Xiaomi-Robotics-1
Xiaomi-Robotics-1 introduces a new training paradigm for robotics that utilizes large-scale, embodiment-free pre-training followed by real-robot post-training. This approach aims to overcome data scarcity issues that have historically limited the scaling of robot policy models.
Why it matters
Advancements in robotics training models could significantly accelerate the development of versatile, autonomous robots capable of performing complex tasks in real-world environments.
Breaking the data barrier. Scaling robot policy models with embodiment-free pre-training.
Foundation models in language and vision keep moving the frontier by riding empirical scaling laws: capability tracks data, parameters, and compute. Robotics has missed out. Large-scale, high-quality data is hard to come by, and that scarcity, more than anything else, has capped how far policy models could scale. What robots can do under genuinely large-scale training remained largely an open question. We take a step toward answering it. Xiaomi-Robotics-1 combines large-scale embodiment-free (UMI) pre-training with a modest amount of real-robot data in a post-training stage. We study how the model behaves as it scales.
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