Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions. We present a generalizable embodiment-centric world model that achieves scalable data generation by synthesizing photorealistic demonstrations with novel objects, in novel scenes, and from novel viewpoints. Our approach anchors generation to rendered robot motion while conditioning on explicit scene and object priors, effectively decoupling trajectory execution from environment synthesis. This formulation has the potential to unlock two powerful data scaling capabilities: (1) retrieval and rebirth, which repurposes existing trajectories into entirely new contexts without new motion data; and (2) prop-free teleoperation, where operators manipulate empty air and the model hallucinates the target objects and scene afterwards, eliminating reset time. We demonstrate with real-world experiments that our generated data consistently improves downstream policy performance and significantly reduces real-world data requirements across diverse manipulation tasks.

翻译：扩展机器人学习需要大规模、多样化的示范数据，然而通过远程操作收集真实世界数据仍然成本高昂且耗时。尽管视频扩散模型为数据扩展提供了有前景的途径，但现有生成方法往往局限于表面视觉增强，或出现具身幻觉导致生成物理上不可行的运动。我们提出一种可泛化的以具身为核心的世界模型，通过合成包含新物体、新场景和新视角的逼真示范数据，实现可扩展的数据生成。该方法将生成过程锚定于渲染后的机器人运动，并基于显式的场景与物体先验进行条件约束，有效解耦了轨迹执行与环境合成。这一框架具备解锁两种强大数据扩展能力的潜力：（1）检索与重生，即无需新运动数据即可将现有轨迹重用于全新场景；（2）免道具远程操作，即操作员仅需进行空手操作，模型随后生成目标物体与场景，消除重置时间。我们通过真实世界实验证明，该方法生成的数据能够持续提升下游策略性能，并在多种操作任务中显著降低对真实数据的需求量。