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Xiaomi-Robotics-1-5B is an open-weight model for robotics from Bri Parales, released under Apache License 2.0. Its published files total 10.2 GB.
This repository contains the post-trained Xiaomi-Robotics-1 (XR-1) checkpoint for further post-training with the Xiaomi-Robotics-1 codebase.
By Bri Parales, published under apache-2.0, revision cce1591069ab.
This repository contains the post-trained Xiaomi-Robotics-1 (XR-1) checkpoint for further post-training with the Xiaomi-Robotics-1 codebase. XR-1 is a Vision-Language-Action (VLA) foundation model for mobile manipulation, initialized from a model pre-trained on over 100K hours of embodiment-free UMI trajectories and subsequently post-trained on over 10K hours of cross-embodiment data. The reference environment requires Python >= 3.9, a CUDA GPU, transformers==4.57.1, and FlashAttention 2. The Qwen3-VL backbone is loaded from Qwen/Qwen3-VL-4B-Instruct. Clone the source repository and install the dependencies: Download modelstates.pt from the Files and versions tab and place it at: Download…
This repository contains the post-trained Xiaomi-Robotics-1 (XR-1) checkpoint for further post-training with the Xiaomi-Robotics-1 codebase. XR-1 is a Vision-Language-Action (VLA) foundation model for mobile manipulation, initialized from a model pre-trained on over 100K hours of embodiment-free UMI trajectories and subsequently post-trained on over 10K hours of cross-embodiment data.
The reference environment requires Python >= 3.9, a CUDA GPU, transformers==4.57.1, and FlashAttention 2.
The Qwen3-VL backbone is loaded from Qwen/Qwen3-VL-4B-Instruct.
Clone the source repository and install the dependencies:
git clone https://github.com/XiaomiRobotics/Xiaomi-Robotics-1.git
cd Xiaomi-Robotics-1/xr1
pip install -e .
pip install flash-attn --no-build-isolation
Download model_states.pt from the Files and versions tab and place it at:
pretrained_ckpt/model_states.pt
Download the XR-1 post-training demo dataset, then launch post-training:
wandb login
RESOURCE_GPU=1 bash scripts/train.sh \
trainer.project="xiaomi-robotics-1" \
trainer.exp_name="posttrain" \
trainer.default_root_dir="outputs" \
data=load_washer \
model=posttrain \
model.params.pretrained="pretrained_ckpt/model_states.pt"
The training configuration expects the demo episodes under data/. See xr1/README.md for the data format, normalization statistics, and multi-GPU training options.
After post-training, serve the resulting checkpoint with:
bash scripts/deploy.sh outputs/project_xiaomi-robotics-1/posttrain 1 1
The runtime client accepts three camera images, a natural-language instruction, and the current robot state, and returns the predicted action sequence. See the deployment documentation before connecting a post-trained checkpoint to a robot.
@article{team2026xiaomi,
title={Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories},
author={Team, Xiaomi Robotics and Guo, Jun and Jin, Piaopiao and Li, Jason and Li, Peiyan and Li, Yingyan and Liu, Futeng and Peng, Wanli and Qin, Optimus and Su, Yifei and others},
journal={arXiv preprint arXiv:2607.15330},
year={2026}
}
3 files, 10.2 GB in total. The weights are 1 file totalling 10.2 GB in pt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model_states.pt | Weights | 10.2 GB | 94d55a791220 |
| README.md | Documentation | 2.8 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
Released by Bri Parales through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 10.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. Xiaomi-Robotics-1-5B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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