A new survey of 100 papers introduces World Action Models that predict physical consequences before a robot moves. Unlike traditional AI, these models learn from unlabeled everyday videos. This shift allows robots to train on vast, non-specialized datasets. Practitioners can now build agents that understand cause-and-effect without expensive manual labeling.