Breaking the data barrier. Scaling robot policy models with embodiment-free pre-training.
Everything Xiaomi-Robotics-1 can do starts from data. For pre-training, we use 100,000 hours of embodiment-free (UMI) trajectories spanning more than 1,700 scenarios (household, commercial premises, industrial sites, and outdoor spaces), covering a diverse range of tasks. We develop a scalable auto-labeling pipeline that first divides trajectories into fixed-length segments and then annotates each segment with language descriptions of scene state transitions.
For post-training, we leverage cross-embodiment datasets containing in-house robot data, filtered open-sourced robot data, and a set of high-quality UMI data. For the in-house data, we collected over 7,200 hours of real-robot data in real homes, covering tasks like tidying a sofa, sorting a shoe cabinet, and putting away kitchenware. The UMI data are manually annotated with temporal segments and instruction prompts, which differ from the auto-labeled state-transition descriptions used in the pre-training data.
Following the training paradigm of LLMs, the training of Xiaomi-Robotics-1 consists of two stages: pre-training and post-training. The first stage learns general representations for action generation from large-scale UMI data, while the post-training stage aligns the model with real robot embodiments and instruction-following capabilities.
Pre-training is about breadth: exposing the model to as much of the real world as possible. We use the embodiment-free UMI data described above, which spans a broad range of environments and tasks. At this scale, manual labeling is infeasible. Thus, we built an automatic annotation pipeline powered by a strong vision-language model. Long videos are split into fixed-length clips, and the VLM describes the state transition of grippers and interacting objects within each clip. The result is a large-scale corpus of real-world manipulation trajectories, each annotated with precise language descriptions. These allow the model to learn action generation that drives the scene toward the state transitions described by the language.