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In the past, nonlinear dynamic systems have been proposed as a suitable representation for motor control. It has been shown that it is possible to learn desired complex control policies by a nonlinear transformation of an existing simpler control policy, which is based on a canonical dynamic system. The resulting control policies were termed dynamic movement primitives. The main result of this paper is an approach to learning parametrized sets of dynamic movement primitives based on a library of example movements. Learning was implemented by applying locally weighted regression where the goal of an action is used as a query point into the library of example movements. The proposed approach enables the generation of a wide range of movements that are adapted to the current configuration of the external world without requiring an expert to appropriately modify the underlying differential equations to account for percepetual feedback.