'Human-like' brain helps robot out of a maze
Date:
December 10, 2021
Source:
Eindhoven University of Technology
Summary:
A maze is a popular device among psychologists to assess the
learning capacity of mice or rats. But how about robots? Can
they learn to successfully navigate the twists and turns of a
labyrinth? Now, researchers have demonstrated they can. Their robot
bases its decisions on the very system humans use to think and act:
the brain. The study paves the way to exciting new applications
of neuromorphic devices in health and beyond.
FULL STORY ==========================================================================
A maze is a popular device among psychologists to assess the learning
capacity of mice or rats. But how about robots? Can they learn to
successfully navigate the twists and turns of a labyrinth? Now,
researchers at the Eindhoven University of Technology (TU/e) in the
Netherlands and the Max Planck Institute for Polymer Research in Mainz, Germany, have proven they can. Their robot bases its decisions on the
very system humans use to think and act: the brain. The study, which was published in Science Advances, paves the way to exciting new applications
of neuromorphic devices in health and beyond.
========================================================================== Machine learning and neural networks have become all the rage in recent
years, and quite understandably so, considering their many successes
in image recognition, medical diagnosis, e-commerce and many other
fields. Still though, this software-based approach to machine intelligence
has its drawbacks, not least because it consumes so Mimicking the human
brain This power issue is one of the reasons that researchers have been
trying to develop computers that are much more energy efficient. And
to find a solution many are finding inspiration in the human brain,
a thinking machine unrivalled in its low power consumption due to how
it combines memory and processing.
Neurons in our brain communicate with one another through so-called
synapses, which are strengthened each time information flows through
them. It is this plasticity that ensures that humans remember and learn.
"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." So how does it work?
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.
The robot is programmed to turn right by default. Each time it reaches
a dead end or diverges from the designated path to the exit (which is
indicated by visual cues), it is told to either return or turn left. This corrective stimulus is then remembered in the neuromorphic device for
the next effort.
"In the end, it took our robot 16 runs to find the exit successfully,"
says Krauhausen. "And, what's more, once it has learned to navigate this specific route (target path 1), it can navigate any other path that it
is given in one go(target path 2). So, the knowledge it has acquired
is generalizable." Part of the success of the robot's ability to learn
and exit the maze lies in the unique integration of sensors and motors, according to Krauhausen, who cooperated closely with the Max Planck
Institute for Polymer Research in Mainz for this research. "This
sensorimotor integration, in which sense and movement reinforce one
another, is also very much how nature operates, so this is what we tried
to emulate in our robot."
========================================================================== Smart polymers 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.
Bionic hands Polymeric materials also have the added advantage that
they can be used in numerous biomedical applications. "Because of their
organic nature, these smart devices can in principle be integrated with
actual nerve cells. Say you lost your arm during an injury. Then you
could potentially use these devices to link your body to a bionic hand,"
says Krauhausen.
Another promising application of organic neuromorphic computing lies
in small so-called edge computing devices where data from sensors is
processed locally outside of the cloud. Van de Burgt: "This is where I see
our devices going in the future, our materials will be very useful because
they are easy to tune, use much less power, and are cheap to make."
So will neuromorphic robots one day be able to play a soccer game, just
like TU/e's soccer robots? Krauhausen: "In principle, that is certainly possible. But there's a long way to go. Our robots still rely partly on traditional software to move around. And for the neuromorphic robots to
execute really complex tasks, we need to build neuromorphic networks in
which many devices work together in a grid. That's something that I will
be working on in the next phase of my PhD research." A 'human-like' brain helps a robot out of a maze:
https://www.youtube.com/ watch?v=O05YVljxrtg ========================================================================== Story Source: Materials provided by
Eindhoven_University_of_Technology. Note: Content may be edited for
style and length.
========================================================================== Journal Reference:
1. Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas,
Scott T.
Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar
Sheelamanthula, Alexander Giovannitti, Fabrizio Torricelli, Iain
Mcculloch, Paul W. M.
Blom, Alberto Salleo, Yoeri van de Burgt, Paschalis
Gkoupidenis. Organic neuromorphic electronics for sensorimotor
integration and learning in robotics. Science Advances, 2021; 7
(50) DOI: 10.1126/sciadv.abl5068 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2021/12/211210140717.htm
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