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In this paper we consider autonomous robot discovery through experimentation in the robotpsilas environment. We analyse the applicability of machine learning (ML) methods with respect to various levels of robot discovery tasks, from extracting simple laws among the observed variables, to discovering completely new notions that were never mentioned in the data directly. We first introduce the XPERO project, and present some illustrative initial experiments in robot learning in XPERO. Then we formulate a systematic list of types of learning or discovery tasks, and discuss the suitability of chosen ML methods for these tasks.