Humanoid robots are now gradually integrating into daily life, capable of deftly picking up eggs, skillfully twisting off bottle caps, and steadily navigating stairs. As a core future industry prioritized by national strategy, the development of embodied intelligence relies heavily on extensive training using massive amounts of real-world data. A group of young people, serving as the "early educators" for humanoid robots, are building a pathway for artificial intelligence to perceive the real world through thousands of personal demonstrations.
How are robots trained to possess agile limbs and human-like perceptual abilities? The answer lies within intelligent real-world data factories. These facilities lack roaring assembly lines but instead feature familiar everyday settings—home kitchens, convenience stores, car washes—resembling movie sets. Here, young workers wear data collection gear, using unique methods to teach robots how to become competent "workers."
In 2025, embodied intelligence was first mentioned in a government work report, with over 140 companies across the country entering this field. High-precision, multi-scenario data supply is a key factor for industry advancement—without sufficient high-quality data, embodied intelligent robots struggle to evolve. Data collection and training centers act as the "data granaries" for the smart industry. Workers repeatedly capture the same basic actions from various angles and positions, with each set of movements replicated thousands to tens of thousands of times to accumulate data for intelligent robots.
One data collector shared that, as a vocational school graduate, he never imagined participating in the robotics industry, which he associated with master's or doctoral degrees. While not involved in deep-level R&D, he contributes by diligently performing foundational tasks.
The precision in capturing subtle force applications and movement details during data collection stems from professional equipment and core technology systems. The data collection suit, equipped with over 3,000 contact points, gathers information across more than 80 dimensions, including size, temperature, hardness, and surface texture. This data not only teaches robots finer perceptions but also trains their operational logic.
Traditional data collection methods, which rely on humans remotely controlling robots, are inefficient, costly, and lack tactile feedback, resulting in mere mechanical repetition. The new technology mimics nerve endings and human skin, collecting comprehensive sensory input from vision to touch.
As the industry upgrades, robots will inevitably enter more life scenarios. Collaborating with data collectors are data reviewers, such as one young worker who checks collected data for accuracy. For instance, if a task meant for the left hand is performed with the right or is incomplete, it is flagged as erroneous. Both correct and incorrect data are fed to embodied robots, enabling them to learn like children—understanding both right and wrong approaches.
In data factories, numerous young individuals meticulously refine details and uphold standards, collectively witnessing the transformation of the robotics industry. Currently, large-scale real-world data collection bases are being established nationwide. Breakthroughs are being made in key areas like core sensors, real-world training, and localized data for humanoid robots, with the domestic production rate of core components steadily rising.
To date, collaborative national clusters have accumulated over 20 million localized real data entries, with more than 8 million added in the first four months of 2026 alone—a year-on-year increase of over 120%. The continuous accumulation of high-quality data lays a solid foundation for industry expansion and the emergence of new professions. Over the next five years, China's embodied intelligence market is projected to reach 400 billion yuan, generating over one million new jobs, including roles like data collectors and robot group coordination operators.
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