Xiaomi Takes AI to the Streets and Homes with Open-Source MiMo Model
Xiaomi Breaks New Ground with Cross-Domain AI Model

In a significant move for artificial intelligence development, Xiaomi today introduced MiMo-Embodied - a versatile AI model that connects two seemingly separate worlds: autonomous vehicles and smart home robotics. What makes this announcement particularly noteworthy? The company is releasing the technology as open-source.
Bridging Two AI Frontiers
The tech industry has been grappling with how to make different AI systems work together seamlessly. While your robotic vacuum cleans floors independently and your smart car navigates traffic, these capabilities rarely communicate. Xiaomi's new model tackles this challenge head-on.
"We're seeing embodied intelligence take root in homes while autonomous driving scales up," explains a Xiaomi spokesperson. "MiMo-Embodied represents our vision for unified AI that learns across environments."

How It Works: Three Key Innovations
The model brings several breakthroughs:
- Cross-domain capabilities - Handling everything from predicting when you'll need household items to making split-second driving decisions
- Knowledge transfer - Skills learned indoors actually improve outdoor navigation, and vice versa
- Reliable deployment - A sophisticated training approach makes the model perform consistently in real-world conditions
Performance That Turns Heads
Test results show MiMo-Embodied setting new standards:
- Topped 17 benchmarks for home robotics tasks
- Outperformed competitors on 12 autonomous driving measures
- Demonstrated surprising adaptability in general visual understanding tasks
The open-source release includes pretrained models and training code available at Hugging Face.
Key Points:
- Open ecosystem: Xiaomi's decision to open-source could accelerate innovation across both industries
- Practical benefits: Future products might feature robots that learn from your car's experiences, and vice versa
- Benchmark leader: The model sets new performance standards across multiple categories


