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In this paper, we develop a technique for learning controllers for a large set of heterogeneous behaviors. By dividing a reference library of motion into clusters of like motions, we are able to construct\n            <jats:italic toggle=\"yes\">experts<\/jats:italic>\n            , learned controllers that can reproduce a simulated version of the motions in that cluster. These experts are then combined via a second learning phase, into a general controller with the capability to reproduce any motion in the reference library. 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