标题： RoboScape：物理信息嵌入的世界模型 显示英文标题

标题： RoboScape: Physics-informed Embodied World Model

摘要： 世界模型已成为具身智能不可或缺的工具，作为强大的模拟器，能够生成逼真的机器人视频，同时解决关键的数据稀缺问题。 然而，当前的具身世界模型在物理意识方面存在局限，特别是在建模三维几何和运动动力学方面，导致在接触丰富的机器人场景中生成不现实的视频。 在本文中，我们提出了RoboScape，这是一个统一的物理信息世界模型，在一个集成框架中联合学习RGB视频生成和物理知识。 我们引入了两个关键的物理信息联合训练任务：时间深度预测，以增强视频渲染中的三维几何一致性；关键点动力学学习，隐式编码物理属性（例如物体形状和材料特性），同时改进复杂运动建模。 大量实验表明，RoboScape在各种机器人场景中生成的视频具有更优的视觉保真度和物理合理性。 我们进一步通过下游应用验证了其实际效用，包括使用生成数据进行机器人策略训练和策略评估。 我们的工作为构建高效的物理信息世界模型提供了新见解，以推动具身智能研究的发展。 代码可在以下链接获取：https://github.com/tsinghua-fib-lab/RoboScape. 显示英文摘要

摘要： World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data scarcity challenges. However, current embodied world models exhibit limited physical awareness, particularly in modeling 3D geometry and motion dynamics, resulting in unrealistic video generation for contact-rich robotic scenarios. In this paper, we present RoboScape, a unified physics-informed world model that jointly learns RGB video generation and physics knowledge within an integrated framework. We introduce two key physics-informed joint training tasks: temporal depth prediction that enhances 3D geometric consistency in video rendering, and keypoint dynamics learning that implicitly encodes physical properties (e.g., object shape and material characteristics) while improving complex motion modeling. Extensive experiments demonstrate that RoboScape generates videos with superior visual fidelity and physical plausibility across diverse robotic scenarios. We further validate its practical utility through downstream applications including robotic policy training with generated data and policy evaluation. Our work provides new insights for building efficient physics-informed world models to advance embodied intelligence research. The code is available at: https://github.com/tsinghua-fib-lab/RoboScape.