Trains a three-stage system that enables humanoid robots to visually perceive and reflexively catch thrown boxes using an egocentric RGB-D encoder, a compact latent object-dynamics representation, and a whole-body RL controller; reports 85.7% simulation success and 65%/40% real-world success at 2.5/5 m.