(PhysOrg.com) -- Robots typically acquire new capacities by imitation. Now, EPFL scientists are doing the inverse -- developing machines that can learn more rapidly and outperform humans by starting from failed or inaccurate demonstrations.

A robot, unblinking, impassive, observes. Its instructor wants it to learn how to put a balloon in a basket 20 meters away. As the researcher demonstrates this task, which is difficult for a human to accomplish, she systematically misses the basket. Isnt the scientist just wasting her time?

Typically looked at simply as useless mistakes, failed demonstrations can, on the contrary, be opportunities to learn better, claim scientists from EPFLs Learning Algorithms and Systems Laboratory (LASA). Their unusual point of view has led to the development of novel algorithms.

We inversed the principle, generally accepted in robotics, of acquisition by imitation, and considered cases in which humans are inaccurate in certain tasks, explains professor Aude Billard, head of LASA. This approach allows the robot to go further, to learn more quickly and above all, outperform the human, notes postdoctoral researcher Dan Grollman, who was recently awarded a Best Paper Award for an article on the subject presented at the International Conference on Robotics and Automation (ICRA), in Shanghai.

Grollman based his work on what he calls the Donut as I do theory. He developed an algorithm that tells the robot not to reproduce a demonstrators inaccurate gesture. The machine will use this input to avoid repeating the mistake and to search for alternative solutions. Thus the choice of the term donut  a play on the words do not and donut. The hole in the middle is the incorrect gesture, which must be excluded, and the surrounding dough represents the field of potential solutions to explore.

We were inspired by the way in which humans learn, explains Billard. Children often progress by making mistakes or by observing others mistakes and assimilating the fact that they must not reproduce them.

This way of learning things is a real step forward, says Grollman, who adds that, after all, isnt the real goal to make robots that can do things we cant?

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Provided by Ecole Polytechnique Federale de Lausanne