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Training data collection is the binding constraint on humanoid robot performance.
Unlike language models, which train on text drawn from the internet, robots learning physical tasks need labeled, embodied demonstrations gathered in the real world, and assembling those demonstrations at scale has proven slow.
Several Chinese AI startups are now attacking that bottleneck directly, each testing a distinct method for collecting and applying robot training data more efficiently. The mechanism behind that slowness is structural.
Humanoid robots learning to fold laundry or handle components on a production line require physical demonstration data: records of hand position, grip force, body movement, and task sequencing, each captured during actual physical activity.
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