Post-training aims to align the strong action-generation capabilities acquired from pre-training with real robot embodiments and natural-language instruction following along two axes. Embodiment alignment uses high-quality cross-embodiment real-robot data to map the general action-generation ability onto actual robots. Instruction alignment shifts the model from "generating actions given a description of scene state transitions" to "understanding a natural-language instruction and executing it directly."

After post-training, Xiaomi-Robotics-1 can be used out-of-the-box to perform a wide range of mobile manipulation tasks in the real world. We evaluate the post-trained model in unseen environments with unseen object instances to understand whether the scaling behaviors from pre-training can transfer to real-robot performance after post-training.

The answer is yes. As we increase the amount of pre-training data and model size, real-robot success rate rises steadily and predictably. That is, a stronger pre-trained model yields better real-robot performance. The scaling gains show no signs of saturation: the real-robot success rate after post-training keeps improving as the model consumes more data or scales up during pre-training.