China's Embodied AI Industrialization Accelerates Toward Real-World Deployment

Deep News
7 hours ago

China is steadily strengthening the industrial foundation for embodied artificial intelligence this year, speeding up the construction of training-ground infrastructure, refining industry standards and norms, and pushing embodied AI to move faster from the laboratory toward industrialized deployment.

At an artificial intelligence company in Zhongguancun, an embodied AI data platform aggregates human video and simulated synthetic data from training grounds across the country. It has already delivered 1.5 million hours of human video data, a scale that leads the world.

At present, more than 70 embodied AI training grounds have been built and put into operation nationwide, with more than 40 under construction or in planning, forming three core clusters in the Yangtze River Delta, the Beijing-Tianjin-Hebei region, and the Pearl River Delta.

While large volumes of practical training data are being collected at an accelerating pace, they are already being applied to real-machine training. In Shenzhen, a wheeled-arm humanoid robot trained on practical data can complete complex tasks such as switching electrical cabinets on and off and opening and closing industrial valves.

Diverse training data drawn from a wide range of real-world scenarios is constantly being enriched, continuously empowering robots to iterate and improve, and China's entire embodied AI industrial chain is advancing at an accelerating pace. In 2025, the scale of China's core artificial intelligence industry exceeded 1.2 trillion yuan, with more than 6,200 enterprises.

Not long ago, the Ministry of Industry and Information Technology and other ministries and commissions launched a special initiative focused on the three major fields of industry, services, and special operations, building an industrial closed loop of "real-scenario practical training, data accumulation, product iteration, and large-scale deployment."

Going forward, China will continue to strengthen training-ground clusters, put industry standards into effective use, expand real-scenario practical training settings, and drive embodied AI products to accelerate their empowerment of all sectors of the economy.

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