
Startups like QJ Robots, Lumos, and Ace Robotics are leveraging industrial data to develop autonomous systems, despite skepticism over commercial readiness.
AI-generated summary
The robotics industry is shifting from text-based AI to 'world models' that simulate physical environments. Startups are utilizing industrial manufacturing ecosystems in China to gather training data.
QJ Robots, a startup that's helping robots learn human skills, has been backed by Temasek, founder and CEO Haichuan Gao told me.
The company's AI model focuses on helping robots predict the world around them and perform tasks more efficiently.
QJ Robots claims it's already made more than 100 million yuan ($15 million) from deploying the model to more than 100,000 robots, mostly by companies in mainland China.
That gives the startup a large base from which it collects data every month, Chief Technology Officer Tianren Zhang said. But it's not enough — he said more kinds of data are needed to train the model effectively.
Zhang and CEO Gao both have PhDs in automation from Tsinghua University, where their three-year-old company got its start in the school's brain computing research center.
It's all part of the tech industry's growing focus on world models, which aim to imitate the physical world more closely than ChatGPT-type "large language models" that focus on text generation.
One of Beijing's approaches is to encourage industrial giants to integrate and develop AI capabilities. It's a manufacturing ecosystem edge that local startups are tapping into.
Another Tsinghua alumnus, Chao Yu, launched industrial-focused robotics company Lumos two years ago. Its backers and partners include Mitsubishi Electric.
Lumos has launched its own center for collecting training data. But Yu said that earlier this year, he realized that for factories and other businesses to use more robots effectively, they needed a platform to translate data and AI instruction into task completion. The startup last week officially launched its NexCore system that claims can work with a range of robots, not just those from Lumos.
But the race to build the best autonomous robotics solution is intensifying.
Just one year ago, Ace Robotics launched with SenseTime Co-Founder Wang Xiaogang as the startup's chairman.
An effective humanoid robot solution, he said, must combine hardware, models and data with use-case scenarios. Real-world data is critical, Wang said, as using online videos for training can introduce unrealistic special effects into the model.
Ace Robotics' products include wearable sensors for precisely capturing data from human activity.
By the end of next year, Wang predicts the industry will have gathered enough data for a breakthrough moment, similar to how ChatGPT's launch in late 2022 transformed what businesses could do with AI.
It's less clear whether markets will be willing to wait that long. Unitree's shares tumbled after founder Wang Xingxing sought to lower expectations for humanoid commercialization, while the public visiting conference exhibition booths were often bluntly unimpressed.
Dancing robots aside, machines folding clothes or selling water moved slowly and stiffly. One person waiting to buy a bottle of water noted in Mandarin Chinese that people were only patient because it was a robot.
"A human would definitely be scolded if it took this long."
AI outlook — possibilities, not facts
Industry breakthrough in data collection by end of 2025.
Speculative · Within months

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