Integrating developmental and conventional Markov decision processes: An application to robotic navigation
2013
This paper proposes an architecture for developmental robot that can learn abstract concepts early on and use these concepts to reason and make decisions. We introduce a frame work of two macro-layers. The bottom layer takes the desired information (e.g., desired heading direction) as the input. The top macro-layer enables human teachers to interactively inject a representation of abstract concepts (e.g., location) into the developmental process. This architecture is applied to a navigation problem, and its superiority over one-layer architecture is confirmed in comparative experiments using simulated Lidar sensor data. The robotic navigation demonstrates its robustness in accomplishing complicated task in clutter outdoor environments.
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