机器人看12万小时视频，零样本成功率翻倍
机器人学动作，不再只靠人类手把手教它操作了。这篇研究让机器人先看海量第一人称视频——人怎么拿杯子、用工具、做长流程的事——把物理世界的规律先学进脑子里，再用少量机器人自己的数据做微调。结果很直接：训练数据从只有机器人自己的轨迹，扩展到12万小时的人类和机器人视频后，机器人在从没见过的任务上，成功率从36.1%涨到77.8%。它不是你明天就能买回家的东西，但这条路线意味着：机器人学新技能的成本，可能从「每个动作都要人示范」变成「看视频就行」。
📄 原文摘要(英文)
Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect and inherently limited in diversity. Egocentric videos offer a far more scalable source of embodied experience, capturing object interactions, contact dynamics, tool use, and long-horizon behaviors across diverse environments. The central challenge is how to convert this abundant but action-free experience into effective robot control. We introduce ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video. ZimaBlue follows a three-stage training curriculum: it first performs causal embodied video pre-training on large-scale human and robot egocentric videos, then grounds the learned visual dynamics in heterogeneous robot trajectories through video-action mid-training with a unified action representation, and finally specializes the model to a target robot for deployment. To make generative WAMs practical for real-time control, ZimaBluefurther adopts an asynchronous Slow-Fast dual-system architecture, where a high-capacity Slow world model provides generalizable spatiotemporal representations and a lightweight Fast branch enables 30 Hz action prediction on NVIDIA RTX 4090. On real-robot zero-shot evaluations, scaling from target-robot data alone to over 120,000 hours of embodied video improves success from 36.1% to 77.8%. ZimaBlue further delivers strong performance across multiple benchmarks, with particularly pronounced gains on unseen tasks.