This tutorial will provide an introduction into the basic algorithms and techniques used in robot learning. It will cover recent work on learning models, learning control, and probabilistic reasoning. In addition, it will highlight some recent success-stories of robot learning and give some guidance for applying robot learning in practice.
The tutorial is targeted towards students, engineers, scientists, and
teachers who are new to the field of robot learning, but who would like
to get an overview of the field (and who would like to share my
Basic knowledge in robotics, machine learning, or statistics will be
helpful but is not required.
About the Lecturers
is a research faculty member at Carnegie Mellon University. His research
interests lie in the areas of machine learning, neural networks and
robotics. Thrun received his Ph.D. in 1995 and his M.Sc. in 1993, both
from the University of Bonn in Germany. He is a consultant for several
companies, including Real World Interface Inc., a leading US mobile
robot manufacturer. Thrun recently co-edited a special issue of the
journal Machine Learning and a book on "Robot Learning," and is
currently editing another book entitled "Learning to learn."