Qwen-RobotManip, a Vision-Language-Action foundation model, enables large-scale training through unified alignment across representation, motion, and behavior. It uses open-source data to build a 38,100-hour pretraining corpus and demonstrates emergent generalization, outperforming prior state-of-the-art models in out-of-distribution settings and ranking first in RoboChallenge with a 20% relative improvement on real-robot platforms.