Robot Learning

Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans of much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late 1950s, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fuelled by recent work in the areas of reinforcement earning, behaviour-based architectures, genetic algorithms, neural networks and the study of artificial life. This book gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in the book include: reinforcement learning; behaviour-based architectures; neural networks; map learning; action models; navigation; and guided exploration.