# Uranus：面向具身智能的下一代仿真基础设施

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-23 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.news/items/cmuf9i85e05unrocspu14uibx
- 原文链接：https://arxiv.org/abs/2609.24815

## AI 摘要

研究者发布数据驱动的机器人仿真器 Uranus，基于关节轨迹条件的自回归扩散模型，可在线接收未来关节位置轨迹并逐步自回归生成隐帧，每步对应四帧 RGB，无固定时长限制。经推理优化后生成速度达 24 FPS，并提供统一接口支持多种机器人形态与相机配置的同步多视角生成。代码与模型权重已开源。

## 正文

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.