Using machine learning techniques in real-world mobile robots

Applying machine learning techniques can help mobile robots meet the need for increased safety and adaptivity that real world operation demands. The techniques also facilitate robot to user communication. Using these techniques, we built increasingly abstract representations of a robot's perceptions and actions. This produced a symbolic description of what the robot knows and can do. Because this task is fairly complex, we first identified those subproblems that a learning method can solve efficiently, and isolated those with good classical solutions. Also, for a robot to solve a complex problem, we had to find solutions for several learning tasks. We identified these learning tasks and the learning techniques appropriate for their solution. To evaluate our approach, we used the mobile robots Priamos and Teseo.<
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