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-