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arXiv · 2609.24815

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

Wenkang Qin·Yukun Zhou·Noah Shen·Jisong Cai·Dongxiao Mao·Baicheng Li·Yue Zhang·Wei Sui
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Abstract

Scalable simulation is essential for robot data generation, policy training, evaluation, and safe iteration, yet real-world interaction is costly and conventional simulators require labor-intensive construction. We present Uranus, a data-driven robot simulator built around a joint-trajectory-conditioned autoregressive diffusion model. Uranus offers three key capabilities: (1) streaming, open-ended rollout, which receives future joint-position trajectories online and autoregressively generates one latent frame per step, corresponding to four RGB frames, without a fixed horizon; (2) low-latency generation, achieving 24 FPS after inference optimization; and (3) scalable, extensible robot control, providing a unified interface for synchronized multi-view generation across diverse robot embodiments and camera configurations. We conduct comprehensive quantitative and qualitative evaluations on both in-distribution and out-of-distribution data, providing an objective assessment of Uranus and clearly identifying its current limitations. We release the code and model weights to empower the community with practical tools and insights.

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Wenkang Qin, Yukun Zhou, Noah Shen, Jisong Cai, Dongxiao Mao, Baicheng Li, Yue Zhang, Wei Sui. 2026-09-21. Uranus: Building the Next-Generation Simulation Infrastructure for Embodied AI. https://arxiv.org/abs/2609.24815

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