Imitation addresses the problem of learning a task representation and/or solution from observations of a demonstration. From such demonstrations it is possible to extract various kind of information, and different approaches exist to extract each type. Approaches have ranged from regression and classification methods, clustering and inverse reinforcement learning. In this presentation we will review some of these approaches, particularly the ones with an active learning generalization. We will also try to have a unified perspective of some of them, particularly regression and inverse reinforcement learning. We will present new results and discuss the main advantages and disadvantages of using active learning in an imitation setting.