RoboScape: Physics-informed Embodied World Model

TL;DR

RoboScape introduces a physics-informed world model that jointly learns video generation and physical properties, improving realism and physical accuracy in robotic scenario simulations for embodied intelligence.

Contribution

It presents a novel unified framework that integrates physics knowledge into world modeling, enhancing 3D geometric consistency and motion dynamics in robotic video synthesis.

Findings

Superior visual fidelity and physical plausibility in generated videos

Effective for robotic policy training and evaluation

Advances in physics-informed world modeling for embodied AI

Abstract

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.