This paper presents a comparative study of approaches to control robots with variable impedance actuators (VIAs) in ways that imitate the behavior of humans. We focus on problems where impedance modulation strategies are recorded from human demonstrators for transfer to robotic systems with differing levels of heterogeneity, both in terms of the dynamics and actuation. We categorize three classes of approach that may be applied to this problem, namely, 1) direct, 2) feature-based, and 3) inverse optimal approaches to transfer. While the first is restricted to highly biomorphic plants, the latter two are shown to be sufficiently general to be applied to various VIAs in a way that is independent of the mechanical design. As instantiations of such transfer schemes, 1) a constraint-based method and 2) an apprenticeship learning framework are proposed, and their suitability to different problems in robotic imitation, in terms of efficiency, ease of use, and task performance, is characterized. The approaches are compared in simulation on systems of varying complexity, and robotic experiments are reported for transfer of behavior from human electromyographic data to two different variable passive compliance robotic devices.