Robot Learning
Sebastian Thrun
Course Description
Robot learning is concerned with algorithms that enable robots
to improve their performance with experience. Learning can make
up for lack of knowledge when programming robots, such as
lack of exact task specifications, lack of environment models,
lack of sensor models, or lack of effective control strategies.
Traditionally, the field of robotics has paid little attention to robot
learning. With a new generation of intelligent service robots in
close reach, and with the number of success stories increasing,
learning is likely to become a fundamental part of robotics.
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
excitement).
Basic knowledge in robotics, machine learning, or statistics will be
helpful but is not required.
About the Lecturers
Sebastian Thrun
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."
higuchi@etl.go.jp
Last modified: Thu Feb 20 14:07:12 JST 1997