Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Authors: name: Xiaomi Robotics Team, name: Jun Guo, name: Piaopiao Jin, name: Jason Li, name: Peiyan Li, name: Yingyan Li, name: Futeng Liu, name: Wanli Peng, name: Optimus Qin, name: Yifei Su, name: Nan Sun, name: Qiao Sun, name: Runze Suo, name: Heyun Wang, name: Yunhong Wang, name: Rujie Wu, name: Caoyu Xia, name: Lina Zhang, name: Jack Zhao, name: Guoliang Chen, name: Wenlong Chen, name: Xinze He, name: Bin Li, name: Qing Li, name: Zhuorong Li, name: Heng Qu, name: Wenxuan Song, name: Diyun Xiang, name: Yifan Xie, name: Peiran Xu, name: Hangjun Ye, name: Wen Ye, name: Han Zhao, name: Quanyun Zhou
Organization: Xiaomi Robotics

We 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