A person can show a robot where a cup belongs without showing how a gripper should hold it. That gap between a visible goal and an executable action shapes how robots learn from human demonstrations. This review examines six studies of physical manipulation, from learning through practice after a single demonstration to estimating robot actions from synthetic video. Some systems use human motion to guide a controller trained on robot demonstrations. Others recover relations between hands and objects or exploit experience acquired across robots and environments. We examine what each system observes, which information must come from elsewhere, and how researchers measure transfer on physical hardware. Success under a change of viewpoint answers a different question from success after an object slips. These distinctions clarify the contribution of the human demonstrator and show why the strongest evidence for learning comes from tasks that test which features of the demonstration matter.
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