Ethan Bennett
Biology 1615
Jennifer M. Cowan
SLCC Redwood Campus
Evolutionary Robotics
A synopsis based on the article, “Evolution of Adaptive Behavior in Robots by Means of
Darwinian Selection” Written by Dario Floreano and Laurent Keller.
The idea of evolutionary robotics began with Alan Turing in the 1950’s. Turing hypothesized that
although the development of artificially intelligent control systems would be too complicated for
humans to achieve, scientists could instead program computers with random genes and let them evolve
by the process of Darwinian natural selection until they display the same phenotypes that we see in
biological organisms. Turing’s hypothesis is what drives the experimentation in evolutionary robotics
today.
Now, almost a half century later, we have developed advanced computer technology that
enables us to carry out these types of experiments at an elementary level. The robots in these
experiments display behaviors of collision free movement, homing, predator/prey co-evolution,
evolution of brain and body morphologies, and even cooperation and altruism. These phenotypes
develop completely independent of human intervention and programming. Robots are given random
genes to begin with and the ones who perform their given objective most accurately are naturally
selected to pass on to the next generation. The details of how these robots produce offspring and pass
on genetic information are not given as I assume they are beyond the scope of this article. The details of
environment and robot morphologies for each experiment are given and I will briefly cover each as I
describe each experiment.
Collision free movement
Scientists experimented with robots to see if they could develop collision free navigation.
Robots were placed in a maze and programmed with random genes. In the first generations of the
experiment, the robots chaotically bumped into walls and made little progress through their
environments. In successive generations the robots improved as the ones who bumped into walls less
frequently were selected to have their genes passed on. Within 100 generations the robots showed
completely collision free movement. One unexpected result the scientists noticed was that the robots
were only moving at half their capable speed. After manually speeding up the robots the scientists
discovered that the vision sensors of the robots were not fast enough to update the central processor in
time to change the trajectory of the robot. This result indicates that the robot’s genes became aware of
not just their environment, but also the morphological limitations of the robots as well.
Predator/prey co-evolution
Beside collision free movement and predator/prey co-evolution, there are three other
experiments carried out that were briefly mentioned at the beginning of this synopsis. Although these
experiments resulted in some very valuable and interesting conclusions, the co-evolution of
predator/prey behavior shows the most supporting and comical evidence for Darwinian selection. So for
the purposes of this exercise I will not cover the other three experiments but the reader is welcome to
cover these in more depth by reading the article attached.
Many technical details are given in the original article about the physical capabilities of the
robots but for my purposes it is sufficient to say that one, the prey robots were given the capability of
higher speed but their vision was impaired and they were completely blind to one side; two, the
predator robots were given the capability of an additional field of vision with a 36 degree field of view
but their speed was impaired; and three, the robots were placed in a square arena for the duration of
the experiment. As usual, the robots were programmed with random genes to begin with and displayed
chaotic behavior in the first few generations of the experiments. The robots quickly developed strategies
of capture and escape and within 100 generations the robots showed 6 different strategies of which
they used to perform their tasks.
At first the prey were simply able to outrun the predators when they came near. In short time
the predators figured out how to intercept the prey from a lateral direction. The predators executed this
strategy so quickly that they began to bump into walls. The prey exploited the flaw in the predator’s
strategy by simply moving backward when the predator came near with the result of avoiding capture
and the subsequent collision of the predators with the walls. Later generations of predators figured out
that the prey were completely blind to one side and used this weakness to approach the prey from the
blind side. The prey responded to this strategy by coasting the side walls at maximum speed so to avoid
blind side and lateral capture. Later generations of predators used a “sit and wait” strategy to capture
the prey as it came near. The prey responded by coasting the wall back and forth in one area of the
arena where they knew the predators were not residing. The experiment ends here.
As we can see, with each subsequent generation, the predators and prey continued to take into
account the failures of the previous generations. In the last strategy the prey knew they were faster
than the predators and that the predators could capture them from a lateral direction; so coasting the
wall at max speed eliminates the chances that the predators could run down the prey and increases the
chances of the predators missing the prey and hitting the wall instead. They prey also knew the
predators were sitting in wait at one place along the wall so they used a strategy of coasting the wall
only in one place at max speed to avoid lateral, blind side, and run down capture. If the experiment had
continued I would hypothesize that the predators would have developed a strategy where they moved
forward at intermittent time intervals until they reached the area of the arena where the prey was
oscillating direction. This experiment results in similar phenotypes that we see in biological systems of
co-evolution and interestingly enough, none of the strategies that predators and their prey used were
perpetually effective. We see just the same result in biological systems.
Research in Evolutionary Robotics gives scientists the opportunity to observe the evolution of
phenotypes in robots that they would not otherwise have with real organisms. The collision free
navigation systems show us that genes are capable of recognizing the physical limitations of the
organisms in which they reside. In nature, scientists to not have a knob that they can turn which would
artificially increase the speed of an organism to discover why it only operates at half its max speed.
Assuming we already know what its max speed is. Although these experiments are only dealing with the
elementary behavior which most insects possess, I predict that with further advancements in computer
technology and knowledge of genetics and how they relate to behavioral and morphological
phenotypes, scientists will begin to experiment with evolutionary robotics at the level human intellect
and consciousness.