- Η Xiaomi released the code and weights of the robotics foundation model Xiaomi-Robotics-1, openly beating the competition.
- The model was trained on over 100.000 hours of real-world UMI data and 10.000+ hours of post-training on different hardware embodiments.
- The complete package includes everything from deployment and post-training code to benchmarking tools in hugging face and GitHub.
Xiaomi just changed the rules of the robotics artificial intelligence game by making its robotics base model open-source Xiaomi-Robotics-1We at TechNoid reviewed the announcement originally published on Weibo and we explain what this move means for the future of autonomous machines.
The Xiaomi-Robotics-1 training mechanism
The biggest challenge in robotics models is not the algorithms but the volume and quality of real-world motion data. According to the technical data, Xiaomi-Robotics-1 was pre-trained on over 100.000 hours of UMI data (Unstructured Manipulation InteractionThis approach allows the model to process visual stimuli and language commands, predicting precise sequences of actions to transition a scene from its current conditions to the desired state.
Unlike closed models that require huge fine-tuning costs from scratch, Xiaomi offers a ready-to-use foundation model. Its architecture is specifically designed to overcome the limitations researchers face when trying to translate algorithmic theory to physical hardware.
Cross-embodiment and practical application
The critical turning point in Xiaomi-Robotics-1 is the post-training stage, which lasted over 10.000 hours on different robotic platforms. This strategy ensures cross-embodiment capabilities, i.e. the ability of the model to adapt to different types of hands, arms or mobile robotic bases.
Unlike other projects that are limited to simulations, Xiaomi provides code that has been tested on real devices. This means that engineers don't have to spend months calibrating sensors and actuators, since the core of the robot's behavior is already trained in real-world object interaction conditions.
Open ecosystem for developers and researchers
The Chinese company's move to make the entire process available—from post-training of real devices to benchmarking evaluation code—places the Xiaomi Robotics at the heart of the global open source community. The full suite is available in official repositories, enabling smaller labs to try out advanced robotics capabilities.
Additionally, the integration of ready-made evaluation scripts allows for immediate performance comparisons on custom tasks. If you are involved in the development of intelligent systems, you can obtain the necessary tools and checkpoints directly from the distribution platforms supported by the project.
| Feature | Data / Approach |
|---|---|
| Pre-training Data | 100.000+ hours of real-world UMI |
| Post-training Data | 10.000+ hours on multiple robotic bodies |
| Architecture | Cross-embodiment Plug-and-Play Foundation Model |
| Availability | GitHub, Hugging Face, Xiaomi Robotics Official |
Our opinion at TechNoid
Xiaomi’s decision to open source Xiaomi-Robotics-1 is not just a marketing move, but a strategic attack on the closed ecosystems of the West. When a consumer goods and hardware giant generously gives away 100.000+ hours of training data to the community, it forces competitors to reconsider their policies. We believe that this move will act as a catalyst, accelerating the commercialization of affordable home and industrial robots within the next three years.
Frequently Asked Questions about Xiaomi-Robotics-1
What exactly is Xiaomi-Robotics-1?
It is an open-source robotics foundation model developed by Xiaomi for controlling and manipulating objects by robotic devices.
Where can someone find the code and models?
The code, model checkpoints, and benchmarking tools are available on the company's official GitHub and on the Hugging Face platform.
How much data was used to train it?
The model was pre-trained on over 100.000 hours of real-world UMI data and underwent 10.000+ hours of post-training.
What does cross-embodiment mean?
It means that the model can be adapted and run on different types of robotic hardware without needing to be redesigned from scratch.
Is it aimed at ordinary users or developers?
The project is aimed exclusively at researchers, robotics engineers and developers who want to integrate it into their own platforms.
What is the source of the original data?
Data was collected from real interaction conditions (Unstructured Manipulation Interaction) for maximum accuracy in the natural world.
How does this move affect the robotics market?
It significantly reduces research costs and time, accelerating the development of autonomous robotic systems worldwide.
If you want to delve deeper into the developments surrounding smart grids and artificial intelligence, take a look at our feature on the cutting-edge technologies in modern networks.
