optimizing a control policy that allows
a two, four, or six-legged robot to move
over rugged terrain—is a popular area
of study in robotics. In mainstream robotics, machine-learning algorithms
can now optimize walking behavior for
a physical two-legged robot in a matter
of minutes. 7 Alternatively, a recent investigation in simulation has shown if
robots are evolved to move over rough
terrain, robots will eventually evolve
from amorphous shapes into robots
exhibiting the rudiments of appendages (Figure 1b). 1
The former experiment can enable
walking behaviors for a certain kind
of robot; the latter experiment can
continuously produce different robots
adapted to different environments.
Put differently, mainstream robotics
aims to continuously generate better
behavior for a given robot, while the
long-term goal of evolutionary robotics is to create general, robot-generat-ing algorithms.
history
The goal of artificial intelligence, since
its beginnings, has been to reproduce
aspects of human intelligence (such
as natural language processing or deductive reasoning) in computers. In
contrast, most roboticists aim to generate noncognitive yet adaptive behavior in robots such as walking or object
manipulation. Once these simpler
behaviors are realized successfully in
robots, it is hoped the behavior-gener-ating algorithm will scale to generate
ever more complex behavior until the
adaptive behavior exhibited by a given
robot might be characterized by an
observer as intelligent behavior. This
operational definition of intelligence
bears a resemblance to the Turing
Test: if a robot looks as if it is acting
intelligently, then it is intelligent.
Note the emphasis in robotics on
“behavior:” the action of a robot gen-
erates new sensory stimulation, which
in turn affects its future actions. This
differs from non-embodied AI al-
gorithms, which have no body with
which to affect, or be affected by the
environment. In non-embodied AI, in-
telligence is something that arises out
of introspection; in robotics, the belief
is that intelligence will arise out of ever
more complex interactions between
the machine and its environment. This
idea that intelligence is not just some-
thing contained within the brain of the
animal or control policy of a robot but
rather is something that emerges from
the interaction between brain, body,
and environment, is known as embod-
ied cognition. 27
applications
Evolutionary algorithms have been applied in several branches of robotics
and thus evolutionary robotics is not
strictly a subfield of robotics. When
applied well, an evolutionary approach can free the investigator from
having to make decisions about every
detail of the robot’s design. In many
cases the evolutionary algorithm discovers solutions the researcher might
not have thought of, especially for
robots that are non-intuitive for a human to control or design. For example
it is often difficult to see how best to
control a soft robot (Figure 1j) using
traditional machine learning techniques, let alone determine the best
combination of soft and rigid materials for such a robot.
Moreover, ideas can flow not just
from biology to robotics but back
again: evolved robots that exhibit
traits observed in nature—such as
a robot swarm that evolves cooperative rather than competitive tendencies—often provide new ways
of thinking about how and why that
trait evolved in biological populations. In this way evolutionary robotics can give back to biology (“Why did
this trait evolve?”) or more cognitively
oriented fields such as evolutionary
psychology (“Why did this cognitive
ability evolve?”).
Evolutionary biorobotics. In biorobotics, investigators implement anatomical details from a specific animal
in hardware and then use the resulting
robot as a physical model of the animal under study. Although much work
in this area has been dedicated to nonhuman animals (see supplemental
material available in the ACM Digital
Library; http://dl.acm.org), many roboticists choose to model the human
animal: a humanoid robot is more
likely to be able to reach a doorknob,
climb steps, or drive a vehicle than a
wheeled robot or one measuring only
a few inches in length. The humanoid
form, however, requires mastery of
bipedal locomotion, a notoriously difficult task. As an example, Reil et al. 30
evolved a bipedal robot in simulation
that first mastered walking and then
evolved the ability to walk toward a
sound source.
In short, bioroboticists attempt to
model, in robot form, the products of
evolution: individual organisms. Evolutionary roboticists in contrast attempt to re-create the process of evolution, which generates robots that may
or may not resemble existing animals.
Evolutionary biorobotics is a
blend of these two approaches: investigators build robots that resemble
a particular animal, and then evolve
one aspect of the robot’s anatomy
to investigate how the corresponding aspect in the animal might have
evolved. For example Long and his
colleagues19 have evolved the stiffness of artificial tails attached to
swimming robots: robots with tails
of differing stiffness have differing
abilities to swim fast or turn well.
This provides a unique experimental
tool for investigating how backbones
originally evolved in early vertebrates.
Developmental robotics. The field of
developmental robotics22 shares much
in common with evolutionary robotics. Practitioners of developmental robotics draw inspiration from developmental psychology and developmental
neuroscience: how do infants gradually mature into increasingly complex
and capable adults? Like evolutionary
robotics, work in developmental robotics tends to have either a scientific or
an engineering aim. Developing robots
can be used as scientific tools: they can
serve as physical models for investigat-