Chinese startup DexForce uses virtual training to cut humanoid robot costs
Chinese startup DexForce trains humanoid robots using simulated, or synthetic, data to cut costs and improve efficiency. The Shenzhen-based firm says the approach could accelerate progress toward "physical artificial general intelligence", and it is backed by major conglomerates and state funds.
Chinese startup DexForce is using simulated training to teach humanoid robots a wide range of real-world tasks, an approach it says is faster and cheaper than collecting data from human workers. The Shenzhen-based company displayed a screen showing a virtual robot repeatedly placing objects on a table while the angle of its arms, the colour of the table and the objects themselves changed.
Chief Executive Jia Kui told Reuters that relying on synthetic data generated in virtual environments could more quickly power a breakthrough in robot capability, similar to the one ChatGPT triggered after its 2022 release helped propel generative AI into the mainstream. "Trying to achieve 'physical artificial general intelligence' purely through real-world data collection would require 10 trillion samples ... manual (data) collection for infinite human tasks is fundamentally inefficient," he said, referring to a milestone where a humanoid can complete new tasks in an unfamiliar environment on command.
China has developed humanoids capable of outperforming humans in running and jumping, but the harder commercial test lies in whether they can reliably carry out useful work in factories, offices and the home. Many Chinese robot makers are trying to meet that challenge by collecting large volumes of data from human workers repeatedly performing tasks in the real world. DexForce, however, develops AI software designed to simulate physical environments and models intended to predict how objects move and interact. It creates a digital version of a workplace or task, trains a robot in the simulation and then transfers that learning to physical machines.
The company says the process is faster and cheaper than collecting data from human trainers, who may need to repeat a movement more than 100 times to generate precise examples. Dozens of humanoid robot data training centres are currently being built nationwide at great expense, and are often heavily reliant on government subsidies. Most Chinese robot makers use a combination of simulated and real-world data, and many say access to high-quality training data remains a key bottleneck.
Jia, a machine-learning professor at the Chinese University of Hong Kong, Shenzhen, founded DexForce in 2021. The roughly 500-employee startup is backed by Lenovo Capital, the investment arm of Lenovo, and Lens Technology, as well as several state-owned funds. It raised 1 billion yuan ($147 million) in a recent Series B funding round at a valuation of more than 10 billion yuan. Bloomberg reported it was considering a Hong Kong IPO, but Jia declined to comment.
The firm's software is used in industrial settings by Chinese and foreign manufacturers including Toyota, Samsung, Panasonic, Foxconn, Haier, and Hisense, while its humanoid robots make coffee and serve customers at malls and tourist sites in more than 15 cities nationwide.