机器人数据的「金标准」:一次拍摄，两用
大模型已经能看着画面直接开机器人，但一碰到「时间长、要反复接触」的活儿就掉链子。这篇的赌注是：卡点不在模型，在数据——而且环境和动作得一起拍。他们放出 1,589 个带真实尺度的 3D 场景扫描，外加 74 类任务、131 小时的第一人称人类操作录像。关键在于每段素材都能二次利用：场景转成能物理交互的仿真环境，人的动作重定向成整身机器人的轨迹，再靠虚拟遥操作和自动生成补出更多机器人行为。一次采集，同时喂出「在哪做」和「怎么做」。它不是你明天能用上的东西；它在赌物理 AI 也能有自己的 scaling law，而这条曲线要先靠这种成对数据画出来。
📄 原文摘要(英文)
Large foundation models such as GPT-6 Astra and Claude Fable 5.1 have recently been shown to control robots directly from visual observations. They nevertheless remain brittle on long-horizon, contact-rich tasks in real-world environments. Further progress depends on two complementary resources: demonstrations of human behavior that supervise how tasks are performed, and interactive environments in which policies can be rolled out, evaluated, and improved in closed loop. Language models have advanced along well-characterized scaling laws; the corresponding question for physical AI is how capability scales with environments and behaviors, which first requires identifying which data to scale and how to produce and reuse such data continuously. To address this question, we introduce Ropedia Xperience 2, comprising 1,589 released metric-scale 3D scans and a collection of 4,230 egocentric recordings of 74 tasks (131 hours). We further show that each capture can be repurposed for robot learning: scenes are converted into physically interactive simulation environments, human motion is retargeted to whole-body robot trajectories, and additional robot behaviors are obtained through virtual teleoperation and automated long-horizon trajectory generation. We propose this joint capture of environments and behaviors as a gold standard for physical AI data collection, laying a data foundation for establishing scaling laws over environments and behaviors.