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

Research Embodied AI

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

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TL;DR - Uranus is a data-driven robot simulator that uses a joint-trajectory-conditioned autoregressive diffusion model for continuous visual rollouts. It aims to make robot data generation, policy training, and evaluation faster and more scalable across different embodiments and camera setups.

  • Supports streaming, open-ended simulation by generating one latent frame per control step, corresponding to four RGB frames, without a fixed rollout horizon.
  • Achieves 24 FPS after inference optimization for low-latency generation.
  • Provides a unified interface for synchronized multi-view generation across diverse robots and camera configurations.
  • Includes in- and out-of-distribution evaluations, explicitly documents current limitations, and releases code and model weights.

Sources (1)

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

arXiv cs.RO Wenkang Qin, Yukun Zhou, Noah Shen, Jisong Cai, Dongxiao Mao, Baicheng Li, Yue Zhang, Wei Sui 2026-09-21 arXiv:2609.24815
Public signals Hugging Face upvotes 1
Providers: Hugging Face · Upvotes 1 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:17:05.705248 UTC

TL;DR - Uranus is a data-driven robot simulator that uses a joint-trajectory-conditioned autoregressive diffusion model for continuous visual rollouts. It aims to make robot data generation, policy training, and evaluation faster and more scalable across different embodiments and camera setups.

  • Supports streaming, open-ended simulation by generating one latent frame per control step, corresponding to four RGB frames, without a fixed rollout horizon.
  • Achieves 24 FPS after inference optimization for low-latency generation.
  • Provides a unified interface for synchronized multi-view generation across diverse robots and camera configurations.
  • Includes in- and out-of-distribution evaluations, explicitly documents current limitations, and releases code and model weights.
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