RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer

arXiv:2610.09254 · cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.RO

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/aigc-apps/VideoX-Fun

Project page: https://robo-render.github.io

Terminology

• Sim-to-Real Gap — 15× in this paper · explained in 6 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Sim-to-Real Transfer — 9× in this paper · explained in 2 episodes
• alpha — 7× in this paper · explained in 1 episode
• Grounding — 6× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 5× in this paper
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Digital Twin — 2× in this paper · explained in 5 episodes
• Embodied AI — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Proceedings of the Conference on Robot Learning — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• International Conference on Robotics and Automation — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
• RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
• EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling
• RoboTransfer: Controllable Geometry-Consistent Video Diffusion for Manipulation Policy Transfer
• GigaWorld-0: World Models as Data Engine to Empower Embodied AI
• Wan: Open and Advanced Large-Scale Video Generative Models
• Cosmos World Foundation Model Platform for Physical AI
• World Simulation with Video Foundation Models for Physical AI
• SkyReels-V2: Infinite-length Film Generative Model
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation

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