# RoboScape: Physics-informed Embodied World Model

arXiv: https://arxiv.org/abs/2506.23135 · HTML: https://mlsanity.com/paper/2506.23135/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 52 of its 57 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 30 of 52)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation](https://mlsanity.com/paper/2310.05737/) (2023)
- [Learning Interactive Real-World Simulators](https://mlsanity.com/paper/2310.06114/) (2023)
- [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025)
- [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024)
- [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023)
- [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024)
- [How Far is Video Generation from World Model: A Physical Law Perspective](https://mlsanity.com/paper/2411.02385/) (2024)
- [Video Depth Anything: Consistent Depth Estimation for Super-Long Videos](https://mlsanity.com/paper/2501.12375/) (2025)
- [PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation](https://mlsanity.com/paper/2404.13026/) (2024)
- [GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2503.20523/) (2025)
- [SpatialTracker: Tracking Any 2D Pixels in 3D Space](https://mlsanity.com/paper/2404.04319/) (2024)
- [PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation](https://mlsanity.com/paper/2409.18964/) (2024)
- [Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation](https://mlsanity.com/paper/2410.05363/) (2024)

## Built on by (most cited first, 30 of 40)

- [WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models](https://mlsanity.com/paper/2602.08971/) (2026)
- [A Comprehensive Survey on World Models for Embodied AI](https://mlsanity.com/paper/2510.16732/) (2025)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [Do World Action Models Generalize Better than VLAs? A Robustness Study](https://mlsanity.com/paper/2603.22078/) (2026)
- [When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs](https://mlsanity.com/paper/2602.17659/) (2026)
- [Omni-WorldBench: Towards a Comprehensive Interaction-Centric Evaluation for World Models](https://mlsanity.com/paper/2603.22212/) (2026)
- [Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond](https://mlsanity.com/paper/2604.22748/) (2026)
- [Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation](https://mlsanity.com/paper/2606.17030/) (2026)
- [World Action Models: A Survey](https://mlsanity.com/paper/2606.20781/) (2026)
- [Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models](https://mlsanity.com/paper/2605.07514/) (2026)
- [Making Foresight Actionable: Repurposing Representation Alignment in World Action Models](https://mlsanity.com/paper/2606.12217/) (2026)
- [GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation](https://mlsanity.com/paper/2607.02642/) (2026)
- [Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence](https://mlsanity.com/paper/2511.00108/) (2025)
- [WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform](https://mlsanity.com/paper/2605.17912/) (2026)
- [World2Act: Latent Action Post-Training from World Model Dynamics](https://mlsanity.com/paper/2603.10422/) (2026)
- [KeyWorld: Key Frame Reasoning Enables Effective and Efficient World Models](https://mlsanity.com/paper/2509.21027/) (2025)
- [LongScape: Advancing Long-Horizon Embodied World Models with Context-Aware MoE](https://mlsanity.com/paper/2509.21790/) (2025)
- [Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos](https://mlsanity.com/paper/2511.12882/) (2025)
- [RoboTrustBench: Benchmarking the Trustworthiness of Video World Models for Robotic Manipulation](https://mlsanity.com/paper/2606.01600/) (2026)
- [MoWM: Mixture-of-World-Models for Embodied Planning via Latent-to-Pixel Feature Modulation](https://mlsanity.com/paper/2509.21797/) (2025)
- [RoboScape-R: Unified Reward-Observation World Models for Generalizable Robotics Training via RL](https://mlsanity.com/paper/2512.03556/) (2025)
- [PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives](https://mlsanity.com/paper/2608.13552/) (2026)
- [Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface](https://mlsanity.com/paper/2512.19402/) (2025)
- [From Generative Engines to Actionable Simulators: The Imperative of Physical Grounding in World Models](https://mlsanity.com/paper/2601.15533/) (2026)
- [Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization](https://mlsanity.com/paper/2511.16602/) (2025)
- [PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics](https://mlsanity.com/paper/2605.18303/) (2026)
- [HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning](https://mlsanity.com/paper/2605.05951/) (2026)
- [World-Ego Modeling for Long-Horizon Evolution in Hybrid Embodied Tasks](https://mlsanity.com/paper/2605.19957/) (2026)
- [Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform](https://mlsanity.com/paper/2512.08478/) (2025)
- [PhysFire-WM: A Physics-Informed World Model for Emulating Fire Spread Dynamics](https://mlsanity.com/paper/2512.17152/) (2025)

## Abstract (arXiv)

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.