Uranus: Building the Next-Generation Simulation Infrastructure for Embodied AI

arXiv:2609.24815 · cs.RO, cs.AI, cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.RO, cs.AI, cs.CV

Submitted: 2026-09-21

Updated: 2026-09-23

Comments: Project Page: https://d-robotics-ai-lab.github.io/large-model-team/blog/uranus/ Inference Code: https://github.com/D-Robotics-AI-Lab/Uranus-OSS Inference Data: https://huggingface.co/datasets/D-Robotics/Uranus-Demo-Data SDK Code: https://github.com/D-Robotics-AI-Lab/Uranus-SDK Model Weights: https://huggingface.co/collections/D-Robotics/uranus

Code: https://github.com/D-Robotics-AI-Lab/Uranus-OSS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embodied AI — 54× in this paper · explained in 2 episodes
• Consistency — 39× in this paper · explained in 3 episodes
• Generalization — 38× in this paper · explained in 13 episodes
• World Model — 27× in this paper · explained in 7 episodes
• Distillation — 26× in this paper · explained in 2 episodes
• Calibration — 24× in this paper · explained in 7 episodes
• KV cache — 21× in this paper · explained in 3 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Benchmark — 19× in this paper · explained in 2 episodes
• Decode — 12× in this paper · explained in 2 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Distribution Matching — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Anchor Tokens — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Abstraction — 2× in this paper · explained in 1 episode
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Phased Consistency Model — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Score Function — 2× in this paper · explained in 4 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Amortization — 1× in this paper · explained in 1 episode
• Causal Inference — 1× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• World Simulation with Video Foundation Models for Physical AI
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• WoW: Towards a World omniscient World model Through Embodied Interaction
• Ctrl-World: A Controllable Generative World Model for Robot Manipulation
• Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
• RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• Flow Matching for Generative Modeling
• Yume: An Interactive World Generation Model
• GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation
• SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
• Evaluating Gemini Robotics Policies in a Veo World Simulator
• GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation
• Advancing Open-source World Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
• RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

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