The ARC (Abstraction and Reasoning Corpus) challenge has been proposed to push AI research towards more generalization capability rather than ever more performance. It is a collection of unique tasks about generating colored grids, specified by a few examples only. We propose object-centered models analogous to the natural programs produced by humans. The MDL (Minimum Description Length) principle is exploited for an efficient search in the vast model space. We obtain encouraging results with a class of of simple models: various tasks are solved and the learned models are close to natural programs.