Roboticists once traded sci-fi ambitions for the narrow utility of industrial arms and vacuum cleaners. This gap between aspiration and execution persisted for decades. Now, new learning paradigms allow machines to generalize tasks. Practitioners can finally move beyond rigid, pre-programmed scripts toward flexible autonomous agents that adapt to unpredictable physical environments.