that could challenge Unitree and AgiBot in the coming years.
But Unitree does not face much competition in the United States or the West more generally. Boston Dynamics sells excellent robots, but they tend to be significantly more expensive. There are a number of American startups aiming to build humanoid robots, including Tesla, Figure, and 1X, but consumers cannot buy a robot from any of these companies today.
That has alarmed some American policymakers. In June, a bipartisan group in the House introduced a bill to restrict importation of Chinese robots. The official press release for the legislation mentioned Unitree by name. Then in July, the FCC imposed broad new regulations on foreign-made robots that are likely to impact Unitree and other Chinese companies.
However, the robot market is global. Even if Chinese companies get locked out of the US market, Unitree or one of its Chinese rivals could still come to dominate the global market for humanoid robots.
There’s an obvious parallel here to electric vehicles, where Chinese companies like BYD are effectively banned from the US but are making rapid gains in the rest of the world. There’s a risk that a ban on Chinese robots could have a similar impact: rather than hampering the growth of companies like Unitree, it could make the US a robotics backwater.
If you enjoyed this article, please support our work with a paid subscription. This week we’re offering 25% off an annual subscription!
Unitree makes larger quadruped robots like the B2, which seems to start around $85,000. It is unclear to me how to do an apples-to-apples comparison with Spot, which also comes in multiple configurations and is not priced transparently.
To avoid logistical hassles, I bought my robot from an eBay vendor in Pennsylvania. This added a few hundred dollars to the cost.