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Robots worldwide learn from each other through database
21 januari 2010 - This week, RoboEarth has started, a project led by TU/e which is intended to ensure that robots in industry and healthcare can learn from each other and thereby acquire new actions much faster. To this end a worldwide database will be set up from which newly learned actions can be downloaded. In the project, which has a budget of 5.6 million euros, six European partners will be working together for the next four years.

“In new environments robots now still have to reinvent the wheel time and time again”, says project leader dr.ir. René van de Molengraft from the Department of Mechanical Engineering. “In project RoboEarth we are developing a kind of World Wide Web for robots, in which they can store each task and action that they have learned. As a result, other robots anywhere in the world no longer need to learn these tasks themselves anymore. This means that robots can acquire new actions much faster.”

One of the challenges for the 21st century is the development of robots that can move around in the human world and carry out tasks there that are useful to society, for example in care for the elderly. As it is, robots cannot react independently to new situations. Each task carried out by the robot has been preprogrammed by humans. There is no collective, worldwide memory that robots can plug into to carry out their tasks in a new environment, for instance. RoboEarth is going to change that now. It should lead to someone in Eindhoven being able to download a task for his robot which a user in Rio de Janeiro, in possession of an identical robot, has taught his mechanical domestic help.

As stated, the team is led by TU/e. Other participants are Universität Stuttgart, the ETH Zürich, Universidad de Zaragoza, Technische Universität München and Philips Applied Technologies. The team will be making six demos, which will demonstrate the use of RoboEarth; the six include a robot that can offer patients in hospital a drink, and a system that will show how the knowledge gained by robot A can improve the performance of robot B. (TJ)/.