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Rather than try to learn skills directly, developmental learning seeks to create a general purpose system inspired by the human developmental process. The premise is that intelligent behavior can only be achieved through the course of experience, and attempts to circumvent this approach will lead to a plateau limiting performance. There are many facets of this approach and some of the common examples are categorization, sensorimotor control and social learning. These are relatively simple tasks for people, yet challenging for an autonomous robot. A survey of research in this recent field is presented. The concepts and the tools used are described so that other disciplines may have a understanding of this field and use the paradigm in their applications.