provide a theoretical foundation for the field.

Morphological computation. As noted earlier, evolutionary robotics builds on the concept of embodied cognition, which holds that intelligent behavior arises out of interactions between brain, body, and environment. 27 An important corollary of embodied cognition is that, given the right body plan, a robot (or animal) can achieve a given task with less control complexity than another robot with an inappropriate body plan. For example, a soft robot hand can grip a complex object simply by enclosing it: the inner surface of the hand passively conforms to the object. A robot hand composed of hard material must carefully compute how to grasp the object. It has been argued that the physical aspect of a robot—its morphology—can actually perform computations that would otherwise have to be performed by the robot’s control policy if situated in an unsuitable body plan. This phenomenon of morphological computation25 cannot be completely abstracted away from the physical substrate that gives rise to it in the way a Turing Machine can. Practitioners in this area would greatly benefit from the aid of theoretical computer scientists to formalize this concept.

Dynamical systems theory. Dynamical systems theory is increasingly a useful tool for creating controllers for autonomous robots. 2 Often these controllers take the form of artificial neural networks that have their own intrinsic dynamics: they exhibit complex temporal patterns spontaneously. Evolutionary algorithms can then be used to shape the parameters of these networks such that they can be pushed by incoming sensor stimuli to fall into desired attractor states. For example, a neural network that falls into a periodic attractor may generate a rhythmic gait in a legged robot. However, it has been demonstrated that a one-to-one mapping between a basin of attraction in a neural network and a distinct robot behavior may be overly simplistic, 14 indicating there is much work to be done at the interface of dynamical systems theory and evolutionary robotics.

Information theory. Typically in an evolutionary robotics experiment, the “fitness” of a robot is measured based on its ability to perform a given behavior, such as how far it can walk or how well it can grasp an object. Surprisingly, it has been found that maximizing certain information-theoretic measures within the neural network of evolving robots can lead to useful behavior. 28 Why information maximization produces desired behaviors rather than useless, random, or uninteresting behavior remains mostly unresolved, although some progress has been made in this direction. 8

In addition to helping with the synthesis of behavior, information theory can also be used to analyze evolved behaviors. Williams et al. have recently shown37 that information flow—the transfer of information from one variable to another—can be employed to measure how behaving robots “offload” computed information to their body and/or their environment. This technique therefore holds promise for formalizing the concept of morphological computation. 25

challenges

There are a number of challenges currently facing the field, including transferring evolved robots from simulation to physical machines; scalability issues; and the difficulty of defining appropriate fitness functions for automatically measuring behavior.

The Reality Gap Problem. Both
biological and artificial evolution are
notorious for exploiting the poten-
tial relationship between the animal
(or robot) and its environment to
produce new behaviors. For instance
the lightweight property of feathers,
which are thought to have originally
evolved for heat regulation, was later
exploited for flight.e
As an example of the exploitative
tendencies of evolutionary algo-
rithms applied to robots, a robot was
initially designed to brachiate along
a suspended beam. 10 The robot was
composed of a main body slung un-
der two arms, and a heavy battery pack
attached to the main body. Gradually,
the evolutionary algorithm discovered
e This tendency of evolution to repurpose traits
is known as “exaptation.”
control policies for the robot that ex-
ploited, rather than fought against
the weight of the batteries. These con-
trol policies would cause the robot to
move such that the battery pack swung
forward under the robot’s body before
it changed hand holds. This would
cause the robot’s center of mass to
move forward, thus requiring much
less force to release contact with the
beam and grasp it further forward.
This mimics the way primates exploit
the weight of their bodies like a pen-
dulum to bring them into reach of a
new tree limb. It is also reminiscent
of the energy-saving passive dynam-
ics of bipedal locomotion (see more
details in the supplemental material).