ST. AUGUSTIN, Germany, April 20 /PRNewswire/ --     Researchers in the European research project XPERO have developed a
machine learning method, which enables a small humanoid to learn rather
fundamental mathematical concepts such as position and orientation in a
coordinate system. The algorithm takes the robot's sensor data recorded while
it moves through the surrounding world and creates a model, which allows the
robot to predict how the objects in its vicinity will change their position
relative to the robot when it moves. "What is a trivial thing for a human is
a rather difficult problem for a robot," explain Jure Zabkar and Ivan Bratko,
from Univ. of Ljubljana, the inventors of the algorithm. Our robot has less
knowledge than a baby. Seeing an object does not mean anything to it. It only
perceives color blobs or edges. It has neither a sense of objects and nor of
a position of an object in a coordinate system and nor how that changes as it
moves. The robot is neither told to learn a coordinate system nor how to
learn it nor what the use of a coordinate system is. We have developed
mechanisms, which allow the robot to extract regularities in its sensor data
and to translate them into models or theories which in turn allow the robot
to better explain and predict what is going on around it. Learning a
coordinate system is just a demonstration of this capability. With the same
algorithm we have learned physical concepts such as "movability" of an object
or "degree of freedom" (number of axes in/around which an object can be
moved).

What seems a rather basic research problem, however, has also a
significant technological relevance, claims Erwin Prassler from
Bonn-Rhein-Sieg Univ. in Sankt Augustin, Germany, the coordinator of the
project. The XPERO project lays the first cornerstones for a technology,
which has the potential to become a key technology for the next generation of
so-called service robots, which clean our houses, mow our lawns, or polish
our shoes. Existing products are rather dumb, pre-programmed devices. They
can only perform a single pre-programmed task. They cannot perform any new
tasks or cope with unforeseen operational conditions. Future service robots
will have to be able to learn entirely new concepts and models based on their
existing knowledge and their sensor observations and with this new knowledge
also perform new tasks.

XPERO's learning robot will be demonstrated during the FET'09 conference
(Future and Emerging Technologies) in Prague, Czech Republic from April 
21-23, 2009.

For more information contact:
    Prof. Dr. Erwin Prassler
    Bonn-Rhein-Sieg University of Applied Sciences
    Grantham-Allee 20
    53757 Sankt Augustin
    Germany
    Email: erwin.prassler@h-brs.de
    Phone: +49-2241-865-257
    Mobile: +49-179-129-1079



URL: http://www.xpero.org, http://www.ailab.si/xpero/