Evolutionary Robotics: The Biology, Intelligence, and Technology of
Self-Organizing Machines. Stefano Nolfi and Dario Floreano. xii + 320 pp. The
MIT Press, 2000. $50.
Just a few decades ago, scientists envisioned the "house of tomorrow,"
complete with robots that would assist us with daily chores, anticipate our
needs and learn from their mistakes. Yet none of us now live with such robot
companions.
The primary reason for this failure lies in our historical approach to
designing and programming robots. The traditional approach attempts to
anticipate every possible required robotic behavior and write programs to
carry out specific routines that address those behaviors. This has proved to
be time-consuming, costly and ultimately unsuccessful. There are simply too
many unconsidered circumstances in any real-world robotics application. For
robots whose behavior relies on a rule-based architecture, extending the rules
to handle cases that were unforeseen or treated inappropriately has often
proved unwieldy. Some efforts have required orders-of-magnitude increases in
the number of rules to handle special cases, while generating little overall
improvement in performance.
An alternative strategy for designing robots relies on inspiration from
nature, where evolution perfects carbon-based machines, which often carry out
complex goal-driven behaviors. Although the proposition for evolving robots
goes back at least to the mid 1950s, with George Friedman's master's thesis
from the University of California, Los Angeles, only recently have significant
efforts been made to apply nature's design principles to real robots-as
opposed to mere computer simulations of those machines. Stefano Nolfi and
Dario Floreano were two of the primary researchers working in this new area of
"evolutionary robotics" in the early 1990s, and they have continued to pursue
investigations of natural computing methods and robotics. Their new book
summarizes some of the important case studies in this growing field.
Nolfi and Floreano offer basic introductory material regarding genetic
algorithms, a branch of evolutionary computation often used to optimize
robotic control architectures. These architectures often take the form of an
artificial neural network, a mathematical model based loosely on the way
biological neural networks are believed to operate. Artificial neural networks
possess many "neurons," each of which acts as a mathematical transfer
function-that is, an input-output device. Each neuron transforms its incoming
activity signal into an output signal that is in turn passed along to other
neurons. Some neurons in the neural network may affect robotic controllers,
such as the rate of turning a single wheel on a Khepera robot. Other neurons
play the role of receptors-for example, reading in data from an infrared
sensor. Connections between the neurons in an artificial neural network are
weighted, and these weights, in large measure, dictate the overall behavior of
the robotic system. Evolutionary algorithms and other optimization techniques
are often used to search for appropriate weight sets, or even optimal neural
architectures.
This book describes experiments (of increasing complexity, perhaps) in
different settings. To illustrate the basic approach, a small robot may be
placed in an arena (essentially a wooden racetrack measuring 80 by 50
centimeters) with the goal of having the robot learn to navigate the arena at
maximum speed without bumping into walls. The robot can perceive its
environment with eight infrared sensors that act either by measuring the
amount of ambient light or by emitting infrared light and measuring the amount
reflected. The robot executes a behavioral strategy represented by an
artificial neural network. In essence, alternative neural networks are
downloaded into the robot, which acts based on this programming. Some neural
networks are better than others at achieving the designer's goal. The best are
saved, and the worst are replaced by variations of the best; the analogy to
random variation and selection is obvious. Over time, a neural network emerges
that maximizes the robot's performance.
This book goes into considerable detail, covering experiments with a variety
of robots, including some that walk rather than roll. In experiments using
coevolution, populations of robots compete against each other in predator-prey
situations, without human intervention. Nolfi and Floreano expect that the
evolutionary approach to robotics will lead to designs for robots that are
faster, more reliable and more robust than those fashioned with traditional
techniques.
This book will be welcomed by those interested in building complex robots or
simple robots that do complex things. I have only two real criticisms. First,
much of the work cited isn't particularly recent. Many of the references
predate 1997; insufficient attention is paid to information about ongoing
efforts published in the most contemporary conference proceedings and
journals. In this rapidly emerging field, information about the most recent
efforts is particularly pertinent. Second, the book is not well copyedited. I
noted numerous agreement errors and misspellings, and at least one quotation
never closed. I found this quite distracting and below the standard of a major
scientific publisher, but I suspect that interested researchers easily will
look past the substandard presentation and benefit from the technical details
assembled.-David B. Fogel, Natural Selection, Inc., La Jolla, California
http://www.sigmaxi.org/amsci/amsci/bookshelf/Leads01/evolrobotics.html
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Evolutionary Robotics: The Biology, Intelligence, and Technology of
Self-Organizing Machines (Intelligent Robotics and Autonomous Agents)
by Stefano Nolfi, Dario Floreano
Hardcover - 384 pages 1st edition (December 15, 2000)
MIT Press; ISBN: 0262140705
AMAZON - US
http://www.amazon.com/exec/obidos/ASIN/0262140705/darwinanddarwini/
AMAZON - UK
http://www.amazon.co.uk/exec/obidos/ASIN/0262140705/humannaturecom/
Book Description
Evolutionary robotics is a new technique for the automatic creation of
autonomous robots. Inspired by the Darwinian principle of selective
reproduction of the fittest, it views robots as autonomous artificial
organisms
that develop their own skills in close interaction with the environment and
without human intervention. Drawing heavily on biology and ethology, it uses
the tools of neural networks, genetic algorithms, dynamic systems, and
biomorphic engineering. The resulting robots share with simple biological
systems the characteristics of robustness, simplicity, small size,
flexibility,
and modularity.
In evolutionary robotics, an initial population of artificial chromosomes,
each
encoding the control system of a robot, is randomly created and put into the
environment. Each robot is then free to act (move, look around, manipulate)
according to its genetically specified controller while its performance on
various tasks is automatically evaluated. The fittest robots then "reproduce"
by swapping parts of their genetic material with small random mutations. The
process is repeated until the "birth" of a robot that satisfies the
performance
criteria.
This book describes the basic concepts and methodologies of evolutionary
robotics and the results achieved so far. An important feature is the clear
presentation of a set of empirical experiments of increasing complexity.
Software with a graphic interface, freely available on a Web page, will allow
the reader to replicate and vary (in simulation and on real robots) most of
the
experiments.
Book Info
Describes the latest technology in robotics and artificial intelligence:
robots
based on the Darwinian principle of survival of the fittest. Reports the
latest
results and achievements with these robots, which are sophisticated enough to
reproduce. Offers software on the companion Web site that will allow to reader
to view simulations of robot activity. DLC: Evolutionary robotics.
About the Author
Stefano Nolfi is Coordinator of the Division of Neural Systems and Artificial
Life, Institute of Psychology, National Research Council, Rome. Dario Floreano
is Assistant Professor of Biorobotics and Adaptive Systems, Institute of
Robotics, Department of Microengineering, Swiss Federal Institute of
Technology, Lausanne.
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--J. R.
Useless hypotheses, etc.:
consciousness, phlogiston, philosophy, vitalism, mind, free will, qualia,
analog computing, cultural relativism, GAC, Cyc, Eliza, and ego.
Everything that can happen has already happened, not just once,
but an infinite number of times, and will continue to do so forever.
(Everything that can happen = more than anyone can imagine.)
We won't move into a better future until we debunk religiosity, the most
regressive force now operating in society.
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