A human is inserted into the training or operation loop: selecting or labeling key data, correcting model errors, rating outputs, or demonstrating desired behavior (e.g. by teleoperating a robot); the system learns from this feedback and improves iteratively.
Purely automated learning can be inefficient or unreliable on complex, ambiguous tasks; human involvement provides high-quality signal (labels, corrections, demonstrations) and safety oversight.
