Researchers at the Eindhoven University of Technology (TU/e) and the Max Planck Institute for Polymer Research in Mainz, Germany, have proven that robots can learn to successfully navigate the twists and turns of a maze. Their robot bases its decisions on a human-like brain. The study, titled Organic neuromorphic electronics for sensorimotor integration and learning in robotics, has been published in Science Advances. This study paves the way to exciting new applications of neuromorphic devices in health and beyond.

Machine learning and neural networks have a drawback because they consume too much power to train the algorithms. This power issue is one of the reasons that researchers have been trying to develop computers that are much more energy efficient. The human brain is a great example for a thinking machine unrivalled in its low power consumption due to how it combines memory and processing.

“In our research, we have taken this model to develop a robot that is able to learn to move through a labyrinth”, explains Imke Krauhausen, PhD student at the department of Mechanical Engineering at TU/e and principal author of the paper. “Just as a synapse in a mouse brain is strengthened each time it takes the correct turn in a psychologist’s maze, our device is ‘tuned’ by applying a certain amount of electricity. By tuning the resistance in the device, you change the voltage that control the motors. They in turn determine whether the robot turns right or left."

The robot that Krauhausen and her colleagues used for their research is a Mindstorms EV3, a robotics kit made by Lego. Equipped with two wheels, traditional guiding software to make sure it can follow a line, and a number of reflectance and touch sensors, it was sent into a 2 m2 large maze made up out of black-lined hexagons in a honeycomb-like pattern.

Another clever thing about the research is the organic material used for the neuromorphic robot. This polymer (known as p(g2T-TT)) is not only stable, but it also is able to ‘retain’ a large part of the specific states in which it has been tuned during the various runs through the labyrinth. This ensures that the learned behaviour ‘sticks’, just like neurons and synapses in a human brain remember events or actions. 

The use of polymer instead of silicon in the field of neuromorphic computing was pioneered by Paschalis Gkoupidenis of the Max Planck Institute for Polymer Research in Mainz and Yoeri van de Burgt of TU/e, both co-authors of the paper. In their research (dating from 2015 and 2017), they proved that the material can be tuned in a much larger range of conduction than inorganic materials, and that it is able to ‘remember’ or store learned states for extended periods. Since then, organic devices have become a hot topic in the field of hardware-based artificial neural networks.