Ask any robotics founder in China what stands between their humanoid and a real job, and the answer is the same word three times. Data, data, data. The models are good enough. The hardware is cheap enough. What nobody has is enough examples of a robot doing the work.
The standard fix is brute force. You hire people to wear motion capture rigs and repeat one task, thousands of hours per skill, and you end up with a robot that can do exactly that one thing in exactly that one setup. It works. It also means every new customer scenario starts the clock again.
At the World Robotics Conference in Beijing I spent an afternoon at the booth of DexForce, a Shenzhen company I have visited several times, because they attack this from the other end.
Train Once in Simulation, Deploy in Hours
DexForce was founded in 2021 and now has around 300 people, roughly 60 percent of them in R and D. That ratio is the tell. When I walk through their office in Shenzhen it looks less like a factory and more like a research lab that happens to build arms.
Their system is a generative simulation environment. You type a description of a scene, the engine builds the 3D assets, and the robot learns the task on synthetic data before it ever touches the real object. What used to take months of collecting real demonstrations now runs in hours.
We collect data, we train a skill, and it takes months. Now we train with synthetic data from our own simulation engine and deploy in hours.
On the screen next to me the instruction was as plain as it sounds: put the cans upright. The robot in the simulation figured it out, and the robot on the floor did the same thing with real cans a minute later.
Four Demos, One Argument
The booth was built to answer the four objections a buyer actually raises.
Can it handle a customer? The robot made me an ice cream, then popcorn. Simple, but it is a machine designed for humans, operated by a machine, in front of a crowd that keeps moving.
Can it run all day? They put a robot in front of a Chinese chess board and let it play visitors. Not a party trick. Chess is a long horizon task under constant visual change, which is the closest thing on a trade show floor to an eight hour shift.
Does it break when the world moves? The retail demo was a full checkout. The robot scanned barcodes rather than working from a stored product list, gripped bottles of different shapes, coordinated both arms, and picked items back up when they were knocked over. DexForce calls that zero shot generalization. In a shop it means nobody has to teach the robot every new SKU.
Does it do real work? The last station was a production line with two robots packing, unpacking, assembling and inspecting.
Why a Humanoid and Not a Cobot
The industrial case is the one Western buyers push back on hardest, and the honest answer has nothing to do with the robot looking human. A cobot on a fixed cell means you design the cell around the robot. Move the job and you rebuild the station. A mobile two armed robot walks to the next bench. Today it packs, next month it inspects, and the line stays as it is.
That flexibility is worth more than speed in most factories I visit. It is also why the deployment stories that hold up are the boring ones: dangerous stations, repetitive stations, the night shift nobody wants.
What I Would Still Ask
A trade show floor is a controlled environment, and every company there is showing its best day. The number that matters is not how fast a skill trains in simulation but how many hours it survives in a real store or plant without an engineer standing behind it. That is the question I put to every manufacturer we visit with clients, and it is the one that separates a demo from a product.
What is no longer in question is the direction. The training bottleneck that defined humanoid robotics two years ago is being engineered away, and it is being engineered away fastest in Shenzhen.
[00:00] Hello. [music] If we want to bring robots into our home, into our hospitals, into our factories, into our daily lives, there are three things that are most important. Data, data, and data. Data is a big bottleneck, but it's still very important. And right now I'm here in Beijing at the World Robotics Conference at the booth of one company that has a really special approach to data. It's called DexForce. They are from Shenzhen. It's a company I have visited a lot of times. And today I want to show you how they tackle this problem. >> [music] >> Here with me is Baiyu. Baiyu, thanks for the invitation. >> Hi. >> Tell us a little bit about when was
[00:47] DexForce founded? >> Uh we founded in 2021 and specialized in body AI humanoid robots. >> Yeah, and this is one of your humanoids. And uh your founder is one of the top 2% scientists in whole China in in this robotics field. So, what is his specialty? >> He specialized in body AI and 3D perception, data collection. And we also have 300 members in our uh company now. About like 60% of them are R&D members. >> Yeah, this is what I saw in Shenzhen. That 60% of their staff are actually engineers, right? And they work in the research and development. That is why they are strong. We will see a lot of robots in the future in the service and industry. And this robot from DexForce
[01:35] is already collecting a lot of data. He can use the ice cream machine and also the popcorn. So, I'm very sure that this robot we will see in the future in a lot of scenarios in real life. >> [music] >> So, here we go. That's a fresh ice cream I got from the robot right here and it looks perfect, right? The way you train it is so different to other companies. Most companies, they will have human beings who repeat one task for 10,000 of hours so they get the data. But you have the matrix which is your training simulation system, right? Can you tell us a little bit about it? >> Yeah, actually we in the greatest software to hardware into our product matrix. And that means we from the
[02:23] robotic brand and to the robot body we can integrate it at all. This demo we already scalable and repeatable landed. So that means you can see our robots is actually in working as Thomas said before. We collect data need training a skill and it need months. Now we use our own self developed a simulation AI engine. That means training our robots with the synthetic data that can deploy in only like hours. >> This is crazy and this is the China speed I'm talking about all the time. is that they will shorten the time for training the robot with a simulation software. >> So you can see here is a generative simulation user interface and it's basically based on the Textor's physics engine developed by Textor's here and you can input your scene description
[03:11] here. The engine will generate 3D assets accordingly. >> Okay, so so here we can see now the task we we gave it, put the cans upright. It's doing it right now which is crazy. So you don't need to train them in real life. With this simulation user interface, you can train it once and then it knows in the future how to put up all the bottles, right? >> Yes. >> As Barry said, it's already deployed in a lot of use case scenarios. You have a lot of customers who use it like in this setting like in shop. But what I'm also very interested in industrial scenarios, right? >> Yes, yes. Let us see. >> Let's go and have a look. What we see here is the robotics version
[04:01] of AlphaGo. We all know this super brain who plays chess with all the world masters and here we have the Dexforce version of this robot who's playing Chinese chess with other people. Whenever you are lonely, he will be here for you to play a game of Chinese chess with you. >> Why we develop this demo is because we want to show our robot's long-term operation capability. >> So actually this is not for just playing. What you can see here is the very strong vision model. As you said the long-term operation, right? So I think for industrial use cases this is really important. It should be fast, it should be reliable and it should be precise. Don't just treat this as a game of Chinese chess. This is actually could be an industrial use case. What we can see here is the demonstration of the
[04:50] brain of the Dexforce robot. This robot actually can work in a retail store so we can see the brain is so smart. have to know all the SKUs in one shop or a factory. It just can take it by themselves, scan the barcode so you don't have to put all the data of the SKU inside. We have different forms of packaging and it can grip the bottles, it can grip the both arms cooperate with each others and cool can do the automatic checkout for 24 hours and they're also very friendly to you. >> Actually for this demo we want to showcase our zero-shot generalization capability. No matter it is two hit or is knocked down, it can always pick it up.
[05:36] >> For the factory we actually also have a use case right here. Let me show it to you. So we have two robots working here on a production line. Can be packed, can be unpacked. >> For this >> demo assembly and inspection product line, FOV is more large and the grasping way is more flexible. >> The flexibility is seen. Today it can do this job, but then you can move it and it can do another job. Why you need humanoids on a production? Because if you just have the cobots, you need to redesign the whole factory and the whole space, but these robots are very flexible and you can easily move them from one place to another. >> Yes, also some dangerous or some very boring stuff can let our humanoid robots do. >> I'm very sure that humanoids will not be here to replace human beings, but to do
[06:24] the work no humans should do. For example, very dangerous places, uh very boring tough repetitive tasks. And this is not the future, this is already reality because the robots of Dexforce, they are already collecting data, they're already working, and they're already doing services and doing real work in real factories, in supermarkets, in shopping malls. So next time you come to China, you can have a look at the shopping mall, at the factory, and also soon we will see them all over Europe and the rest of the world. Thank you so much, Bei Yu. >> Hello. >> [music]
Get These Insights Every Monday
Join 18,000+ professionals reading Asiabits. Free, every Monday, straight from Shanghai.
Subscribe Free →
