Biologically Inspired Robot Behavior Engineering

449 Pages·2003·17.396 MB·English

by  Jean-Arcady Meyer, Stéphane Doncieux (auth.), Professor Richard J. Duro, Professor José Santos, Professor Manuel Graña (eds.)

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(cid:8)(cid:11)(cid:16) (( B(cid:8)(cid:18)(cid:5)(cid:15)(cid:12)
Springer-Verlag Berlin Heidelberg GmbH
Professor Richard J. Duro
Universidade da Corufia
Escuela Politecnica Superior
c/Mend.izabal s/n
15403 Ferrol (La Corufia)
Spain
richard @udc.es
Professor Jose Santos
Universidade da Corufia
Departamento de Computaci6n
Campus de Elvina
15701 La Corufia
Spain
santos @udc.es
Professor Manuel Grana
Universidad del Pafs Vasco, UPV/EHU
Departamento de Ciencias de la
Computaci6n e Inteligencia Artificial
20018 San Sebastian
Spain
ccpgrrom @sc.ehu.es
ISSN 1434-9922
ISBN 978-3-7908-2517-6 ISBN 978-3-7908-1775-1 (eBook)
DOI 10.1007/978-3-7908-1775-1
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Die Deutsche Bibliothek - CIP-Einheitsaufnalune
Biologically inspired robot behavior engineering I Richard J. Dnro ... ed. - Heidelberg; New York: Physica
VerJ., 2003
(Studies in fuzziness and soft computing; Vol 109)
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©Springer-Verlag Berlin Heidelberg 2003
Originally published by Physica-Verlag Heidelberg New York in 2003
Softc over reprint of the hardcover 1st edition 2003
The use of general descriptive names, registered names, tradernms, etc. in this publication does not imply,
even in the absence of a specific statement, that such names are exempt from the relevant protective laws
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Printed on acid-free paper
"Was the Setaur intelligent? Well yes, in its own fashion! That
"unnecessary" -and at the present moment highly dangerous- "wisdom"
of the machine angered many participants in the action; they couldn't see
why in the hell the engineers had endowed such freedom and autonomy on
a machine made strictly for mining tasks. McCork calmly explained that
this "intellectronic redundancy" was - in the current phase of
technological development- the same thing as the excess of power
generally found in all conventional machines and engines: it was an
emergency reserve, put there in order to increase safety and dependability
off unction. There was no way of knowing in advance all the situations in
which a machine, be it mechanical or informational, might find itself. And
therefore no one really had the foggiest notion of what the Setaur would
do."
The Hunt, p. 156 in Stanislaw Lem, Mortal Engines, Orlando, Fla:
Harcourt Brace Jovanovich 1992
Preface
Engineering is concerned with the construction of artificial objects that accomplish
some required task or fulfill some needed function. This book is primarily about
engineering in the sense that its contents deal with artificial objects and some specific
ways to conceive and construct them. On the other hand, it strays from the general
mainstream in this field in the methodological aspects of the design process. The
general kind of objects considered here belong to the branch of robotics defined as
"mobile robotics" or, in a more general definition, "autonomous robotics". This type
of systems share many engineering features with other machines and robots, but differ
from traditionally engineered ensembles in that, irrespective of their final function,
they must carry out this function as independently as possible from human
supervision, even under changing environmental and/or internal conditions. Thus, a
higher design level not contemplated for most machines that involves certain
capabilities such as adaptability, learning, behavior modification and fault tolerance,
among others, is needed. Design at this level introduces requirements that are very
hard to state precisely and in conventional methodological engineering terms about
how to learn, what is important in an environment, what is the relationship between
body and function, etc. Consequently, many researchers in this field have resorted to
biology for inspiration. Biological agents are autonomous and manage to survive in
their ecological niches. Therefore, we can draw some ideas from them on how to
obtain different types of behaviors and on the structures that are required for
autonomy. Conversely, by implementing what we think are biological concepts on
VI
artificial systems and combining them with more traditional engineered solutions we
can gain insight into how biological systems work and relate to their environment.
The words "Biological Inspired Engineering" have a multitude of meanings, of
which the contents of the present book are a privileged sample. The backgrounds of
the authors contributing to this volume encompass a broad spectrum of fields, and
therefore their views on the area reflect this variety. Some are in the biological
sciences trying to replicate biological behaviors, while others belong to more classical
engineering fields and are more interested in efficiency questions: efficient design,
efficient control, and efficient computation. Still others are in the nebulous area
between the established scientific fields.
Biological inspiration may affect
The object itself,
The purpose of the construction of the object,
The construction methodology,
The building blocks for the objects.
We would like to provide a neat classification of the work contained in this book,
but it is not possible, as is often the case with fast evolving research areas.
Nevertheless we can consider the above list as possible axes in an abstract graph that
allows us to locate the contributions and to relate them.
The object:
The physical design of the robot may be heavily influenced by biology: grasping
hands, legged insect-like robots, etc. In this volume, two exotic designs are studied:
the swimming lamprey-like robot (Chs. 1 and 4) and the brachiation robot (Ch. 2).
Other authors consider more conventional legged and/or wheeled robots (Chs. 1, 6 and
9) and some take as the object of their study individual subsystems, proposing
different solutions to enhance sensing or actuation capabilities (Chs. 5 and 12 to 15).
Finally, the robot morphology used to test the ideas under development may be closely
entwined with theory and condition its shape. i.e.: Ch. 6 makes an explicit effort to
abstract from the Khepera robot, but is still conditioned by its broad morphological
features.
The purpose:
Some papers deal with the modeling of biological entities and try to obtain new
insights through the simulation, analysis and parameter optimization of their artificial
counterpart (Chs. 2, 3, 4 and 11). However, in this same volume other authors strive to
solve very precise technological problems (Chs. 12 to 15). Others (Chs. 1, 9) fall in a
nebulous region where the results may be interpreted from a biological perspective and
have a technological value. They are not concerned with the existence of a biological
VII
counterpart, they profit from the biological metaphor in terms of the power and
flexibility that is derived from it.
The work on lamprey swimming robots reflects the divergence in purposes that
may be characteristic of the field and the contents of this book. While the authors in
Ch. 4 aim to learn about the dynamic implications of Ekeberg's model parameter
settings, the authors of Ch. 1 seek the spontaneous generation of the control structure
and parameter settings, following a general methodology that is also applied to other
robot types.
The design methodology:
Automatic robot design implies some kind of optimization process, either for
parameter estimation or for structural design. Evolutionary Algorithms are becoming
the standard tool for these processes and most of the chapters involve their use in some
way or another. The reason lies in the enormous modeling flexibility allowed by
coding the problem into individual genotypes and the fact that some guarantees of
convergence are conveyed by the use of these algorithms. Finally, it is usually easy to
obtain relative performance values for complex robots operating in different
environments, which is what an evolution based algorithm requires, but very hard to
provide the exact gradient or error values needed by other types of optimization
techniques. Notwithstanding this fact, in some cases the parameter estimation can be
achieved by simple techniques, such as gradient descent algorithms.
The building blocks:
Some neural architectures have become standard tools for data fitting and modeling:
Self Organizing Maps (SOM), Multiple Layer Perceptrons (MLP), etc. Given the fact
that data clustering and categorization pervades the autonomous learning paradigm,
competitive neural networks are present in many control system designs (Chs. 6, 8, 11
and 12). However, there is still room for some recent neural networks (Ch. 13) or
derivations of traditional networks, such as those including trainable delayed
connections (Ch 9). We have included within the scope of biologically inspired some
computational models such as fuzzy modeling (Ch. 2 and 15), inspired on the
"computation with words" ability of humans; and regular grammars for syntactic
modeling and synthesis of (control) structures (Ch. 1).
VIII
Questions
This book poses rather than solves a number of questions. These questions are
defining the boundary of research in this area and the contributions included in this
book are motivated by the quest for answers to some or all of them.
Autonomy
What are the limits and conditions of achievable autonomy?
Autonomy is the key term in this volume and in the whole area of robotics. The
goal is to obtain systems that work without human intervention.
The Autonomy question can be stated in another way: When is the Deus ex
machina required to restart the processes or to correct undesired or unstable
behaviors?. Truly autonomous systems will never need the intervention of the God
like supervisor. The degree of intervention required for the system to work is a
negative measure of autonomy.
There are several degrees of autonomy that are easily acknowledged by the
scientific community:
Autonomous behavior: how long does the robot live by itself?
Autonomous adaptation: To what degree is the robot able to change itself to
survive? We can distinguish between the following:
• Parameter fitting.
• Strategy learning.
• Morphological adaptation
The ultimate goal is to propose systems that, starting from the zero structural state,
learn the optimal control structure and parameter settings to cope with a specified task
in a given (noisy) environment. However, the unavoidable teacher appears most of the
time in the form of an objective function or some kind of reward (reinforcement).
Even unsupervised systems hide some implicit type of supervision in the computation
of the objective function.
Although the length of the active life is the basic measure of autonomy, there are
few studies that involve the duration of the life cycle of the robot. Those studies that
exist need to provide complete strategies for robot refurbishment and "feeding" and to
define appropriate environments. Some studies found in the literature, introduce
hunger as the driving force for the robot and measure fitness of the individual by its
length of life. These studies seldom wander off the simulation desk. They do,
however, provide a way to obtain some degree of autonomy, at least in the form of a
short life before starving. In this volume only Ch. 11 gives some lateral consideration
to this issue. Some of the authors found in this volume, (Chs. 1, 3, 4 and 9) build
(often by means of evolutionary algorithms for parameter optimization) their systems
IX
offline, out of the real-life operation loop. Life is an experiment that is restarted each
time, in an infinite trial and error process. Although some kind of evolutionary
algorithm implements the search process, there is no truly autonomous breeding and
selection in a closed environment.
We can distinguish two cases of autonomous parameter fitting. The first
corresponds to systems that perform some type of on-line learning or "learning while
performing". Ch. 2 estimates the parameters of its brachiation controller during
operation. Ch. 8 proposes a neurocontrol architecture with some ability to learn and
categorize new sensorial data to integrate the desired behaviors. Others, such as Chs.
12, and 13, propose visual information processing schemes intended for adaptation
during the real-time operation of the robot. Ch. 10 also proposes on-line learning once
the control structure is set.
The second case of autonomous parameter fitting corresponds to the consideration
of non-stationary environments. Changing environments imply changing sensory
conditions as well as structural conditions. It is not only the map of the world that may
change. The lighting conditions may affect the camera or the infrared proximity
sensors. Also the internal state of the system may change, e.g. batteries drain and so
motors reduce their response to control commands. In Ch. 5 the author claims that his
system is naturally adapted to external changes, given that the model of the world is
embedded in the neural structure. Ch. 12 considers the robustness of non-stationary
image capture implicit in the adaptation performed by the competitive neural networks
that compute the filtered image.
The second and third levels of autonomous behavior are subjects of active research.
Ch. 11 induces control structures from sensory motor interaction with the goal of
obtaining high-level control structures, i.e. planning. However an example of strong
structural adaptation is lacking.
Generality
Is there any kind of no-free lunch theorem for computational intelligence in
autonomous systems?
Traditional robotics is based on the mathematics of (linear) control theory and
mechanical modeling. The power of these formal tools to face real life problems has
been extensively proven, and also their limitations. Biologically inspired modeling and
design tools have been proposed as a way to overcome these limitations, but the
question remains: Is it possible to propose a general design strategy and general
modeling tools for autonomous (robotic) systems?
At any level, adaptation implies some kind of optimization process, which
sometimes (but not always) can be formalized. The translation of the no-free lunch
theorem, already formulated for optimization problems to the design of autonomous
systems, means that no general strategy defined in an absolute way would be
X
applicable to every situation. Research in this line must include work on defining the
range of applicability of the proposed tools.
The work of Ch. 1 addresses the formulation of a general design strategy that may
be easily transported from one problem space to another. Modeling is based on the
definition of a given grammar structure that must be tailored to the problem at hand.
Ch. 9 provides another view on this issue, studying the reusability of control modules
that perform precise behaviors. Chs. 6, 10 and 11 propose general control
architectures, but they are still very conditioned by robot morphology. It is not an easy
task to translate them to other robot-environment-task settings.
Sensing versus control
Poor sensing (i.e. infrared) needs sophisticated information processing, such as the
time integration shown in some of the virtual sensors of Ch. 9, or some means of noise
reduction, as in the work presented in Ch. 1 on obstacle avoidance of the rolling robot.
Rich sensing (i.e. vision) overcomes the complexities of the post-processing, but it is
much more expensive to obtain and process the information provided by the sensor.
Ch. 5 proposes a flexible neural planning system, which relies on a very exhaustive
observation of the environment. Propioceptive sensing in the form of wheel revolution
counting, as in the case presented by Ch. 11, is the most basic sensing that must be
considered for localization and orientation. The mixture of several sensory channels,
i.e. propioceptive and visual, is a new and promising venue of research under the name
of "sensor fusion". Chapters 6 and 11 introduce propioceptive sensing for enhanced
control architectures.
The quantization of the input sensor values, or their categorization, is a central
problem in many approaches. This quantization is a means to assign numerical values
to the input and allows for further processing in the control modules. Ch. 8, 10 and 11
make extensive use of quantization via competitive neural networks. Ch. 7 poses the
interesting problem of the interdependence of task definition, sensing and internal
control and tries to set the basis to elucidate when sensor information time integration
is really needed.
Robot objective
Does robot behavior have any useful meaning?
What are the tasks that can be learnt autonomously?
The distinction between artificial life and robotics lies in the (external) definition of
the utility obtained from the robots behavior. Artificial life does not expect any reward
from the robots behavior, whilst roboticists focus on the utility of robot behavior.
Robotics talks about "tasks" whilst artificial life talks about "behaviors" and
mechanisms of reproduction. The chapters in this volume smoothly traverse the