Abstract
An autonomous developmental robot typically requires a considerable amount of developmental experience before it is able to learn, master and use abstract concepts, even if its speed of cognitive development is not limited by the speed of cell growth in humans. How can a developmental robot learn abstract concepts early on and use these concepts to reason and make decision? This paper introduces a frame work of two macro-layers. The upper macro-layer enables human teachers to interactively inject a representation of abstract concepts (e.g., location) into the developmental process. The lower layer takes the desired information (e.g., desired heading direction) as the input. Autonomous navigation with a global path planner is experimented with as example
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