RoboScape: Physics-informed Embodied World Model
Authors: name: Yu Shang, name: Xin Zhang, name: Yinzhou Tang, name: Lei Jin, name: Chen Gao, name: Wei Wu, name: Yong Li

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.