一个模型同时学会人和机器人走路干活
一个模型，同时学会人和机器人怎么走路、怎么伸手拿东西。研究者把 1 万小时的人类第一视角视频和 3 千小时的机器人数据揉在一起，用一套统一的「骨架+镜头」表示法，让模型既懂人的动作、也懂机器人的动作，还能互相迁移。它不只是看，还能预测动作后果：在 5 个跨形态基准上拿了最好成绩，蒸馏后能实时跑一分钟，支持 VR 里直接操控。它不是你明天能用上的产品，但这是「世界模型」第一次把人和机器人的动作统一进一个模型，意味着以后机器人学动作，可能不用再从零训练，而是直接借人类的经验。
📄 原文摘要(英文)
World models enable embodied agents to anticipate action consequences, yet jointly modeling locomotion and manipulation across diverse embodiments remains challenging due to heterogeneous action spaces and limited multimodal supervision. We present Ropedia W0, the first egocentric loco-manipulation world foundation model spanning humans and robots with diverse embodiments. We curate Xp-Data, comprising 10k hours of egocentric human data and 3k hours of robot data with unified multimodal annotations and systematic quality control. A unified kinematic representation encodes locomotion through camera poses and manipulation through projected skeletons, with robot skeletons combining embodiment-specific forward kinematics with extensive manual keypoint annotation and correction. We also introduce Xp-Teleop, a long-horizon loco-manipulation benchmark based on high-quality dexterous humanoid teleoperation data. Evaluations demonstrate state-of-the-art performance across five cross-embodiment benchmarks (DreamDojo, Ψ_0, ω-HOME, Xp-Teleop, and AgiBot) and on held-out human egocentric video, from zero-shot inference to few-shot and full-data post-training. Scaling human pre-training data improves performance on both human and robot evaluations. After distillation, our model supports real-time, minute-long rollouts, enabling live VR interaction. Applications in policy evaluation, model-based planning, and world-action modeling further demonstrate the model's utility for embodied decision-making.