Shenzhen robotics startup DexForce is using synthetic data and virtual environments to train humanoid robots before transferring learned behavior to physical machines. Reuters reported on October 15 that the company raised 1 billion yuan, about $147 million, in Series B funding at a valuation above 10 billion yuan.

DexForce’s own technical site describes its EmbodiChain engine as a Real2Sim2Real system that builds simulated versions of physical scenes, generates large volumes of synthetic training data and transfers the resulting policies back to robots. Its W1 Pro platform combines stereo vision, up to 40 degrees of freedom and real-time joint control. The approach attacks one of embodied AI’s biggest bottlenecks: collecting enough varied physical demonstrations to train robots safely and economically.

DexForce’s second-generation W1 Pro shows the physical platform receiving the company’s embodied-intelligence software.

The distinction from conventional robot programming is important. Instead of writing a fixed routine for every task, simulation can expose a model to many object positions, lighting conditions and failure cases before the robot touches a real production line. BitcoinVersus.Tech has been tracking the same training problem through Schaeffler’s humanoid robot gym and the broader field in our Top 7 Humanoid Robots ranking.

Reuters said DexForce technology is already being used by manufacturers including Toyota, Samsung and Foxconn, while its robots serve public-facing roles in more than 15 Chinese cities. Those deployment claims show why synthetic-data economics matter, but they do not by themselves prove general-purpose autonomy. The real benchmark will be repeatable performance outside curated demonstrations, especially as commercial humanoids such as Digit push toward longer production duty cycles.

BitcoinVersus.Tech Editor’s Note:
We volunteer daily to ensure the credibility of the information on this platform is Verifiably True. If you would like to support to help further secure the integrity of our research initiatives, please donate using the QR code provided

For best QR-code scanning results, please scan this donation code from a desktop or larger screen. Mobile screens may prevent camera recognition.