Figure CEO：机器人数据基础设施是物理AI瓶颈

"If we could snap our fingers and get a pile of data... we would solve general robotics right now."

• Figure CEO Brett Adcock

The big bottleneck in Physical AI / robotics is not better models, but better robotics data infrastructure. That is the gap @cyberorigin_ai is building around with CyberCode.

Robotic data is insanely expensive and brutal to collect. Real-world manipulation data is messy.

A robot policy does not learn from "clips" the way a human watches a demo. It needs training data that can be searched by task, scene, action, device, collector, quality result, and data ID.

It needs every useful frame traceable back to where it came from.

It also needs different signals aligned on the same timeline, because a model can learn the wrong thing if vision, motion, language, robot state, and other sensor streams are slightly out of sync.

CyberCode turns real human manipulation data into an operating layer where the data is searchable, inspectable, traceable, synchronized, quality-checked, and evaluation-ready before it reaches the model.

That sounds less flashy than a humanoid demo, but it is closer to where a lot of the real bottleneck sits. For manipulation policies, world models, and vision-language-action models, better data infrastructure can matter as much as better model architecture, because the model can only learn from the structure, coverage, timing, and quality the data system actually exposes.

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