One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self- motivated, open-ended way Barto, Singh, and Chentanez (Barto, Singh, AND Chentanez 2004; Singh, Barto, AND Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a task-nonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.
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