ing biological development. Alternatively, engineers can draw on insights
from biological development to build
better robots.
Evo-devo-robo. Developmental robotics tends to focus on post-natal
change to a robot’s “body” and “brain”
as the robot learns to master a particular skill. Evolutionary robotics experiments on the other hand generate robots that become more complex from
generation to generation, but typically
each individual robot maintains a fixed
form while it behaves.
Biological systems however exhibit
change over multiple time scales: individual organisms grow from infants
into adults, and the developmental
program that guides this change is in
turn altered over evolutionary time.
This process is known as the evolution
of development, or evo-devo. This biological phenomenon has recently been
exploited in evolutionary robotics: 3 At
the outset of evolution, robots change
from a crawling worm into a legged
walking machine over their lifetime. As
evolution proceeds, this infant form is
gradually lost until, at the end of evolution, legged robots exhibit the ability to
walk successfully without the need to
crawl first. It was found this approach
could evolve walking machines faster
than a similar approach that does not
lead robots through a crawling stage.
In the initial experiments of evo-devo-robo, 34 the genetic instructions
were encoded as a specific class of
formal grammars known as Linden-mayer systems, or L-systems.c
L-systems were initially devised to model
plant growth: their recursive nature
can produce fractal or otherwise
symmetric forms. Hornby12
demonstrated that robots evolved using
such grammars do indeed produce
repeated forms (Figure 1a). He also
showed this repetition can make it
easier for evolutionary algorithms to
improve such robots, compared to
robots lacking in genetically determined self-similarity.
The evolution of robot bodies
and brains differs markedly from all
other approaches to robotics in that
it does not presuppose the existence
c Sims’ work had a large impact on the computer graphics community and L-systems remain
a popular technique within that field.
the evolution
of robot bodies
and brains differs
markedly from
all other
approaches
to robotics in
that it does not
presuppose
the existence of
a physical robot.
of a physical robot. Rather, the user
provides as input a metric for measuring robot performance along with
a simulation of the robot’s task environment, and the algorithm produces
as output the body plan and control
policy for a robot capable of performing the task. This can then be used
to manufacture a physical version of
the evolved robot. Such an algorithm
could, in principle, continually receive new desired behaviors and task
environments and continuously generate novel robots.
In this way, the roboticist can make
fewer assumptions about the final
form of the robot and have greater
confidence the final evolved robot is
better adapted to the environment in
which it must operate. For example,
there is often a debate about whether
a wheeled or legged robot is more appropriate for moving over a given surface. Although not yet demonstrated,
an evolutionary robotics algorithm
should generate wheeled robots if
supplied with a simulation of flat terrain and legged robots if supplied
with a simulation of rugged terrain.
Recent work in mainstream robotics
has demonstrated the possible advantage of combining wheels and legs in
the same robot: an evolutionary system should rediscover this manually
devised solution if it is indeed superior to either wheels or legs alone.
Another advantage of this approach over mainstream robotics is
its potential for better scalability: by
genetically encoding assembly instructions rather than the blueprint
of a robot, more complex machines
can be evolved with little or no increase in the amount of information
encoded in the genome. For example,
consider an approach in which robots are specified by a formal grammar such that the invocation of a
rewrite rule replaces one part of the
robot with two or more parts. Thus
the more times a given set of rewrite
rules are invoked, the more complex
the resulting robot becomes. If evolution increases the number of rewrite
rule invocations, then simple robots
can evolve into more complex robots
with no increase in the information
content of the underlying genomes
describing those robots.
Despite the promise of this ap-