Small Humanoid Nao learning mathematical and physical concepts
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: www.xpero.org | Prof. Dr. Ivan Bratko
Faculty of Computer and Information Sc.
University of Ljubljana
Trzaska 25, 1000 Ljubljana, Slovenia
Email: bratko@fri.uni-lj.si
Phone: +386 1 4768393
URL: www.ailab.si/xpero/ |
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