Evolutionary robotics
Evolutionary robotics (ER) is a methodology that uses evolutionary
computation to develop controllers for autonomous robots. Algorithms in ER frequently operate on populations of
candidate controllers, initially selected from some distribution.
This population is then repeatedly modified according to a fitness function. In the case of genetic algorithms (or "GAs"), a common method in evolutionary
computation, the population of candidate controllers is repeatedly
grown according to crossover, mutation and other GAoperators and then culled according to the fitness function. The candidate controllers
used in ER applications may be drawn from some subset of the set of artificial neural
networks, although some applications (including SAMUEL, developed at
the Naval Center for Applied Research in Artificial
Intelligence) use collections of "IF THEN ELSE" rules as
the constituent parts of an individual controller. It is theoretically possible
to use any set of symbolic formulations of a control law (sometimes called a policy in the machine learning community) as the space of possible
candidate controllers. Artificial neural
networks can also
be used for robot learning outside of the context of evolutionary
robotics. In particular, other forms of reinforcement
learning can be
used for learning robot controllers.
Developmental
robotics is related
to, but differs from, evolutionary robotics. ER uses populations of robots that
evolve over time, whereas DevRob is interested in how the organization of a
single robot's control system develops through experience, over time.
Contents
History[edit]
The
foundation of ER was laid with work at the national research council in Rome in
the 90s, but the initial idea of encoding a robot control system into a genome
and haveartificial evolution improve on it dates back to the late 80s.
In
1992 and 1993 three research groups, one surrounding Floreano and Mondada at the EPFL in Lausanne and a second involving Cliff, Harvey, and Husbands from COGS at the University of Sussex and a third from the University of Southern
California involved M. Anthony
Lewis and Andrew H
Fagg reported
promising results from experiments on artificial evolution of autonomous
robots.[1][2] The success of this early research
triggered a wave of activity in labs around the world trying to harness the
potential of the approach.
Lately,
the difficulty in "scaling up" the complexity of the robot tasks has
shifted attention somewhat towards the theoretical end of the field rather than
the engineering end.
Objectives[edit]
Evolutionary
robotics is done with many different objectives, often at the same time. These
include creating useful controllers for real-world robot tasks, exploring the
intricacies of evolutionary theory (such as the Baldwin effect), reproducing psychological
phenomena, and finding out about biological neural networks by studying
artificial ones. Creating controllers via artificial evolution requires a large
number of evaluations of a large population. This is very time consuming, which
is one of the reasons why controller evolution is usually done in software.
Also, initial random controllers may exhibit potentially harmful behaviour,
such as repeatedly crashing into a wall, which may damage the robot.
Transferring controllers evolved in simulation to physical robots is very
difficult and a major challenge in using the ER approach. The reason is that
evolution is free to explore all possibilities to obtain a high fitness,
including any inaccuracies of the simulation[citation needed]. This
need for a large number of evaluations, requiring fast yet accurate computer
simulations, is one of the limiting factors of the ER approach[citation needed].
In
rare cases, evolutionary computation may be used to design the physical
structure of the robot, in addition to the controller. One of the most notable
examples of this was Karl Sims' demo for Thinking
Machines Corporation.
Motivation
for evolutionary robotics[edit]
Many
of the commonly used machine learning algorithms require a set of training examples consisting of both a hypothetical input and
a desired answer. In many robot learning applications the desired answer is an
action for the robot to take. These actions are usually not known explicitly a
priori, instead the robot can, at best, receive a value indicating
the success or failure of a given action taken. Evolutionary algorithms are
natural solutions to this sort of problem framework, as the fitness function
need only encode the success or failure of a given controller, rather than the
precise actions the controller should have taken. An alternative to the use of
evolutionary computation in robot learning is the use of other forms of reinforcement
learning, such as q-learning, to learn the fitness of any
particular action, and then use predicted fitness values indirectly to create a
controller.
Conferences
and institutes
Main conferences
·
GECCO
·
ALife
Octavia interactive robot of Navy Center for Applied
Research In Artificial Intelligence
Academic institutes and researchers[edit]
·
University of Sussex: Inman Harvey, Phil Husbands, Ezequiel Di Paolo, Eric
Vaughan, Thomas
Buehrmann
·
CNR: Stefano Nolfi, Domenico
Parisi, Gianluca
Baldassarre, Vito
Trianni, Onofrio
Gigliotta, Gianluca
Massera, Mariagiovanna
Mazzapioda
·
Center for Robotics and Intelligent Machines, North
Carolina State University: Eddie Grant, Andrew Nelson
·
U.S. Naval
Research Laboratory's, Navy
Center for Applied Research In Artificial Intelligence: Alan
C. Schultz, Mitchell
A. Potter, Kenneth
De Jong
·
University of the Basque Country (UPV-EHU): Robótica
Evolutiva, Pablo González-Nalda (in Spanish) PDF (in English)
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