Humans frequently engage in activities for their own sake rather than as a step towards solving a specific task. During such behavior, which psychologists refer to as being intrinsically motivated, we often develop skills that allow us to exercise mastery over our environment. Singh, Barto, & Chentanez (2004) have recently proposed an algorithm for intrinsically motivated reinforcement learning (IMRL) aimed at constructing hierarchies of skills through self-motivated interaction of an agent with its environment. While they were able to successfully demonstrate the utility of IMRL in simulation, we present the first realization of this approach on a real robot. To this end, we implemented a control architecture for the Sony-AIBO robot that extends the IMRL algorithm to this platform. Through experiments, we examine whether the Aibo is indeed able to learn useful skill hierarchies.
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