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。
"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.