Abstract
Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of robotics and artificial intelligence. This chapter presents the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. The chapter illustrates a learning mechanism, explanation-based neural-network learning, that transfers knowledge between related tasks via neural-network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic, and laser sensors. In less than 10 minutes of operation time, the robot is able to learn to navigate to a marked target object in a natural office environment.
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