Shenzhen startup DexForce is training humanoid robots in computer-generated virtual environments instead of relying mainly on repeated real-world demonstrations, aiming to cut training costs and speed up development.
The company builds a digital version of a workplace or task, trains the robot in simulation, and then transfers that learning to physical machines. DexForce 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.
Chief Executive Jia Kui, 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, Lens Technology and several state-owned funds, and raised 1 billion yuan (about $147 million) in a recent Series B funding round at a valuation of more than 10 billion yuan.
DexForce'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 across China. Bloomberg reported the company was considering a Hong Kong IPO; Jia declined to comment.
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 — so simulated training complements rather than fully replaces real-world demonstrations.