Department of Computer Science
Informatik 9
Boltzmannstrasse 3
85748 Garching
Germany
Tel: +49-89-289-17759
Fax: +49-89-289-17757
Office:
Mail: michael.beetz@in.tum.de
CCRL II:
Tel: +49-89-289-26902
Office: 3002
At present, I receive large amount of personal Email messages per day. If you need an urgent or a certain reply, please cc my personal assistant Doris Walter or one of my students. Thank you.
Michael Beetz is a professor for Computer Science at the Department of Informatics of the Technische Universität Muenchen and heads the Intelligent Autonomous Systems group. From 2006 to 2011, he was vice coordinator of the German national cluster of excellence CoTeSys (Cognition for Technical Systems) where he is also co-coordinator of the research area “Knowledge and Learning”.
Michael Beetz received his diploma degree in Computer Science with distinction from the University of Kaiserslautern. He received his MSc, MPhil, and PhD degrees from Yale University in 1993, 1994, and 1996 and his Venia Legendi from the University of Bonn in 2000. Michael Beetz was a member of the steering committee of the European network of excellence in AI planning (PLANET) and coordinating the research area ``robot planning''. He is associate editor of the AI Journal. His research interests include plan-based control of robotic agents, knowledge processing and representation for robots, integrated robot learning, and cognitive perception.
@Article{Rusu08RAS,
author = {Radu Bogdan Rusu and Brian Gerkey and Michael Beetz},
title = {{Robots in the kitchen: Exploiting ubiquitous sensing and actuation}},
journal = {Robotics and Autonomous Systems Journal (Special Issue on Network Robot Systems)},
year = {2008},
abstract = {
Our goal is to develop intelligent service robots that operate in standard
human environments, automating common tasks. In pursuit of this goal, we
follow the ubiquitous robotics paradigm, in which intelligent
perception and control are combined with ubiquitous computing. By
exploiting sensors and effectors in its environment, a robot can perform
more complex tasks without becoming overly complex itself. Following this
insight, we have developed a service robot that operates autonomously in a
sensor-equipped kitchen. The robot learns from demonstration and performs
sophisticated tasks in concert with the network of devices in its
environment. We report on the design, implementation, and usage of this
system, which is freely available for use and improvement by others in the
research community.
},
bib2html_groups = {EnvMod},
bib2html_pubtype = {Journal},
bib2html_rescat = {Perception},
bib2html_funding = {CoTeSys},
bib2html_domain = {Assistive Household},
}