SHENZHEN: At the offices of Chinese startup DexForce, a screen shows a virtual robot placing objects on a table. It repeats the task as the angle of its arms, the colour of the table and the objects themselves change.
DexForce, based in the southern tech hub of Shenzhen, is betting that such simulated training can help solve one of the humanoid-robot industry's biggest challenges: teaching machines to perform a wide range of real-world tasks without human assistance.
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 is relying on synthetic data generated in virtual environments to cut costs and improve training efficiency.
Chief Executive Jia Kui told Reuters the approach could more quickly power a breakthrough in robot capability, akin 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," said Jia, referring to a technological milestone where a humanoid can complete new tasks in an unfamiliar environment on command.
Reaching that point will require AI systems that can perceive and navigate their surroundings and adapt to variation. - (Reuters)
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