# Figure CEO：机器人瓶颈是数据基础设施，CyberOrigin 推出 CyberCode

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-30 03:47
- AIHOT 分数：39
- AIHOT 链接：https://aihot.virxact.com/items/cmqzn4akj0068slnwzfodj6t8
- 原文链接：https://x.com/rohanpaul_ai/status/2071682053578592277

## AI 摘要

Figure 公司 CEO Brett Adcock 表示，若能获得大量数据，就能解决通用机器人问题。他认为物理 AI / 机器人领域的真正瓶颈不是更好的模型，而是更好的机器人数据基础设施。CyberOrigin 推出的 CyberCode 正是为解决该问题构建：将真实的人类操作数据转化为可搜索、可检查、可追溯、多模态信号精准同步、质量检查、评估就绪的运营层。机器人策略、世界模型和视觉-语言-动作模型只能从数据系统暴露的结构、覆盖范围、时序和质量中学习，因此更好的数据基础设施与更好的模型架构同等重要。

## 正文

"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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