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Ph.D., Prof. (Brown University, Providence/USA) Michael J. Black

Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen

Phone: +49 7071 601-1801
Fax: +49 7071 601-1802

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Robots learn human perception

February 17, 2011

Michael J. Black teaches computers to analyse data on their environment as quickly and reliably as the human brain.

Text: Tim Schröder

How do robots learn to move? Michael J. Black, founding director of the  Max Planck Institute for intelligent systems, tries to answer this  question. Zoom Image
How do robots learn to move? Michael J. Black, founding director of the Max Planck Institute for intelligent systems, tries to answer this question. [less]

"I really like Tübingen," says Michael J. Black. "The city, its surroundings. My wife and I love hiking. The Swabian Alb is fantastic!" Michael J. Black intends to stay here permanently. He is now in his late forties. "That means I still have about 20 years to carry out my research." And he is pretty certain about what he wants to achieve in this time. "I want to teach a robot to be as familiar with the world as a two-year-old child." This may not sound like a lot at first, but it would actually be a sensation – after all, two-year-olds are pretty darned clever.

Newborn babies have a strong grip. They have strong grasp reflexes, which is evident when they grab your finger for example - but that is about all they can do. A two-year-old child, however, is already an expert when it comes to grasping and has dozens of gripping variations. For instance, they can gently lift objects and hold a spoon. Small children can competently move round angular and pointed objects in their hands, and they are also capable of abstraction. They can recognise angular objects as angular objects and round objects as round objects, regardless of whether the object has three, four or five corners or curves – and regardless of whether this is the first time they have seen the object.

It is this abstraction ability that is still missing from the brain of a computer today. "Human beings analyse their environment within fractions of a second," says Black. "All we need to do is glance at a surface to know whether it is slippery or not." A computer has to carry out extensive calculations before it can disentangle the input from its sensors and identify what something is. The human brain, however, picks a few basic characteristics from the storm of sensory stimuli it receives and comes to an accurate conclusion about the nature of the world around us.

This is precisely what Black envisages for his computers: to be capable of generalisation and abstraction, to be able to infer characteristics from a small amount of information. Yes, a technical system can process thousands of data, figures and measurement values and analyse the atomic structure of a floor tile – yet a robot would probably still slip on a floor that has been freshly mopped. Black's central question is which environmental stimuli are important. How does the brain manage to deduce so much from so little – and safely guide us through our lives? And how do we teach this to computers and robots?

 
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