Xiaomi · embodied AI & robot learning小米 · 具身智能与机器人学习
3 curated papers, code, datasets and hardware from Xiaomi (1 with code), with one-line takeaways.3 个来自小米的精选论文、代码、数据集与硬件（1 个附代码），均配一句话要点。
Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配（data/labs.yaml），合作工作会出现在所有署名机构下。仅列出精选条目，因为每日 arXiv 论文不含机构信息。
Xiaomi's compact embodied agent that plans, tracks progress and recovers from failures across manipulation and navigation, distilled from stronger teachers with a rubric-driven self-improvement loop.小米具身智能团队的轻量统一具身智能体：在操作与导航中负责任务理解、规划、进度跟踪和失败恢复，由更强的教师模型多教师在线蒸馏而来，并配有基于评分细则的自我改进数据飞轮。
@misc{team2026omnibrain,
title = {{OmniBrain-0: A Unified Agentic Model for Embodied Intelligence}},
author = {Xiaomi Embodied Intelligence Team and Yuechen Luo and Chong He and Fang Li and Shaoqing Xu and Jingkai Wang and Menglun Su and Jiaxin Liu and Shengyin Jiang and Zhiyi Lai and Long Chen and Longlong Wang and Liang Hu and Haochen Liu and Yuncheng Jiang and Hongwei Xie and Jieming Cui and Hanbing Li and Junjie Huang and Zhixiang Duan and Hexin Zhang and Weixiang Liang and Ying Gong and Yuhan Wang and Yong Pan and Pengna Li and Kangyi Wu and Ken Deng and Kun Ma and Fuxi Wen and Zhi-xin Yang and Hangjun Ye},
year = {2026},
eprint = {2610.36013},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2610.36013},
}
A streaming multi-view causal world model. Its 3D Gaussian reconstruction feeds renderings back as scene context for long rollouts; the model targets closed-loop simulation, training and world action models.流式多视角因果世界模型：用 3D 高斯重建的渲染结果作为场景上下文来维持长时生成，面向闭环仿真、闭环训练和世界动作模型应用。
@misc{zhu2026xiaomi,
title = {{Xiaomi Auto World Model 2.0: Causal World Models for Real-World Driving and Embodied Intelligence}},
author = {Zhenxin Zhu and Zhijian Huang and Cheng Chi and Yuru Wang and Jianhua Wu and Jiayi Guan and Yonggang Li and Mingfei Tu and JiaKui Hu and Tao Xu and Xianqi Wang and Hao Li and Yuechuan Pu and Qizeng Jia and Ruiling Yang and Hongxiao Yu and Xuhan Sheng and Jian Mao and Ruiqiang Zhang and Yu Zhu and Guang Chen and Hangjun Ye and Bing Wang},
year = {2026},
eprint = {2611.01677},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2611.01677},
}
A 38-billion-parameter multimodal autoregressive model that treats embodied generation as an extension of image and video generation, keeping the prior of the pretrained world foundation model.380 亿参数的多模态自回归模型，把具身生成视为图像和视频生成的延伸，以保留预训练世界基础模型的先验。
@misc{li2026xiaomi,
title = {{Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model}},
author = {Xinghang Li and Jun Guo and Qiwei Li and Long Qian and Hang Lai and Yueze Wang and Hongyu Yan and Jiahang Cao and Xi Chen and Jingen Qu and Jiaxi Song and Nan Sun and Hanye Zhao and Futeng Liu and Wanli Peng and Heyun Wang and Yunhong Wang and Caoyu Xia and Jack Zhao and Diyun Xiang and Hangjun Ye and Heng Qu and Huaping Liu and Jason Li},
year = {2026},
eprint = {2607.11643},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.11643},
}