Researchers of Freie Universitat Berlin, of the Bernstein Fokus Neuronal Basis of Learning, and of the Bernstein Center Berlin used the relatively simple nervous system of the honeybee as a model for the robot's working principles.
They installed a camera on a small robotic vehicle and connected it to a computer. The computer programme replicated in a simplified way the sensorimotor network of the insect brain.
The input data came from the camera that - akin to an eye - received and projected visual information. The neural network, in turn, operated the motors of the robot wheels and could thus control its motion direction.
"The network-controlled robot is able to link certain external stimuli with behavioural rules," said Professor Martin Paul Nawrot, head of the research team and professor of neuroscience at Freie Universitat Berlin.
"Much like honeybees learn to associate certain flower colors with tasty nectar, the robot learns to approach certain coloured objects and to avoid others," Nawrot said.
In the learning experiment, the scientists located the network-controlled robot in the centre of a small arena. Red and blue objects were installed on the walls.
The simultaneous processing of red colour and the reward now led to specific changes in those parts of the network, which exercised control over the robot wheels.
As a consequence, when the robot "saw" another red object, it started to move toward it. Blue items, in contrast, made it move backwards.
"Just within seconds, the robot accomplishes the task to find an object in the desired colour and to approach it," said Nawrot.
The scientists are now planning to expand their neural network by supplementing more learning principles to make the mini brain even more powerful and the robot more autonomous.
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