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Prof. Dr. Ralf Der

Phone: +49 341 9959-564
Fax: +49 341 9959-658

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PD Dr. Nihat Ay

Head Information Theory of Cognitive Systems Group

Phone: +49 341 9959-558
Fax: +49 341 9959-555

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Complex Systems . Computer Science . Mathematics

Robots start learning

March 23, 2009

Ingeniously designed machines learn to move without receiving any instructions from control programs. Similarly, robots are learning about their bodies and their environment.

Text: Stefan Albus

Future robots will be able to water flowers only if they can act in a flexible and sensitive way, That is what they are trained for by sophisticated mathematical expressions. Zoom Image
Future robots will be able to water flowers only if they can act in a flexible and sensitive way, That is what they are trained for by sophisticated mathematical expressions. [less]

That’s supposed to be a robot?” Parents who show their science-fiction-loving children an industrial robot for the first time are probably quite familiar with such questions. No wonder: these complex machines are not at all reminiscent of the cinema hero C3PO from the film Star Wars, or the visions we may have of the domestic robots of the future. What’s more, even the very latest mechanical helpers that neatly place little plastic caps on trays in a factory are basically brainless. If the tray feed snags, it will let all the plastic objects entrusted to it fall without reacting.

For researchers like Nihat Ay and Ralf Der at the Max Planck Institute for Mathematics in the Sciences in Leipzig, there is a simple reason for this: “Robots today are still highly rule-based systems,” says Ralf Der: they simply implement predefined programs. And we attempt to predefine effective responses on their behalf for every eventuality. Ultimately, however, this approach mak­es robots rigid and inflexible.

Highly developed machines can now perform ballet on their own, climb stairs, and even react to laughter and language – but strictly in accordance with the program. Consequently, they inevitably fail at some point, as the world is too complex to fit into any rulebook: if you want to make a stair-climbing robot falter, all you have to do is put a brick on one of the steps. That is why, when it’s essential to get everything right, for example on space missions, engineers rely on remote-controlled machines. Such difficult missions would require robots that can adapt deftly to their environment and can solve problems autonomously.

This is precisely what is on the minds of researchers like Nihat Ay and Ralf Der. In Ralf Der’s words: “You can train children by telling them exactly what they should do. This is the rule-based approach. However, you can also observe what they are best at and foster this activity.”

But how is a robot that, without its control programs, is little more than a pile of metal, supposed to display behavior that is worth fostering? In the opinion of Der and Ay, this is precisely the misconception that has misguided the advocates of so-called strong artificial intelligence (AI) for years. Instead, in his view, the solution lies in such concepts as self-organization and embodied intelligence.

 
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