Xiaomi-Robotics-1 shows that more data beats bigger models when training robots to move

Xiaomi-Robotics-1 shows that more data beats bigger models when training robots to move Jonathan Kemper View the LinkedIn Profile of Jonathan Kemper Jul 21, 2026 Xiaomi Xiaomi has released an AI model for robots that follows the same scaling pattern as large language models. Its performance improves as it trains on more data. To build that dataset, Xiaomi largely avoided using physical robots. Robot AI faces a data problem that language models don't: LLMs can train on huge parts of the public internet, while useful data on robot movement is scarce. Robots usually have to learn from scratch how to grip, lift, and put away objects. The standard method has people remotely guide a physical robot through every movement. That process is slow and expensive, and it often produces repetitive data from the same tasks in the same settings. That's the gap Xiaomi-Robotics-1 is trying to close. The model is designed to follow spoken or written commands in unfamiliar environments without prior exposure and adapt to new tasks with little extra training. A handheld gripper replaces expensive robots To get around the data bottleneck, Xiaomi mostly ditched real robots during data collection. Instead, the team used portable handheld grippers with attached cameras that a person simply picks up and operates by hand. This setup lets you record manipulation tasks in kitchens, offices, stores, factory floors, and outdoor spaces without a robot even being present. The result was over 100,000 hours of motion recordings. The pretraining data comes from about 100,000 hours of UMI recordings across more than 1,700 different environments. | Image: Xiaomi A dataset that large creates another problem because each recording needs a description the model can learn from. Labeling it all by hand wasn't practical, so Xiaomi used another AI model to describe each motion segment in text. The team says it labeled the full dataset in about two weeks. Xiaomi then transferred that training to physical robots, including wheeled models and dual-arm systems. The model still had to account for the differences between a handheld gripper and a robot arm. For post-training, Xiaomi combines its own recordings from real apartments with open-source robot datasets and annotated UMI data. | Image: Xiaomi More training data matters more than model size Tests showed that a larger model improved performance, but more training data produced much bigger gains than more compute. The researchers say progress in robot AI will depend mainly on collecting larger and more varied datasets. The finding matches what researchers showed for visual data in early March. For language models, the long-standing rule is that model size and data volume should grow at roughly the same rate to make the best use of a fixed compute budget. That balance changes when a model must process images rather than just text. In that case, adding data helps far more than increasing model size. More training data and larger models both reduce prediction errors. With limited data, however, the model's performance begins to decline later in training. | Image: Xiaomi The same pattern appeared in tests with physical robots. As Xiaomi increased the amount of training data, the model's success rate in unfamiliar environments rose from about 25 percent to 75 percent. The researchers say they haven't reached a point where more data stops improving performance. The same scaling pattern holds for physical robots, with more pretraining data raising success rates in unfamiliar environments. | Image: Xiaomi Xiaomi says Xiaomi-Robotics-1 posted the best results to date across standard robot AI benchmarks. In one demo, a robot reportedly packed a suitcase without human help. The task took more than ten minutes and required the robot to move across a