In this article, we present some preliminary work on integrating an artificial curiosity mechanism in PROPRE, a generic and modular neural architecture, to obtain online, open-ended and active learning of a sensory-motor space, where large areas can be unlearnable. PROPRE consists of the combination of the projection of the input motor flow, using a self-organizing map, with the regression of the sensory output flow from this projection representation, using a linear regression. The main feature of PROPRE is the use of a predictability module that provides an interestingness measure for the current motor stimulus depending on a simple evaluation of the sensory prediction quality. This measure modulates the projection learning so that to favor the representations that predict the output better than a local average. Especially, this leads to the learning of local representations where an input/output relationship is defined. In this article, we propose an artificial curiosity mechanism based on the monitoring of learning progress in the neighborhood of each local representation. Thus, PROPRE simultaneously learns interesting representations of the input flow (depending on their capacities to predict the output) and explores actively this input space where the learning progress is the higher. We illustrate our architecture on the learning of a direct model of an arm whose hand can only be perceived in a restricted visual space. The modulation of the projection learning leads to a better performance and the use of the curiosity mechanism provides quicker learning and even improves the final performance.