Galbot trains AstraBrain through a combined approach: a small amount of human demonstration to capture task intent, massive high-fidelity simulation and synthetic data (including hard cases like transparent objects and deformable cloth) to broaden coverage, reinforcement learning to sharpen precision and collision-free behavior via trial-and-error, and lightweight real-world fine-tuning to close the sim-to-real gap. The result is a system that emphasizes generalization—one autonomy stack handling diverse tasks (in-hand dexterity, transparent-object pickup, packed-shelf retrieval, cloth folding, and bimanual tool use) by adapting to the live environment rather than “memorizing” a single scripted sequence.