In this paper we describe an approach of controlling an autonomous robot by means of a hierarchical organised control structure. The action selection mechanism that was realised is capable of learning. Controlling mobile robots in an efficient way is one of the greatest challenges in current research in robotics. Since Damasio's "Descartes' error" in 1994 the number of approaches to action selection that use internal values, derived from psychological models of emotions or drives, has increased significantly. We present an approach that realises a learning action selection mechanism in a hierarchy of sensory and actuatory layers. The sensory values yield the internal states which serve as a basis for the action selection. In addition the internal states are used to calculate the reinforcement signal that trains the action selection.
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