titleXiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
authorsXiaomi Robotics Team, Jun Guo, Piaopiao Jin, Jason Li, Peiyan Li, Yingyan Li, Futeng Liu, Wanli Peng, Optimus Qin, Yifei Su, Nan Sun, Qiao Sun, Runze Suo, Heyun Wang, Yunhong Wang, Rujie Wu, Caoyu Xia, Lina Zhang, Jack Zhao, Guoliang Chen, Wenlong Chen, Xinze He, Bin Li, Qing Li, Zhuorong Li, Heng Qu, Wenxuan Song, Diyun Xiang, Yifan Xie, Peiran Xu, Hangjun Ye, Wen Ye, Han Zhao, Quanyun Zhou
date2026-07-16
venuearXiv (preprint, not peer reviewed)
idarXiv:2607.15330
affiliationXiaomi Robotics
upvotes70 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
primerVision-language-action models
abstractWe present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural languages describing scene state transitions, providing rich and precise conditioning for action learning. During post-training, we aim to align these capabilities with robot embodiments and imperative instructions that humans naturally use to prompt robots. Extensive experiments demonstrate strong scaling behavior. Xiaomi-Robotics-1 consistently improves with increased data scales and model sizes during pre-training. This scaling behavior directly transfers to post-training, where a stronger pre-training model yields better out-of-the-box real-robot performance in unseen environments. Furthermore, Xiaomi-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency. Across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods. Notably, it establishes a new state-of-the-art with a 57.6% success rate on RoboCasa365, surpassing the previous best of 46.6%. Furthermore, it achieves an average score of 20.07 on RoboDojo, significantly outperforming the prior state-of-the-art (13.07). Code and model checkpoints will be released. Project page: https://robotics.xiaomi.com/xiaomi-robotics-1.html

cite

@misc{team2026xiaomi,
  title={Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories},
  author={Xiaomi Robotics Team and Jun Guo and Piaopiao Jin and Jason Li and Peiyan Li and Yingyan Li and Futeng Liu and Wanli Peng and Optimus Qin and Yifei Su and Nan Sun and Qiao Sun and Runze Suo and Heyun Wang and Yunhong Wang and Rujie Wu and Caoyu Xia and Lina Zhang and Jack Zhao and Guoliang Chen and Wenlong Chen and Xinze He and Bin Li and Qing Li and Zhuorong Li and Heng Qu and Wenxuan Song and Diyun Xiang and Yifan Xie and Peiran Xu and Hangjun Ye and Wen Ye and Han Zhao and Quanyun Zhou},
  year={2026},
  eprint={2607.15330},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2607.15330}
}