Introd uction Thomas R. Consi andBarbara Webb
nimals have long servedas inspiration to roboticists. Their adaptability, flexibility of motion and great variety of behaviors has made them the often unspoken) benchmarks for robot performance. Beyond inspiration , however, animals have not always yielded much in the way of concrete mechanisms
that
could
be used to build
robots , and robots
have been almost
comical representationsof animals. The problem was that engineered components did not have the performance or form factor of their biological counter-
parts. Recentlythings havebegunto changeand robotsare now capable , to a limited degree, of accurately mimicking the behavior of animals. Advances that have made this possible include microprocessors of ever increasing computational power and ever decreasingsize, tiny solid-state sensors, low-power electronics , and miniaturized
mechanical
components
. Robots
can now
be built
that have some of the sensingcapability of, for example, a desert ant or the motor skills approaching that of a cockroach. Animal -like robots (termed biorobots in this book but also known as biomimetic or biomorphic robots) are serving an increasingly important role as a link between the worlds of biology and engi.
neenng
.
Biorobotics is a new multidisciplinary field that encompassesthe dual usesof biorobots as tools for biologists studying animal behavior and as testbedsfor the study and evaluation of biological algorithms for potential applications to engineering. There have been severalrecent reviews of biorobotics as a way to apply biological algorithms and mechanisms to engineered systems (e.g. Beer et al. 1993, Dario et al. 1993, Hirose 1993, Srinivasan and Venkatesh 1997, Bekey 1996, Beer et a11997, Sharkeyand Ziemke 1998, Chang and Gaudiano 2000) This book particularly concerns the role of robots as tools for biologists, a more recent phenomenon. Understanding how animals work is essentiallya problem of "reverseengineering" i .e. rather than building something with a certain func-
VIII CONSIANDWEBB tional capability, we have something with a certain functional capability and want to work out how it works. Thus the application of engineering methodologies for modeling animals seemsan appropriate and promising approach. However such a task is far from easy. An animal can be describedas mobile vehicle with a multimodal , high-bandwidth interface to its environment. One merely has to look at a cricket with a hand lens to see the thousands of sensory hairs studding its exoskeleton. Each hair is invested with many sensorycells and different types of cells "see" the environment in different ways (e.g. some respond to mechanical stimulation and others to chemical signals). Animals are therefore, deeply embedded in their environments and they are profoundly affected by the subtle and complex signals within those environments (e.g. turbulence , polarization patterns, acoustic noise, thermal micro-climates, etc). It is the complexity of the environment and the high degreeto which animals can senseand respond to that complexity that makes it difficult to obtain a detailed understanding of the world as seen by an animal through its sensesand interpreted through its behaviors. The situation is made even more complicated becausethe animal invariably disturbs its environment and createsa new set of stimuli that may also be important to the creature's behavior. Biorobots are now enabling biologists to understand these complex animalenvironment relationships. They can be thought of as micro-environment exploration robots that can detect and map sensorysignals at the level of the animal and can measurehow the presenceand motion of the animal affects those signals. This data, coupled with observations of the animal itself, can lead to very sophisticatedhypothesesas to what is causing a behavior and what is shaping the behavior as it plays out. These hypothesescan then be tested in laboratory or field-basedexperimentswith the biorobot robot aswell aswith the real animals. The robot offers two distinct advantagesover the real animal in such studies. First, the behavior under test in the robot is not affected by competing, uncontrolled , behaviors. Second, orders of magnitude more data can be obtained from a robot, compared to an animal, on its actions, its sensory input , and its internal states. Despite theseadvantagesit must not be forgotten that the biorobot , however sophisticated , is only mimicking part of the animal . Biorobotics is a tool-baseddiscipline, much like the microscopy, and one should never lose sight that biorobots are tools for use in studies of animals, not replacementsfor such studies. Computer simulation has long been another tool used by biologists to model biological phenomenaat all levelsof organization, from populations of animals to individual creatures, to "subassemblies " such as the ear, down to individual components, neurons, sensoryreceptor cells and musclefibers. The question naturally comesto mind as to why bother building robots at all when computer/numerical models havebeenso useful?The answerto this question comesfrom the complexity of the sensoryworld discussedabove. A hypothesis implemented on a robot
INTRODUCTION IX operating in a real environment can be tested more rigorously than in simulation becausethe hypothesiswill be challengedwith real, complex and often unmodelable stimuli . For example, hydrodynamic simulations of underwater turbulence are very complex yet still do not adequately representreal turbulent flow. This type of flow is what shapesthe olfactory signal sensedby lobsters, fish and many other underwater creatures. It is far easierand cheaper to generatea real odor plume to test a plume following algorithm than to use a plume simulation that will produce an inferior stimulus compared to the real thing . Biorobots can be usefully thought of as physical models of animals that enable a level of investigation beyond that possible with simulation. Note that biorobots do not replace simulation, just as they do not replacereal animals in experiments. Simulation is very useful both as a hypothesistesting methodology and as a design tool for developing biorobots. It often happensthat there is a cyclic iteration of animal observations, simulations, and biorobotic experimentsduring the courseof an investigation that, in the best case, leadsto an increasinglymore accuratepicture of the animal's behavior and its physiologicalunderpinnings.
A Brief History of Biorobotics The attempt to make machines behave in a lifelike manner is as old as science (De Solla Price 1964). Ingenious mechanical deviceshave been built to mimic animal behaviors, sometimes with impressive detail e.g. Vaucanson's duck (de Vaucanson 1738, Chapuis and Droz 1958). However their clockwork mechanisms did not noticeably resemblethe inner workings of biological systems. A more or lessdirect scientific lineage to biorobotics can be traced starting at the end of the nineteenth century with the advent of the then new discipline of electrical engineering. Nikola Tesla conceived "the idea of constructing [an] automaton that would . .. respond, as I do myself, but of course, in a much more primitive manner, to external influences. Such an automaton evidently had to have motive power, organs for locomotion , directive organs, and one or more sensitive organs so adapted to be excited by external stimuli ... ." He built and demonstrated a radio controlled boat in the 1890s and discussedplans for an automaton that "will be able, independent of an operator, left entirely to itself, to perform , in responseto external influences affecting its sensitive organs, a great variety of acts and operations as if it had intelligences [sic] " (cited in Rosheim 1994). The pioneering physiologist JacquesLoeb compared the behaviors of "lower" animals to that of an artificial heliotropic machine, a light following device made of motors , photocells and relays (Loeb 1918) . Breder (1926) developedtwo model boats, one propelled by a flapping fin and the other by an undulating fin , to study fish propulsion. Fifty years ago the advent of cyberneticssaw the building of a seriesof electromechanicaldevicesintended to
X CONS ! AND WEBB explore aspectsof animal behavior, such as the "homeostat" machine (Ashby 1952 ) and the reactive "turtle " (Walter 1961 ) . A number of similar devices built
around this time are describedin Young (1969).
Although the adventof moderntransistortechnologyand computersmight have been expected to support rapid further progress in building animal-like
robots, in fact the main research emphasisdivergedin somewhatdifferentdirections. One might be termedinvestigationof the "disembodied " brain: an emphasison building machineswith human reasoningpowers(artificial intelligence ) ratherthan human (or animal) physicalpowers.Although someof these mechanisms were"biologically-inspired," suchasthe neuralnetworkapproach , the tasks investigated were still largely cognitive. Even within biology, where
"analog" (i.e. electricalcircuit models) of hypothesizedanimal control systems continued to be used as simulation tools (e.g. Harmon 1961, Collewijn 1972; Collett 1980) till replaced by today's software simulations, the systemsrarely "closed the loop" with real actuators and sensors. On the other hand, investigation of the physical problems of sensing and control for artificial systemswere somewhat subsumedby mechanization, with much robot researchderiving from industrial concerns (with the main exception some notable research on humanoid robots in Japaneseresearchgroups such as that of Ichiro Kato). Cyber-
netic theory for operatingthesesystemsdevelopedsophisticatedmathematical formalisms , but wasin the main not closelyrelatedto biology. One reasonmay have been the limited understanding of how biological systemsactually worked. Thus a parallel development in biology that was critical to the emergenceof biorobotics was the application of control system theory and other engineering techniques to the study of animal behavior, most notably by the "European School" of neuroethology. This work was primarily focused on the sensory-motor behavior of arthropods and began with the work of von Holst and Mittel -
staedt(reviewedin Schone1984). Ground breakingstudieson manyarthropod systemswere carried out in the mid to latter twentieth century. A few examples of the many systemsstudied include: fly vision (reviewed in Buchner 1984), ant navigation (Wehner 1989), walking in the stick insect (Cruse 1990) and crab oculomotor behavior (Horridge and Sandeman 1964). This work , and other similar studies, provided a rich baselineof quantitative data on the performance
of animalsthat wasreadyto be incorporatedinto the biorobotsthat beganto emerge in the last decades of the twentieth
century .
Two notable event in the development of current biorobotics were the publi -
cationof the slendervolume Vehicles , Experiments in Synthetic Psychology (Braitenberg 1984) and the emergence of behavior-based robots (Brooks 1986a). Braitenberg in "Vehicles" showed how animal-like behaviors might be produced in simple "thought " robots and how thesevehicles may be used to interpret behavioral data. Brooks and colleaguesexpanded the field of artificial intelligence to consider the problems faced by relatively simple insect-like robots that must navigate within the real world . Other influential work done in this period in-
INTRODUCTION XI cludes the highly impressiverunning robots developedat the MassachusettsInstitute of Technology's Lego Lab (Raibert 1986), and the application of Arbib 's (1972) biologically based "schema theory" to autonomous robots by Arkin (1987). The fields of artificial life (Langton 1989) and adaptive behavior or "animats" (Meyer and Guillot 1990) also emergedaround this time, with their emphasis on artificial replication of precognitive behaviors, though still largely in simulation . What these interdisciplinary movements helped generate was a meeting point between robot technology on one hand and mainstream biological models on the other. This has resulted in recent years in a rapid increasein the number of models of specific animal competencies being implemented in robot devices, driven both by advancesin technology, as mentioned above, and in our expanding knowledge of the sensory, motor, and nervous systemsof animals. In table 1 we list papers from the last decadethat fall within this description (not including the large amounts of work in biologically-basedsensoryprocessingexceptwhere it is used in behavioral control , i .e. on a robot), demonstrating both the quantity and breadth of current work in this field.
Overview of the Book This book is an edited collection of papers that were presentedat the "Robots and Biology: Developing Connections" American Association for Artificial Intelligence Symposium held on October 23-25, 1998 in Orlando Florida. The purpose of the symposium was to bring together scientists from a diverse array of disciplines all of whom are using biorobots as probes of animal behavior and brain function . The chapters are ordered with those primarily involved with sensory biology presented first, followed by chapters that focus on motor systems, and ending with chapters concerned with higher-level or cognitive processes . This ordering is, of course, artificial becauseit is difficult if not impossible to cleanly separatefunctional subsystemswithin animals. A prime example of this is the use of visual motion for object detection and navigation (Viollet and Franceschini, chapter 4) in which the motor and visual systemsare closely coupled to perform this function . Nevertheless, the ordering does serve as a convenient organizational framework for the book and to direct readerswith specific intereststo specific chapters. A chapter on neural mechanismsin cricket phonotaxis by Barbara Webb begins the Sensory Systemssection. A robot model of a cricket is used to test a neuronal model for sound localization by these noisy insects. Next we dip underwater where Frank Grassoexaminesthe world of olfactory-basedguidance in lobsters. The robot presentedin Grasso's chapter is one of the first examplesof a marine biorobot . Polarized light navigation in insects has long been of interest
XII CONSIANDWEBB to biologists and the next chapter by Ralf Moeller and colleagues presents a robot with a visual system modeled after that of the desert ant Catagiyphis . "Sahabot" is being used to test hypothesis on how Cataglyphisusesthe pattern of skylight polarization to find its way back to its desert burrow. This is followed by an invited chapter by Nicolas Franceschini, a pioneer in biorobotics. In this chapter Franceschiniand coauthor StephaneViolett present a novel and robust visual tracking system that utilizes of low amplitude scanning of a photodetector. Such a systemmay have an analog in the compound the fly in which a tiny muscle oscillatesthe photoreceptor array. Two chapters are presented in the Motor System section. In the first, Roger Quinn and Roy Ritzman review their work in developing hexapod robots with cockroach kinematics and control . This work is an excellent example of how the close collaboration of an engineer and a biologist can lead to advancesin both fields. The chapter by Holk Cruse presentsthe intriguing argument that an understanding of how the brain controls complex, multiple degreesof freedom motor systems, such as the six legs of the stick insect, may give us important insight into how the so-called higher cognitive functions are implemented. The issuesaddressedin Cruse's chapter lead us into the final pair of chapters on the use of robots to explore higher brain function . Olaf Sporns and Nikolaus Almassy explore the development of perceptual invariance in a neural model patterned after the mammalian inferior temporal cortex. This model was incorporated into a mobile robot with a visual systemand was shown to develop pattern-selective "neurons" over time as the robot was permitted to move about within the real world . In the final chapter of this book Brian Scassellatidiscusses the application of humanoid robots to the study of human social development. The book ends with a discussionof the outstanding issuesin biorobotics, given the current state of the art, that were derived from the lively discussionsthat occurred during the AAAI symposium. It is our hope that the readerwill find thesechaptersinformative and insightful and perhaps inspirational. We do hope, however, that the reader also views these chapters with a critical eye. Biorobotics is an emerging field that will become scientifically strong only through vigorous debate and the application of rigorous standardsof scientific utility . It must not be forgotten that a biorobot is a model of a living animal and, like all models, has its appropriate usesand its limits . To aid our readersin the evaluation of work in this field, and to help them develop their own research, we provide the following list of dimensions (Webb 2001) on which biorobotic modeling decisionsneed to be made: . Realism : whetherthe modeltestsandgenerates hypotheses applicableto biology. . Level:the elementalunits of the modelin the hierarchyfrom atomsto societies . . Generality : the rangeof biologicalsystemsthe modelcanrepresent . . Abstraction : the complexity , relativeto the target, or amount of detail includedin the model.
INTRODUCTION XIII Subject area Examples Simple sensorimoror con rol Chemical Moth pheromone racking Amtrail following Lobster plume follo \\'ing
References
Kuwana ,Shimoyama .and Miura 1995 ; Ishida , Kobayashi , Nakamoto .and Moriisumi 1999 ; Kanzaki 1996 ;Willis 2000 Sharpe and Webb 1998 ;Russell 1998 Grasso .Consi .Mountain .and Atema 1996 ; Grasso .Consi , Mountain .and Atema 2000 ;Ayers etai,1998 C,elegans gradient climb Morse , Ferree .and Lockery 1998 Auditory Cricket phonoraxis Webb 1995 ;Lund ,Webb ,and Hallam 1998 ;Webb and Scutt 2000 Owl sound localisation Rucci .Edelman .and Wray 1999 Human localisation Horiuchi 1997 ; Huang .Ohnishi ,and Sugie 1995 Bat sonar Kuc 1997 ; Peremans .\X'alker .and Hallam 1998 VisualLocust looming detectionBlanchard .Verschure .and Rind 1999 ;Indiveri 1998 Frog snapping Arbib and Liaw 1995 Flymotion detection to Franceschini . Pichon .and Blanes 1992 ;Weber .Venkatesh .and Srinivasan 1998 ; Hoshino , control movement Mura ,Morii ,Suematsu .and Shimoyama 1998 ;Huber and Blilthoff 1998 ;Srinivasan and Venkatesh 1997 ; Harrison and Koch 1999 Praying mantis peering Lewis and Nelson 1998 Human Horiuchi and Koch 1999 ;Takanishi .Hirano .and Sato 1998 ;Shibata and Schaal 1999 oculomotor reflex Tracking /Saccade con rol Clark 1998 ;Wagner ,Galiana ,and Hunter 1994 ;Schall and Hanes 1998 OtherAntpolarized light Lambrinos etal.1997 .Lambrinos .Moller , Labhart .Pfeifer .and Wehner 2000 compass Lobster anemotaxis Ayers etal.1998 Cricket wind escape Chapman and Webb 1999 Trace fossils Prescott and Ibbotson 1997 Complex motor con rol Walking Stick insect Cruse etal.1998 , Kindermann etal.1998 . Ferrell 1995 ;Pfeiffer etal.1995 Cockroach Espenschied etal.1996 ,Delcomyn and Nelson 2000 ,Nelson and Quinn 1998 . Binnard 1995 Four -legged mammal Ilgetal.1998 , Berkemeier and Desai 1996 Swimming Tailpropulsion Triantafyllou and Trianrafyllou 1995 . Kumph 1998 Pectoral tin Kato and Inaba 1998 Undulation Patel etal.1998 Flagellar motion Mojarrad and Shahinpoor 1997 FlyingInsect wings Mikiand Shimoyami 1998 .Fearing 1999 , Fearing etal.2000 .Dickinson etal.1999 . Pornsin Bat Sirirak and Tai1999 Arms /handsSpinal circuits Hannaford etal.1995 ;.Williamson 1998 Cerebellar con rol Fagg etal97.van der Smagt 1998 .Hoff and Bekey 1997 Grasping Leoni etal.1998 .Hauck etal.1998 Haptic exploration Erkman etal.1999 Humanoid Special issue Advanced Robotics 116 : 1997 ; Brooks and Stein 1993 Hirai etal.1998 ;Yamaguchi and Takanishi 1997 OtherRunning and Hopping Raibert 1986 .Pratt and Pratt 1998 Brachiation Saito and Fukuda 1996 Mastication Takanobu etal.1998 Snakes Hirose 1993 . Review inWorst ,Miller forthcoming Paper wasp nest constructHonma 1996 Navigarion Landmarks Ant /bee landmark homing Moller 2000 ;Moller etal.1998 MapsRat hippocampus Burgess etal.1997 .Gaussier etat.1997 ;Recce and Harris 1996 Search review Gelenbe etal.1997 Collective behaviours Beckers etal.1996 ;Holland and Melhuish 1999 ; Melhuish etal. 1998 ; Kube and Bonabeau 2000 Learning Edelman etal.1992 ;Hallam etal.1994 ;Sporns forthcoming ,Scutt and Damper 1997 .Saksida etal.1997 ,Voegclin and Verschure 1999 ,Chang and Gaudiano 1998 Table1: Examplesof biorobot research . This is intended to bea representative sampling not a fully comprehensive listing. .
Accuracy: how well the model representsthe actual mechanismsof behavior.
.
Medium: the physical basisby which the model is implemented.
.
Performancematch: to what extent the model behavior matches the target behavior.
.
Utility : the biological understanding, technical insight or communication of ideas
thatthemodel provides .
XIVCONS I AND WEBB Acknowledgements The editors thank the American Association for Artificial Intelligence for sponsoring the AAAI Fall 1998 Symposium Seriesand our symposium organizing committee colleagues, Randall Beer and Holk Cruse. We thank our contribut ing authors and our other colleagues who participated in the symposium . Thanks also goes to our editor, Kenneth Ford, and our publisher the AAAI Press. Finally, we thank the following publishers for their generouspermission to reprint some of the articles in this book: ConnectionScience , SPIE. Note
1. Willis, M . 2000. ModelingApproachesto UnderstandOdor-Guided Locomotion. This paperis availableat http://flightpath.neurobio.arizona.edu/Model / index .html .
CHAPTER
A Spiking NeuronController for RobotPhonotaxis Barbara Webb
In Thorson their 1988 paper the auditory behavior of the cricket, Weber and suggest as a about "first model" of phonotaxis: " ... the simple rule 'turn toward the ear more strongly stimulated.' We usethe word simplebecause a two-earedrobot programmedto obeythis rule (if suitable noisewereincorporated ) could be madeto track a soundsourcein a mannerlike that of the female." This chapter reports the latest in a seriesof studies(Webb 1994, 1995; Webb and Hallam 1996; Lund, Webb, and Hallam 1997, 1998) of what is required to make a two-eared robot track sound sourcesin a manner like the female cricket. In the
~
processmany questionshave been raised, both about the "simple rules" of phonotaxis, the more generalproblems of understandingneural control of behavior, and what can be learned from robot models. The ultimate aim in theseinvestigations has been to gain an understandingof how the sensoryand neural systemsof animals, embeddedin appropriateenvironments, successfullycontrol behavior. A general strategyhas been to look for alternativesto the standard"information process ing" conception of perception by focusing on how sensorscan act as matched filters, how temporal dynamics of neurons can interact, and how environmental conditions control behavior. Consequentlythe modelshavethe following features: . As far aspossiblethe modelsarebuilt asrealsystemswith actualsensorsand motors, behavingin the real world. They are alsobuilt aswhole systems , solvinga completeproblemof sensorimotorcontrol ratherthan partial problemsof sensing or movIng. . The architecturesrepresentneural processes at appropriatelevelsof detail rather than usingstandardartificial neuralnet abstractions . Individual neuronproperties and identified connectivit)r are included, rather than training methodsbeing applied to genericarchitectures .
4 WEBB
.
The
systems
detail
built
with
explains
the
The
are
,
observations
explanation
of
the
.
-
The
quickly
behavior
in
test
evident
over
this
female
of
song
of
sary
)
for
large
finding
distance
terrain
in
-
,
the
and
mill
full
In
to
the
1985
;
Huber
neuroethological
,
nervous
nections
( e
1994
;
.
Stumpner
Thorson
,
derlies
Weber
most
"
of
Once
a
sensing
.
to
-
Thus
an
However
,
it
falls
.
is
More
-
.
(
as
;
(
is
a
sounds
. g
.
;
tractable
a
tread
and
of
,
Huber
and
for
the
response
,
neural
con
critical
;
-
Thorson
system
stimulus
1988
)
e
a
covered
on
1982
number
1995
)
It
Schildberger
Horseman
and
an
-
Hu
-
.
suggested
system
a
basic
hypothesis
that
still
un
-
:
correct
,
is
.
defined
small
1982
source
)
-
-
available
-
cover
other
periods
Wendler
neces
to
and
long
are
1989
able
vegetation
for
the
not
is
males
and
towards
though
female
uneven
well
Stout
this
the
other
,
relatively
flying
(
to
the
female
the
apparently
right
or
walks
left
and
towards
making
it
suitable
by
correc
-
.
assumes
sessed
that
of
by
a
been
song
"
when
information
simply
is
"
does
beyond
is
evidence
not
scope
. e
.
explicit
the
-
and
"
,
,
recognized
thus
it
the
that
start
independent
is
make
)
stop
above
follow
of
to
an
song
it
1988
i
for
necessarily
the
towards
failure
from
is
a
Thorson
fashion
played
songs
whether
walks
and
is
correct
,
indicates
phonotactic
,
this
However
female
( Weber
sound
of
.
the
argued
typical
even
recognition
sound
whether
approach
It
neu
applied
research
or
sound
Loher
and
,
ability
1982
on
sound
"
The
coming
is
sufficient
despite
track
a
Huber
recognized
the
localization
low
and
model
.
system
walking
is
Scharstein
and
Huber
,
by
tracking
a
Atkins
"
.
" turns
This
in
is
really
explanations
negotiating
,
involving
research
song
.
has
,
the
whether
rive
to
,
-
,
and
and
-
the
current
cues
will
,
,
Wohlers
auditory
the
Moore
system
. g
only
Schmitz
approach
accessible
ber
;
useful
advanced
plausible
cue
male
of
1982
sensory
female
details
a
forth
of
male
This
single
the
be
replica
explanation
meters
a
lab
Huber
Thorson
Using
twenty
many
and
some
model
nontrivial
well
alternative
conspecific
.
locate
thus
,
a
.
ten
.
and
Weber
mate
reliably
vicinity
in
the
Phonotaxis
produces
a
compared
well
is
yet
relatively
should
a
some
find
male
is
build
a
suggest
can
the
the
-
,
neurons
to
short
models
cricket
calling
stereotyped
is
how
phonotaxis
Cricket
The
is
behavior
assessing
system
trying
far
the
resulting
of
cricket
terms
of
how
building
the
aim
behavior
of
rigorous
:
the
studied
themes
understanding
when
models
with
.
system
these
roethological
as
data
particular
explore
treated
biological
is
is
possible
that
the
with
contains
, "
but
event
as
failure
corrective
fact
movement
hence
recognition
that
suitable
the
prior
generally
will
chapter
to
review
the
extensive
,
directional
discussed
be
.
current
It
turns
useful
be
.
moves
corrective
no
as
turns
cricket
-
to
evidence
-
A SPIKING NEURO ~ CONTROLLER
FOR ROBOT PHONOTAXIS
5
on cricket phonotaxis (seee.g. Huber, Moore, and Loher 1989; Pollack 1998).
The main finding that will be addressed is that the femalecricketonly turns towardsand approaches the calling songof conspecificmales. To be more exact, the carrierfrequencyof the songand the temporalrepetitionstructureaffectthe likelihoodand accuracyof taxisin the female: in generalthe besttaxisis seento the conspecific song. Cricket song generally consistsof short bursts (ten to thirty millisecond "syllables") of fairly pure tone (around 4-5 kHz ) grouped in various distinctive patterns ("chirps"). A number of studies have shown frequency preference in the approach to temporally identical songs. For example, Popov and Shuvalov ( 1977), Stout and McGhee ( 1988) and Oldfield (1980) all report a higher
thresholdand decreased accuracyfor taxisasthe frequencyis changedfrom the typical carrier, and no taxis below 2 kHz or above 12 kHz . The most important temporal pattern cue for recognition is given by the syllable repetition interval (SRI) in the song (Popov and Shuvalov 1977; Thorson , Weber, and Huber 1982; Stout and McGhee 1988). Thorson and colleaguesargue for a "thirty Hz" hypothesis for Gryllus campestris : that a repetition rate of around this speed is necessaryand sufficient for taxis in the female, whatever the length of syllables or higher order grouping of syllables. Doherty (1985) and Wendler (1990) suggest that chirp structure alone may also be necessaryor sufficient for taxis in Acheta domesticaand Gryllus bimaculatusrespectively, but there is general agreement that syllable rate is a primary cue. In previous work (Webb 1994, Webb 1995) it was hypothesizedthat the frequency selectivity of the cricket could be a simple consequenceof the physical means by which it detects the direction of sound sources, and the temporal se-
lectivity a consequence of usingthe relativetiming of firing to control behavior rather than firing rates. The work discussedhere will show that both these hypothesesare borne out by the robot model.
Robot
Model
i ng
A good way to evaluate the completenessand usefulnessof a hypothesis is to implement it in a model system. This will alwaysquickly revealgapsin information or inconsistenciesin evidence, as well as potentially producing predictions for further testing. In the caseof hypothesesabout sensorimotor function , there are a number of reasonswhy using a physical model rather than a simulation can be advanced.Al -
thoughtherehasbeenmuchpseudo -philosophicaldebateoverthe meritsof using real-world systemsI believethat the pragmatic advantagesare the onesto stress: . It is easierto model physical constraints physically than symbolically. By using realworld physics there is little risk that the model violates the possible. Noise is natural
6 WEBB rather than an arbitraryaddedrandomness . Good simulationsof real world constraintsrequiresubstantialcalculations , it is easierto let the "physicsdo the walking" (Flynn and Brooks1989). Finally the testingof mechanisms is morestraightforwardandmorerobust- ideallyonecanusethe sameexperimentalparadigmson the robot asareusedon the animalbeingmodeled. Implementinga robot forcesyou to look for simplemechanisms that might make it work, ratherthan immediatelyimplementinga givenhypothesis . Thus eachassumptionaboutthe systemis questionedfor its necessity andplausibility. The constraintsimposedareappropriatebecause they aresensorimotoronessuchassignal processing time, noise, andeffectsof selfmotion.
Implementation The robot model of the cricket has undergone a number of incarnations. The main features of the current model (discussedin detail below) are (1) an electronic sound processingcircuit that mimics the auditory processingof the cricket ear in real time, with programmable control of parameters; and (2) a neuralnetwork model that operates at the level of dynamic changes in membrane potential The robot base is a Khepera (K-Team 1994) miniature mobile robot . This measuresroughly six centimeters in diameter and four centimeters high: the ears circuit described below adds six centimeters to the height . It has two drive wheels and two castors. It has a 68332 processorprogrammed in C. This platform was chosen becauseit is closer than most robots to the scaleof a cricket, has precisemotor control, and is relatively straightforward to interface. The processorspeed turned out to be a limitation , requiring the invention of a pareddown version of the model of neural spiking previously used (Scutt and Webb 1997) to enable it to run in real time on the robot. The Robot 's Ears The cricket has evolved a unique solution to the problem of determining the direction of the sound source it wishes to approach (Michelsen, Popov, and Lewis 1994) . There is little direct amplitude difference in sound at the two earseither from distance attenuation or sound shadowing, and the cricket lacks the specialized neural processesfound in other animals for detecting microsecond phase differences. Instead the phasedifference is physically converted into an easily detected amplitude difference as follows. The ear-drums on the legs are connected by an air-filled tracheal tube to each other and to two additional openings on the cricket body. Sound thus reachesthe tympani both directly and indirectly, via the tube, so that its vibration reflects the summation of thesedifferent waves.
A SPIKING NEURON
CONTROLLER
FOR ROBOT PHONOTAXIS
7
The phaserelationships of thesewavesdepend on the distancestraveled (and internal delays in the tube), which depend on the direction of the sound source.
Thus a soundsourceon the left sideproducesa largervibration in the left tympanum than the right tympanum, and consequently quickerfiring of auditory neurons
on that side .
This mechanism was modeled electronically (Lund , Webb, and Hallam 1997), using two microphones with a small separation. The output of each mi crophone was combined with the inverted, delayed output of the other microphone. The delay representsthe time for sound to travel through the tracheal tube and the inversion
the fact that direct
and indirect
sound
operate
on oppo -
site sides of the ear-drum . The result is that sound amplitude measuredon the combined signal varies with sound direction in an approximately cardioid func.
tlon
.
The implemented circuit is mounted on top of the robot. The two microphones face forward and their exact separationcan be varied. In the experiments below it was set at eighteen millimeters, approximately one-quarter the wave-
lengthof 4.7 kHz sound, which is the carrierfrequencyof Gryllusbimaculatus song. The circuit is programmable so that the relative delay between the ears and gains of the combined signal can be altered. In the experiments below it was set to a fifty -three microsecond delay- the time for sound to propagate onequarter wavelength of4 .7 kHz , and gains of one were used. An important feature of this system is that the tuning of the ears separation and the delay result in the circuit being most directional for a particular signal frequency and providing progressively less directionality as the frequency increasesor decreases . Thus behavior, such as turning to the sound, that depends on the directionality, should becomeprogressivelyworse as the carrier frequency
of songis alteredfrom the ideal. Although the systemasdescribedhasno direct frequencyfiltering, the behaviorwill nevertheless appearfrequencyselective . Experiments on the robot demonstrated that this does in fact occur. Full details are given in Lund , Webb and Hallam (1997) but in summary, the robot would approach cricket sounds or a synthesizedsignal with a 4.7 kHz carrier frequency, would ignore a 9.4 kHz carrier frequency, and had a raised threshold for taxis to a 2.35 kHz signal. Moreover the robot was able to "choose" a 4.7 kHz signal over a 6.7 kHz signal when played simultaneously. The
Robot
' s Neurons
The model neurons implemented are a simplified version of the Scutt-Dampner (Scutt and Dampner 1991, Scutt 1994) model, which has previously been used in a phonotaxis simulation (Scutt and Webb 1997). Implementing such a model on the robot produced strong constraints. While it was consideredessentialto retain the dynamic membrane potential (and spiking) elementsof the neurons, it was also necessarythat the model run at real time speed(e.g. spiking at up to two
8 WEBB
hundredto threehundredtimesa second , or at leastthreeto five timesduring a cricket's fifteen to twenty millisecond syllable) on the robot's microprocessor. General
Features
of the
Model
The implemented model usesa two-state system to describe neuron membrane
potential, modeledasa singlecompartment . In the pre-firing statethereis leaky integration of input . When threshold is reacheda spike (rapid increasein potential) occurs and the neuron goesinto a-post-firing state. In the latter state input is still added but there is also a rapid decreasein potential. While the neuron is above a recovery level it passesproportional activation via weighted synapsesto
other neurons.When it dropsbelowthis levelit returnsto the pre-firing state. The time-constantand the recoverylevelcanbe varied; to mimic specificproperties of identified
neurons .
Neuronsareconnectedby "weightedsynapses ." During the post-firing (prerecovery) state, a spiking neuron passespotential to those neurons with which it connects, the amount of potential dependent on the membrane potential of the neuron and the sizeof the weight of the connection. These synapsesalso exhibit depression, i .e. the efficacy is decreasedafter firing and takes time to recover. It is also possible for synapsesto connect to other synapses , in which casethey fa-
cilitateor inhibit that connectionby alteringits weight. Neurons can be defined to have sensoryinput , in which casethe signal is represented as a change in membrane potential; or motor output , in which case
spikesin the neuron are interfacedwith the motor control of the robot. The original model (Scutt 1994) included mechanisms for long-term alteration of
connectionweight, to modelbiologicallearningmechanisms . Theseandvarious other details have been stripped out of the code and it has been optimized as far as possible . The four -neuron model described below runs faster than a cycle/ millisecond: one cycle updates all neuron membrane potentials including input and output functions. The
Phonotaxis
Circuit
The current robot controller consists of only four neurons and four synapses (figure 1). The two input neurons are basedon the identified "ANI " neurons in the cricket (Wohlers and Huber 1982), which are closely associatedwith taxis (Schildberger and Horner 1988). The amplitude signal from the auditory circuit is scaled-down by a factor of 4 and a "noise" constant subtracted. The resulting value is added to the membrane potential of the respectiveinput neurons. If this is higher than the constant "leak," the potential will eventually sum to threshold. The AN 1 in the cricket has spikessuperimposedon a raisedpotential so recovery (i .e. ability to spike again) in the model neuron is set near threshold rather than near the resting level. This means that continued input will rapidly lead to further spikes for the duration of the stimulus (seeexamples in figure 2). The amplitude of the input will be representedboth by the initial
A SPIKING NEURON CONTROLLER FORROBOT PHONOTAXIS 9
latency to start spiking and the consequentfiring rate. The time constant of this neuron is matched to the cricket neuron such that it decayshalf-way to resting potential after around ten milliseconds (Wohlers and Huber 1982). This gap appearsto set a limit on the fastesttemporal patterns that can be encoded in its spike pattern (Schildberger 1984). In the cricket the AN 1 send axons to the brain, but although a number of brain neurons involved in auditory processinghave been identified (Schildberg-
10 WEBB
er
1984
ty
is
a
motor
;
Bohm
not
and
known
.
neuron
tial
of
the
means
that
further
.
if
can
tial
also
will
opposite
the
speed
ond
for
tion
of
sence
If
turn
cricket
to
the
recovery
to
for
to
gap
to
is
occur
opposite
of
it
large
Each
,
AN
- MN
effect
the
drive
too
.
AN
any
of
MN
the
this
-
synapse
significantly
to
if
poten
this
potential
leading
,
-
to
the
of
the
1 ,
suppress
before
the
of
turns
fifty
control
is
of
the
set
at
turns
by
1
synapse
the
is
will
.
opposite
walking
reported
in
is
or
as
terms
of
it
is
,
of
a
the
fixed
extended
.
.
If
Currently
degrees
per
substantial
sec
-
simplifica
cricket
simple
body
spikes
reversed
- five
a
flying
a
it
and
,
be
fifty
this
If
speed
elapses
turn
adopted
.
fixed
approximately
a
was
way
a
duration
the
Obviously
it
data
following
at
duration
milliseconds
information
the
direction
occurs
during
duration
in
corresponding
spike
actual
raises
sufficiently
will
behavior
the
fires
behavioral
control
but
in
-
the
approximation
ab
:
movement
rather
-
most
than
leg
.
Experimental
Variables
The
robot
the
membrane
can
of
robot
limit
( less
be
selected
tions
.
be
altering
the
run
how
can
robot
be
worth
in
" nonrecording
off
to
download
it
in
at
a
varied
of
but
,
generally
be
mode
data
a
set
which
at
around
it
rate
ten
,
can
direc
the
-
sound
recording
,
mo
allowing
Finally
the
is
comparable
of
Hz
data
Second
other
directly
The
.
performs
in
the
robot
towards
data
consecutively
case
or
the
,
that
this
.
of
speaker
.
Hz
required
moving
First
of
thirty
speed
generating
treadmill
collected
in
.
,
on
worth
at
is
forward
of
.
so
on
cycles
record
the
data
cycle
limitations
vary
detailed
instead
each
Memory
hundred
towards
position
is
to
a
of
at
patterns
default
that
walking
data
"
so
fixed
crickets
The
moves
0
.
four
such
collection
recorded
produced
input
which
.
the
be
around
relevant
recorded
set
rotates
can
the
fast
be
from
seconds
be
be
to
as
facilitate
can
can
in
can
to
neuron
recordings
but
purposes
collected
modes
each
behavior
)
speed
position
sixty
spiking
most
,
,
that
several
of
behavior
This
source
in
second
for
motor
breaking
the
half
sufficient
the
run
consecutive
than
tor
be
potential
recordings
to
in
detailed
raise
AN
threshold
first
spike
integration
connection
firing
connectivi
connection
.
hundred
the
below
circuit
depression
contribute
leaky
far
motor
MN
of
1
,
I
later
and
AN
inhibitory
AN
another
one
in
not
the
the
excitatory
initial
the
of
)
an
an
do
firing
too
controls
a
.
the
,
Thus
spikes
the
performs
decayed
the
.
makes
However
in
also
1998
AN
side
.
gap
MN
slightly
makes
same
spike
As
further
MN
duration
a
further
that
firing
robot
is
have
a
Each
the
following
.
means
ANI
on
Staudacher
each
threshold
there
a
makes
This
)
;
model
towards
threshold
poten
1992
robot
immediately
,
above
the
( MN
MN
Only
synapse
Schildberger
In
the
behavior
up
robot
without
-
to
can
A SPIKING NEURON
CONTROLLER
FOR ROBOT PHONOTAXIS
11
Results The
data below
comes from
a series of tests run in " treadmill
" mode , i .e. with
the robot turning in responseto MN spikesbut not moving closer to the sound.
Using recordingsof the neuronsand the motors, the behaviorwas examined with
both recorded
cricket
songs and artificial
stimuli
.
Turning to Sound The graphs below (figure 2) show how this circuit controls turns towards the sound source . The stimulus is recorded cricket song (fifteen millisecond
bursts
of approximately 4.7 kHz sound), looped to repeat the syllables continuously, and the robot is initially facing about forty -five degreesto the right . The output of the auditory circuit is thus much stronger for the left ear than the right ear; in fact the right ear is initially below threshold. It can be seenthat each syllable of sound in the left ear produces a ramp in potential in the left AN followed by four spikes, and then a slow decreasetowards resting level once the syllable has ended. The first spike of each group increasespotential in the left MN , but due to synaptic depression, the immediately following spikes have little effect. MN potential decays between syllables but slowly enough such that after two or three syllablesit exceedsthreshold and producesa spike. The spikes turn the robot left, towards the sound, as can be seen by the increasein input amplitude for the right ear and decreasein the left. The right AN begins to spike, but close comparison will show that it takeslonger to reach threshold and spikeslater than the left AN . This meansthat the left AN activity will have inhibited the connection between the right AN and MN , so the right MN
does not yet fire , while
the left AN
continues
to do so .
Selectivity for Syllable Rate The turning responsecan be madeselectiveto a bandpassof syllableratesby tuning the time constants. The first critical factor is illustrated in figure 3, which shows the neural responseon one side of the circuit to syllables faster than normal- this is a synthesizedsignal consisting of 4.7 kHz tone bursts of ten millisecond
duration
with
ten millisecond
gaps . The time constant
of AN
is
sufficiently slow that it is unable to code this pattern clearly, but instead contin ues to fire almost constantly. This means that the MN -AN synapseis depressed and never gets an opportunity to recover, so MN does not receivesufficient in-
put to fire, and no turn is madeeventhough the directionof soundis clearly representedin the input . Figure 4 shows the same neurons when the synthesizedsyllablesare doubled to last twenty
milliseconds
with
twenty
millisecond
gaps . Note
that
these gaps
12WEBB
Figure 2. The modelneuron response to cricketsong- 15 millisecondburstsof 4.7 kHz tone. At the start, only the left input is strongenoughto causespikesAN, andthesesumto a spikein MN everytWoor threesyllables . This meansthe robot turnstowardsthe sound. The right ear amplitudeincreases , but it is still lowerthan the left. Thusthe right AN spikesafterthe left AN, whichhasinhibitedits connectionto MN . Note that the amplitudeandmembrane potentialare arbitraryunits, cyclesreferto processor cyclesof average durationjust underonemillisecond .
A SPIKING NEURON CONTROLLER FORROBOT PHONOTAXIS 13
are about the samelength as those in the cricket song stimuli in figure 2. In this case the AN firing clearly representseach syllable with a burst of spikes. The first spike in a burst produces an increasein potential in MN . The gap is long enough for the depressionof the AN -MN connection to recover, so that the following burst again increasesMN potential. Though MN potential decaysin the gap, each increasebrings it closer to threshold, and MN fires after two or three syllables, producing turns towards the sound source. When the length of syllablesis increasedfurther the circuit becomesless effective at locating sound as shown in figure 5. Here each syllable and gap lasts 40 milliseconds Though these gaps are long enough to allow the AN - MN connection to recover, they are also long enough for the MN potential to decay
14WEBB
Figure
As
in
quickly
most
tWo
MN
back
to
medium
or
three
(
twenty
millisecond
syllables
)
produces
a
spike
syllables
( left
in
side
MN
.
The
are
needed
only
)
robot
.
responds
to
threshold
,
hence
more
syllables
to
produce
a
.
ability
of
by
(
Response
every
way
behaviorally
Huber
.
,
sound
the
in
The
2
to
of
spike
4
figure
1982
)
degrees
separation
switched
to
the
robot
.
The
robot
other
locate
the
,
the
to
adopting
starts
right
and
after
sounds
treadmill
about
of
different
paradigm
facing
between
left
.
ten
The
two
signal
seconds
syllable
used
speakers
is
,
and
by
rates
Thorson
that
produced
switched
was
,
are
in
back
tested
Weber
one
again
,
at
and
ninety
speaker
,
after
a
A SPIKING NEURON CONTROLLER FORROBOT PHONOTAXIS 15
further ten seconds . Cricketsperformingphonotaxisshowa characteristic oscillation around the speakerdirection and follow the switch between the speakers, provided the signal has the "preferred" temporal properties. The results are shown in figure 6. For continuous sound the robot does not move , and at ten milliseconds
one turn
occurs when
the sound
is switched
back .
At fifteen milliseconds the robot actually turns the wrong way although it produces some periods of oscillatory behavior. At twenty and thirty milliseconds the robot tracks the sound in the characteristic fashion and quickly follows the shift in direction . The tracking at thirty milliseconds shows slightly larger oscillations. At forty milliseconds, slower oscillation around the sound direction occurs, and the robot fails to follow the switch between the speakers.
16 WEBB
Figure~ Behaviorof therobotwith differentsyllablerates . Soundis switchedfrom +45 to- - 45 degreesafter approximatelyten secondsand switched back at tWenty seconds. The robot successfullytracks the switch only for syllables of tWenty to thirty milliseconds.
A SPIKING
Recognition
As
and
a further
ditional ed
Choice
test
tests
of
the
were
oscillation can
recognition
't
the
with
two
confusing
the
direction
males
three
trials
fully
tracks
sounds one
, the sound
robot
switches
robot
briefly
the
FOR
has
ROBOT
PHONOTAXIS
17
mean
the
one
is
calling
between
speakers
oscillates
between
, initial
silence ten it
, producing by
no
" the
nondirectional
" recognized
with
tracks
from
above
-
two
and
behavior
directly
7
success
, faced
turning
figure
robot
towards
a sound
than choose
) . In
, the
turns
-
, crickets
can
1993
first
pre
rather
cricket
second
then
study
them
Dill
the
the
them
seconds
had
. In
. In
of
-
they
independent
same
female and
shown
that of
the
ad -
nondirect
them
one
( Hedrick
are
following
perform
evidence
) . In
to
than
song
as
1988
track
taken
as though
taken
to
is
the
to
above
shown
this
cricket
shown
directly
Thorson are
circuit
been
been
and
sources
about
implemented have
, and
third after
oscillate
this
sound
more
the
broadcast
( Weber
recorded
replaced
to
a sound
, and
and
. In
is
begins
if
using
of
. Crickets
to
signal
presented
between
capabilities
localize
of
CONTROLLER
?
performed
behavior
sumably
NEURON
the
. The
robot
signal
.
Discussion
The
robot
which
cricket
crickets
ing
issues
for
plex
neural
cuit
. Finally
, the
a real
tional So
how
rons
the
or
. Our in
the to
mutually
strategy model reproduce inhibitory
can
be
relate
has only the
the
it
even
support
of
by
interest
reveals
the
a simple
-
com
model
models
considered ) the
sounds
, and
of
of
-
cir -
alternative
the
neurons
neural
of
sound
tracking
sources
behavior
actual
circuitry
in
the
cricket
with
auditory
processing
been
to
the
connections
and
ON
include
they
appear
. For
1 neurons
of
that
can
, selectivity
without
direc
-
.
associated
behavior
in
" model
circuitry
been
when
complexity
embedded
as a " minimal
simplest
: tracking
rates
the
demonstrates
usefully
syllable
to
robot properties
( probably
competing
have
to
dynamic
behaviors
and
this
neurons
. It
observed
with
might
identified
tempts
the
from
used
mechanism
a number
.
on the
represents
frequencies
input
from
device
support
specific
, simple
raises
generally
emerge
be
systems
implemented
is , it
. More
can can
. It
Phonotaxis
result
sensorimotor
actually
havior
can
, that
that
sensorimotor
circuit
that
system
described
Cricket
neuron
phonotaxis
for
other
a plausible
phonotaxis
this
interactions
for
four
demonstrates
selective of
methods
and
behavior
out
understanding
Implications
of
here
carry
/ behavior
hypotheses
The
described may
example that
to
be
functionally
, it
might
appear
to
? More
than
and properties
increase
useful the
be -
of
demanded prove
four
taxis
neu in
to
-
at -
include
difference
in
18WEBB
The ability to track one speaker out of two and to make phonotaxis like turns when there is no directional information have been taken as evidence of independent "recognition" in the cricket, but the simple robot circuit can reproduce these behaviors.
firing of the AN 1 neurons (Horseman and Huber 1994). This additional suppression of firing of the wrong side may solve some of the problems causedin the current circuit by the latter firing sufficiently long after the right side to causea motor spike in the wrong direction . An important implication for cricket neurophysiology of the model is that
A SPIKING NEURON CONTROLLER FORROBOT PHONOTAXIS 19
more
caution
should
information
directly
already
tion
. "
the
particular
the
rates
role
neuron
show
spike
effect
,
most
not
The
a
clue
what
.
for
What
relative
versus
the
Are
time
and
in
of
How
to
should
rectly
approach
iments
Further
both
rate
to
rates
)
the
,
-
-
to
the
MNs
syllable
in
a
the
four
syllables
but
in
imply
preferred
one
this
is
-
(
simply
this
inherent
of
direcrionality
a
able
i
. e
.
side
be
may
and
the
robot
?
example
other
if
,
movement
a
then
short
latency
.
neural
The
responses
results
explain
correct
are
neurons
?
?
Many
ex
-
.
to
the
For
the
of
in
interpreted
suffice
like
?
on
drawn
the
number
auditory
system
turns
syllable
be
of
auditory
cause
a
:
directions
to
studies
the
to
suggests
including
localization
distinction
It
tuning
to
opposite
from
more
,
length
suggest
sound
system
in
normal
answer
?
frequency
alone
a
plausible
phonotaxis
the
firing
choice
crickets
firing
most
,
-
-
behavior
necessarily
model
sig
informa
success
the
not
"
employed
fire
do
provides
of
hypothesis
the
on
thus
by
this
bias
to
-
current
underlies
disambiguated
female
sensory
be
best
with
appears
the
the
can
rates
the
.
AN
allow
spikes
)
spike
of
provide
correlate
syllable
here
would
be
fail
In
correct
preceded
spikes
recognition
simple
the
played
side
of
sufficient
on
patterning
not
that
of
limitations
one
periments
.
is
differences
number
Can
.
provide
operations
temporal
that
investigation
role
on
timing
mechanism
further
may
rates
that
1984
function
frequency
syllable
.
correlation
given
neural
directions
.
same
their
model
-
rates
to
to
rates
Schildberger
firing
frequently
and
neuron
spike
and
scale
rates
brain
(
the
minimal
question
a
those
the
firing
crickets
for
circuit
they
of
And
of
by
functional
firing
.
evidence
preferred
,
time
changing
behavior
that
neurons
The
interactions
control
assuming
of
.
that
subtle
to
in
function
behavior
suggest
More
fully
exercised
the
control
nal
of
be
about
the
calling
needed
here
fact
song
to
.
follow
suggest
that
better
up
a
can
However
this
that
females
di
-
exper
-
.
Work
Although
the
sis
about
used
to
fied
robot
described
phonotaxis
,
test
be
in
the
and
era
inputs
research
same
gripper
ple
compositions
the
and
The
and
these
is
,
robot
it
(
is
quite
to
the
neural
.
could
This
be
speci
different
,
base
cam
by
means
,
-
other
that
explored
-
can
sensors
robot
circuitry
be
be
add
light
same
-
easily
circuits
to
already
the
systems
can
various
plausible
are
on
investigated
hypothe
it
can
so
there
particular
that
connections
mechanism
implemented
interface
a
design
neurons
Further
the
implement
the
vari
for
-
exam
-
.
system
control
provides
in
.
.
to
controllers
behavior
What
constraints
device
neuroethologically
escape
motor
)
to
of
processing
capabilities
and
other
.
general
robot
motor
groups
ous
the
used
feature
hypotheses
of
tested
been
important
alternative
independently
sensory
has
an
This
can
and
is
terms
of
produce
information
the
a
tool
for
thinking
dynamics
alternate
processing
about
of
the
neural
theories
to
accounts
problem
interaction
the
.
of
and
standard
One
sensori
real
-
-
world
functional
hope
is
that
de
a
"
tool
-
-
20 WEBB box)' of circuit ideas may accumulate allowing the design of more complex neural processors , in a manner analogous to analog electronic circuit design. Even better would be to develop a formalism for this kind of processor: however this is likely to be a difficult task as a balance must be continually maintained between abstraction to enable mathematical tractability and realism so that impor tant low -level properties of neurons are included . An alternative route is to
adopt the "neuromorphic " approachof building explicit electroniccircuitry to modelneuralprocesses ratherthan usingsoftware . This would offer advantages of speedand parallelism but perhapsat the cost of flexibility .
Acknowledgments The earscircuit was built under EPSRC grant GR/ K 78942 at the University of Edinburgh , designedby John Hallam and Andrew Haston and implemented by Henrik
Lund . The neural
model
is based on collaborative
work
with
Tom
Scutt .
CHAPTER
2 Environmental Information , AnimalBehavior , and BiorobotDesign Reflections on Locating Chemical Sources in Marine Environments FrankW Grasso
here are two usesof biorobots. They can be employed, first, as tools to aid biological research, and second, as the end products of biological researchthat perform useful tasks with an efficiency that approachesthat of animals. My focus is primarily on using them as aidesto developing a deeper understanding of animal behavior and its physiological substrates. The idea of producing useful robots along the way, while desirable, is a secondaryconsideratIon . In the study of biological systems there are two broad areas in which biorobots contribute . First, they allow for the direct test of biological hypotheses themselves. If an appropriate implementation of a theory fails to replicate the biological process it is intended to explain , that failure constitutes strong grounds for rejecting the hypothesis. I shall return to this point in detail later. A second application for biorobots is exploring the constraints of the world of an animal. The behavior produced and data collected from a properly scaled autonomous robot in the natural environment of the animal it emulatesare about as close as we can come to experiencing the world of another species. I shall reflect on both theseapplications of biorobots in detail in this chapter. To produce useful robots, one must have a well-defined problem and a work able solution to that problem. Certainly animals can be found with capabilities that are desirable in robots. Researcherswho aim is to implement animal algorithms and hopefully approach animal efficiency in these areas add to their labors the burden of having the animal solution in hand before they begin construction . The extent of this self-imposed burden depends upon the vagueness with which the builder is "inspired" by the natural system. Future designersof
22 GRASSO practical biorobots will rely on understanding of the model animals gleaned from biological research. For such an understanding to be adequateit will most likely have one foot on traditional biological researchand the other foot on previous generationsof biorobots used as tools of biological research.
Three Issues As a result of my experiences in the study of robots and animals , three issues have been promoted biorobots :
to high relief in my thinking
about projects involving
1. What constitutes a sufficient understanding of the organism to ensure that a biorobotic implementation will lead to useful results? 2. What constitute good measuresof robot performance? 3. What is the proper control algorithm (s) to represent a biological function in a robot- more specifically, how does one decide the relative importance of environmental cuesand internal representations? These are good questions for researchers to ask themselves in the early plan ning phases of a biorobotic project , regardless of whether they are planning on using robotics to understand biological phenomena or are interested in the development of practical robots based on biological inspiration . The answers- even partial answers- to these questions , obtained from the perspective of a particular task or biological system , are more useful to have in hand at the start of a biorobotics project than they are as "discoveries" at the end of one . In this chapter I will review the results of studies exploring the nature of lobster chemo -ori entation with our autonomous underwater biorobot , RoboLobster and use them to illustrate the effect answers to these questions have on the biorobotic approach to understanding biological phenomena and the production of useful robots .
The RoboLobster Experiments The RoboLobsterexperimentsand designspecifications havebeendescribedin previouspublications . In this sectionI summarizethe key featuresof that work that illustratethe generalpoints. The readerinterestedin the detailsof the robot and chemo-orientationis directedto thosepapers(Consi, Atema, et ale1994; Consi, Grasso , et ale1995; Grasso , Dale, et ale1996; Grasso , Dale, et ale1997; Grasso , Basil, et ale1998; Grasso , Consi, et ale2000). Three Lines of Attack Marine speciesappearto have evolved a variety of strategiesto deal with chemoorientation in the sea (Dodson and Dohse 1984; Moore, Scholz, et ale 1991;
ENVIRONMENTAL
Dunsenberry et
1992
ale
1995
Moore
;
and
um
and
.
, Grasso
The
aim
lobster
the
chemo
lobster animal
In
following
distribution of
The
under chemo
I
the
two
will
can
Grasso
,
lines
of
been
a
multiple on
ale
1996
;
of
americanus
.
aimed
involved
have
-
hypotheses
research
of
medi
switch et
Homarus
environment
at
ex -
understanding the
plume
tangle
of
- tracking
free
parame
-
.
review of of
,
;
each
under they
alternative
components
constraints
action
- orientation
evaluate
would
conditions
) .
fluid
,
1997
studies
of
turbulent
turbulence
algorithms
the
flow before
in
chemo
and
- orientation
the
ruming
RoboLobster
be
physics to
of
the
-
chemi
-
implemen
-
.
Animal
In
the
case
studied gies
.
when
it
is
are
that
paragraph
the
the
( Moore
lobster
unequivocally tion
about
flow
on
source Rheotaxis
of
lobster
ale
odor
)
a its
downstream
line
of
- gated
the
animal
source
to
angle from
the
would source
is
based
direction
the
likely
source
the
it
of
were is
form
to ( the
walking the
of
pattern
approach definition
-
mean
the of
parallel of
-
This
informa of
a
in
2 ) .
direction
were
of of
source
and
-
their
closeness the
1
the
flow
- in to
predictions the
figures
lobster
it
,
following
turbulence
) ,
some
mean
unless
-
using
odors
to
from
the
to ) . Al
strategy
the
direction ( see
extracting
. If
plume 1997
animals
track
1986
distinct
of
increase . The
to
a al .
. The
using
contrast
plume is
that
is the
)
the
-
convoluted
antennule
to
( Kennedy angle
far
.
be
) . In
et
strategies
likely
so strate
track , that
two
two
plumes
less
still
Grasso
plume
1994
through
the
the
are
rheotaxis
rheotaxis
parallel
heading
; Basil
,
possibility
least
odor
the
- orientation
can ,
actually
the
( heading
that
direction
which along
1991 - gated
travel
demonstrates the
of
they
of
at
for
chemo straighter
the
might
least
Beglane
strategies they
progresses
;
exclude
using
at
take
form
of
multiple
odor
they , but
degraded
that
, et
direction
the
to
their
1982
time
structure
as
that
inform
antennules
a
why
clear
to
( i .e . ,
capable
suggests
such
is
Atema
this
is
, Scholz
's as
at
- temporal
strategies
creases
and
illustrate
spatial
source
lateral
employing
behavior
duced
both ( Devine
lobster
will
Lobster
other
use
, it
- sensors efficiently
possible
antennule
appears
cherno more
one not
lobster
two
they with
though
American
use
perform
source
one
the
they
They )
its
of
-
paths
rectly
,
under
1999
,
, Finelli
strategy
studied
Oliver
23
Dale
the the
that ;
parallel
;
of
yet
lobster
the
of
sections
lobsters
other
RoboLobster sources
to
three
understanding
with
ale
American of
. The
and
was
the
component
two
, et
1997
dominate
not
DESIGN
- Faust
ale
strategies 1992
project
et
dynamics to
species
, Grasso
BIOROBOT
; Zimmer
,
spatial
multiple
; Mjos
1994 Hunt
appear
many
in
one
these
rhe
ration
1997
efforts
of
cal
ale
,
( Dunsenberry
chemotaxis
without
havior
context
Cox
) . The
evolved
on
behavior
. Our
ters
probably have
;
1998
, AND
- Faust
1996
locomotion
RoboLobster
was
lobster
and
BEHAVIOR
Zimmer
ale
of
- orientation
RoboLobster plaining
et
; Barbin
to
, et
of
,
speed
-
appear
depending
Beglane
the
's
Some
-
and
Johari 1997
animal
uses
off
,
Lepper
conditions
, ANIMAL
; Weissburg
Baird
the
species
and
INFORMATION
intermit
di
-
ENVIRONMENTAL INFORMATION , ANIMAL BEHAVIOR , AND BIOROBOT DESIGN 25
HeadingAngle is measured betweenthe direction of the animal or robot's shortest
route to the source
and its actual
direction
of motion
.
Heading angle
s0
Direction of the source .
~ ~; ~~~~~-~j~ '~ ~ ~~i~~{) Path
Figure2. Themethodof extractingheading angle(a) from a digitizedpath asviewed from overhead The heading angle may be computed from any pair of position measurementsalong this hypothetical path.
a lobster walks a number of meters distance. The lobster placesits lateral antennules abovethe viscoussublayer(the sheetof water nearestthe substrate) up into the flowing medium that lies above it to sensethese passingchemicals (as perceived chemicalsknown as odors that can be individual compounds or mixtures of chemicals). The lobster can sensethis passingflow and sample the chemicals that are carried within it . The lobster obtains a frame of referenceto evaluatethe relative motion of the flow-borne patchesof odor through its contact with the substrate. Turbulence produced by physical featuresthat effect the motion of the seawatergeneratesspatial patterns of flow, and therefore spatially heterogeneous distributions of chemical. Thus, what can be thought of as confusing noise to a concentration-tracking strategy may provide guidance cues to help the lobster track the chemical to its sourceusing a feature detection-basedstrategy. Studies of chemical distribution in turbulence in the field and in the laboratory provide insights into the nature of the signal environment that animals actually encounter (Moore and Atema 1991; Murlis , Elkinton , et ale 1992; Finelli , Pentcheff, et al. 1999). The period of time required to obtain a statically stationary estimate of concentration depends on the magnitude of the turbulence. In our seawaterconditions , the time scale for such estimates is typically several minutes. On purely physical evidence, these studies demonstrate that the idea that animals (or at least lobstersand similar-sizedcrustaceans ) track chemicalsto their source by ascendingthe concentration gradient in environments that contain even moderate levelsof turbulence (Re > 1 X 102) is untenable. Studies of the dynamics of the concentration signal have revealed features (such as characteristic rates or patterns of change) that could provide the lobster (or other animals) with guidance cuesfor certain plumes (Dittmer , Grasso, et ale 1995; Dittmer , Grasso, et ale 1996; Finelli , Pentcheff, et ale 1999; Moore and Atema 1991; but see Hanna, Grasso, et al. 1999). Examples of cues extracted
26 GRASSO
from the concentrationsignalinclude peakheight (the maximum valueof a pulse of odor), peak slope (the rate of change of concentration in time on the
rising phaseof an odor pulse), and interpeak-interval (the delaybetweenthe passingof one peak and the arrival of the next at a sensor) . There are many oth-
ersthat havebeenstudied. Suchcuesmight providethe informationlobsterbehavior indicatesthey are usingto decrease their headinganglesduring chemoorientation(figures1 and2). The
Robot
The constraintssuppliedfrom the physicsof odor plumesand the behaviorof lobsters provided a suite of testable hypotheses on how lobsters locate odor sourcesin turbulence. They also provided suitable constraints for the construction of a robot that could be used to test those hypotheses. Why a Robot ? Hypothesesof chemo-orientation mechanismsare difficult to test in animal behavior experiments becausemeasuresof the cue stimuli are very difficult to obtain- and harder still from a freely moving animal. The viscosity of water guaranteesthat the effects of physical perturbations from obstaclesor changesin the shapeof the channel persist far downstream from the agentsthat produce them. Once a perturbation is introduced (say from a plume-generating device or the animal itself) , it evolvesin a way that is only predictable in a stochasticway that requires large numbers of repetitions before statistical stationarity is achieved. This makesthe construction of apparatusfor testing lobster reactions to specific features highly problematic (if not technically impossible) for two major reasons: First, the control of flow through a stImulus delivery device to produce odor pulsesof a predetermined duration , slope, or other characteris exceedingly difficult , and second, good methods are not yet availableto reproduce arbitrary patterns. This would suggesta restrained animal preparation that would allow
tight control of the stimulusdeliveryto the chemo-sensors . However , this approach also removesthe possibility to observenatural behavioral responsesfrom the animal. Sequential effects would also be very difficult and time consuming to extract from such a preparation. At first glance, simulation studies appear to be a means to circumvent these difficulties . An artificial plume environment constructed from calculations with in a theoretical framework would provide the experimenter with perfect control of the environment . Such direct numerical simulation
(ONS) methods exist in
the field of fluid dynamics, but they come with serious caveatsfor the biologist or bioroboticist . First, they describelong-term averagebehavior of fluid dynam-
icssystems ; the second -by secondspatialstructureof the signalsencounteredby the animals is not available from such simulations. Second, the computational load of DNSthat approachesthe appropriate spatial scaleis so great as to practi-
ENVIRONMENTAL
INFORMATION
, ANII 'v1ALBEHAVIOR , AND BIOROBOT
DESIGN
27
cally prohibit the large repetitions required for evaluation of orientation algorithms in a reasonableamount of time (Belangerand Willis 1996).
We haveimplementeda numberof algorithmsin simulation, with "playback " plumes. Theseplume simulationsare basedon the playbackof data recorded from actual odor plumes rather than the theoretical approach described in the previous paragraph. These simulation studies have identified guidance features,
which areavailableto the lobsterthroughits peripheralchemoreceptors . The use of thesefeaturesspeedsup the simulation's orientation by an order of magnitude
comparedto concentration -averagingtechniques(Grasso , Gaito, et al. 1999). Suchstudiesareusefulasa meansof screening algorithmsandtuning parameters prior to biorobotic studies, but they cannot, for reasonsdiscussedabove, completely capture a number of important environmental featuressuch as the effect of the robot or lobsters hydrodynamic shadow on the guidance cues. This is a problem that must be solved in all simulation studies of behavior.. The resultsof the simulation study are only as good as the simulation of the task environment. In the caseof chemo-orientation in turbulence, a robotic implementation is a critical and unequivocal test of the quality of an orientation algorithm. By constructing an artificial lobster and placing it into the sameenvironment as the biological lobster, we had a method for testing hypothesesof chemo-ori entation that circumvented these difficulties . Tom Consi, Jelle Atema, David Mountain , and I undertook a seriesof experiments to test simple hypothesesof lobster chemo -orientation (Consi , Grasso, et al . 1995 ; Grasso, Dale , et al. 1997 ; Grasso, Basil , et al. 1998 ; Grasso, Consi , et aI. 2000 )
There is an additional advantage to a biologist who has a biorobot in his or
her tool-kit . By placing a scaledsurrogateof the animal under study in the exact environment , the biologist can view the sensory world of a given species. Data from sensorswith spatial and temporal sampling scalesmatched to that of the animal could be logged to on-board data-storage media. (This was the case in Robolobster.) Offline analysis of these logs can be of great value in evaluating the reasonsfor an algorithm 's failure to reproduce animal behavior. By suggesting alternative hypotheses for testing of the animals, such data closesthe loop between robot and animal experiments.
Bio-Mimetic Scaling The first biorobot we used to study lobster chemotactic strategies- dubbed RoboLobster- was designed and built by Tom Consi and Cliff Goudey who were then working at the Massachusetts Institute of Technology's AUV lab (Consi, Atema, et ale 1994) . It had wheels instead of legs and was not intend -
ed to mimic lobsterbiomechanicsor morphology(figure 3). Insteadit incorporated those features essential for the evaluation of the chemotactic hypotheses: RoboLobster was built to the proportions of the American lobster and had two chemo-sensors that can be positioned at the same height and separation as the lobster's two lateral antennules.
ENVIRONMENTAL INFORMATION , ANIMALBEHAVIOR , ANDBIOROBOT DESIGN29
Overall Length
Speed (cm/ s)
(cm) Lobster Robot
Sensor Height &
Chemo -sensor
Separation(cm)
ampling Rate(Hz)
,..,30 .0
.-9 .0
9 .0 & 3 .0
.-2 .5
24 .0
.- 9 .0
9 .0 & 3 .0
2 .0 - 5 .0
lable 1. RoboLobsterscritical points of biomimetic scaling.
1991; Dittmer , Grasso, et al. 1995). As a consequence , we are forced to consider other biologically reasonablealgorithms in searchof a reasonableexplanation. The negative result was significant becausethe conditions of the seriesof experiments were carefully constructed to succeedif and only if the plume structure supported the algorithm . The plume structure failed to do so in an interesting way. The behavior of the robot as it tracked the plume showed two statistically distinct patterns of locomotion in two regions of the plume using the samealgorithm . In the downstream region, the robot did not make significant progresstoward the source. In the upstream region, where progresstoward the sourcewas possiblewith a tropotactic algorithm , the paths produced showed little resemblanceto those of lobsters (figure 4). Thus RoboLobster revealedto us something about the lobster's world that we had previously only suspected: the need to switch tracking strategiesbetweendifferent regions of the plume. In one seriesof experimentswe systematicallyvaried the intersensorseparation. Since tropotaxis relies on an instantaneousestimateof the gradient, the increased distancebetweenthe sensorsshould haveimproved performance. This was the case in the upstreambut not the downstreamregionsof the plume. Theseexperiments, which varied one of the biologically derived constraints of the robot, representa type of evolutionary experiment in that they test the sensitivity of an algorithm to architecturalvariation. They place the question of why the lobsterspossessthe intersensorseparationthat they do (other specieshave both greaterand lesssepara tions) in high relief and open the door to further experimentsthat can explore the relationshipbetweenenvironment and evolutionarily derivedstructure.
Sufficient Description for Biorobotic Algorithm Implementation A biologically meaningful biorobot cannot be createdde novo from first princi ples. A good practical robot might arise from first principles but not a biorobot . There must be a sufficient descriptive databaseavailable to provide appropriate constraints for the design parameters. Without this set of constraints one who
30 GRASSO
setsabout building a looselyconstrainedbiorobot will, unlessvery lucky, produce neither a practical robot nor good biology. They would also probably never finish building the robot.
Thesestatements might appearto posea paradox . If the biologicalsystemis so sufficiently well understood that a biological replica can be built , why bother
building it as a tool to study the biology? If the biology is imperfectlyunderstood how does one proceed with the development of a robot? This reducesto the old question of judging the dividing line between essentialfacts and irrele-
vant details. The basisfor the experimentor 's determiningthe relevanceof particular featuresof the description of the system should be strong theoretical in-
ENVIRONMENTAL INFORMATION , ANIMALBEHAVIOR , ANDBIOROBOT DESIGN31
Figure 4b. Representative robolobsterand lobsterpaths and sensorprofiles. Theseparticular pathswere chosenbecausethey show the closestsimilarity betweenlobster and robot paths obtained. PanelsC and 0 the correspondingsensorlogs. PanelC showsdopamineconcentrationprofiles recordedwith a pair of electrochemicalelectrodes mounted on the back of the lobster and positionedin the olfactory samplingareaof the lobster. Dopaminewas usedas a tracerin the food odor plume the lobstertracked. Panel 0 showsconcentrationsignalstaken from the robot's sensorlogs. Both C and 0 are expressedas percentof maximum signal. RoboLobster 's counter turning near the sourceis evidentin the sensorlog.
ference (Platt 1964). That is, do they fulfill the requirement that the included featuresare both necessaryand sufficient to explain the systemunder study? In our studies with RoboLobster, we made an effort to explain the observation that lobsters reduce their heading-angle error as they progressthrough the plume. Basedin part on the observation that lobstersperform well with two antennules, we formulated a chemo-tropotaxis model as an explanation . This
32 GRASSO
framework
allowed
that
us
appropriate
temporal
of
information
to
size
of
the
rithm
.
the
the
lobster
These
's
and
This
is
petent
a
and
that
ceed
to
If
then
required
or
If
to
too
sign
test
)
one
suggested
to
obtain
are
that
In
answer
If
the
aim
of
be
cloud
the
comparison
on
vant
mance
.
measured
versed
true
a
the
trials
the
These
is
not
as
Motivation
.
We
-
issue
.
are
one
not
un
-
possible
or
case
for
legs
,
greater
de
for
-
one
technical
implementation
the
in
the
performance
mod
-
the
an
measures
system
animal
for
.
As
the
adequate
be
derived
from
to
permit
the
a
meaningful
with
constraints
relevant
and
description
irrele
of
descriptions
of
determination
-
perfor
the
-
bio
that
-
the
.
good
description
the
paths
using
those
used
" missed
"
cannot makes
is
parameters
delineates
in
of
measured
then
and
tested
a
descriptors
RoboLobster
.
have
free
an
Constraints
biorobotic
biological
operating
to
were
,
the
robot
adequate
conditions
algorithms lobster
or
,
not
pro
Measures
should
of
where
implemented for
is
same
no
can
that
it
our
make
a
of
you
that
here
if
.
-
the
.
behavior
essential
number
.
the
and
preferably
being
are
assume
( in
com
by
constraints
animal
for
is
acceptability
is
only
the
testing
the
I
hypothesis
evaluated
in
biological
was
plume
exactly The
it
from
hypothesis
measures
,
,
be
between
study
case
lobster
that
the
those
.
issues
understood
that
under
our
We
the
the
Performance
algorithm
-
test
suggested
testing
only
the
robot
of
question
test
two
hypothesis
behaviors
In
is
the
of
algo
critically
algorithm
hypothesis
database
or
you
Performance
made
,
features
logical
der
be
to
been
biological
biological
scientific
to
well
design
include
sufficient
one
biorobot
to
robot
need
directly
the
a
hypothesis
sufficiently
and
to
Adequate
must
the
of
a
inform
the
robots
a
required
most
the
are
below
explanation
have
consideration
answer
words
to
of
one
the
at
by
us
Construct
that
behavior
possession
under
not
:
hypotheses
practical
to
to
of
's
in
known
other
leaves
that
tropotactic
allow
an
that
to
the
to
but
and
certain
and
inform
as
robot
spatial
reasonably
frequency
sufficient
strategy
set
hypothesis
likely
.
is
the
that
are
difficulties
el
not
study
costly
to
our
the
comparable
Hz
hypothesis
robot
are
of
robotic
features
example
the
you
is
a
be
was
allowed
biorobotic
or
production
algorithm
dertakes
the
allow
.
the
the
of
hypothesis
hypothesis
could
five
were
for
determine
.
essence
test
we
not
tropotaxis
behavior
the
to
biology
were
essential
to
guidance
above
constraints
- based
- tracking
not
us
that
for
signals
antennules
were
allowed
used
Plume
similar
concentration
plume
It
so
robot
.
legs
.
sensors
the
lobster
that
essential
the
that
available
suppose
was
resolution
the
to
speed
exactly
in the
be the
of
the
lobster
"
animal
rates
same
must
,
methods
due
, the
as
.
it
and
tra
-
un
-
. be
however of
behavior
took
studies
source
certain " hit
the
RoboLobster
lobsters
that
to
failures
the difficult
of
same
is to
ENVIRONMENTAL INFORtv1ATION , ANIMALBEHAVIOR , ANDBIOROBOT DESIGN33 contrast with RoboLobster becausethe robot is programmed to alwaysseekthe source, while the lobster may not be driven by a single motivation . Thus we used a finer-grained performance measure. We digitized the paths taken by the robot at intervals that are shorter than the variation in lobster locomotor performance. From these, we were able to measure, in both the lobster and the robot, the distance from the source, time to various points from the source, locomotory speed, and the path tortuosity (a measureof the deviation of the path from a straight line). These parametersallowed us to make direct, quantitative comparisons between lobster and robot performance with specific relevance to the tropotactic hypothesis. This narrow view of the performance measurehas a cost, however. Given that the performance matches some reasonablecriterion of the biological system, there is no guaranteethat the function implemented in the robot is the strategy used by the animal. It merely constitutes evidence that the two are consistent. The narrowing of the performance measuresto just those that are adequateto evaluate the hypothesis may mask inconsistencies in other aspectsof performance. In contrast to the robot design, performance measuresshould be chosen to at least permit testing the hypothesis. A discrepancyin some aspectof performance that is unessentialfor the hypothesis points to a limitation of the model (maybe the hardware, maybe the software implementation , or maybe the theory itself) that is in need of explanation.
Control
Algorithm
Selection
and
Internal
Representations
The choice of the sophistication of the algorithms to be tested in an experimental biorobot is also of importance. The biorobot could be designed with elaborate internal representationsof the world that it will encounter, and detailed contingency mechanisms that are activated when certain conditions are met. Alternatively, it could be designedso that its behavior is driven by the stimulus world . In a biorobot intended to evaluatea biological hypothesis, it is likely that actual algorithms will fall between these extremes. With the exception of simple unicellular animals (Berg and Purcell 1977), most animals produce behaviors that suggest they plan their actions, make use of environmental features, remember past tribulations and successes , and use this information to adjust their behaviorsaccordingly. These information -usestrategiessuggestsome form of internal representation. Biorobotics is an ideal tool to explore such hypotheses. On the other hand, it is possibleto generatequite complex behaviorsfrom a relatively simple algorithm if the environment is complex (Braitenberg 1984, Kennedy 1992). Whichever side one chooses, the implications of this assumption must be considered.
ENVIRONMENTAL
INFORMATION
, ANI1 \1AL BEHAVIOR , AND BIOROBOT
DESIGN
35
environment as the animal, we are, in reality, placing the hypothesis directly in to the environment
.
Summary Recently, Vogel (1998) has strongly argued against the common prejudice that biological solutions to problems are necessarilyoptimal . He points out that evolution has shapedbiological systemsto be adequateto ensurethe survival of the individual animal until it can reproduce. In this, optimality is not required-
merelyadequacy . At the sametime, biologicalsolutionsexist(to problemssuch as chemo-orientation in turbulence) that are superior to our present ones. The animals themselvesare existenceproofs of such superior solutions. Biorobots are a powerful tool that can aid our efforts to uncover thesenatural strategies. To achieve this however, we must acknowledge that the science of biology, with its own brand of methods and logic, must be at the center of any such effort . The performance measures , hypotheses, and design parametersmust be bi-
ologicallymeaningfuland not merelybiologicallyinspiredif we are divine nature's solutions to difficult problems.
Acknowledgements The experimental work summarized here has been previously published. Nevertheless I must acknowledge the contributions a number of people. The work was conducted in the laboratory of Jelle Atema and rests largely on the many yearsof lobster researchconducted in that laboratory in earlier years. Tom Consi and David Mountain were also central to the RoboLobster project. I must also thank Jennifer Basil for generouslythe supplying the lobster data in figure 4. Jonathan Dale, Paul DiNunno , Kevin Dittmer , Diana Ma, and Rainer Voigt all made significant technical contributions to the project. SteveGaito, Megan Mahaffy, Christina Manbeck, and Christian Reilly made significant contributions to the development and study of theoretical chemo-tactic algorithms. Photocredit for figure 3 goesto Tom Consi and Marjorie Steele. This work was largely supported by two NSF grants to Jelle Atema - BES-9315791 and IBN 9631665
.
CHAPTER
3 InsectStrategiesof VisualHoming in Mobile Robots Ralf Moller, Dimitrios Lambrinos, ThorstenRoggendorfiRolf Pfeifer, and Rudiger Wehner
nimals of many speciesare impressivenavigators. Especially insectssuch as bees or ants solve demanding navigation tasks, despite their tiny rains. Desert ants of the genus Cataglyphis(seefigure 1), for example, unfailingly return to the nest from foraging excursions that took them several hundred meters away (Wehner 1987). Unlike other ants, Catagiyphiscan not use chemical cues for navigation, since no marker substancewould endure long enough in the heat of its desert habitat. Experiments revealedthat the ants instead, besides using path integration , rely on visual information about landmarks in their environment (Wehner and Raber 1979; Wehner, Michel , and Antonsen 1996). The limited brain size of insects restricts the complexity of the mechanisms that could underlie their navigational capabilities. Hypothesesabout theseparsimonious and yet efficient navigation strategiescan serve as a guideline for the development of navigation strategiesfor mobile robots. Vice versa, in a process of "synthetic modeling," the understanding of a natural systemcan be improved by constructing an artificial agent that mimics some aspectsof its behavior, and observing the behavior and internal states of the artificial agent, preferably in environments similar to the habitat of the biological agent. Synthetic modeling is an indispensablecomplement to computer simulations, and will especiallybe used in cases, where the agent-environment interaction is too complex to be simulated with sufficient accuracy. The work presented here focuseson modeling the visual homing capabilities of insects with mobile robots. Two navigation models- the "snapshot model" and the related "averagelandmark vector model," which both widely explain the navigation behavior observed in beesand ants- were implemented and tested on mobile robots. The snapshotmodel was tested in a softWareimplementation on the mobile robot Sahabot 2, while the averagelandmark vector model was
38 MOLLER,LAMBRINOS,ROGGENDORF,PFEIFER,ANDWEHNER
Figure1. Thedesertant Cataglyphisbicolor(courtesy R. Wehner).
implementedin analoghardwareon a robot with fullyanalogcontrol (called analog robot in the following). The sectionVisual Homingof Insects brieflyoutlinesthe experimentsthat
havebeendonewith insectsto analyzetheirvisualhomingstrategies.The snapshot modeland the averagelandmarkvectormodelare presentedin the section Navigation Models. The section Robot Implementation describes,how the navigationmodelsare implementedon the two mobile robots. The experimental setupsand the resultsof the robot experimentsare presentedin the section Robot Experiments. The Discussion sectionprovidesa discussionof the experimentalresultsand relatesthe study to other work in both the fieldsof synthetic modelingand visualrobot navigation.It also providesfirst experimental
resultsof an applicationof the averagelandmarkvectormodelto navigationin indoor
environments.
Visual Homing of Insects Desert ants of the genus Cataglyphis are solitaryforagersthat have to return to important locations in their environment, which can be either food sources or
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1m Figure 2. Landmark navigation experimentwith Caraglyphis. Ants were trained to locate the center of an array of three landmarks (black cylinders, depicted as circles). The figure shows the trajectory of a single ant that was displaced to a test area with an identical landmark configuration . R marks the releasepoint (adapted from \Y/ehner, Michel , and Antonsen
[ 1996 ] ) .
the nest entrance. Experiments revealed that the ants resort to three different navigation strategies: path integration (dead reckoning), visual landmark navigation , and systematic search (Wehner, Michel , and Antonsen 1996). Path integration guides the animal to the vicinity of the target location, but is subject to cumulative errors that limit the precision of this strategy. Ants that by meansof path integration returned to a target location that is not surrounded by landmarks have to start a systematicsearcharound the expectedgoal position. How ever, their behavior is completely different, if landmarks are visible: in this case, the ants preciselypinpoint the goal (Wehner, Michel , and Antonsen 1996). The typical experimental procedure to analyze the visual homing strategies that was used for experiments with both ants (Wehner and Raber 1979; Wehner, Michel , and Antonsen 1996) and bees(Cartwright and Collett 1983) is depicted in figures 2 and 3. In a training area, landmarks are positioned close to a target location , e.g., the nest entrance. When the animals have learned to locate this goal, they are displaced to a test area with another landmark configuration , where their search path is recorded. The place where the
INSECT STRATEGIES OFVISUAL HOMING41
Navigation Models Severalmodels have been devisedto explain the visual navigation abilities of insects. These models differ in the information that is assumedto be stored about the target location, and in the mechanismsused to derive a home direction from currently availableand stored information . An early algorithmic model was suggestedby Anderson (1977) and studied in computer simulations. He supposed that beesconsider the overall "Gestalt" of a landmark configuration- specifically the "surroundedness " by landmarks- rather than remembering the landmarks around the goal pictorially . However, this concept was later given up basedon evidence from the experiments by Wehner and Raber (1979) and Cartwright and Collett (1983) in favor of a pictorial matching process. The corresponding algorithmic description is the "snapshot model," studied in computer simulations by Cartwright and Collett ( 1983). Recently it could be shown that the Gestalt concept and the snapshot model are actually closely related; the bridge between the two domains is establishedby the "averagelandmark vector model" (Lambrinos et ale 1998, 2000). While the snapshot model is already considered parsimonious, the averagelandmark vector model turns out to be extremely cheap, both with respect to the memory capacity and computation required. The algorithms of both the snapshot model and the averagelandmark vector model are describedin the following subsections. Snapshot Model Cartwright and Collett (1983) proposed an algorithm of the snapshotmatching procedure that reproducessome aspectsof the behavior observedin bees; figure 4 (left) visualizesthe matching processof this model. It assumesthat the views on which the matching processis operating are horizontal images comprising dark and bright sectors corresponding to the landmarks and the gaps between them, respectively. These views are supposed to be aligned with an external compassdirection . A pairing between sectorsin the two views is establishedby searchingthe closestsector of the sametype (dark or bright) in the current view for each sector in the snapshot. Each pair of sectorscontributes two vectors to the final home vector: a tangential vector pointing from the snapshot sector towards the paired sector in the current view, and a radial vector that points centrifugally from the snapshot sector, if the apparent width of the current view sector is smaller than the width of its counterpart in the snapshot, and vice versa. A movement in the direction of the tangential vector reducesthe difference in bearing of the paired sectors; a movement in the direction of the radial vector reducesthe difference in their apparent size. The model could also take into account differences in apparent height, though this was not considered in the original snapshot model. Averaging all contributions gives a home vector point -
42 MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , AND WEHNER
ing approximatelyto the positionof the target(figure4, top right); a trajectory following thesehomevectorsthat are attachedto eachpoint of the planewill approachthe locationwherethe snapshotwastaken(figure4, bottom). Despitethis rathersimpleprocedure , homing is highly reliableevenfor more complexlandmarkconfigurationslike the oneshownin figure5. As canbe seen in the centerimageand in the view traceon the right, the algorithm tolerates the occlusionof landmarksand the fusionof landmarksectors .
INSF ~Cl~Sl~RA1 'EGIES OI~VISUAL HOMIN(; 43
Figure 5 . S'imulation of the snapshot model (with proportional
vector contributions ) for a
configuration of eight landmarks (black circles) . lop: lrajectories running tangentially to the home vectors. Bottom left: Number of landmark sectorsvisible from each point ; darker areascorrespond to higher numbers of visible sectors. A selected trajectory is depicted with a white line . Bottom right: '[ ransformation of the current view over time (upper part) towards the snapshot (lower part) for the trajectory depicted in the center image.
Average landmark Vector Model Surprisingly , there is an even more parsimonious method , the "average landmark vector (ALV ) model ," which can be derived mathematically
from the snapshot
model (Lambrinos et al. 1998 , 2000 ) . In this model , views are characterized by two -component "average landmark vectors ," which are computed as the average (or sum ) of unit vectors pointing towards the selected image features; see figure 6. Instead of a snapshot image , only the average landmark vector of the target
44 MOLLER , LAMBRINOS , ROGGENDORF , PFE1FER , ANDWEHNER
location has to be stored. An image matching procedure as in the snapshot model is not required; in the ALV model, this step is simplified to a subtraction of the averagelandmark vectors of the current and the target location. Like the snapshot model, the ALV model presumesthat the vectors are aligned to the sameglobal referencesystem. For the analog implementation of the ALV model, it is advantageousthat only a vector has to be rotated and not an image as in the snapshotmodel, which would require more hardwareeffort .
INSECT STRATEGIES OFVISUAL HOMING45
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48 MOLLER,LAMBRINOS, ROGGENDORF, PFEIFER,ANDWEHNER
Figure9. Detailedviewofthe360 degreecamerasystemof theSahabot2. The 360 degreecameraconsistsof a camerawith verticalopticalaxis(withinthe case)and a
conicalmirrorthat is alignedwiththe axisof the cameraand mountedwithinan acrylicglass tube.The axialalignmentand the distancebetweenmirrorand cameracan be adjusted.An infraredand a neutral density filter are mounted betweencameraand mirror.
50 MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , AND WEHNER
1994
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INSECT STRATEGIES OF VISUAL HOMING
51
Figure 12. The analog robot (height22 centimeters,diameter 12.8 centimeters). The black ring in the center contains thirty-two photo diodes, the boards above implement the ALVmodel, the boards below belong to the motor control circuitry.A magnetic compass is mounted on the top of the robot.
boards implementing the ALV model, and the also frilly analog motor control boards are mounted on a small robot base (seefigure 12). An overviewof the ALVcircuit is given in figure 11; technical details are presented in Mller (1999, 2000). Visual input comes from a ring of thirty-two
52
MOllER
, LAMBRINOS , ROGGENDORF , PFEIFER , AND WEHNER
photo diodes (seefigure 12) mimicking the portion of ommatidia of both insect eyesfacing the horizon. The angle between two neighboring diodes is p = 11.25 degrees.
An aperturein front of eachdiode restrictsthe openingangleto a = 8 degrees. The signalsof the photo diodesare amplified. Edgesof one polarity (clockwise black-to-white ) are used as landmark cues; they are detected by thresholding the difference of the signals of two neighboring sensors. U nidirectional lateral inhibition between neighboring edge filters ensuresthat only one pixel per edgebecomesactive; this is a prerequisite for the operation of the ALV model. For opening anglessmaller than the inter-sensorangles, and for sharp visual edgesas they are used in the experimental setup, lateral inhibition can be
restrictedto immediateneighbors . From the outputsof the edge-filters, which deliverbinary signals(high constant voltage for an active edge, or 0 V) , the robot-centered AL vector is com-
puted. A radiallandmarkvectoris assigned to eachedgepixel, and all landmark vectors are added simultaneously. This can be achieved in a simple way by encoding the two components of each landmark vector in two resistors that are connected to the input of two adder circuits ("convergent pathway" in figure 11) . The
home
vector
is the difference
of the AL
vector
of the current
location
and the AL vector of the target location, with both vectors relating to the same coordinate system. Before the target AL vector is stored in two capacitors, it is
rotatedto world coordinatesusing a circuit with four analogmultipliers. The orientation of the robot in the global referencesystem is determined by a mag-
netic (fluxgate) compass . In the returnphase , the storedvectoris rotatedbackto robot coordinates according to the current orientation of the robot , and subtracted from the current AL vector, which gives the home vector in robot coordinates .
The robot usesdifferential steering. Each of the two motors can be directly controlled by one component of the home vector, if the robot coordinate system
is rotatedby forty-five degrees with respectto the front of the robot, so that the vector (1, 1) points to the front . The component of the axis on the left side de-
terminesthe speedof the right motor andviceversa.This arrangement will stabilize a home vector in the frontal direction; if the home vector is pointing to the rear, the robot will automatically turn around, since the opposite direction is
unstable . As in the proportionalvectormodel, the lengthof the homevectorof the ALV model correspondsto the distance from the goal. Since the home vector components determine the speed of the motors, the robot automatically slows down when it approaches the target location, which avoids oscillations around
that location
.
INSFCT STRATEGIESOF VISUAL HOMING
53
Figure13. Exampleofa landmarkarrayusedfor the experiments with the Sahabot2.
Robot Experiments Snapshotmodel and averagelandmark vector model were tested in setups with comparablecomplexity;however,whilethe Sabahot-2experimentson the snapshot model were done outdoors under varyingconditions of illumination, the analogimplementationof the averagelandmarkvector model is so far restricted to a laboratory setup. Both series of experiments, described below, aim at demonstrating the applicabilityof the models under real-worldconditions. As a quantitativecriterion, the precisionof homing achievedin the experimentsand factorsinfluencingthe precisionare analyzed. Experiments with the Sahabot 2
Experimentswith the Sahabot2 weredone in one of the habitatsof Cataglyphis in the Tunisian part of the Sahara desert. The experimentalfield was a sandy salt-panflat. A grid was painted on the desertground to simplifythe alignment of landmarksand robot as well as the registrationof the final points. Figure13 showsa typicalsetup. Blackcylinderswere used as landmarks;the same type of landmarkswas also used for the ant experiments. Beforeeach experiment,the robot was placedat the starting positionwithin the landmarkarray.The landmarkarray used for the experimentsshown in figure 14 consistedof three black cylinderswith a height of eighty centimetersand a diameter of thirty centimeters,forming an equilateraltrianglewith three-me-
54 MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , ANDWEHNER
Topleft: Twotypicaltrajectories of therobotin anarrayof threelandmarks (circles ). Fromthe endof thepreprogrammed outWard journey(dashed line) towards thetargetposition(0,0) therobotisguidedbythevisuallandmark navigation algorithm(solidline). Topright:Transi tionsof therotatedcurrentviewovertimetowards thesnapshots (images underneath each trace ) for thetWotrajectories . Bottom : Timecourse of disparity betWeen snapshot andcurrent viewfor thetWotrajectories . Thedashed linedepictsthethreshold usedfor terminating the runs. ter side length. The starting position was situated on a symmetry line of this tri angle in a distance of one meter from one of the landmarks. The samesetup was used in the simulation presentedin figure 4. Data from two of the experiments are visualized in figure 14. At the beginning of each experiment, a snapshot was taken at the starting position , processedas described in the section " Image Processing," rotated by the angle ob-
INSECT STRATEGIES OFVISUAL HOMING 55 tained
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angles
configu
trajectories
obtained
comparison
the
eight
and
be
trajectory
different
landmark
.
can
simulation
points
termination
snapshot
of
in
this
overlapping
in
For
with
the
final
with
.
two
of
locations
the
)
this
centimeters
that
on
final
14
between
are
points
figure
with
twenty
threshold
on
the
be
phase
The
located
not
to
that
to
the
the
using
experiment
parts
locations
from
in
final
and
not
for
.
B
deviation
points
x
the
points
but
those
1 . 5
with
are
assumed
the
,
part
are
,
seems
outward
computation
of
the
two
foraging
this
,
,
in
executed
precision
obtained
left
The
final
,
disparity
the
bands
closer
the
below
passed
signal
for
the
;
maximal
threshold
position
these
since
and
were
seven
the
the
was
) .
between
reflect
,
in
the
direction
,
short
to
,
accomplished
snapshot
shown
the
threshold
A
point
( bottom
curves
data
shot
be
15
dropped
iso
set
and
had
A
of
" Modifications
are
journey
starting
was
of
parity
outward
same
figure
evaluation
a
trajectories
the
the
not
ing
views
a
end
image
and
the
section
to
the
black
robot
trajectory
to
At
camera
,
to
of
the
for
that
the
direction
than
meters
) .
from
the
,
algorithm
view
form
corresponding
homing
used
lower
)
the
snapshot
manually
aligned
was
( including
axis
the
left
compass
current
vector
of
and
shown
rotated
top
the
in
in
the
) .
distances
ration
,
view
the
became
bottom
to
described
the
disparity
14
14
the
stored
( four
displaced
horizontal
home
and
taking
distance
figure
to
,
After
certain
been
matching
of
resulting
) .
over
model
Transitions
The
a
see
sectorized
by
compass
right
have
according
vector
tional
top
handed
a
view
;
also
was
of
this
B
could
control
home
,
for
trajectory
extraction
of
light
14
direction
( it
trip
polarized
( figure
can
even
algorithm
target
point
.
Experiments with the Analog Robot The experimentswith the analog robot were done in a one meter by one meter arenawith white walls (30 cm high) and floor ; seefigure 16. Three black pieces of paper (21 cm x 29 cm) attached upright to the walls served as landmarks. Light came from the ceiling lamps of the room. Figure 17 (left) shows the AL vector voltagesmeasured(using multimeters with computer interface) while the
INS1( I Si tA II Gil S OF VISUAl HoMING
57
Figure16 Arena usedfor the experimentswith the analogrobot.
tion markedwith the cross-circlein the same setup. The robot was first placed at the target location, and the AL vector of that location was stored. Then the
robot was rotated by sixtydegrees(to test the vectorrotation system)and moved to sixty four positionson a grid, where the two voltagesof the home vector were measured. Evidently,all home vectors point approximatelyto the target location; their length decreasesin the vicinityof the target. For the homing experiment presented in figure 17 (bottom), the robot was first placed at the target location, where the AL vector was registered,and then moved to different starting points close to the walls of the arena. A pen was mounted in the center of the robot between the two wheels. After some seconds, a time switch released the motors and the robot started to move. The tra-
jectory was drawn by the pen on paper coveringthe floor of the arena; the trajectorieswere afterwardsdigitizedfrom a photo of the paper (this method causes the smalldistortionsof the diagram).The V-shapedparts in some of the trajectories result from changes in the movement direction of the robot from backwards to forward (see the section Implementation of the ALV Model). In the
upper left trajectory,the robot temporarilylost sight of one of the landmarks, which caused a short disruption. On average,the final points of the trajectoriesshown in figure 17 (bottom) deviatedfrom the target point by 58 – 17 millimeters.The experimentwas re-
58 MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , ANDWEHNER
Threelandmarks (depicted by bars ) whereattached to thewallsof thearena . Thedetectable edges aremarked byanarrow . Topleft: Average landmark vectormeasured for 64positions on a grid. Thevectorscalingis shownin theupperrightcorner(1 V) . Topright: Homevector field for the targetlocationmarkedwith the cross -circle. Bottom : Robottrajectories . The cross -circlemarksthetargetlocationwheretheAL vectorwasstored . Thefinalpositions of therobotaredepicted bydots. peated nine times with different target locations; the mean deviation over all 99 runs was 68 :t 35 millimeters. The deviations in this and other experiments can almost entirely be explained as an effect of the low visual resolution of the robot. In a relatively large areaaround the target point , the edge-filtered view perceived by the robot is constant. Consequently, the target location can not be located with higher precision. This "iso-view" region has been constructed geometrically for the experiment in figure 18. The view sector of each active edgepixel, which
INSECT STRATEGIES OFVISUAL HOMING59
has an angular size equal to the inter-sensor angle fJ = 11.25 degrees, was attached to the corresponding landmark; the crosssection of all view sectorsgives the iso-view region (hatched area). Depending on the orientation of the robot, different edgedetectorsare activated, which results in iso-view regionswith similar size but different shapes; in the geometrical reconstruction, the orientation was chosen in a way that the shape of the iso-view region was in accordance with the final points of the trajectories obtained in the experiments. Apparently, an iso-view region could be found where the final points are located closeto the margin or inside the region.
Discussion Mobile robot experimentsproved to be useful tools for testing hypothesesabout information processingin biological agents, especiallyin the field of visual navigation. Franceschini, Pichon, and Blanes (1992) constructed a vision systemfor
60
MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , AND WEHNER
a robot based on the marion detection circuits in the compound eye of the housefly. Elementary motion detectors of insectshave also been modeled in several analog VLSI implementations (see chapter 1). Visual dead-reckoning evi-
dently performedby honey beeswas testedon a mobile robot by Srinivasan , Chahl, and Zhang (1997). Mechanismsof path integrationusing a polarized light compassthat mimics the celestialcompassfound in certaininsectspecies havebeenreproducedwith mobilerobotsby Larnbrinoset al. (1997, 2000) and Moller et al. (1998). An overview of projects in biomimetic robot navigation is given by Franz and Mallot (2000).
The investigations presented herefocuson syntheticmodelingof visualhoming strategiesof insects. It was demonstrated that the snapshot model- which was only tested in computer simulations before- works under real-world conditions. The experiments with the mobile robot Sahabot 2 have shown that the deviations of the final points of the robot's return journeys from the target position
are in the range of ten centimeters
. From
this distance , the nest entrance
should be visible for the ant. Therefore it is justifiable to assumethat the precision of the visual homing is comparable to the precision achievedby desertants. To directly compare the homing precision of ants and robot is legitimate, since the visual processingis not affected by differencesin body size and propulsion.
Moreover , the high precisionof homing could be achievedwith a relativelylow visual
resolution
, which
is in accordance
with
animal
data -
the resolution
of
the camera image along the horizon is approximately 2 degrees, whereasa reso-
lution of 3 degrees wasdeterminedfor the horizonalbelt of the Catagiyphis eye (Zollikofer , Wehner, and Fukushi 1995). The robot could successfullycope with sensor noise in the visual system and in the polarized-light compassthat was used for the view alignment required by the snapshotmodel. In the robot experiments, the same type of artificial landmarks were used as in experiments with Catagiyphisants. Attempts have been made to use those landmarks
that are relevant
to the ants in their
natural
habitat . However , where -
asthe ant's eyesareabout0.5 centimeteraboveground, the mirror of the robot's visual system is at 27 centimeter height. Objects like small shrubs or stonesthat
stick abovethe horizonof the antsand arethereforevisibleasa skylineagainst the bright sky as background are usually below the horizon seen by the robot. From its higher perspective, the frequent shrubs in the vicinity of the test field
form a singlebandaroundthe horizon, which cannot be separated into distinct landmarks. For the setup with artificial landmarks, only very few and simple im ageprocessingstepswere necessaryto link the visual input to the representation required by the navigation method, since the landmarks are clearly distinguishable from the background. In more complex environments, however, the effort required for the extraction of landmark cues may increase(seethe section "Ap plication for Robot Navigation"). The experimentswith the analog robot have shown that insectsmight employ an even simpler navigation method, the ALV model. Snapshotmodel and ALV
INSECT STRATEGIES OFVISUAL HOMING61 model are closely related and it is known that a version of the snapshot model and the ALV model evenyield identical home vectorsin the vicinity of the target location
(Lambrinos
et al., 2000 ) . There are also versions of the AL V model ,
where the apparent size of the landmarks is considered (Lambrinos et al., 2000 ) .
With the experimental proceduresdescribed in the section "Visual Homing of Insects" it is therefore difficult to decide which strategyis actually used by the insect. Only experiments, where beeshad to decide between two different landmark arrays, provide some evidencethat it may actually be a picture that is stored in the animal's brain (Cartwright and Collett 1983, figure 15).
The homing precisionachievedin the experimentswith the analogrobot seemsto be limited only by the very low visual resolution (11.25 degrees ) . How -
ever, alsothe precisionof the compassis critical for the homing precision; a redesign of the compasssystem was necessary , since the original , less precise sys-
tem entailedlargeposition errors. With the successful operationof the analog implementation of the ALV model it is possible to define a lower limit of complexity for visual-homing mechanisms: the apparently computationally complex task can be realized in a very simple analog electronic circle (the current implementation contains ninety-one operational amplifiers, eight analog switchesand twelve analog multipliers ). The analog implementation also adds plausibility to the ALV model as a model of insect navigation, since analog electronic circuits and biological nervous systemssharea number of information processingprinciples. The process-
ing in both architectures is analog, continuousin time, inherentlyparallel, and the sameoperations are easyto realize (like weighted addition of signals) or dif ficult (like shifting an array of data). It therefore seemslikely that the analog cir-
cuit hasa closebiologicalcounterpart , which maynot be the casewith a system relying on digital computation (where, for example, the shifting of an array can be done by changing a single pointer variable, while weighted addition is a time-
consumingprocess with considerable underlyinghardwareeffort). The first processingsteps from the sensor information to the robot centered AL vector are widely compliant with the architecture of biological nervous systems, since they
are mainly basedon weightedaddition of signalsand thresholdingoperations . Differences
will
occur
in the part
for rotation
and storage
of the AL
vectors ,
where precision analog components were used that are unlikely to have direct correspondencesin biological neural systems. Starting from the architecture of the analog implementation , it may be possible to interpret the responsesof biological neurons that are involved in the navigation system; neurons that encode the AL vector, for example, would be identified aslarge field neurons with a specific sinusoidal responseover the horizon. From the simplicity of the analog implementation and the fact that the largest part of the circuit (sixty-four of the ninety-one operational amplifiers) is organized in a retinotopical manner it may
be concludedthat visualhoming capabilitiesareimplementedin the peripheral parts of the insect 's visual brain .
62
MOLLER , LAMBRINOS , ROGGENDORF , PFEIFER , AND WEHNER
Application for Robot Navigation Besidesapproachesthat employ mobile robots for the "synthetic modeling" of animal behavior, a growing number of approachesin robotics get inspiration
from the navigation capabilitiesthat insectsshow despitetheir diminutive brains. Related to this, there seemsto be a shift from "computational," "quantitative" towards "qualitative" methods. Traditional approaches in visual robot navigation are basedon determining the metric position of the robot using accurate geometrical two-dimensional maps or CAD -like three-dimensional world models (reviews can be found in Kuipers and Byun 1988, Kortenkarnp 1993, and Bartlett et ale 1995). Typically, the amount of required computation and information that has to be stored about the environment by far exceedsthe limits that can be assumed for insects .
Recent changesin the robotics paradigm towards "cheaper" methods affect both world models and navigation strategies. Concerning the type of world
models, thereis a transitionfrom the aforementioned topographicalto topological maps that only represent " interesting places" in the environment and relate theseplacesby notions of order, proximity , or instructions for the transition between them. This idea is appealing, since it eliminates the problem of dealing with movement uncertainty in mobile robots (Kortenkamp 1993). Many of the "cheap" navigation strategiesthat currently replacethe "computational," position-basedmethods regard "homing" as the basic navigation abili-
ty. In particular, homing methodsare usedin conjunction with topological maps (Kuipers and Byun 1988; Franz et ale 1998a). There are two different approachesto image-basedhoming , called "local" and "associative " homing (Hong
et ale1992). Localhoming methodsgraduallyalign a currentview with a snapshot stored at the target position by deriving a direction of movement from the differencesbetween the two images (Hong et ale 1992; Franz et ale 1998b). Associative homing methods associatestored sceneswith movement vectors point -
ing towardsthe goal (Gaussieret ale 1997; Cassinis , Grana, and Rizzi 1996; Nelson 1989). The method presentedin this study belongs to the local homing
methods. It is a disadvantage of the associative approaches that they requirean exploration phase and some external means like path integration to determine the home direction
associated with
a scene .
Localhomingmethodshaveto copewith the problemwhat featuresto useas landmarks for the comparison of images, and how to establish the correspondence between featues in the current and the stored view (an exception is the
"warping" approachsuggestedby Franzet ale 1998b). The computationally most expensive part of these methods is the correlation of image regions (Gaussieret ale 1997; Hong et ale 1992). An alternative is the approach presented here, which is basedon the snapshot model (Cartwright and Collett 1983). Only a small set of features, like two different types of edges, has to be extracted
INSECT STRATEGIES OFVISUAL HOMING63 from the images. To establishthe correspondence, the closestfeaturesin the two views are paired with each other, without identifying the features as belonging to the samelandmark. Whereas the neural apparatusrequired to perform a correlation of image regions will be rather complex, it could be shown that a neural architecture realizing the matching procedure of the snapshot model is actually very sil1)ple (Moller , Maris, and Lambrinos 1999). The "averagelandmark vector model" is even cheaper: only a reliable detection of landmark featuresis necessary, but no correspondenceshave to be established between features in the two Views. The two robot implementations described here are restricted to artificial setups. The detection of landmarks in the desert experiments with the Sahabot 2 relies on the fact that the dark artificial landmarks can be separatedfrom the bright background using a simple thresholding mechanism. The analog implementation usesa very simple feature detection mechanism (threshold and lateral inhibition ) that only works under room light conditions and in the artificial setup with its sharp contrast edges. While the navigation methods themselvesare very parsimonious, it still has to be investigated, how much effort has to be investedfor a reliable detection of landmark cuesin more complex visual environments without artificial landmarks and under changing light conditions. First experiments in an indoor environment provide some evidencethat actually relatively simple image processingstepsmight be sufficient for an extraction of landmark cues. An album of images was taken with the camera of the Sahabot-2 in an area of 3 meters x 4.5 meters on grid positions with a spacing of 30 cm; the light conditions were constant for all imagesof the album. Figure 20 showsthe environment, an entrance hall, which was left unmodified for the registration of the album. All images are aligned in the same direction . Figure 19 explains the image processingstepsthat were afterwards applied to extract landmark vectors for the ALV model. A stripe above and including the horizon was extracted from the camera image. The gray values in the columns of this image were averaged. The resulting one-dimensional image was low-pass filtered . Landmark vectors point to those pixels, where the low-passfiltered view crosses a threshold. The threshold is adjusted automatically in the following way: In the low-pass filtered image, regions with small absolute value of slope were detected (dots on the curve). The correspondinggray valueswere marked in a table of the 256 possible values(right diagram). The gray value in the center of the largestunmarked region (bracket) was used as threshold. This method increasesthe stability of the detected features when the robot is displaced from the target position . Small changesin the gray valuesof the low-passfiltered image will not add or remove detectedlandmarks; this directly affects the sizeof the region of attraction. Mter the threshold has been determined for the target location, it is stored together with the AL vector and kept constant during the return to this location. The home vectors obtained with this method are visualized in figure 21. In
66 MOLLER , LM1BRINOS , ROGGENDORF , PFEIFER , ANDWEHNER
was implemented in analog hardware that sharesa number of information processingprinciples with nervous systems. The results also indicate that the parsimonious strategiesof insect navigation can provide a guideline for robotics research. Future work will try to adapt the mechanisms to environments with more complex visual featuresand to integrate multiple snapshotsin topological maps. Acknowledg ments This work was supported by grants from the SwissFederalOffice for Education and Science (VIRGO TMR network), the SwissNational ScienceFoundation, the Human Frontier ScienceProgram, by a personal grant from the Kommission zur Forderung des akademischenNachwuchsesder Universitat Zurich to R.M ., and by RosysAG (Hombrechtikon ). Many thanks to Christian Gfeller, Koh Hosoda, Hiroshi Kobayashi, Thomas Labhart, Lukas Lichtensteiger, Mari nus Maris, and the workshop of the Physicsdepartment for their contributions and the fruitful cooperation.
CHAPTER
4 Aerial Minirobot that Stabilizesand Trackswith a Bio-inspired Visual Scanning Sensor Stephane Viollet and Nicolas Franceschini
he present study was carried out inthe context of our previous studies using a biologically inspired minimalistic robotic approach , which were based on the idea of reconstructing natural visuomotor processes in or der to better understand them . Here we show that an elementary visual sensor composed ofonly two pixels is able to accurately detect contrasting features within its visual field , provided it uses a particular scanning mode , such as that recently discovered in the compound eye of flies . This low complexity , low weight , low power , low cost scanning sensor was found to deliver an output that is (1 )quasi -linear with respect to the angular position ofthe acontrasting object in its field of view and ( 2 ) largely invariant with respect to distance to the ob ject and its level of contrast . The most novel feature ofangular this visual sensor ,pulse which differs from other systems based on scanning at constant speed and s canning , is that it scans the visual field at an angular speed that varies gradually with time ( variable speed scanning ).The sensor 'soutput then becomes athe graded function ofthe position of a contrast feature within its visual field , with interesting consequenc that if this sensor is incorporated into a visuomotor control loop , it is able to stabi lize a robot such as a micro air vehicle with respect to the environmen fea tures . Inthe first part ofthis chapter ,we simulate the pure rotation ata-variable speed of two photoreceptors in front of a stationary contour ( a light to -dark edge ) and show that the processing of the apparent motion yields a signal that gradually varies with the angular position of the edge in the visual field . On the basis ofour simulation results ,we then describe the structure ofaminiature scanning sensor coupled with an elementary motion detector (EMD ),the prin -ciple ofwhich was again derived from the fly compound eye .Finally ,we de
68 VIOLLET AND FRANCESCHINI scribe
a miniature
a visual signal ing
, which
robot
onto
to the
only
where
the
tains
fixation
and
stiff
scanning
but
two
also
and
sensor
and
the
for : detecting
its environment
; rejecting a nearby
. Some
a slowly
the
" see -
locked
speed
environmental
examples
are
dark
a gyro described
given
edge
below
,
main
-
or
loop
we
have
motion
between
by
maintaining
disturbances , regardless
one
.
control relative
with
, the
minutes
visuomotor
target
of
an error
visually
be achieved
moving
seventeen
low
or distant
can
characteristics
drift
for
up
, causing
so as to remain
, which
tracks
target
by means
picks
.
low - cost of
in yaw
latter
differentially
of sight
target
either
stabilizes
sensor . The
propellers
its line
devoid
) aircraft
, which
stabilization
a stationary visual
onto
the
adjust
of yaw
appropriate
al fixation
scanning
low - weight
( tethered
are
robot
the
contrasting
kind
onto
aerial
on
drive
and
similar
is not
The
to
or distant
having
oped
based
is used
Contrary scope
- engine
loop
" to reorient
a nearby
here
twin
feedback
of its contrast
devel a craft visu -
and
distance
within a given range; and stabilizing the attitude of a craft optically with respect to environmental features.
Scanning at a Variable Angular Speed In
this
section
variable
(
,
we
angular
see
figure
view
simulated
the
speed
la
)
.
in
Each
of
by
rotation
a
photoreceptor
characterized
Photoreceptor
concerted
front
light
-
can
the
angular
of
dark
receive
sensitivity
two
photoreceptors
brightness
at
transition
light
only
function
(
within
a
an
a
edge
small
)
field
of
.
Model
The
angular
sensitivity
mated
by
tion
,
a
the
bell
-
bottom
The
(
lp
)
shaped
a
lens
-
photoreceptor
function
part
angular
S
of
of
.
which
sensitivity
is
S
( < p )
combination
In
the
present
truncated
of
is
case
below
,
the
photoreceptor
i
is
we
use
usually
a
threshold
-
Gaussian
so
therefore
approxi
func
-
.
given
by
:
=
(
abS
(
Gj
(
lp
)
-
so
2 )
+
(
Gj
(
lp
)
-
So
)
)
(
1
)
,
with
Gj
S
(
height
cp
)
(
lp
)
is
=
exp
[
-
~
characterized
~
p
.
]
by
Both
parameters
(
its
total
can
angular
be
width
expressed
Lv
with
and
respect
its
angular
to
width
a
as
follows
at
:
half
2
)
AERIAL MINIROBOT 69 1
~p=2CT [21n (~ )]2
(3)
1
Lv =2a [21n (~)]2
(4)
Variable Angular Speed As sketched in figure 1a, we have simulated the concerted rotation of two photosensors, 1 and 2, separatedby a constant angle dcp, called the interreceptor angle, in front of an elementary panorama consisting of a single segmentAB . The pair of photosensorsis assumedhere to rotate clockwise at an angular speed Q that decaysexponentially with time . The angle 1/1is given by figure 1b:
(5)
t/f(t) = A( 1- exp( - ~ ) ) Hence the angular speed! 2(t) is:
(6)
Q(t)=1exp (-~)
The position of the segment is defined by the points A (Xl' Yl) and B(X2' Y2) withYl = Y28 At any time, the output Rft ) from photoreceptor i is obtained by integrating the angular sensitivi ty S(cp) weighted by the in tensity 1(cp):
'/zI(lp 2 )S(lp)dlp Ri(t) = fLV -Lv Processing the Photoreceptor
(7) Signals
To calculate the output signal from each photoreceptor, we used a discrete version of equation 7. The signal sampled, Rfk1 ) , with T in seconds, results from a convolution of the light intensity I with the Gaussianmask S(kTs) (Tsin degrees). In other words, at each value of 1/1, Ri(k1) results from the filtering of 1 by a filter with a Gaussian-shapedimpulse response. In all thesecalculations, the Gaussian mask S(kTs) based on equation 1 was processedwith a spatial sampling step Tsequal to 0.004 degrees. Ri(k1) is maximal and equal to 1 when a black segment (1 = 10) covers the whole visual held Lv and it is null when the black segmentis completely outside the visual held (1 = 0). In order to show that the time lag between the two photosensorsvaries with the position of the contrast feature, we refer to the schemeof figure 1a, in which we put the dark edge at 3 positions (1, 2, 3) along the x axis. For each of these
70 VIOLLET ANDFRANCESCHINI
Figure 1. Simulated response of two adjacentphotosensors . (a) Simulatedresponse of two adjacentphotosensors rotating clockwiseat a variableangular speed.Q, and encounteringa dark edgeplacedat variouspositions1, 2, 3 (a). (b) Variationof angle'Jf(t). (c, d, e) Output from photoreceptors1 and 2, dependingof the position of the dark edge(1, 2, 3). (f, g, h) High-passfilteredversionof (c, d, e) (cut-off frequency: 2Hz) and thresholdingof the output signalfrom eachphotoreceptor . L\p = 2 degrees,L\
stationary positions, figures 1(c, d, e) give the output from photosensors1 and 2 as they turn clockwise at a variable angular speed {] . In figures 1(f, g, h), the output from each photosensor was passedthrough an analog high-pass filter with a cut-off frequency of 2Hz .
AERIAL MINIROBOT 71
Figure
1 ( f ,
thresholded
g
,
h
)
angular
be
tion
( 1 ,
cp c
noticed
2
receptor
,
3
)
of
~
can
to
that
increases
.
tionally
Like
many
.
OFF
contrasts
derivative
in
eliminate
the
( ON
)
A
" )
the
The
pulse
the
basis
1986
sually
We
second
1996
.
an
It
and
can
driven
array
be
similar
will
vary
of
of
in
of
the
can
the
to
with
miniature
ly
at
sections
position
scanning
a
variable
an
-
out
-
inputs
is
- dark
.
it
two
EMD
- to
as
an
its
direc
-
transitions
The
use
makes
it
of
the
possible
between
to
positive
.
with
llt
an
( called
,
in
on
patented
1991
it
onboard
50
sample
Institute
larger
detector
at
and
California
grows
we
obstacles
" facilitate
the
and
kind
avoiding
hence
motion
flies
this
decaying
and
analog
on
of
exponentially
vi
-
centimeters
"
of
in
a
scheme
)
was
Technology
in
simulations
shown
the
at
angular
a
in
figure
variable
position
1 ,
angular
cp c
of
a
dark
that
if
an
speed
edge
,
in
EMD
its
is
output
the
visual
.
,
Scanning
we
have
sensing
sensor
angular
-
variable
yielding
clockwise
an
of
rotating
sensor
previous
lead
2
for
capable
The
In
-
inter
)
analog
light
,
figure
detectors
photosensors
scanning
it
.
from
pair
the
a
EMD
discriminate
inversely
at
the
essential
scheme
authors
1998
,
multiplying
principle
,
posi
.
in
varies
motion
by
gradually
the
contrasts
shown
this
EMD
predicted
a
0
robot
patented
by
field
A
V
at
between
only
findings
independently
,
.
llt
is
is
than
circuit
lag
and
from
designed
autonomous
discovered
)
is
pulse
analog
direction
Moreover
.
neurons
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photosensors
Elementary
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-
72 VIOLLET AND FRANCESCHINI
Band -Pass Photodiode filter I-~-Q c-)-.0 ~ [2~~~~-- [;;g}-~ ' --~ (-.-) X Va = 'c-) -a '.-)'Q 'C ~ Q -CJ [8}Kt 8_"'C IPh =_l_J----fIl tlog l~_~;l~_J--.--rr~ 1/ \L I ~I::D l:===_ L~_ ~:_J--A ~ ~ multiplier Figure 2.Structure a/the EMD . The Scanning Sensor
We built a miniaturescanner , the componentsof which aresketchedin figure3. A dual photosensorand a lens (focal length: 8.5 millimeters), mounted opposite each other on a blackened perspex drum
(diameter
15 millimeters ) , form a
miniature "cameraeye," which is driven by a DC micromotor (diameter 10 mil limeters). The angle of divergenceL\ cpbetween the visual axesof the two photoreceptors ("interreceptor angle") is: ~cp= 4 degrees The angular position of the drum (and hence, that of the sensor's mean line of sight) is monitored by a magnetoresistive sensorresponding to the angular position of a micromagnet glued to the hub of the drum . This sensoris part of the loop of an accessoryposition-servo, which controls the eye so that it will follow any imposed signal. This servo-eye is positioned by a composite periodic signal at 10 Hz (cffigure
4a), which eventuallyleadsto two scanningphases(cf figure 4c). During the first phase (duration : 25ms), the angular speedQ of the eye is made to decrease quasi-exponentially with time. During the longer "return phase" (duration 75 ms), the eye is made to return to its original position at a quasi- constant speed. The amplitude L\~ of the scan is expressedin terms of the interreceptor angle L\cpby meansof the scanning factor a :
a =g I1lp
The EMD has a preferred direction such that it respondsonly during the first part of the scan (short phase), delivering an output that varies in a graded manner with the position of the contrasting edge within its visual field. Imposing a slow and constant angular speedduring the return phase improves the robustnessof the scanning sensor, making it responsiveto environmental features far beyond the simple edgeconsideredhere.
AERIAL MINIROBOT 73
Sensor Output Versus Contrast and Distance The complete scanning sensor was mounted vertically onto the shaft of a resolver and a 16-bit resolver-to-digital converter was used to accurately determine the angular orientation of the sensor. The two photoreceptor outputs were connected to an EMD via soft microwires. We varied the orientation of the scanning sensor manually and at each orientation recorded the voltage output Vofrom the EMD and the angular position cpc of the sensor. The object was a contrast step (an edge) made of grey paper that stood out from the background. The contrast m was determined by measuring the relative illuminance of the paper (11) and its background (12) and calculating:
74 VIOLLET ANDFRANCESCHINI
Figure 4. Measuredperiodic signalsfrom theposition servoof the scanningeye. (a) referenceinput voltageimposedupon the positionservo. (b) actualmotor input voltage. (c) resultingorientationof the "eye" asmonitoredby the magnetoresistive sensor
m = II - 12 II + 12 Contrast was measured in situ with a linear photo device having the same spectralsensitivity as the dual photosensorused. Figures 5(a, b, c) show that the sensor output varies with the position of a contrasting edge within its visual field , as predicted on the basisof the simulation results shown in figure 1. The responsesare quasilinear with respect to
AERIAL MINIROBOT 75
Accuracy : :t SOm V (a) response at D = 60 centimeterswith respectto a dark edge(solid) and a dark stripe 10 millimetersin width (dotted). (b) response at 0 = 100 centimetersto a dark edgeof variouscontrasts . (c) response at 0 = 140centimetersto the samedark edges . (~
of the scanning sensor and largely invariant
to the contrast m and the distance D of the dark edge. Subtracting
with respect a reference
signal r = 5.5 V from all the data gives an odd function , which is a useful fea-
76 VIOLLET ANDFRANCESCHINI ture for incorporating the scanning sensor into an optomotor loop. Moreover, invariance with respect to the distance- within a limited range- is an impor tant feature for the dynamics of an optomotor loop of this kind , becauseit does not introduce any gain variations liable to cause instability . The sensor was designed to be robust against any spurious modulations caused by neon room illumination ( 100Hz intensity modulations ).
Visual
Control
of a Miniature
Twin - Propeller
Robot
We built a miniature aerial robot equipped with its scanning sensor described in the previous section. The robot is made of carbon, wood, fiberglass, and EPS (figure 6a). Its total weight is 100 grams, including the engines, scanning sensor, gyroscope , and the complete electronics . The onboard battery (LiMnO2 ' 9V -O.SA.h) weighs another 54 grams and gives about one hour autonomy. In order to minimize the inertia around the yaw axis, the DC engines are positioned close to the axis of rotation and transmit their power to their respective propellers (diameter 13 centimeters) through a 12 centimeter-long carbon fiber shaft and bevel gear (reduction ration 1/ 5). The SMD electronic components occupy five decks of printed circuit boards interconnected via miniature bus connectors. Each board (diameter 55 millimeters ) is dedicated to a particular task: (a) power conditioning and (b) 433 MHz RC receiver and PWM motor controller for each propeller, (c) EMD circuit , (d) optomotor analog controller circuit (e) servoloop controller of the scanner. Dynamical Modeling of the Twin -Propeller Robot We analyzed the dynamics of the twin -propeller system around its yaw axis from its responseto a step input . We mounted the robot onto the shaft of the resolver, which was selected becauseof its negligible friction and inertia . The 16 bit resolver-to-digital converter was then used to measure the angular speed of the robot in yaw. This tachometer has a scale factor of 2.27.10-3 VIols , a scalefactor error often percent and a bandwidth of 125Hz. In order to convey a step input voltage to the autonomous plant , we designed a remote control system operating at 433MHz (1,000 bauds). At the transmitter , the throttle and yaw commands were adjusted by means of "digi tal" joysticks. Onboard the robot , these signals were received by a miniature receiver and dispatched to two digital potentiometers via a PIC16F84 microcontroller . These potentiometers (8-bit digital -to-analog converters) generated a differential signal udto adjust the yaw and a signal u, to adjust the common throttle (figure 7). In order to make sure that the two thrusters reached the same nominal speed (i .e., the same "operating point ") during each experimen-
AEIUAE,nMINER)I
77
Figure6 Twin-engineautonomousminirobotand test-bed. (a). Twinengine autonomousminirobotequippedwith its scanningvisualsensor. Ihe robots
body has a diameterof 55 millimetersand the robot weights100 grams,includingall the electronicsignal processing,motor control systems,radit) throttle control, and start arid kill switches.The robot is usuallytetheredto a 100 pm wire securedto the ceiling.The robot controlsits yaw orientationby performingvisualfixationand tracking,evenin the presence of pendulumoscillations.The robots headingdirectionis the sameas the mean eyeaxis.
(b) Test-bedused to characterizethe robot and identifyits dynamics.Insteadof being suspended,the robot is here mountedonto a low-frictionresolverto assessits dynamicalproperties or to a lowfriction potentiometerto measureits orientation in yaw. lhc visualpattern (herea dark bar) can be movedin front of the robot to test its trackingability.
78 VIOLLET ANDFRANCESCHINI
tal rUll , the DC level uc was memorized in the microcontroller flash memory and called back via a third remote control channel. The open loop model G(s) of the robot obtained from its step responseis as follows: ()
2700
G(5)=~ =(0.02652 +0.2325 +1) . where8ais the angular speed(o/s) of the robot and Udis in volts.
AERIAL MINIROBOT 79
Figure 8. Block diagram of the closed -loop visual control systemincorporating the scanningvisual sensor(outer loop) and a gyroscope(inner loop) .
The Visual Inertial FeedbackSystem The completepositionfeedbacksystemwe havedesignedis shownin figure8. The scanningsensoris modeledby a simplegainKs == 1V/o (cf figure5), and its output signalwasmaintainedby a zero-orderhold (ZO H) until the completion of eachscan(T = 100 milliseconds ). Sincethe transferfunction from the controllingyaw input to the yaw anglehasthreemain poles, we incorporateda velocityfeedbackusinga miniaturepiezogyroscope . The latterwasmodeledby the followingtransferfunction H (s):
-30 Y 6 . 7 . 1 H(s)=0-a=(0.0032s +1)
The gain K D = 5 was chosen so as to give a closed loop step responsewith a settling time of 100 milliseconds with a minor overshoot. The lead compensator D (s) was chosen so as to raise the phase margin further, thereby increasing the damping and reducing the settling time to 100 milliseconds. The transfer function of O (s) is given by :
+ 8s 1), D(s)=0.3(0.014s
andKe= 6.10-2
The frequencyresponse of the open-loop transferfunction
80 VIOLLET ANDFRANCESCHINI
20 0 cc -c - -20 ~ -c ~ -40 ~ c= C ~) -60 ~
-80 -100 -90 .
- .--.==--=...- - .--..-.. -...-. -..----.-.-.-..-....- .-......- ..._~.. .-.-..~.... . -..._...._......._...~.._.....-.-.- -......- ...- ..-.....- .. ..
- -135 -0 ~ (/) ~= -180.._-M .~~..._.__..- ......--- .--.- -. ..c 0-225
......-........-. -
-
-------------270 ._ _.-.""--~_.- "..""~....h --- ~.~. .- - ."...~. -...-...-.- .- ._..~.- .-.- - .~.-- .. .~. - ._."'~.h...._.. 100 1 101 102
103
Frequency (rad/s)
Figure9. Frequency response of thecomplete system (figure8) obtainedwith Matlab. In this Bodediagram , thevisualscanning sensor is modeled asa simplegain.
B (s) = ~ 0 (s) is shownin figure9.
TargetTrackingand VisualFixation We assessedthe performance of the visual-inertial feedback system on the testbed described in figure 6b. Both the angular position of the dark edge (input) and the angular orientation in yaw of the robot (output ) were measured with miniature servopotentiometers, asdepicted in figure 6. Figure 10 illustrates the actual tracking performance of the robot with respect to a sinusoidal variation of the position of a dark edge in the visual field of the . scanrungsensor. The responsewas found to lag behind the command by about 100 milliseconds, i .e., by hardly more than the scan period, which turned out to be the main limiting factor. The maximum linear speedof the dark edgethat the aerial robot was able to track was about twenty-five centimeters per second at a distance of one meter (i .e. 14 /s). The histogram in figure 11 shows that the robot steadily controls its two propellers so as to gazesteadily on a stationary target during a long (seventeenminute) experiment.
AERIAL MINIROBOT 81
Figure 10 Dynamic performanceof the overall twin -propeller robot (cffigure 6) tracking a dark ed:l;e (contrastm=O.2) locatedat a distancel? -f 1 meterin _-front '?-f the eyeand oscillatin:l;
at 0.2 Hz perpendicularly to its line of sight. r = 5.5K
Conclusion We describeda low-level, low complexity, miniature sensordesigned to provide a micro-air vehicle with visual stabilization and tracking possibilities. We established that a hundred gram twin -propeller robot equipped with this miniature scanning eye can track an object with various levels of contrast (down to ten percent) at various distances (up to 1.5 meters), using its onboard electronics and energy in a completely autonomous manner. The aim here was not to achievea high performance closed-loop systemsuch as those based on a CCD camera that recognize a target and track it at high speeds. Such systemsrely on more complex control laws, which require larger, heavier, and more consuming computational systems. Our low-level visual system is not basedon any "understanding of the environment," and consequently cannot detect and track a specific target among many others. Yet this moderately complex, inexpensive (:= 400 USD ) system can be used to stabilize a micro air vehicle around a given axis of rotation , where the time-constant is small (the small time-constants of micro air vehiclesmake it difficult for them to be stabilized by a human operator); to replace a gyroscope by maintaining an angular
82 VIOLLET ANDFRANCESCHINI
position with respect to environmental features, for a long time and with no drift ; and to track a target visually with relative insensitivity to shape, contrast, and distance, using a single pair of photoreceptorsscanning concomitantly. It is worth noting that the optical position-servoing method we have developed is basedon a position sensorthat actually relies on a motion sensor . An essentialmessageof this chapter is that a simple, low amplitude scanning of two adjacent photoreceptorswith motion detection can provide positional information to an accuracyfar better than that expectedfrom their angular sampling basis, hence leading to subpixelaccuracyin target localization and tracking. Extending theseresults to a 10 or 20 photoreceptor array will retain this most interesting property while enlarging the sensor's field of view.
AERIAL MINIROBOT 83
Acknowledgments We thank M . Boyron for his expert technical assistanceand I . Daveniere and J.
Blanc for improving the Englishmanuscript. This researchwas supportedby CNRS (Life Sciences, Engineering Sciences, and Program on Microsystems) and EEC (1ST/FET program). S. Viollet received a predoctoral fellowship from Ministry of National Education and Scientific Research(MENRS ). The content of this chapter is largely basedon a paper entitled "Visual Servo System Based on a Biologically Inspired Scanning Sensor" by S. Viollet and N . Franceschini, appearing in SensorFusion and DecentralizedControl in RoboticSystems II edited by G. T. McKee and P. Schenker, SPIE Volume 3839, pp. 144- 155. Bellingham, Washington: International Society for Optical Engineering, 1999.
CHAPTER
Construction ofa Hexapod Robot withCockroach Kinematics Benefits bothRobotics andBiology RogerD . Quinn and RoyE. Ritzmann
he goal ofthis project was to demonstrate the advantages ofcareful bio logical emulation in robot design . The robot would be used as a plat form on which both biologists and engineers could test hypothese about posture and locomotion . We did not intend to build a robot with all of the remarkable locomotion capabilities of the cockroach . The state of actuator and power source technology precludes this .Therefore ,we limited the locomo tion goals of Robot III to walking , turning , and climbing in a manner similar to a cockroach . To determine the aspects of the insect ' s legs and their control that are critical to those behaviors ,we made detailed kinematic and neurobiolo observations of the animal while performing those tasks . Inthis chapter we first describe the biological observations that were made , providing detailed kinematic data on cockroaches running on a treadmill and climbing over barriers .that We then describe how those data were incorporate into a dynamic simulation predicted motor and structural loads that were used indesign of the robot .Finally ,we describe the construction of the robot and de velopment of its posture controller . The posture controller is an important part ofthe locomotion controller that iscurrently under development . Biological Observations In order to collect the necessary data on the mechanics of cockroach walking ,
.
our first task was to build a walking platform
that would allow us to monitor
the movements of all leg joints as the animal performed ments . Considerable
real walking
move -
work had been done in the past on cockroach walking
88 QUINNANDRITZMANN
Flexor EMG
CTF Anglej Depressor
FTAngle Extensor
r t,r*. *
d1J1UIE Dep
I
Ext
Lrt -
ittdi u*-t $
200 400
Flex
600 800 1000 1200 1400
Time (msec)
!or
E::J Video Came
Figure1. Schematicshowingdata collectionmethodology. The leg in the upper left corner shows the placement of insulated wires for EMG recording. The record in the upper right corner showsjoint angle data from a rear leg (trace 1 from the coxa-trochanter-femur(CTF) joint and trace 2 from the femur-tibia (FT) joint) combined with three electormyograms(trace3 from the trochanterdepressor,trace 4 from the tibia!extensor and trace 5 from the tibia! flexor).
(Pearsonand lies 1970; Delcomyn 1971; Pearson 1976; Spirito and Mushrush 1979; Delcomyn 1985). However,most of that work focused upon the timing of foot contact with the ground (i.e. footfallpatterns). In order to provideaccurate informationon leg movementsand on the control of leg movements,we had to move beyond footfallpatterns to monitor individualjoint movements. Fortunately, advances in high-speed video systems made these observations possible. However, an important problem had to be overcome. Insects make complex leg movements in three-dimensional space. Because of the inherent problemsassociatedwith viewinga three dimensionalmovementprojectedonto a single plane, any singleview could readilygeneratefalsedata on joint movement. In order to accurately determine joint kinematics, we had to view leg movementsfrom two orthogonal directions,measurethe joint angles and then reconstruct the true three dimensional movements from those data (Biewener
and Full 1992).To accomplishthis task,we constructeda miniaturetransparent treadmill that allowed us to view the animal simultaneouslyfrom the side and from the ventral surface (underside),via a mirror positioned at a forty-fivede-
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMATICS 89 gree angle under the belt (Watson and Ritzmann 1998a) (figure 1). Video im ageswere taken at either two hundred or two hundred and fifty frames per second. The videotape was then played back frame by frame so that we could digitize specific landmark points on the legs. The digitized data from both views were entered into a custom program along with measurementsof the actual dimensions of each leg segment. The program then reconstructed the true angles of the leg joints in three dimensions as the animal moved along the treadmill . Each cockroach leg is divided into severalsegments(figure 4a). Although the segmentsare reproduced in each of the three pairs of legs, their dimensions are very different in the front , middle and rear legs. The leg segmentsfrom the most proximal to the most distal segmentare called the coxa, trochanter, femur, tibia and a seriesof foot joints collectively called the tarsus. The joint betweenthe body and coxa is made of two plateswith soft tissuebetweenthem. The complex musculature coupled with complex mechanicsconfersupon this joint three degreesof freedom much like that of a ball and socket joint (Laurent and Richard 1986). The distal joints are for the most part more straightforward. The joints between the coxa and trochanter, between the trochanter and femur, and between the femur and tibia are simple one degreeof freedom joints . The joint between the trochanter and femur makesonly a small movement and has often been referred to as fused. However, our observationssuggestthat becauseof the 450 orientation of the joint coupled with its proximal location on the leg, even the small movement of the trochanter femur joint allows the animal to rotate the tibia and tarsus relative to the ground. This movement is important to the actions associatedwith severalmovements, especiallyclimbing. The tarsaljoints eachhave severalpassive degreesof freedom making for agile foot placement. Finally, a claw located on the end of the tarsuscan be raisedor lowered to engagethe substrateduring locomotion on slippery surfacesfor climbing (Frazieret ale1999). We also have an interest in the control of the leg joints . Thus, we combined our kinematic analysiswith electrophysiologicalrecordingsof motor activity (figure 1). Neuromuscular activity can be readily monitored in a freely behaving animal by recording the electrical currents in the muscle extracellularly (Watson and Ritzmann 1998a) . The resulting records are referred to as electromyograms (EMGs). In insects, this technique is particularly useful because , unlike vertebrates, eacharthropod muscle is typically innervated by relatively few motor neurons. For example, the musclesthat are believedto provide most of the action in extending the femur of each leg during walking are innervated by only two motor neurons, the fast and slow depressors(Df and Ds respectively) (Pearsonand lIes 1971). Moreover, muscle records from these two motor neurons are readily distinguished basedupon the sizeof their related EMG potentials. Thus, activity in the muscle can be directly related to activity in specificmotor neurons. In order to make a useful robotic vehicle, we wanted it to be able to walk through complex terrain and interact with its environment in three dimensions. Specifically, the tasks that we wanted the robot to be capableof performing in-
90 QUINNANDRITZMANN
cluded efficient walking, turning and climbing over objects. We, therefore, set out to study the leg movements and joint control in horizontal walking at various different speeds, aswell asduring turning and climbing . Horizontal Walking Even in our initial observations, it was clear that the movements of the three pairs of legswere not similar. The typical gait during most speedsof walking is an alternating tripod gate (Delcomyn 1971). In this movement the front and rear right legs move in synchrony with the left middle leg forming a tripod that alternateswith the tripod that is made up of the remaining three legs. Although the feet of the three legs move in synchrony, the joint movements of the front , middle and rear legs are distinct . Each leg's joint movements are specializedfor unique roles played by each pair of legs. '"[ his observation is consistentwith data on ground reaction forces indicating unique patterns of behavior for each pair of legs (Full, Blickhan and Ting 1991). The rear (metathoracic) legs are specializedfor producing power to accelerate the animal forward. The movement of the two joints that are responsiblefor extending the leg, coxa-trochanter-femur (CTF ) and femur-tibia (FT) joints extend in synchrony moving through approximately the same excursion in both
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMATICS 91 joints (Watson and Ritzmann 1998a) (figure 1). As a result, the tarsal segments are driven directly backward in a line essentiallyparallel to the animal's long axis, providing powerful forward propulsion on the body (figure 2). In the middle (mesothoracic) legs, the two joints also move synchronously, but the FT joint goesthrough a smaller excursion than the CTF joint (Watson and Ritzmann 1998a). This movement, along with the orientation at the body coxa joint , results in a sweepingmovement, which first deceleratesand then acceleratesthe animal (Full et al. 1991). Other observationsdescribed below suggest that the middle legs are particularly important in turning the animal and in climbing movements. The front (prothoracic) legs are the most unique. To form a functional tripod movement, the tarsus of these legs must extend forward during swing and then be pulled rearward while the middle and rear legs of a given tripod extend rearward. To do this the front leg must make much more useof the body-coxa joint to swing the coxa forward than is seenin the middle and rear legs. With the forward swing of the coxa and the required forward movement of the tarsus, the CTF and FT joints must come out of synchrony to make the appropriate foot placement. Indeed, in some casesthese two joints actually move in antiphase (Tryba and Ritzmann 2000). Although the movement made by the prothoracic legs is complex, it can be at least partially visualized by realizing that, unlike the other two pairs of legs, the front legs are extended during swing. Then during the stancephase, they are drawn back toward the body and then extendedagain. The first part of the stance movement is accomplished by flexing the FT joint while extending the CTF joint and rotating the coxa backward. As the tarsi are drawn under the body, the FT joint stops flexing and the CT joint continues to extend pushing the tarsi rearward. During swing the process is essentially reversed, while the leg is lifted off the ground. Also unlike the middle and rear legs, the movements of the front legs are somewhatvariable. Indeed, the forward projection and variability of the prothoracic leg movements are reminiscent of human arms, suggestingthat they may be used assensori- motor appendagesfor investigating the local environment. The different joint movementsfound in the three pairs of legsshould be associated with equally different motor activity. This was found in the related EMG records (Watson and Ritzmann 1998a). In the rear legs, activity in the slow depressormotor neuron (Os), which controls extension of the CTF joint occurs in near synchrony with activity from the slow extensor of the tibia (SETi), which controls extension of the FT joint (figure 1). In the middle legs the two motor neurons are also in synchrony, but SETi is active at a lower frequency consistent with the smaller movement at the FT joint in that leg. In the prothoracic legs, activity in Ds and SETi are not in synchrony (Tryba and Ritzmann 2000). Rather, SETi fires before Ds to produce the initial FT Flexion. After the FT flexion and as the CF joint is extending, there is a second lower frequency burst in SETi. This occurs as the leg is passingunder the animal's body. It is consistent
92 QUINN AND RITZMANN with a hypothesisthat, at this point, the front leg is beingloadedand a small amount of tension is required in the FT joint to prevent collapseof the leg. This burst could result from a reflex initiated by sensory receptors called campani-
form sensilia(Ziii 1990; Ziti and Seyfarth1996) that arelocatedin the leg's cuticle anddetectcuticularstrainsuchaswould occurduring loading. Change in Walking Speed In order to walk faster, the cockroach increasesthe velocity of joint movement, in particular in the stance phase (depression and extension of joints ). This change is accomplished by increasing the rate of activity in the slow motor neurons that control movement of those joints (Watson and Ritzmann 1998a). In fact there is a linear relationship between the firing rate of Ds and SETi and the related joint velocity. At very high speeds, the second motor neuron that controls each of these joint movements is recruited. These neurons are called the fast depressor(Df ) and the fast extensorof the tibia (FETi). They are easily distinguished from the slow motor neurons becausethey generate much larger muscle potentials in the EMG records (Watson and Ritzmann 1998b). In responseto activity in Df , the CTF joint appearsto become much stiffer (Watson and Ritzmann 1998b). The joint rebounds more rapidly from swing to stancethus shortening the transition time. Timing of activity in Df comesat the right time for this change in transition duration to occur. Stiffening of the CTF joint appearsto be an important event for walking at any speed. The Df potential occurs at the same time in which Ds normally fires a high frequency burst. Thus, it appearsthat at this time of transition it is very important for the joint to becomestiff in expectation of the increasedload during foot touchdown, regardlessof walking speed. A similar high frequency burst has also been found at the onset of extensor activity in cats (Gorassini, Prochazka, Hiebert and Gauthier
1994). Finally, joint stiffeningat touchdownwasfound to be a critical problem that has to be overcomein walking movementsof Robot III . This observation is just one exampleof where hurdles that have to be overcomein Robot III point to an explanation for universalproperties of leggedlocomotion in animals.
Turning In order to turn , the cockroach must create a mismatch in joint movement in the right and left legs that at any given time are generating forward-directed force. This can be accomplished by altering the velocity of movement in the middle leg relative to the activity of the rear leg of the sametripod . For example, to turn to the right the animal would increasethe activity in the left middle leg relative to the right rear leg. However, if the alternate tripod is on the ground it would turn towards the middle leg by decreasingactivity in the right middle leg relative to the left rear leg. Observations of animals turning on the treadmill in-
CONSTRUCTION
OF A HEXAPOD
ROBOT WITH COCKROACH
dicate that this does, in fact occur (Watson and Ritzmann
KINEMATICS
93
1998b ) . Thus , the
middle legs play an important role in turning the animal. They are also critical to changesin body attitude during climbing .
Climbing In order
to climb
over a barrier , the cockroach
first
appears
to measure
the
height of the object. It then pitches its body up and finally extendsits legs forcefully to push it up and over the obstacle. We have very little information on how the insect measuresthe height of the object. However, we are confident that such a measurement
is done , because in over half of the climbs
that we ob -
served, the front legs are preciselyplaced on top of the barrier without bumping into the side of the object. Reasonablesensorydevicesfor making the measurement include the compound eyesand the antenna. They may in fact be acting in tandem
to make the calculation
.
In order to alter the attitude of the body in preparation for the climb, the middle legs are rotated so that the tibia is more nearly perpendicular to the substrate
(figure3) (Watson, Tryba, Ritzmann, and Zill 1997). Oncethis is done, extension of the middle leg will pitch the animal up in preparation for the climbing movement
. The rotation
of the tibia
occurs via a combination
of movements
at
the body-coxa (BC) and trochanter-femur (TF ) joints . Both the TF joint and the BC joint of the middle leg are located at an angle, so that movement of the BC joint will rotate the leg segmentsdistal to the joint . Becauseboth of thesejoints are located proximally on the leg, relatively small movementsat the BC joint can result in large rotations at the tibia and tarsus. The contraction of the BC joint rotates the coxa along its long axis. This movement is very different from the movement that occurs in the front and rear legswhere contraction of that joint causesthe coxa to rock forward increasingthe angle between the coxa and body as viewed from the side. In fact during the climb the side view angle of the coxa and body do not change significantly from what occurs during walking. Nevertheless, the coxa rotation will swing the tibia to a more perpendicular position. The movement of the TF joint is subtle. In fact, this joint was previously thought to be fused. However, we have found that a small flexion does occur and that such a flexion
can contribute
to the rotation
of the tibia
relative
to the
substrate. Indeed, when we did fuse the TF joint with staplesor pins through the tendon, we found that the rotation of the tibia during climbing was significantly reduced, and the animal had difficulty executing the climb . Once the animal has reared up, it can then extend its rear legs forcefully to push it up and over the barrier (Watson et al. 1997). It does this by activating Ds at a particularly high frequency to generategreaterforce and push the animal upward. We are currently performing experiments to test whether this increased activation is yet another example of a reflex adjustment initiated by the campaniform
sensilla , which
serve as strain detectors
in the cuticle .
94 QUINNANDRITZMANN
Summaryof BiologicalData The biological data describedaboveprovides the essentialparametersfor design of the robot . By incorporating the insights that were gained in those experiments it was possible to create an agile hexapod vehicle with the potential to walk and run at various different speeds, turn and climb over barriers. We found that the most efficient way to incorporate thesedata into the design of the robot was to use a dynamic simulation tool that could utilize the digitized kinematic data to generateappropriate dynamic properties and then scalethose factors up to the sizeof the actual robot.
Dynamic Model of Blaberus We developed a dynamic model of the Blaberus cockroach to help us understand the biological data and to aid in the design of the robot (Nelson and Quinn 1995; Nelson 1995). The model is basedupon a quasicoordinateformulation developed in the Biorobotics Laboratory that can be used to accurately model any leggedanimal or vehicle (Nelson and Quinn 1996). The model has a total of thirty -six degreesof freedom, three translations and three rotations of the body, and five actuated degreesof freedom in eachof its six legs.
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMATICS 95 Each cockroach leg has ten segmentsand seven degreesof freedom. Six of thesesegmentsare in the tarsus (foot) and are actuated by a single muscle group. We found that the majority of the locomotion data of interest could be modeled using the three major segments(coxa, femur and tibia) and five degreesof freedom in eachleg (Nelson and Quinn 1995). In the interest of limiting actuation, this ignores for the time being the relatively small movement of the TF joint . We believe that we can capture that action in climbing by making adjustments to the BC joint . The BC joint has three degreesof freedom and the CF joint and FT joint are each modeled with one degreeof freedom. The inputs to the simulation include the lengths and inertia of the leg segments and the body and joint angle trajectories from the behavioral studies discussedin the previous section. These joint angle trajectories were used as equilibrium point trajectories in the simulation . The joint torques are computed using a proportional -derivative control law: torquefor a joint = (fJactual - fJdesired ) k + (dfJactual / dt - dfJdesired / dt) c wherefJactualis the actualangle, fJdesired is the desiredangle, and k and c areconstants. The joint torques causedthe model's legs to move and when they contacted the ground they caused the body to move. The foot -ground interaction was modeled using a spring in parallel with a viscous damper and slip was permitted using a Coulomb friction model. Two setsof walking data were used, one for a tripod gait and one for a slower gait. In both simulations, the model walked similarly to the animal. The outputs from the simulation include the joint torques, the body motions, the ground reaction forces and the structural bending moments in the leg segments. The body motions of the model are not input and therefore, are a good measureof the successof the simulation. Becausethe mechanical properties of the model are similar to the insect, the joint torques are good predictions of those used by the cockroach. This information is useful for the biologists becausejoint torques are difficult to measurein a walking animal and can be related to EMG data. The ground reaction forces are also a measureof the successof the simulation . Our predicted ground reaction forcesqualitatively agreewith measureddata (Full 1993). The ground reaction forces and structural bending moments can also be usedin robot design to choosematerialsand sizethe components.
A Robot based on Biological
Observations
We designedthe robot to be 17 timeslargerthan the animalto easethe robot's developmentand to make useof commerciallyavailableactuatortechnology (Bachmannet al. 1997; Nelsonet al. 1997). It is convenientto constructand work with a robot that it is desktopsize, about 1-3 feetin length. It is alsoeasier
96 QUINN AND RITZMANN to visualize its motions and conduct locomotion studies. We chose a length of 30 inches becausewe wished to use commercial air cylinder actuators. For this robot sizethe actuatorsare readily available. Pneumatic piston actuatorswere chosenbecausetheir power to weight ratio is
much higherthan standardDC motorsand they offer the possibilityof passive spring-like properties. It has been shown that the passivespring-like properties of musclesare important for efficient locomotion of animals (Alexander 1988). Our previous robots, Robot I and Robot II (Quinn and Espenschied1993; Espenschied et ale 1995; Espenschiedet ale 1996; Beer et ale 1997), used DC mo-
tors that aregoodfor slowlocomotionasobservedin stick insect, but they are not energeticenoughfor the complexlocomotiongoalsof this robot. We wished to reduce the number of degreesof freedom of each leg as much as possible to further simplify the robot design. The joint angle trajectory from the biological data for the modes of locomotion of interest showed that two of the three degreesof freedom in the BC joint in the rear leg exhibited small excursions. Furthermore, one degreeof freedom in the BC joint in the middle leg
wasfound to moveonly slightly and could be eliminated(Nelsonet ale1997). We also felt that one degreeof freedom in the body-coxa joint in the front leg
could be eliminatedeventhough all five degrees of freedomof that leg showed significant excursions. The dynamic model of the cockroach discussedin the previous section was
scaledup seventeen timesin lengthand usedto testthe reduceddegrees of freedom design: three degreesof freedom in the rear legs, four degreesof freedom
eachin the middleand front legs(Nelsonet ale1997). The middleand rearlegs functionedsimilarlywith or without the eliminateddegrees of freedomfor both walking gaits. However, the front legs did not reach forward as far with the four
degreesof freedomconfiguration. We could havechangedthe joint trajectory input to try to compensatefor this or we could have returned to the five degrees of freedom design.
The choicebetweenthesetwo strategies wasinfluencedby our originalgoals. The goals of the robot include climbing and turning as well as walking. As discussedabove the animal propels itself forward with its rear legs and this can be
performedusing the three degreesof freedomconfiguration. The middle legs pitch the body for climbing and yaw the body for turning. The four degrees of freedomconfigurationfor the middlelegsis sufficientfor thesefunctions. However, as describedabove, the cockroachusesits front legs as sensori-motor "arms" as it reachesand climbs over barriers. This function requires theselegs to be the most dexterous pair and for this reason we chose to design the robot's
front legswith five degrees of freedom(figure4b). The robot was designed to be energetic and mechanically robust (Bachmann
et ale1997). Our dynamicsimulationwasfurther usedasa designtool for sizing actuators and structural members. Each leg was designedto be capableof with standing 1.5 times the robot's weight in its most extendedconfiguration because
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMKfICS 97
(::C)N,~'[IZU (:l '[ONC)!-; A I-If~xAPC )l.) I{C)Rc )'!' WI'[ fi C()(:KIZ ()AC :li K!NI'~M;\I'I(:S 99
Figure 6. L')ide view ~f ' RobotIII .
with roller bearings and hardened steel shafts. rI'he tarsi are made of flexible plastic hosesclamped to the distal ends of the tibias. rI'he hose material was chosen for its stiffnessand frictional characteristics. C:oil springs are inserted inside the plastic tarsi of the middle and rear legs to increasethe stiftl.1essof these .~load bearing legs. 14hisconstruction models the passivestiffness of the animal's tarsi in a sil~plified manner and the friction is necessaryto support horizontal loads in the stancelegs. rl'he.~robot's total weight is 29.5 pounds including thirty -six double-acting air cylinders, f()rty-eight air valves (six blocks of eight valves), air hoses, and wires. rl-1he air valves are located
on the robot 's " abdomen " such that the fc)re- aft loca -
tion of its center ot~massis at the body-coxa joints of the rear legs as it is in the animal. A potentiometer is mounted at each joint to measur(~joint anglesand a stt~ain gage is mounted at the proximal end of' each tibia to measure :.' loading of tht~legs. rrheseplay the roles of chordotonal organsthat monitor joint angle and campaniform sensillathat monitor cuticular strain in the animal (Zill 1990). rI'he robot has t\lventy-four active degreesof freedom. rT'he minimum number of va.lves to (~ontrol the robot is forty -eight three-way valves, one valve on each side of~a double-acting air cylinder (or pair of cylinders). In this configuration the valve is alwayseither pressurizing the one side of~a cylinder or exhausting it and no air storageis possible. Unfortunately, the inability to store air eliminates the potentially useful property of passivestiffl1.ess.
100 QUINNANDRITZMANN The valveson the robot have a maximum frequency of about 200Hz and are controlled using pulse-width -modulation (PWM ) at about 50Hz. Before choosing pneumatic cylinders to actuate the robot, we tested the control systemusing a prototype leg with four degreesof freedom and proportional control of indi vidual joints (Nelson et ale 1997). The system was demonstrated to have the necessary smoothness, path following and repeatability characteristicsfor loco. motIon . The biological data show that the rear legs typically operate with the CF and FT joints coupled, in-phase, and moving through equal excursions (figures 1 and 2). This provides the powerful piston-like motion that drives the animal forward. We designedthe robot with three degreesof freedom in its rear legs in caseall 3 were later found to be important , but we left provisions for a linkage to be attached to reduce it to two degreesof freedom. We now have installed a four-bar, parallel linkage mechanism on the rear legs and therefore have reduced the rear legs to two degreesof freedom. This mechanism simplifies the control of the robot becauseit stiffly couples the CF and FT joints , thereby reducing wobble in the posterior of the robot. At thirty inches in body length, the robot is seventeentimes larger than a typical adult Blaberuscockroach. Its leg segmentlengths are also scaledto be seventeen times larger than those in the animal. The rear legs are large and powerful and have two degreesof freedom, the middle legs are smaller and have four degreesof freedom and the front legs are smallest and have five degreesof freedom. Design of Robot Ill 's Posture Controller Posturecontrol is the control of body motion in three translations and three orientations. Biological data on mammals suggestthat robust posture control is essential for locomotion (Horak and Macpherson 1995). Furthermore, thesestudies have shown that the higher centers of the nervous system are important for posture. Although decerebratemammals are fully capable of moving their legs in a pattern reminiscent of walking , they cannot stand on their own. Thus, normal posture requires an interaction between local reflex circuits in the spinal cord and computational centers in the brain . These centers process various kinds of sensorydata such as leg position, head orientation and visual cues and then act through the local circuits in the spinal cord to actively control posture in a task and context dependent fashion. Insects have a more sprawled leg design that could require lessattention to active posture control . Nevertheless, our own preliminary observations indicate that even in insects, information from the brain is critical to maintaining balance during active behavior. A cockroach that has experienceda lesion of both neck connectivesdemonstratesgrossly extended leg joints and has a strong tendency to overturn with any rapid movements. Although some of these problems are reversedwith time , the animal
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMATICS 101 does not recover its agility even after a period of almost two months. Our conclusion from these observationsand those on mammals is that posture is more than local reflex interaction. It appearsto be the orchestration and tuning of reflexes in lower regions of the central nervous system according to some desired behavior. Robot Ill 's posture controller reflects these ideas (Nelson and Quinn 1998; Nelson et ale 1998): It is a centralized controller based on the virtual model approach (Raibert et ale 1986; Pratt 1995; Pratt et ale 1997). There are no sensorson cockroachesto directly measuretheir total body position or orientation , but there are many proprioceptors that monitor joint angle, strain on the cuticle, and position in extreme flexion. Total body posture can be calculatedwithin the central nervous systemfrom those proprioceptive measurements. In Robot III , body posture is also measuredindirectly using proprioceptive data: from potentiometers on the stancelegs. Even in humans, proprioceptive data are very important for posture control . Patients who have lost proprioceptive input to the brain have difficulty remaining upright even though their vestibular systemis functioning normally (Horak and Macpherson 1995). The input to the robot's posture controller is the desired body position and orientation . The desired forces F (in the x, y and z directions) and moments M (about the x, y and z axes) on the body required to causethis motion are calculated basedon the robot being modeled as a body attached to six springs, one for each of the body's six degreesof freedom, for example: Pi = Ki (actuali- desiredi ) where actuali is the actual body position in the i direction , desirediis the desired body 0 position in the i direction, and the subscript i denotesany of the three directlons x, y, or z. The posture control problem then is one of assigning the load bearing responsibilities to the stancelegs such that their summation causesF and M to be exerted on the body. As the robot walks, the stancelegs typically number from three to six. Therefore, the number of equations neededto solve this load distri bution problem varieswith the gait and the phaseof the gait. The problem is separatedinto a solution for the vertical force distribution (z direction ) and a solution for the horizontal force distribution (x and y directions) (Nelson and Quinn 1998). The coordinates are further defined in figure 7. F and M are used to determine the desired center of pressure(COP) for the robot (figure 7). Equations are developed that constrain the sum of the vertical forces from the stancelegs to equal Fz and the COP to be positioned as desired. Typically, depending on the number of legs in the stance phase, there are not enough constraint equations to solve directly for the vertical force required from each stanceleg. Therefore, in addition to the constraint equations, an optimization problem is solved that encouragesan equal sharing of vertical load among the stancelegs. Note that normally the legswill not sharean equal vertical load becauseof the enforcement of the constraint equations.
102 QUINNANDRITZMANN
The desired center of pressure (COP ) for the body can be found based upon the desired
forcesF and momentsM. Note that z is the verticaldirection, x is the headingdirectionandy is the lateraldirectionthat followsfor a right-handedcoordinatesystem .
Independentconstraintequationsbasedon satisfyingF and M on the body are formed to solve for the horizontal forces. Again, the problem is typically under-constrained and an optimization is used in the solution for the horizontal forces that encouragesminimization of the joint torques in each leg. This has been shown to causehorizontal ground reaction forces directed in toward the body. A force distribution similar to this has been measuredin walking cockroaches (Full 1993 ) .
With this posturecontrollerthe robot standsand resistslargedisturbances . When pushedfrom the side repeatedlywhile standing, it appearsto exhibit a swaying reflex similar to that observedin animals (figure 8). To further demon-
stratethe posturecontrollerand the robot's strength, we haveshownthat it can
CONSTRUCTION OFA HEXAPOD ROBOT WITHCOCKROACH KINEMATICS 103
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CONSTRUCTION
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signs for Robot III will ultimately make it behavein a more animal-like fashion, and this has been born out in the posture control . However, controlling the front legs has proved challenging becauseof their five degreesof freedom and relatively small inertia. A typical inverse kinematics approach such as that used in Robot II is difficult becauseof their kinematic redundancy. Even with these difficulties , there is benefit. The problems that we have encountered in controlling Robot III point to similar problems that animals face in controlling their legs, that if anything are even more complex. Thus, asa result of the closeatten-
tion to biologyin the designof this robot, the problemsin control haveled to insights in biology. Although we are excited about the results of this project, we recognizethat a detailed attention to biology is not necessaryor desired in all biorobotics projects. Depending on the ultimate goals of the project, a group might be more inclined to use more traditional engineering methods and only look to biology for specific solutions. Indeed, in other projects that our group has undertaken, we are using this strategy. In deciding which strategy to take, a biorobotics group must first define the ultimate goals of the project. The goals of Robot III dictated a more detailed use of biology. Acknowledg ments The work that is described in this chapter was supported by ONR grant NOOO14-96- 10708 and NASA Marshall Space Flight Center grant NGT 52832 . The work
was carried
out in the authors ' laboratories
. Our
talented
and
diligent students and researchassociates , in particular JamesT. Watson, Andrew K. Tryba and Alan J. Pollack (biology) and Gabriel M . Nelson, Richard J. Bachmann, W Clay Flannigan and Matthew C. Birch (engineering) played critical roles. A slightly different version of this chapter first appearedin 1998 in ConnectionScience , volume 10(3- 4): 239-254. We are grateful to ConnectionScience for permission to reprint the paper.
CHAPTER
6 Building Robots with a Complex Motor System to Understand Cognition Holk Cruse
ould an ethologistbe interestedin building robots? At first sight, this seemsto be implausible. The central interest of an ethologist is to understand how the behavior
of an animal
is controlled
. To this end he or she
observesthe behavior of animals and performs experimentsin order to searchfor information concerning the control principles underlying thesebehaviors. However, alreadyquite simple control rules can produce unexpectedlycomplex behaviors, in particular when the loop through the world is taken into account (e.g. Braitenberg 1984, Brooks 1991a, Holland et al. 1994, Pfeifer 1996). This is becausesystemswith recurrent information flow- in caseslike the simple Braiten-
bergvehiclesformedby the animatplusits environment - canhavecomplexdynamicproperties . If, in addition, the biologicalexperiments indicatethat several control rules cooperatein parallel within the brain of the animal, it becomesvery difficult if not impossible to judge by intuition whether or not the observedbehavior of the whole systemcan be traced back on these hypothetical rules. The only way to solve this problem appearsto perform a simulation using theserules and by this way to test whether theserules form a sensiblehypothesis. Typically such simulations are performed using digital computers. As the world usually representsan important part of the complete system, it is generally necessaryto include some properties of the world into the simulation. This may prove diffi cult , in particular becausesome relevant properties of the world may be unknown to the programmer. A sensibleway to overcometheseproblems inherent in such a " software " simulation
is to use " hardware " simulations
. This
means to
build a physical robot, or animat, which has to act in the real world . Such a hard-
waresimulationhasthe advantage that the world no longerneedsto be simulated, it is already there. Therefore, the above question has to be answered: yes, ethologistsshould be interestedin the construction of animats.
108
CRUSE
Four Types of Animats Real animals are, of course, far more complex systemsthan any animat yet built . Nevertheless, many researcherspursue the task of building simple animats in or-
der to understandthe principlesunderlyingthe constructionof real animals, particularlythe constructionof their brains. So the questionarisesto what extent might sucha reductionisticapproachbe sensibleand helpful. To approach this question I will distinguish between four different types of animats. Many interesting investigations have been performed using the simple Braitenbergtype animats. These robots contain a simple sensoryinput (often two sensors), a simple motor output (usually two active, i .e., motor driven wheels) and a corre-
spondinglysimple connectionschemebetweensensorand motor units (e.g. Webb 1996, Scheierand Pfeifer1998, Smithers2000). Although of quite simple structure, thesesystemscan show unexpectedly complex behavior, in particular behaviorsthat resembleconsiderablythose observedin animals. This type of animat can be made more complex when it is provided with more complex sensoryinput . This may either include different types of sensors like tactile and vision sensors(for example to be able to learn conditioned reflexes as in the work of Pfeifer and Verschure 1992) or may use only one type of sensor, often visual sensors, which form complex subsystemsshowing a sensory array with many pixels and many, possibly nonlinear, connections. Examplesare the robots of Franceschini et ale (1992) exploiting visual movement detection for obstacle avoidance, or Mel 's system (1997) for pattern recognition. The underlying schemais given in figure 1a (animat type 1). Becauseonly feed-forward connections are considered here, these systemsare strictly dependent on their sensoryinput and are therefore called sensory-driven or data-driven systems. A major step to make the control structure of an animat more animal-like is
to introduceinternalrecurrentconnections(animattype2, figure 1b). With an appropriate recurrent architecture such a system is no longer solely dependent on the actual sensory input , but can adopt internal states. When for example provided with an internal oscillator like a circadian rhythm generator, the re-
sponseof the animatto a givensensoryinput might dependon the stateof this oscillator. More complex versions are animats with a set of motivations (Maes 1991). Systemswith internal states can also be considered as containing some kind of dynamic (short-term) memory. However, most of these animats still have a simple two degreesof freedom motor output . Although these recurrent systemsare very interesting becausethey can show even more complex behaviors
than do feed-forward systems , they haveonly rarelyappliedto the control of robots. One reasonfor this might be that the theoretical background of the dynamic properties of such recurrent systemsis only poorly understood in most cases , which means that each individual version has to be separatelystudied in detail (seeBeer [1996] for an interesting casestudy).
BUILDING ROBOTS WITH ACOMPLEX MOTOR SYSTEM 109
110 CRUSE Nevertheless, I would also like to argue that also these systems are still far simpler than real brains. In what respect? The general task of a brain is to control behavior. In the caseof an animal, this means to control a high number of degreesof freedom, not just two. One reasonfor this is that there are no wheels in natural systemsand locomotion is therefore very often basedon the control of severalarticulated limbs. This is, of course, a more complicated task that requires an expansion of the control system. An obvious possibility to expand the systemshown in figure 1b is simply to add a "motor system," which replacesthe two wheels by, say, two legs, an articulated backbone, a neck, and maybe two arms. This motor system might consist of feed-forward connections only or it might be complicated by the introduction of recurrent connections as depicted in figure 2a (animat type 3). The assumption that such a "bottleneck" architecture approximatesthe architecture of real brains might be intuitively supported by the qualitative observation that only an extremely small fraction of the rich sensory input passesour consciousness , and this small amount of information than seemsto be used in a fan-out network to control our actions. Although probably nobody assumesthat mammalian brains work that way, this idea still appearsto guide our intuitions in someway. Many authors at least implicitly assumethat insect brains may better be describedby such bottleneck architectures. However, even in insectslowlevel activities, for example the activation of a leg muscle, appear to be represented in the head ganglia , too (Elsner 1994, Edwards et ale 1999) . The existenceof a high number of low-level reflexesclearly showsthat also for insects a bottle neck architecture in the strict senseis only able to describeselectedsubsystems, but not the complete brain. Therefore, at least as far as mammalian brains are concerned, all knowledge indicates that a better approximation to the structure is given by the schemadepicted in figure 2b (animat type 4), showing a massivelyparallel architecture including recurrent connections. If a bottleneck structure was an appropriate description of a brain, the approach considering animats of type 1 or 2 (figure 1) is justified . But even if the holistic structure of type 4 (figure 2b) is a better approximation to reality, investigation of type 1 and type 2 animats can be helpful, in any casewith respectto questions concerning application, but also with respect to brain research, becausethere might be caseswhere the brain is appropriately modularized making a reductionistic separation possible (e.g. the examplesmentioned above). How ever, the strict concentration on only thesetypes of systemsprecludesthe investigation of possible "holistic" properties. By considering only animats of type 1, 2, or 3, the most interesting properties, and maybe the really essentialproperties of brains, might be completely neglected. This concentration on bottleneck architectures might mean more than just leaving out some modules. It rather might correspond to removing the essentialpart of the system. In an unpublished keynote lecture at the Conference on Simulation of Adaptive Behavior 1998, Zurich , Steven Grand formulated this as: "there is no such thing as half
BUILDING ROBOTS WITH ACOMPLEX MOTOR SYSTEM 111
Figure 2. Two animats with complexsensoryinput and complexmotor output. a. A "bottleneckbrain." b. A massively parallelholisticsystem .
an organism," which led him to the recommendation that we should "embrace complexity." Agreeing with this view, I would like to argue that investigation of only type 1, 2, or 3 animats comprisesa, probably dramatic self-limitation when approaching the goal of understanding the function of brains. At the sameconference, Keijzer (1998) arguesalong similar lines. He distinguishes two scalesat which the animal and its environment can be coupled: the sensory-motor cou-
112 CRUSE plings for proximally guided behavior and perception-action couplings for distally oriented behavior. Concerning "wheeled behavior" he states: "wheels allow one to study distal behavior by simplifying the proximal mediating process: What if the proximal mediating mechanism is a much more central aspect of adaptive behavior than many tend to think ?" Two reasonsmay have causedresearchersto stay away from studying complex recurrent systems. One is the lack of knowledge concerning properties of (massively parallel) recurrent systems. But there is also a more practical reasonwhy researcherstraditionally prefer to study systemswith simple motor output as, for example, pressinga button or deciding between left and right etc. In such experiments the situation can be nearly completely controlled by the experimenter even when the input is arbitrarily complex, and the tools of classical systems analysiscan well be applied. This is very different when studying systemswith complex motor output . Already a simple stimulus may elicit a complex behavior and becauseof the autonomy of the systemclassicalinput -output analysisis not easily applicable. However, despite these problems, investigation of complex motor systemshave to be performed in order to understand the principles underlying such autonomous systems. The essentialproperty of a brain is its capability of controlling behavior in the context of dealing with complex motor output , i .e., a high number of degreesof freedom. This , in general, means that the control system has to deal with the problem of redundancy, i .e., with situations in which the systemhas to choose between a number of possible solutions. The higher the number of degreesof freedom of the system the more intelligence, or autonomy, is required for its controller. It may not be by accident that in the domain of invertebrates, for example, those animals are consideredas to be the most intelligent ones that have to deal with the most complex motor system, namely the Cephalopods, in particular Octopus. This leads to the conclusion that we should much more concentrate on the investigation of animals or animats that have not only complex sensory input , but also sufficiently complex body kinematics becausethe latter require a correspondingly complex motor control system. Two Examples Two examples presented below will illustrate - and propose solutions forproblems occurring when dealing with the control of nontrivial motor systems. The first example concentrateson an eighteen degreesof freedom system, namely a six-legged insect being able to walk on irregular substrate. This system is a comparatively simple casebecauseit is organized in a modular way. The legs representsix quasi- parallel, mechanically coupled systemswith three degreesof freedom- three active joints- each. As the body-ground distance is essentially fixed (one of the leg joints servesas a height controller ) there remain twelve joints to be controlled.
BUILDING ROBOTS WITH ACOMPLEX MOTOR SYSTEM 113 How is the movement of these twelve joints organized to find a common solution ? This task posesseveralmajor problems. It is not enough simply to specify a movement for each leg on its own: the mechanical coupling through the substratemeans that efficient locomotion requires coordinated movement of all the joints of all the legs in contact with the substrate. However, the number and combination of mechanically coupled joints varies from one moment to the next, depending on which legs are lifted . The task is quite nonlinear, particularly when the rotational axesof the joints are not orthogonal, as is often the case for insect legs and for the basal leg joint in particular. A further complication occurs when the animal negotiatesa curve, which requires the different legs to move at different speeds. In machines, theseproblems can be solved using traditional , though computationally costly, methods, which consider the ground reaction forces of all legs in stanceand seekto optimize some additional criteria, such as minimizing the tension or compressionexerted by the legs on the substrate. Due to the nature of the mechanical interactions and inherent in the searchfor a globally optimal control strategy, such algorithms require a single, central controller; they do not lend themselvesto distributed processing. This makesreal- time control difficult , even in the still simple caseof walking on a rigid substrate. Further complexities arise in more complex, natural walking situations, making solution difficult even with high computational power. These occur, for example, when an animal or a machine walks on a slippery surfaceor on a compliant substrate, such as the leavesand twigs encountered by insects. Any flexibility in the suspension of the joints further increasesthe degreesof freedom that must be considered and the complexity of the computation . Further problems for an exact, analytical solution occur when the length of leg segmentschanges during growth or their shapechangesthrough injury . In such cases , knowledge of the geometrical situation is incomplete, making an explicit calculation diffi cult , if not impossible. Despite the evident complexity of these tasks, they are mastered even by insectswith their "simple" nervous systems. Hence, there has to be a solution that is fast enough that on-line computation is possible even for slow neuronal systems. How can this be done? Severalauthors (e.g. Brooks 1991a) have pointed out that some relevant parametersdo not need to be explicitly calculated by the nervous systembecausethey are already availablein the interaction with the environment . This means that, instead of an abstract calculation, the system can directly exploit the dynamics of the interaction and thereby avoid a slow, COffiputationally exact algorithm . To solve the particular problem at hand, we propose to replacea central controller with distributed control in the form of local positive feedback(Cruse et ale 1998). The positive feedbackoccurs at the level of single joints : the position signal of each is fed back to control the motor output of the samejoint . How does this systemwork ? Let us assumethat anyone joint is moved actively. Then , because
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116 CRUSE
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human graspingmovements, an arbitrary selectionof one out of all the geometrically possiblesolutions is possible. The number of possiblesolutions increasesdramatically when the leg or arm has redundant degreesof freedom. For example, consider the simple but generic caseof a three joint arm moving in a two-dimensional plane. In this situation the control systemhasto choosein a continuous solution space(this is different to the above mentioned network of Maes (1991) where a decision betweendiscretebehaviorse.g. fight, flight , etc. has to be made that can be done by using a WTAsystem, i .e., a specialform of Hopfield network). To solve this ill -posedproblem in a way that does not usefixed, prescribedsolutions (e.g. Brtiwer and Cruse 1990), but that allow the control systemto really choose between all geometrically possible solutions, I seeno other way then to apply an internal representationof the arm, i .e., some kind of body model. How could such a body model look like ? Based on the "passive motion paradigm" (Mussa-Ivaldi et al. 1988), a body model has been proposed in the form of a relatively simple recurrent network (Steinktihler and Cruse 1998). A linear version of this "mean of multiple computation" or MM C net is shown in figure s. This recurrent network can be used to solve the inverse kinematic problem, the direct kinematic problem and any mixed problem. According to the passivemotion paradigm theseproblems can be solved in the following way. Imagine that a mechanical model of the arm to be controlled is used and the joints are provided with elastic springs. The inversekinematic problem is solved simply by pulling the endpoint of the mechanical model to the desired position. During this movement the other joints automatically move to new angle values
118 CRUSE and the final configuration of the mechanical model comprisesa solution of the task. Now the anglescan be read off the model arm and then be used to control the real arm. To solve another problem, one or more selectedjoints could be fixed before the endpoint of the mechanical model is moved. Then , of course, another solution is found (if it exists) that fulfills these additional constraints. The recurrent network proposed is a neuronal version of this mechanical model. As mentioned, it is not only able to solve these inverse kinematic tasks, but also the direct kinematic task and any mixed problem. Forming a holistic system, its internal structure does not have to be changed when switching between these tasks. This changeis simply triggered by providing different input (seeleft hand part of figure 5b) to the network. Figure 6a, and figure 6b shows an example of how the system behaveswhen relaxing from a given starting configuration to an end position defined by the coordinates of the end effector. The system always finds a solution for the inverse problem. The system is quite "tolerant" as it provides approximate solutions also to problems that cannot be solved exactly. Such a caseis presentedby the example in figure 6c, and figure 6d. Here, the coordinates of the endpoint cannot be reached by the manipulator becausethey specify a point outside its workspace. Nevertheless, the network tries to follow the desired coordinates as well as possible. The manipulator finally points in the direction of the desired endpoint coordinates. This neuronal model can, as described, be used asa network to control movement, even in ill -posed situations. When disconnected from the motor output , it could likewise be used for motor planning. As Calvin (1996) argues, thinking is movement that has not yet taken place. Therefore, this internal body model could be regarded as being the basis for a simple form of thinking . This is in agreementwith severalresearchers(e.g. Fuster 1995, Ito 1993) who argue that thinking is some kind of imagined movement. Similarly, in a more generalview this model can be consideredas a neural system that is able to mentally deal with complex geometrical systems, a task similar to but more difficult than the mental rotation of geometrically fixed bodies (Shepardand Metzler 1971, Georgopoulos et ale 1989). In other words, the system can manipulate knowledge, in this case knowledge concerning the own body. This means that, according to the definition of McFarland and Bosser (1993), this network may be regardedasa basisfor cognition . Furthermore, the MM C net provides a solution for the problem of sensor fusion , occurring when different sensory inputs , for example visually or mechanoreceptively given input values, represent the same value. Using tradi tional feed-forward systems, becauseof limited exactnessof these systems, in general numerically different results are produced for the same value. The holistic MM C network integrates the values and concatenatesboth the visual and the . mechanoreceptively given spacesto form a common , dynamic representatlon.
BUILDING ROBOTS WITHA COMPLEX MOTORSYSTEM 119
Figure 6: Movement of a three-joint manipulator
using the network of figure 5 .
(a) The manipulator moves from a starting configuration (dotted lines) to the prescribed position of the end effector (cross). (b) The temporal changesof the three joint angles a , (3, and () during relaxation. (c, d) Movement of the manipulator when the desired endpoint coordinates (cross) lie outside its workspace. Only the manipulator configuration of every fifth cycle is shown in (c). (d) and (b): Abscissais the number of iteration cycles (i.t .).
120 CRUSE
Concl usion Thus, both examplesshow that the investigation of systemswith complex motor output addressesproblems that may not occur when dealing with systems having only a small number of degreesof freedom. In the first case, the degrees of freedom are not exploited to solvevariable tasks. The high number of degrees of freedom result from a repetition of more or lessidentical segments- a milli pede would be a more extreme case- that have to cooperateto servea common goal, namely the propulsion of the body. This task does not appear to require a "manipulate-able" body model, i .e., a model that can be used to "play around" in order to find possible solutions by exploiting all the possibilities of the complex motor system. Instead, the body itself is used. The second example considersthe control of a complex kinematic body that might be expanded by including external objects like tools and therefore might servenot only asan internal body model, but might also be extendedto form an internal world model. This leads to the assumption that the ability to control complex kinematic systemsmay have formed the prerequisite to deal with more general problems, i .e., to become an intelligent system. Examples of systems with the need to control complex kinematics are the grasping and manipulation movementswith an arm and a hand shown by different mammals, in particular primates, the control of the trunk in elephants, manipulation of objects with legs and beak in parrots or the above mentioned invertebrate example of Octopus. All appearto be good problem solvers. As a conclusion, therefore, I assume that the understanding of how to control a complex kinematic system is an essential prerequisite to understand the functioning of cognitive systems. This proposal may also be relevant for the development of artificial cognitive systems. If one is only dealing with the control of a cylindrical, twowheeled robot, such holistic (recurrent) networks may not be necessarybecause the task to control such a "trivial " body is simple enough to be solved by using feed-forward systems. This kind of researchmay, however, preclude the finding of solutions for the very task for which brains have been developedto deal with . Also artificial cognitive systemsmay have to be built on the basisof holistic systems for motor control (Cruse 1999).
CHAPTER 7
Perceptuallnvariance and Categorization in an Embod ied Model of the Visual System Nikolaus Almdssy andOlaf Sporns
erceptualinvarianceis an important prerequisitefor the discrimination and categorization of stimuli in a naturalenvironment.Biologicalnervous systemscontain neurons, i.e., within the mammalianinferior temporal cortex, that respondinvariantlyto specificvisualstimuli. Little is known about the roleof phenotype , behavior , andenvironmentin the developmentand experience-dependentplasticityof inferior temporalneuronalresponses . To investigatethis question, we designeda neuronalmodelbasedon visualcortexthat is embeddedin a realworld devicecapableof autonomousbehavior . Initially, visual responses arenonselective and noninvariant. In the courseof autonomousbehavior, synapticchangesin visualand sensorimotornetworksaccumulateresulting in neuronalunits that are pattern selectiveand translationinvariant. In analyzingthe model, we find that the continuity of self-generatedmovementsis essentialfor the developmentof thesecomplexresponse properties ; no translation invariancedevelopsif behavioris disruptedand substitutedwith passive presentations of static stimuli. Voltage-dependentintrinsic connectionsin the inferior temporalcortexaid in the developmentof largeand sharplydefinedreceptivefieldsand supportrapidand accuratebehavioraldiscrimination. In summary, our studiessuggestthat at leastsomeperceptualinvariancesmay be the resultof the interplaybetweenplasticityintrinsic to the nervoussystemand extrinsic factors("embodiment ") dueto phenotype , behaviorand environment. Organismsareableto behaviorallydiscriminatehighly diverseand often novel visualstimuli and to acquirenew perceptualcategories throughouttheir lifetime (Edelman1987). Traditionally,theoreticalframeworksof patternclassifica tion have approachedthe problem by attempting to devisestrategiesfor partitioning often high-dimensionalinput spacesinto distinct regionscorrespondingto categories . Theseapproaches do not typicallytakeinto accountsev-
124 ALMASSY ANDSPORNS
eral key characteristics of how biologicalsystemsachievetheir superiorperformancein categorizingnatural environments . Biologicalperceptualcategoriza tion is not the result of disembodied information processingor of (even neurally
implemented ) algorithmicprocedures . Rather,viewedfrom a biologicalperspec tive, categorization necessarilyinvolves exploration and active movement resulting in sensoryexposureand sampling. The construction of basic feature dimen-
sionsdependsupon statisticalpatternsin sensoryinputs. The generationof thesepatterns may depend critically on an organism's body structure (including physical properties of its sensory and motor organs, i.e., its "phenotype"), its movement repertoire and on the previous history of its interactions in the real world .
One key prerequisite for successfulcategorization is the extraction of invari-
antsfrom the highly variableactivity patternpresenton the sensorysheet(Epstein 1977,Walsh and Kulikowski 1998). In the visual domain, many organisms (in particular higher vertebrates) are able to categorizestimuli no matter where they are located within the visual field (translation invariance), and, in addition , discount differences in rotational angle, perspective, and illuminant (see schematicin figure 1). In mammalian brains, a possibleneural basisfor thesevisual invariancescan be found in the receptivefield properties of neurons located in the inferior temporal cortex (for reviews see Gross 1992, Logothetis, Pauls, and Poggio 1995, Tanaka 1996). These neurons respond selectively to various kinds of complex visual stimuli (Gross, Rocha-Miranda , and Bender 1972, Des-
imone et al. 1984, Tanakaet al. 1991). They havelargereceptivefieldsgiving rise to responses that are invariant with respect to stimulus translation
(Gross
and Mishkin 1977, Tovee, Rolls, and Azzopardi 1994, Ito et al. 1995). Other invariant properties of visual neurons include rotational invariance, size invariance, viewpoint invariance, and invariance with respectto illuminance. Here, we addresstwo questions concerning the highly characteristic patterns of neuronal responsesobserved in mammalian inferior temporal cortex. The first question concerns the developmental origin of the complex responseproperties of such neurons. There is relatively little empirical data on the development and experience-dependence of these properties. Evidence suggeststhat tuning properties of visual cortical neurons are subject to significant postnatal modification
and refinement (Rodman 1994 ), at least in part as a result of expo -
sure to actual stimuli (Kobatake, Wang, and Tanaka 1998). Our modeling studies investigate what developmental mechanisms might be involved in shaping theseresponses . The second question concernspotential roles for a classof connections linking neurons within the inferior temporal cortex (also called tangential , horizontal , or long-range intrinsic connections). Such connections have been describedin primary visual cortex (Gilbert and Wiesel 1983, Callaway and Katz 1990, Gilbert 1993) as well as in inferior temporal cortex (Fujita and Fujita 1996, Tanigawa et al. 1998). They link distinct groups of pyramidal neurons over distances of up to several millimeters . We investigated what effects such
126 ALMASSY ANDSPORNS Our most recent set of studies (Almassy, Edelman, and Sporns 1998, Almassy and Sporns 1998, Sporns, Almassy and Edelman 2000) was aimed at the role of behavior and changesin environmental input during visual development. We designedDarwin V, a neuronal model basedon visual cortex embeddedin a real world device, and behaving in an environment containing objects that differed in their visual patterns. In brief, Darwin V operated as follows: Visual input acquired by a video camera was transmitted to a primary visual area (VAp) and from there to a secondaryvisual area (VAs) resembling inferior temporal cortex. Connections of VAp to VAs were widely divergent and modifiable according to a synaptic rule allowing for potentiation and depression. Connections of VAs to motor centers were modifiable depending upon value elicited by "taste" (conductivity ). Before visual experience, VAs responseswere weak, nonselectiveand noninvariant , and behaviorally Darwin V did not discriminate between stimuli . In the course of visual experience, groups of VAs units emergedthat were more sharply tuned, and responded selectively for distinct stimulus patterns and invariantly with respectto their location. After conditioning , Darwin V was able to discriminate objects, approaching nonconductive ones, while avoiding conductive ones. The emergenceof translation invariance was impaired if the temporal continuity of stimulus sequencesdue to self-generated movements was disrupted. The map of units within VAs reflected the history of stimulus encounters and was sensitive to changesin sensory input due to behavior. In the course of development, a patchy network of long-range intrinsic connections developedwithin area VAs, resulting in larger and more sharply defined receptive fields of neurons within VAs aswell as improved categorization behavior. Overall, our results are consistent with the view that the development of pattern selectiveand translation invariant neuronal responsesdepends critically on self-generatedmovement, the stimulus content of the environment and the dynamics of ongoing behavior.
Implementation In this chapter, the implementation of Darwin V, in particular the construction of its robotic phenotype and the simulated nervous system, are only describedin outline . Details of the implementation (including parameter values) are contained in previous publications (Almassy, Edelman, and Sporns 1998, Almassy and Sporns 1998, Sporns, Almassy, and Edelman 1999). A mobile device called NOMADwas equipped with a miniature CCD camera, six infrared sensorsthat trigger collision-avoidance reflexes, and a rigid electromagnetic "snout" that provides a binary "taste" signal to the nervous system. The experiments were conducted in an enclosed area with black cloth-covered walls and a black floor. Six to nine metal cubes (ten centimeters wide, subtend-
PEnCE P!UA [
INVARIANCE ANI) CAIvconIAzAIION
127
Figure 2. NOMAD in its environment.
ing a visual angle of about 7.5 degrees)were evenly distributed in the environment, and the robots control architectureallowedthe explorationand sampling of two types of test stimuli. Appetitive tasting stimuli were marked with five white stripes (width five millimeters, evenly spaced) and aversive stimuli showedblobs (four white patches, 0 2.5 centimeters). During autonomous behavior NOMAD performs obstacle avoidance, visual cx-
128 ALMASSY ANDSPORNS
Boxesindicatedifferentanatomicalareas(numberswithin boxesindicatetheir dimensionsin neuronalunits), shadedboxesindicateinhibitory units, arrowsindicatemajorpathways , typically comprisinghundredsor thousandsof individual connections . The openarrowhead(in areaVAs) indicatesvoltage-dependentlong-rangeintrinsic connections . All other connections arevoltage -independent . Cross-hatchedareasindicateplasticconnections . Valueprojections arerepresented by a stippledarrowand areaof terminationindicatesprojectionsthat aresubject to value-dependentmodification. Inserton top is a sketchof postsynaptic function F (see equation4).
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 129 ploration , tracking, gripping and conductivity (taste) sensing, as well as appetitive and aversivebehaviors. Appetitive behavior consists of prolonged gripping ,
movementand sensingof taste, while aversivebehaviorconsistsof releasingthe object and turning away. The last two modes of behavior are activated either by the sensing of taste (the unconditioned stimulus, US, triggering an unconditioned response, OR), or by inputs from another sensory modality, such as vision (the conditioned stimulus, CS, followed by a conditioned response, CR). Appetitive or aversivebehavior is triggered as soon as the difference in instantaneous activity between motor areasMapp and Mave (see below) exceedsa behavioral threshold fJ. If visual inputs act to trigger aversivebehavior, the aversive turning response(CR) is executed instantaneously, resulting in immediate removal of the aversiveobject from the visual field; no tastesensing(OR) occurs. Nervous System Darwin V 's nervous system consistsof a number of areasrepresenting different brain regions containing neuronal units of different types. Such units are taken to represent local populations of neurons, often corresponding to neuronal groups (Edelman 1987). In the instantiation used in the present experiments, the simulated nervous systemcontains a total of approximately 15,000 neuronal units and 600,000 synaptic connections. There are five major components: a visual system, a taste system, setsof motor neurons capableof triggering behavior, a visual tracking system, and a value system. A schematic diagram of the major components of Darwin V 's nervous systemis shown in figure 3. The pixel image captured by NOMAD's camerais relayed to a receiving areaR and transmitted via topographic connections to a primary visual areaYAp. The spatial arrangement (resembling feature matrices) of theseconnections results in various stimulus selectivitieswithin interleaved subpartitions of YAp. There are three subpartitions, one each selectivefor visual "blobs," "horizontal" and "vertical" stripes. Topographic, mutually inhibitory connections result in sharpening and disambiguation of responses . Dynamically, responseswithin YAp are brisk and closely follow stimulus onset and lateral displacements. All subpartitions of YAp project to a secondaryvisual areaVAs, containing both excitatory and inhibitory units. Local excitatory-excitatory, excitatory-inhibitory , and inhibitory excitatory interactions produce firing patterns that are characterizedby focal regions of excitation (diameter about three to five neuronal units) surrounded by inhibition ; responsestend to be more long-lasting than those in VAp. The taste system consists of two kinds of sensory units responsiveto either the presenceor absenceof conductivity acrossthe surfaceof stimulus objects (as measuredby sensorsin NOMAD's snout). These units emit all-or-none responses
that are transmittedto appropriatemotor centersby innately specified("prewired") connections. In addition , both taste units send a uniform , nonplastic input to the value system .
130 ALMASSY ANDSPORNS The motor system consists of two groups of units capable of triggering two distinct behaviors , appetitive (Mapp ) and aversive (Mave ) . These two groups receive pre -specified and fixed connections from the taste system (Tapp to Mapp and Tave to Mave ) and have mutually inhibitory connections . In addition they receive initially weak nonspecific connections from the visual system (VAs to Mapp and Mave ) that are subject to value -dependent modification . The visual tracking system controls navigational movements , in particular approach of objects identified by brightness contrast with respect to the back ground . The visual receiving area R emits a projection Ius" ) , containing
excitatory
(Ce ) and inhibitory
to an area C ("collicu -
(Ci ) units . The pattern
of
connectivity within area C helps to sharpen Ce responses to visual targets and partially disambiguates such responses when multiple targets are present . Ce activity triggers translational and rotational motion of NOMAD (via activation of Mtra and Mrot , respectively ) ultimately Connection
producing
visual approach behavior .
strengths in projections from Ce to Mtra and Mrot were assigned
initial values resembling distributions dent synaptic modification Cell Activation
obtained in earlier work by value -depen -
during sensorimotor training (Edelman et al. 1992 ) .
and Synaptic
Rules
As in previous models (such as Reeke, Sporns , and Edelman 1990 ) , activity val ues sIt ) of neuronal units at cycle t represent the average firing rate of a local population of neurons . Synaptic strengths Ci" may be subject to activity -depen dent modification (see below ) . Y Synaptic inputs can be voltage -independent or voltage -dependent . The total input to unit i from voltage -independent (VI ) connections is given by M N( A ~ (t ) = LLCjjSj f=l j =l
(t )
( 1)
where M is the number of different anatomically defined connection types and N e is the number of connections per type .e projecting to unit i . Negative values for cij correspond to inhibitory connections . The total contribution age-dependent (VO ) connections is given by M Np A ~D( t ) = L tanh ( A ~I ( t ) + (1Jsi ( t ) )Lc ijs) ( t ) e=1 )=1
from volt -
(2)
where OJdetermines the persistence of unit activity from one cycle to the next . Voltage -dependent connections have no effect unless there is sufficient postsynaptic activity due to other , voltage -independent inputs . The activity level of unit i is given by Si( t + 1) = c/Jaj( tanh ( g;[ AiVI( t ) + A ~D( t ) + (1Js i( t )])) where l/JO "l is a unit -specific firing -threshold function
(3) and gi is a scale factor . In
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 131
the
model
,
dependent
ing
to
a
L \ Cij
si
fixed
types
see
e
( cij
and
( o
as
rates
-
for
,
( e . g
more
detail
. ,
a
to
C ij
( t ) F
of
decay
In
this
function
,
solely
depending
of
,
how
relates
the
and
,
"
Almassy
,
of
behavior
visual
behaving
of
Darwin
of
VAs
,
in
figure
voltage
- in
-
accord
3
-
this
and
-
visual
particular
) ,
-
which
and
sets
the
) .
The
chapter
( t
-
limits
and
connec
on
-
value
- range
( for
intrinsic
modifiable
activity
)
-
connec
some
long
is
0
postsynaptic
For
are
11
=
postsy
of
depends
postsynaptic
and
,
with
.
obtained
Sporns
1998
These
results
) .
development
's
its
development
.
development
of
from
,
how
First
over
neuronal
units
of
experiments
Almassy
and
address
and
embodiment
properties
the
pre
upon
results
during
and
inset
) .
,
initial
)
Its
,
system
respectively
the
both
1982
2000
this
is
weakening
modification
1999
V
~
Munro
exemplary
Edelman
,
,
'( O )
depending
in
Edelman
arise
a
development
area
selectivity
and
invariants
aspects
the
,
upo
Edelman
pre
some
( Almassy
ci
or
and
studied
on
and
depend
( see
) ,
,
units
1 ) ,
depression
Mave
VAs
present
perceptual
to
scribe
er
Sporns
overall
object
we
presynaptic
strengthening
Cooper
Almassy
area
and
( e
F
and
Sporns
-
either
,
Mapp
previously
1998
and
modification
( 4
changes in
and
within
chapter
- dependent
- dependent
( t ) )
post
Synaptic
the
( sj
Resu
reported
voltage
activity
constant
Bienenstock
VAs
to
1 ] sj
potentiation
see
in
+
result
by
connections
changes
is
can
synaptic
( both
subject
activities
e
C ij ' and
( compare
tions
connections
are
( t ) )
are
determined
activity
cij
connection
activity
tions
)
rate
of
naptic
)
:
Sj ( t )
learning
weight
3
rule
=
( t )
of
figure
synaptic
( t )
where
a
several
,
translation
,
the
their
we
emergence
briefly
time
.
Sporns
problem
describe
Then
within
,
the
invariance
we
de
" high
-
-
and
.
Overall Behavior of Darwin V Figures 4 and 5 provide a summary of relevant neural and behavioral states recorded during severalstimulus encounters. Neural statesof visual, taste, motor and value networks are indicated as averageactivity levels over time (figure 4). Before development, Darwin V indiscriminately approachesall visual objects until they are located within the visual fovea. Then , all objects are gripped to allow sensing of their conductivity ("taste"). Taste sensing triggers either appetitive or aversivebehavior. Visual neuronal responsesin areaVAs are still nonspecific and do not allow the translation invariant recognition of objects . Therefore, all behavior is initially driven by "taste" sensing only. After some time, synaptic changesin connections linking visual areasVAp and VAs have ac-
132 ALMASSY ANDSPORNS
Figure 4. Averageneural statesof severalsensoryand motor networksof Darwin V recorded before(left) and after (right) visual developmentand conditioning. Tracesfor tWoindividualencounterswith a "blob" and a "stripe" areshown, with (top to bottom) tracesfor averagevisual activity in VAs, tastesensing(Tave, broken line; Tapp, solid line), behavioralmotor activity (Mave, brokenline; Mapp solid line), and the valuesystemS. For descriptionseetext. cumulated and neuronal responses within VAs have developed that allow the vi sual recognition of objects . This results in visual responses becoming linked di rectly to behavior (figure 5) . After this developmental stage is completed , Dar win V will still approach objects that "taste good ," but will avoid objects that "taste bad ," i .e., if encountered , trigger avoidance behavior . Development Object
of Translation
Selective
Neuronal
During visual exploration
Invariant
and
Responses
and approach , neuronal units in the primary
(V Ap )
and secondary (VAs ) visual areas show responses with different temporal char -
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 133
On the left, symbolsindicate the differencein averageactivities of Mave and Mapp for "stripes " (+) and "blobs" (0 ), with a stippledline indicatingthe behavioralthreshold(/3= 0.3) usedin the presentexperiments . On the right, the percentincidenceof conditionedresponses (aversiveand appetitive) areindicated. Note that after about 1,000 seconds(30 minutes) of experience only conditionedresponses occur, i.e., responses that aretriggeredby visionalone.
acteristics. Neuronal units in VAp have small receptive fields and respond to local features of the image, i.e. short segmentsof vertical lines. Due to stimulus displacement during approach, line segments quickly move in and out of the unit 's receptive fields and unit responsesare brief in duration . In our experiments, no modification of VAp responsesoccurred with development. At higher levels of the cortical model (VAs), neuronal units receivehighly convergent inputs from a large region within VAp. Due to local dynamics of excitatory / inhibitory interactions, lateral displacement of stimuli within the visual field is accompanied by sustained activation or suppression of subsetsof units within VAs. For many units, such responseseither increaseor decreasein both amplitude and temporal duration during development, producing an overall sharpening of unit responses . Next, we tested the neuronal responsepatterns we obtained from areaVAs for translation invariance (figure 6). For this purpose, we presentedvisual objects at different locations within the field of view and compared the activity patterns within areaVAs. Recording the activity values for all nine hundred units within areaVAs for a given pair of object locations yielded two activation vectors a and b. Their mutual overlap (degreeof pair-wise correlation) is estimated as
ab C 0S (cj > )=1-~.I~I-bl
(5)
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 135 So far, we have shown that translation invariant and pattern selective neuronal responsesemerged in the course of development as Darwin V interacted with numerous objects in its environment. A key question arises: does the emergenceof theseproperties depend on movement and how is it affected if the continuity of Darwin V 's movements is disrupted? To address this question, we compared Darwin V 's neuronal responsesbefore and after "normal" development to those of a separatelytrained Darwin V that had been immobilized and presentedwith sequencesof stationary images of visual patterns flashed at random positions within its narrow-angle field of view ("discontinuous" development). Patterns used consistedof a training set of "blobs" and "stripes" (in hori zontal and vertical orientations) that were presented with equal frequency. To keep the overall amount of sensory stimulation comparable to "normal" development, individual patterns were presentedfor about sevenseconds(twenty cycles) separatedby about twenty-five seconds(seventyblank cyclesthat simulated the times for visual searchand exploration). We recorded activity levels and calculated cos(t! (equation 5) for pairs of object presentationsat a distanceof sixteen and thirty -two pixels (corresponding to a viewing angle of 6.4 and 12.8 degrees, respectively) distributed evenly over all parts of the field of view. The overall level of activity within VAs (si(t) > 0.0) was unchanged between "normal " and "discontinuous" modes of stimulation , although a significantly larger number of units showed strong (Si(t) > 0.5) and sustained responsesin the former versus the latter condition . While "normal" development significantly increased the overlap measure cos(t! , which is an indicator of the degree of translation invariance, "discontinuous" development did not result in a similar increaseand cos(t! remained at baselinelevels. Receptive field maps of individual VAs units were examined and compared before and after "normal " and "discontinuous " development. VAs responses were recorded while presenting an object in a regularly spacedarray of 32 X 32 locations within the narrow-angle field of view. An individual receptive field map was obtained by plotting the steady-state activity level of a single neuronal unit for all spatial locations. Figure 7 shows representative examples of such maps for a unit located near the center of VAs, which received highly convergent anatomical input from a large, central portion of VAp . Before development, receptive field maps were patchy and showedweak pattern selectivity over much of their spatial extent (figure 7, "before"). After "normal" development, neuronal units with high selectivity for a particular visual pattern (in this case "horizontals") had receptive fields that were coherent in their selectivity, consisting in most casesof a single large region; at the sametime, parts of the receptive field showing responsesto other patterns decreased(figure 7, "normal"). After "discontinuous" development, receptivefield maps changedonly slightly and retained many of their initial characteristics(figure 7, "discontinuous"). In particular, these receptive field maps did not extend acrosslarge regions of the visual field and did not show coherent regions of high pattern selectivity. No improve-
138 ALMASSY ANDSPORNS
Figure 9. Population vectoranglesbetweenVAsactivity sampledat individual
cycles afterstimulusonsetandanaverage "late"pattern(cycles 6-10). Without long-rangeintrinsic connections(LRIC), the angleis significantlylargerduring the first three cycles after stimulus onset (ttest2: p-values = 2.6 x 10-7, 0.0079 , 0.027, respective-
ly). In addition, arrowsindicatewhen, on average , behavioris triggered .
This pattern allowed distant groups of neuronal units to interact through a patchy network of voltage-dependent connections.
Long-rangeintrinsic connectionsaffectedthe dynamicsof firing patterns within VAs. During early development, thesefiring patterns tended to fluctuate and showed little stability and selectivity with respect to particular visual stimuli . This was due to the still immature patterns of afferent and intrinsic synaptic strengths. A prerequisite for reliable categorization of visual stimuli is a relatively stable firing pattern that ensuresthe consistent development of sensorimotor connections linking VAs to the motor units. To quantify how quickly the population responsestabilized after stimulus onset we evaluated the overlap in VAs
response patterns(activityvectors ) betweenindividual time points during stimulus encounter. In particular, we compared the "late" (stabilized) pattern (sampled after the time when behavior is initiated ) and "early" firing patterns sam-
pled directly after stimulus onset (figure 9). We found that when long-range intrinsic connections were present, the overlap (vector angle <1 between early
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PERCEPTUAL INVARI .ANCE ANDCATEGORI .AZATION139
140 ALMASSY AND SPORNS
units
sampled
pared
to
not
have
ence
.
from
the long
We
trinsic
connections long
" sharpness
" )
Models
that
2 .5 ,
=
,
are
,
in in
see
,
figure
a
long
for
a
the
,
receptive
differences
are
in
-
model response
Furthermore the
-
- range
neuronal
,
connections
- range
model
intrinsic
f3
=
the
aver
field
-
( the
statistically
categorization
simulation
one
was
sig
)
as
VAs
-
- way
long
Anova
to
,
- range
present connec
df
=
, with
five
a was
intrinsic
reliable
one
showed
, behavior were
- range
more
from
segment
area
average
connections long
0 .3 ;
increasing
model
in . On
without
threshold
9 ) . Also
the
. of
performance
discrimination
from
. All
intrinsic
when
3 . 5
representative
omitted
line
-
did
experi
long to
average
percent
that
visual
with
compared
the
thirteen
percent
- range
behavioral
behavioral
percent
com
model
same
model
contour
categorizing
earlier versus
trials
by
a
the
the
and
- eight
long in
,
thirty
maximal
thirty
received in
were
from
) .
included
cycles
twelve
0 . 023
errors als
by shown
significantly
( after tions
increased
improvement
triggered
P
not
but
about
connections
sampled
sizes
connections
half
intrinsic
units
field by
field the
increased
( data
significant
receptive
intrinsic
along
- range
neuronal
connections
the
receptive
long
90
increased
gradient
nificant
with of
intrinsic
that
- range
the
age
model fields
- range
found
without within
a
receptive
the
( out
of
intrinsic
-
1 , F
=
6 ,
rate
of
twelve
tri
-
connections
.
Discussion
The
development
ronal this
,
of
,
study
ment
behavior activity
velopment movement and
. Overall of
,
organisms
subsequent
and
behavior
of
recognition natural
our
study
in
their
and
is
consistent
econiche of
Object
events
is transformations
is
roles
Selectivity
an
in
its
such
the
of
in
in
Self
-
the
be
-
the
de
-
- generated organism
to
.
Translation
as
-
move that
an
-
selective
factor
ability
cat
modeling
cortical
.
ca -
phe
Our
notion
circuitry
is
or
- generated
important
environment
achieved ,
self
the
neural
and
typically
of
with
their
in
.
In
based
perceptual
behavior
development
.
and
recognition or
on
neu
objects
device
form
critically
visual
biologically
world
sensory
ongoing the
a
real
of
purely
depend
important and
a
models as
systems may
in of
from in
previous
elaboration
objects
obtained
activity
play
discriminate
to
motor
invariance discrimination
embedded
processes
autonomous
and
object respect
these
responses
Development
Biological with
in
translation
behavioral
results is
. Most
with of
that
the
that
behavior
neuronal
and
havioral
cortex
dealt
suggests
and
for exemplary
visual
independent
invariant
selectivity
prerequisite
describe of
have
nomena
object
a
autonomous
egorization
The
we
model
pable
sense
is
chapter
neuronal
and
of
responses
a
Invariance
manner
translation
that ,
size
is or
invariant rotation
.
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 141 Neurophysiological evidence (Gross, Rocha-Miranda , and Bender 1972, Logothetis, Pauls, and Poggio 1995, Tanaka 1996) suggeststhat the object selective and translation invariant responsesof neurons in the inferotemporal cortex play a key role in this process. While recognition of some visual patterns may be in-
nate, developmental studiesin primates(Spinozzi1996) and humans(BertenthaI 1996) strongly suggestthat exposureto visual stimuli is required for normal (selective and invariant ) neuronal responses to emerge. In Darwin V , we mod -
eled the development of such responsesin the course of successiveexposureto various objects present in its environment. In the primate visual system, areaswithin "lower" hierarchical levels contain neurons that are selectiveonly for elementary (simple) featuresof visual objects, such as line segments. Invariant and object-selectivevisual responsesarise only at "higher" hierarchical levels and depend for their construction on the progressive elaboration of visual input from "lower" to "higher" areasvia convergent/ divergent feed-forward connectivity (for a review see Rolls 1992). In our model, for reasonsof computational expediency, we have simplified the multi -level hierarchical arrangement of the primate visual system to include only two areas (VAp and VAs), with properties characteristic for primary/secondary (V1 / V2) and inferior temporal cortices (IT ), respectively. These two areasare linked by widely convergent/ divergent connections, arising from neurons with different stimulus selectivity in VAp and terminating on a single set of neurons in VAs. Intrinsic connections within VAs link distinct populations of excitatory and inhibitory units. Activity patterns within VAs are sustained (lasting for several time steps, corresponding to severalhundred milliseconds of real time) and are characterizedby local foci of activation surrounded by lateral inhibition . Before visual experience, these foci are highly dynamic and unstable, appearing at dif -
ferentlocationswithin the VAs map dependingupon smallfluctuationsin the input . During synaptic modification , repeatedsustained coactivation of neighboring units tends to produce similar patterns of synaptic change in their afferent and efferent connectivity. Inhibition tends to spreadapart and limit the size of emerging functional neuronal groups selective for specific objects. Intrinsic excitatory -inhibitory interactions within VAs enhance competition between neuronal populations in different parts of the array, sharpening their responses and leading to the emergenceof distinct neuronal groups. Severalcomputational models of inferior temporal have been developed previously (Gochin 1994, Wallis and Rolls 1997). Different proposals have been made to account for the translation invariant properties of inferior temporal neurons. An important set of models (Foldiak 1991,Gram and Foldiak 1996, Wallis and Rolls 1997 ,Wallis and Blilthoff
1999 ) has utilized a Hebbian -type
learning mechanism (the so-called trace rule) to develop translation invariant neuronal responses . The trace rule incorporates temporal delays into the learning mechanism, providing an effective way to link successiveimagesof objects.
In our model, suchlinkageis achieveddirectlythroughneuronaldynamics , i.e.
142 ALMASSY ANDSPORNS
the
sustained
activation
junction
with
synaptic
,
arriving
series
1992
of
, Wang
in
our
modeled
coupling
tional
is
interactions
Gochin
et
ale
1991
over
these
)
jita
Fujita
tions
found
to
that
areal
and
after
patterns
the
of
allowed
an
responses
were
fields
and
suggestive
on
visual
of
earlier
within
motor
thus
contributed
trial
,
.
within
of
such
an
more
towards
dynamic
effect
motor
effects
our
size
increase
appropriate
These
on
modeled
connections
We
overall
rapidly
This
average
behavioral
roles
to
.
on
-
Fu
-
emerged
more
-
the
both
VAs
net
.
on
increase
-
(
connec
properties
area
response
,
connections
functional
to
converged
that
a
intrinsic
potential
a
found
strong
effects
tended
within
and
patchy
response
-
in
cortex
,
significant
and
patterns
We
the
development
they
stimulus
.
of
;
in
dis
changes
temporal
similar
-
longer
Synaptic
resemble
had
firing
initiation
triggered
After
fields
,
behavior
behavior
of
cortex
the
effect
.
func
dynamic
over
inferior
have
VAs
receptive
Also
a
)
connections
within
of
of
1998
that
these
.
onset
ale
neurons
fields
sharpness
that
produced
poral
of
invariance
quickly
are
groups
receptive
translation
tive
et
local
of
through
that
the
that
Such
micrometers
linked
.
-
units
.
hundred
generated
patterns
striking
incidence
one
ale
development
contain
mutually
within
shown
is
visual
higher
are
connection
Tanigawa
-
et
organization
connections
present
presence
of
extent
in
link
the
shape
,
-
sig
Fujita
responses
about
groups
been
(
groups
observed
intrinsic
in
of
invariant
the
has
during
neuronal
become
dependent
to
studies
pattern
within
they
connections
1996
tend
and
areal
this
These
with
,
result
-
.
neuronal
-
;
cortex
emerging
located
While
voltage
intra
and
.
)
responsive
imaging
translation
distances
connections
of
6
con
with
.
temporal
optical
)
generate
neurons
short
through
work
figure
in
Cortex
groups
consistent
for
teractions
tances
(
and
,
commensurate
become
field
inferior
1996
neuronal
VAs
to
visual
process
scale
VAs
the
of
dynamical
time
Temporal
Tanifuji
selectivity
This
a
within
Inferior
of
area
object
functional
(
and
pattern
.
at
and
,
the
VAs
of
organization
Tanaka
to
share
units
electrophysiological
,
similar
area
objects
region
columnar
a
allow
extended
of
functional
ly
to
an
Organization
in
in
of
serves
over
Intrinsic
neurons
displacement
changes
nals
A
of
lateral
recep
area
in
-
VAs
inferior
tem
-
.
Conclusion
The
essential
ment
of
role
responses
develop
if
if
if
equivalent
of
behavioral
translation
such
the
and
invariance
is
interactions
are
continuity
levels
of
of
sensory
environmental
by
impaired
.
stimulus
input
interactions
demonstrated
displacement
and
No
the
effects
translation
is
synaptic
in
drastic
changes
the
develop
on
invariant
selectively
responses
destroyed
are
allowed
-
neuronal
,
.
We
even
con
-
PERCEPTUAL INVARIANCE ANDCATEGORIAZATION 143 clude that in systemsshowing autonomous behavior, complex neuronal responses are in part determined by self-generated behavioral interactions in the environment. Such interactions can help in sampling and generating statistical regularities in the environment that can be captured by developing neuronal circuitry. Similar mechanismsmay also be responsiblefor generating other perceptual invariances, such as those for object size, rotation , view-independence, or color (see figure 1). In general, our study is consistent with the hypothesis that the ongoing behavioral and motor activity of organisms constitute essential prerequisitesfor the extraction of invariants by specializedneurons. Acknowledg ments This work was carried out in the WM . Keck Foundation Machine Psychology Laboratory at The NeurosciencesInstitute and supported by a grant from the WM . Keck Foundation to NeurosciencesResearchFoundation.
CHAPTER
8 Investigating Models of Social Development Using a Humanoid Robot Brian Scassellati
esearchon humanoid robotics has been motivated by a variety of different goals. Some researchgroups have focused on the construction of mahines with humanlike form and motion to meet anticipated commercial needsas a flexible factory worker, a domestic assistant, or to operate in areasthat are dangerousto humans (Hirai et ale 1998, Kawamura et ale 1996). Other researchhas focused on the construction of humanoid robots to examine issuesof human-robot interaction and cooperation (T akanishi, Hirano , and Sato 1998; Morita , Shibuya, and Sugano 1998), or to examine issuesof sensory-motor integration and architectural techniques from artificial intelligence (Kanehiro et ale 1998). The ma,jority of these researchefforts have focused on the challenging engineering issuesof building intelligent and adaptive systems. My colleaguesand I have proposed that humanoid robotics researchcan also investigate scientific questions about the nature of human intelligence (Brooks et ale 1998). We believe that humanoid robots can serveas a unique tool to investigators in the cognitive sciences. Robotic implementations of cognitive, behavioral, and developmental models provide a test-bed for evaluating the predictive power and validity of those models. An implemented robotic model allows for more accuratetesting and validation of these models through controlled, repeatable experiments. Slight experimental variations can be used to isolate and evaluate single factors (whether environmental or internal ) independent of many of the confounds that affect normal behavioral observations. Experiments can also be repeatedwith nearly identical conditions to allow for easyvalidation . Further, internal model structures can be manipulated to observe the quantitative and qualitative effects on behavior. A robotic model can also be subjected to controversial testing that is potentially hazardous, costly, or unethical to conduct on humans; the " boundary conditions'" of the models can be explored by test-
146 SCASSELLATI
ing
alternative
can
be
plying
to
them
In
be
,
I
robot
ferent
.
abstraction
actions
infant
,
spinal
we
cial
development
tion
behaviors
,
has
1998
,
on
that
One
the
to
critical
repertoire
of
cues
,
that
on
1996
the
,
tion
.
These
tion
,
are
tion
precursors
social
to
,
to
the
objects
to
attention
is
also
by
allowing
the
by
providing
a
language
and
Joint
perts
child
on
disorders
to
etiology
as
social
issues
( Whiten
have
autism
,
on
been
by
so
-
atten
-
is
,
to
initial
mechanism
meanings
atten
-
atten
-
for
.
the
Joint
skills
and
environment
the
social
-
)
Joint
shared
of
of
.
their
large
postural
shared
leverage
behavior
and
children
about
the
a
considera
( or
in
the
learn
these
skills
extremely
Moore
behavioral
,
caregiver
in
and
in
communication
Asperger
's
) .
of
syndrome
such
Evolutionary
these
both
to
social
Baron
as
a
a
stereotyped
) .
Additional
on
disruptions
skills
are
in
- Cohen
an
theory
Ex
-
de
-
throughout
hu
1995
-
) .
explanation
of
and
skills
.
developmental
these
,
fields
normal
in
focused
psychologists
simple
and
the
pervasive
1997
precursor
of
of
1995
vital
attention
a
variety
,
have
how
joint
as
part
Dunham
Volkmar
in
and
a
as
of
demonstrated
and
in
rapidly
and
manifestations
and
1991
evolution
researchers
in
,
interested
Dennett
the
negotiate
forms
acquire
grounding
1991
focused
as
to
to
interested
( Cohen
contextual
this
of
have
under
joint
skills
the
1975
and
interactions
have
of
In
joint
Humans
currently
infants
order
complex
infants
mechanisms
Philosophers
neural
) .
model
gestures
is
serves
and
investigated
Bruner
and
attention
and
development
.
pointing
social
manipulate
are
and
such
joint
man
the
of
.
been
that
( Scaife
)
of
human
of
allowing
more
development
course
sequence
,
world
others
to
for
has
velopmental
work
infant
basis
-
development
behavioral
mechanisms
in
-
inter
1998
interest
object
of
for
the
production
in
mutual
direction
the
caregiver
gestures
attention
in
in
dif
) .
learning
which
with
many
- caregiver
1996
one
on
at
Attention
of
gaze
mechanism
part
joint
development
adult
-
questions
Williamson
and
1998
named
events
a
an
of
object
as
of
,
recognition
observer
normal
part
as
such
an
experiences
of
ap
development
Williamson
1996
social
an
collectively
and
share
knowledge
,
to
abilities
vital
infants
cues
indicate
model
before
infant
models
and
Scassellati
to
to
of
behavioral
,
of
attend
all
) ,
implementation
focuses
selectively
robotic
models
models
( Williamson
- going
( Scassellati
of
a
social
biological
Scassellati
Models
ability
,
engineering
human
implemented
neurons
an
and
of
interaction
Scassellati
present
Finally
strategies
biological
models
including
motor
.
intervention
potential
group
( Marjanovic
of
chapter
Our
and
reaching
models
the
implementing
levels
( Breazeal
potential
.
discuss
by
conditions
evaluate
subjects
examined
humanoid
environmental
and
human
chapter
can
and
suggest
to
this
that
a
learning
used
for
other
minds
primatologists
the
ani
-
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 147 mal
kingdom
as a means
as a measure Premack
social
investigation
origins
ily
master
in
shared
seems
of
of the : What
is the
in
these
primates
these
comparisons
, and
what
the
about
our
purposes
to
) . The
attention
are present
skills
that
occurs
tal
in
also
disorder
and 1996
,
and
from
can
other
nisms
of joint
1995 ) . In
this
comparison
Williams
and
quite
specific
( Karmiloff
Evidence
tological can
also
plex
set
from
joint
, but
all
and
for
a collec
many
( Hobson
before
they
-
different
monolithic
sys -
mechanisms
for
progression
rate
of
1993 ) . For can
interpret
have
ex and
to other Downs
mental
to
and
of Men -
to
have
socially
appropriate
eye contact
often
fail
of
autism that
and
social
, and
to grasp
certainly
the
deficiencies
missing
developmental
the
cover mecha
( Baron
deficiencies
nor -
relevant
, they
other
) , the
tend
-
cri -
(Diagnostic
certain
believe
the
frac -
develop
-
- Cohen disorders
of
autism
are
et ale 1995 ) .
into
the
social
skills
be decomposed
joint
autism
deficits
retardation
Syndromes
- Smith
the
researchers
be critical
skills
with
to make
and
- 1O Classification
with
is looking
limit
by a set of behavioral
ICD
difficulty fail
, some
, that
is a pervasive
communicative
. While
may
can
as autism
is diagnosed
and
a person
attention
be seen
as an evolutionary
of
can
skills
not
1990 ) . Autism
, 1993 ) . Individuals
abilities
of
in
a single
that
same
" 1994 ; The
individuals
research
progression
1991
is a stereotypic
, such
that
social
where
attention
the
( Frith
Disorders
skills
attention
there
disorders
information
cognitive
are not shows
, and
etiology
, autistic
( like
ed that
in
from
as developmental
, repeated
to eye direction
system
abnormal
recognize
of
many
for
be seen
.
motor
example
implications
sensitive
Disorders
and
they
can
of communication
occured
finding
at roughly
of Mental
Behavioral
tasks . For
, as it
do we have
interplay
basis
so eas to engage
development hope
" ( see Whiten
attention
birth
infants
of this
around
mind
development
developmental
Manual
sensory
development
humans
acquire
this
what
complex
have
of
of joint
of unknown
centered
while
all
components
Statistical
the that
must
in
, and
the
important
childhood
, infants are always . . polllting gestures
mental
" theory
mechanisms
joint
are
wrong
learn
studies
from
mal
of mind
, Hauser
Skills
tem . Evidence
the
about skills
humans
occur
we
most
There
goes can
can
studies
, is that
that
of this
tion
teria
1995
?
relevant
of these
of skills
pans
of
ture
of theory
Preuss
questions
? What
investigations
generate
presence and
asks
problems
evolutionary
ample
the
communication
something
, what
Social
most
attention
progression
problems
Decomposing
forms
both ( Povinelli
nonverbal
? When
autism
other
joint
complex
attention
to do
correcting
The
of evaluating functioning
1988 ) .
The and
of
be mapped
of other
into skills
that
progression to
animals
animals
has also
a set of subskills
in that
. The
is evident
in
which
increasingly
are
the
increasingly
indicat same
human
closer
-
on -
infants com to
hu -
148SCASSELLATI mans on a phylogenetic scale (Povinelli and Preuss 1995). For example, skills that infants acquire early in life, such as sensitivity to eye direction, have been demonstrated in relatively simple vertebrates, such as snakes (Burghardt and Greene 1990), while skills that are acquired later tend to appear only in the primates (Whiten 1991). A Theoretical Decomposition One of the most influential models of joint attention comes from Baron-Cohen (1995). Baron-Cohen's model givesa coherent account of the observeddevelopmental stagesof joint attention behaviors in both normal and blind children, the observeddeficienciesin joint attention of children with autism, and a partial explanation of the observedabilities of primates on joint attention tasks. Baron-Cohen describes four Fodorian modules: the eye-direction detector (EDD), the intentionality detector (10), the shared attention module (SAM), and the theory-of-mind module (TOMM) (seefigure 1). In brief, the eye-direction detector locateseyelike shapesand extrapolatesthe object that they are focused upon while the intentionality detector attributes desiresand goals to objects that appear to move under their own volition . The outputs of these two modules (EDD and ID) are used by the shared attention module to generaterepresentations and behaviors that link attentional states in the observer to attentional statesin the observed. Finally, the theory-of-mind module acts on the output of SAMto predict the thoughts and actions of the observedindividual . In normal development, the interaction of EDD, ID, and SAMproduce a variety of normal behaviors. Furthermore, the model proposes that autistic behavior can be explained by including the EDDand ID modules without any of the competencies of the sharedattention module. Decomposition
Based on Observable Behaviors
In order to implement and test a complex social model, representativebehaviors that can be independently tested and observedmust be identified for each part of the model. A behavioral decomposition allows us to evaluatethe performance of the system incrementally and to match the observed behavior of our robot with observedbehavior in humans. The skill decomposition that we are pursuing is a set of representative behaviors from EDD, ID, and SAMfor two social modalities (eyecontact and pointing ). This decomposition includes four observable and testable behaviors: maintaining eye contact, gazefollowing , imperative pointing , and declarative pointing . Figure 2 shows simple cartoon illustrations of thesefour skills in which the smaller figure on the left in each cartoon representsthe novice and the larger figure on the right representsthe caregiver. These skills were selectedas representativebehaviors becausethe ontogeny and phylogeny of the skills have been intensively studied, becausethey are possiblewith
INVESTIGKfING MODELS OFSOCIAL DEVELOPMENT 149
Figure1. Overviewof Baron-Cohens modelof the development ofjoint attentionandtheoryof mind. current robot technology, and becausethey are significant improvements to the behavioral repertoire of our humanoid robot (Scassellati1998). The simplest behavioral manifestation of Baron-Cohen's eye direction detector (EDD) is the recognition and maintenance of eyecontact. Many animals have been shown to be extremely sensitiveto eyesthat are directed at them, including reptiles like the hognosed snake (Burghardt and Greene 1990), avians like the chicken (Scaife 1976) and the plover (Ristau 1991), and all primates (Cheney
and Seyfarth1990). Identifying whetheror not somethingis looking at you provides an obvious evolutionary advantage in escaping predators. In many mammals, especiallyprimates, the recognition that another is looking at you also carriessocial significance. In monkeys, eye contact is significant for maintaining a social dominance hierarchy (Cheney and Seyfarth 1990). In humans, the reliance on eye contact as a social cue is even more striking . Infants have a strong preferencefor looking at human facesand eyes, and maintain (and thus recognize) eyecontact within the first three months. Maintenance of eye contact will be the first testablebehavioral goal for the eyedirection detector. The simplest shared attention behavior is gaze following , the rapid alternation between looking at the eyesof the individual and looking at the distal object of their attention. As part of the shared attention module (SAM), gazefollowing utilizes information about eye direction and mutual gaze from the eye direction detector (EDD) and extrapolates to external objects of focus. While many animals are sensitiveto eyesthat are gazing directly at them, only primates show the capability to extrapolate from the direction of gazeto a distal object, and only the great apeswill extrapolate to an object that is outside their immediate field of view (Povinelli and Preuss1995). This evolutionary progressionis also mirrored in the ontogeny of social skills . At least by the age of three months, human infants display maintenance (and thus recognition) of eye contact. However, it is not until nine months that children begin to exhibit gaze
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 151 is believedto be critical for socialscaffolding(Thelen and Smith 1994), development of theory of mind (Baron-Cohen 1995), and providing sharedmeaning
for learninglanguage(Wood, Bruner, and Ross1976). While gazefollowing and eye contact constitute one mechanism for joint attention
, I believe that it will
also be instructive
to examine
a second mechanism
for establishing joint attention . I selectedpointing as the second behavior. The development of pointing to direct attention is basedupon much more complex sensory-motor control than eye gaze; pointing forces us to utilize the robot's arms and to recognizegesturecues. However, a pointing gesturecan be used for purposes
other
than
to direct
attention
. The
same arm motion
can also be uti -
lized to reach for an object. Developmental psychologistsdistinguish between imperative pointing , which is a gesture to obtain an object that is out of reach, and declarative pointing , which is a joint attention mechanism. Imperative pointing is first seenin human children at about nine months of age (Baron -Cohen 1995 ) , and occurs in many
monkeys (Cheney and Seyfarth 1990). However, there is nothing particular to the infant 's behavior that is different from a simple reach- the infant is initially as likely to perform imperative pointing when the caregiveris attending to the infant as when the caregiveris looking in the other direction or when the caregiver is not present. The caregiver's interpretation of the infant 's gestureprovides the shared meaning. Over time, the infant learns when the gesture is appropriate. One can imagine the child learning this behavior through simple reinforcement. The reaching motion of the infant is interpreted by the adult as a request for a specific object, which the adult then acquires and provides to the child . The acquisition of the desired object servesas positive reinforcement for the contextual setting that precededthe reward (the reaching action in the presence of the attentive caregiver) . Generation of this behavior is then a simple extension of a primitive reaching behavior. Declarative pointing differs from imperative pointing in both form and function . Declarative pointing is characterizedby an extended arm and index finger designed to draw attention to a distal object. Unlike imperative pointing , it is not necessarilya request for an object; children often use declarativepointing to draw attention to objects that are clearly outside their reach, such as the sun or an airplane passing overhead. Declarative pointing also only occurs under specific social conditions; children do not point unlessthere is someoneto observe their action. From the perspective of Baron-Cohen's model, we can formulate declarative pointing as the application of SAMand ID to the motor abilities of imperative pointing combined with imitative learning. When the intentionality detector identifies motion that matches a pointing gesture, the sharedattention module extrapolates to identify the distal target. The recognition of pointing
gesturesbuilds upon the competencies of gazefollowing; the infrastructurefor extrapolation from a body cue is already present from gaze following , it need only be applied to a new domain. The generation of declarative pointing ges-
152 SCASSELLATI tures builds upon the motor capabilities of imperative pointing ; by imitating the successfulpointing gesturesof other individuals, the child can learn to make use of similar gestures. The involvement of imitation as a learning mechanism is consistent with ontological and a phylogenetic evidence. From an ontological perspective, declarative pointing begins to emerge at approximately twelve months in human infants, which is also the sametime that other complex imitative behaviorssuch as pretend play begin to emerge. From the phylogenetic perspective, declarative pointing has not been identified in any nonhuman primate (Premack 1988). This also correspondsto the phylogeny of imitation ; no nonhuman primate has ever been documented to display complex imitative behavior under generalconditions (Hauser 1996). Evaluating the Robotic Implementation A robotic implementation of a behavioral model provides a standardizedevaluation mechanism. Behavioral observation and classification techniques that are used on children and adults can be applied to the behavior of our robot with only minimal modifications . Becauseof their use in the diagnosis and assess ment of autism and related disorders, evaluation tools for joint attention mechanisms, such as the Vineland Adaptive Behavior Scales, the Autism Diagnostic Interview, and the Autism Diagnostic Observation Schedule, have been extensively studied (Sparrow et ale 1997, Powers 1997). With the evaluations obtained from these tools, the successof our implementation efforts can be tested using the samecriteria that are applied to human behaviors. The behavior of the complete robotic implementation can be compared with developmental data from normal children. Furthermore, operating with only the EDDand ID modules should produce behavior that can be compared with developmental data from autistic children. With these evaluation techniques, we can determine the extent to which our model matchesthe observedbiological data. However, what conclusions can we draw from the outcomes of thesestudies? One possible outcome is that the robotic implementation will match the expected behavior evaluations, that is, the complete systemwill demonstrate normal usesof joint attention . In this case, our efforts have provided evidencethat the model is internally consistent in producing the desired behaviors, but says nothing about the underlying biological processes . We can verify that the model provides a possible explanation for the normal (and abnormal) development of joint attention , but we cannot verify that this model accurately reflects what happensin biology. If the robotic implementation does not meet the samebehavioral criteria, the reasonsfor the failure are significant. The implementation may be unsuccessful becauseof an internal logical flaw in the model. In this case, we can identify shortcomings of the proposed model and potentially suggestalternate solutions.
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 153 A more difficult failure may result if our environmental conditions differ too significantly from normal human social interactions. While the work of Reeves and Nass (1996) leadsus to believe that this result will not occur, this possibility allows us to draw conclusions only about our implementation and not the model or the underlying biological factors.
Building SocialSkills A robotic approach to studies of joint attention and social skill development has three main advantages . First, human observersreadily anthropomorphize their social interactions with a humanlike robot. Second, the construction of a physically embodied systemmay be computationally simpler than the construction of a simulation of sufficient detail. Third , the skills that must be implemented to test thesemodels are useful for a variety of other practical robotics tasks. Interactions with a robotic agent are easily anthropomorphized by children and adults. An embodied systemwith human form allows for natural social interactions to occur without any additional training or prompting . Observers need not be trained in special procedures necessaryto interact with the robot; the samebehaviorsthat they use for interacting with other people allow them to interact naturally with the robot. In our experience, and in the empirical studies by Reevesand Nass (1996), people readily treat a robot as if it were another person. Human form also provides important task constraints on the behavior of the robot. For example, to observean object carefully, our robot must orient its head and eyestoward a target. These task constraints allow observersto easily interpret the behavior of the robot. A second reason for choosing a robotic implementation is that physical embodiment may actually simplify the computation necessaryfor this task. The direct physical coupling between action and perception reducesthe need for an intermediary representation. For an embodied system, internal representations can be ultimately grounded in sensory-motor interactions with the world (Lakoff 1987); there is no need to model aspectsof the environment that can simply be experienced(Brooks 1986, Brooks 1991). The effects of gravity, friction , and natural human interaction are obtained for free, without any computation . Embodied systems can also perform some complex tasks in relatively simple ways by exploiting the properties of the complete system. For example, when putting a jug of milk in the refrigerator, you can exploit the pendulum action of your arm to move the milk (Greene 1982). The swing of the jug does not need to be explicitly planned or controlled, since it is the natural behavior of the system. Instead of having to plan the whole motion , the system only has to modulate, guide, and correct the natural dynamics. The social skills that must be implemented to test these models are also im -
154 SCASSELLATI
Figure3. Cog,an upper-torsohumanoidrobot,
Coghastwenty-one degrees of freedom anda varietyofsensory systems including visual,auditory,tactile,kinesthetic,and vestibularsystems.
portantfroman engineering perspective. A roboticsystemthat can recognize and engage in joint attention behaviorswill allow for human-machine interac-
tionsthat havepreviously not beenpossible.The robotwouldbe capableof learningfroman observerusingnormalsocialsignalsin the samewaythat humaninfantslearn;no specialized trainingof the observerwouldbe necessary. The robotwouldalsobe capableof expressing its internalstate (emotions,desires,goals,etc.) throughsocialinteractionswithoutrelyingupon an artificial vocabulary. Further,a robot that can recognizethe goalsand desiresof others willallowfor systemsthat can moreaccuratelyreactto the emotional,attention-
al, and cognitivestatesof the observer,can learnto anticipatethe reactionsof theobserver, andcanmodifyitsownbehavioraccordingly.
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 155
Robotic Hardware Our humanoid robot, called Cog, was designedto investigatea variety of scien-
tific and engineeringissues ; constraintsimposedby socialinteraction studies were balancedwith constraints from other parallel investigations, as well as constraints from cost and availability of components (Brooks and Stein 1994,
Brookset ale1998). To allowfor naturalsocialinteractions , and to providesimilar task constraints
, our
robot
was built
with
humanlike
sensory
systems
and
motor abilities (seefigure 3). To approximate human motion , Cog has a total of twenty-one mechanical degreesof freedom. Cog's torso has six degreesof freedom: the waist bends sideto-side and front -to-back, the "spine" can twist, and the neck tilts side-to-side, nods front -to-back, and twists left-to-right . To approximate human eye motion , each eye can rotate about an independent vertical axis (pan) and a coupled hori -
zontalaxis(tilt ). Eacharm hassix compliantdegrees of freedom, eachof which is powered by a serieselastic actuator (Pratt and Williamson 1995), which provides a sensible
" natural " behavior : if it is disturbed
, or hits an obstacle , the arm
simply deflectsout of the way. To obtain information about the environment, Cog has a variety of sensory systemsincluding visual, vestibular, auditory, tactile, and kinesthetic senses . The visual system mimics some of the capabilities of the human visual system, including binocularity and space-variant sensing (Scassellati1998a). To allow for
both a wide field of view and high resolutionvision, therearetwo camerasper eye, one that captures a wide-angle view of the periphery [88.60 X 115.80 field of view] and one that captures a narrow-angle view of the central (foveal) area [ 18.40 X 24 .40 field of view ] with the same resolution . Vestibular function is ap-
proximated with three rate gyroscopesand two inclinometers. The robot has two microphones for ears, and simple pinnae. We have also begun implementing a tactile systemusing arraysof resistiveforce sensorsfor the torso and hands. Kinesthetic information , including joint position from shaft encoders and potentiometers
, temperature
measurements
from
the motors
and motor
driver
chips, and torque measurementsfrom strain gaugeson the arms, is also available on our robot .
Cog has a distributed , heterogeneouscomputational network. Similar to the decomposition in humans, specializedsubsystemsoperate on specific aspectsof the robot's behavior. Each joint has a dedicated on-board motor controller that performs low-level functions and simple reflexes, similar to the spinal cord. A network
of industrial
Pentium
processors , a network
of custom - built
Motorola
68332processor boards,and a digital signalprocessornetworkfor auditoryand visual processingcombine to provide higher-level functionality .
156SCASSELLATI Implementing
joint Attention
Eventhe simplestof joint attentionbehaviorsrequirethe coordinationof a large number of perceptual, sensory-motor, attentional, and cognitive processes , in-
cluding basiceyemotor skills, faceand eyedetection, determinationof eyedirection, gesture recognition, attentional systemsthat allow for social behavior selection at appropriate moments, emotive responses , arm motor control , image stabilization, and many others. We have begun to construct many of thesecomponent pieces, and many results from this work have been published previously (Brooks et al. 1998, Scassellati1998d, Scassellati1998, Marjanovic, Scassellati , and Williamson
1996 , Breazeal and Scassellati 1998 , BreazeaI1998 ) . In this sec-
tion , we will review some of the capabilities of our robot that have direct bear-
ing on implementingjoint attention. Implementing Maintenance of EyeContact Implementing the first stagein our developmental framework, recognizing and responding to eye contact, requires mostly perceptual abilities. We require at
leastthat the robot be capableof finding faces , determiningthe locationof the eyewithin the face, anddeterminingif the eyeis looking at the robot. The only necessarymotor abilities are to maintain a fixation point . Many computational methods of face detection on static imageshave been investigatedby the machinevision community, for exampleSung and Poggio (1994) and Rowley, Baluja, and Kanade (1995). However, these methods are computationally intensive, and current implementations do not operatein real time. How-
ever,a simplerstrategyfor finding facescanoperatein realtime andproducegood results under dynamic conditions (Scassellati1998). The strategy that we use is basedon the ratio-template method of object detection reported by Sinha (1994).
In summary , findinga faceis accomplished with thefollowingfivesteps : 1. Use a motion -basedprefilter to identify potential face locations in the peripheral Image .
2. Use a ratio-template basedface detector to identify target faces. 3. Saccadeto the target using a learned sensory-motor mapping. 4. Convert the location in the peripheral image to a foveal location using a learned mappIng
.
5. Extract the image of the eye from the foveal image. A short
summary
of these steps appears
below , and additional
details
can be
found in Scassellati ( 1998 ) .
To identify face locations, the peripheral image is converted to grayscaleand
passed througha prefilterstage(seefigure4). The prefilterallowsusto searchonly locations that are likely to contain a face, greatly improving the speedof the detection step. The prefilter selectsa location as a potential target if it has had
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 1S7
Figure4. Blockdiagramfor theprefilteringstageoffacedetection . The prefilter selects target locations based upon motion information and past history. The prefilter allows face detection to occur at 20 Hz with little accuracy loss.
motion in the last 4 frames, was a detected face in the last 5 frames, or has not been evaluatedin 3 seconds. A combination of the prefilter and some early-rejection optimizations allows us to detect facesat 20 Hz with little accuracyloss. Facedetection is done with a template-basedmethod called "ratio templates" designed to recognize frontal views of faces under varying lighting conditions (Sinha 1996). A ratio template is composedof a number of regions and a number of relations, as shown in figure 5. Overlaying the template with a grayscale image location, each region is convolved 'with the grayscaleimage to give the averagegrayscalevalue for that region. Relations are comparisons between region values, such as "the left forehead is brighter than the left temple." In figure 5, each arrow indicates a relation, with the head of the arrow denoting the lesser value. The match metric is the number of satisfied relations; the more matches, the higher the probability of a face. Once a face has been detected, two sensory-motor mappings must be used to extract the eye image (seefigure 6). First, the face location is converted into a motor command to center the face in the peripheral image. To maintain portability and to ensureaccuracyin the sensory-motor behaviors, we require that all of our sensory-motor behaviors be learned by on-line adaptive algorithms (Brooks et ale 1998). The mapping between image locations and the motor commands necessaryto foveate that target is called a saccademap. This map is implemented as a 17 X 17 interpolated lookup table, which is trained by the following algorithm: 1. Initializewith a linearmapobtainedfrom self-calibration. 2. Randomlyselecta visualtarget. 3. Saccade usingthe currentmap. 4. Find the targetin the post-saccade imageusingcorrelation. S. Updatethe saccade mapbasedon L2error. 6. Go to step2.
158 SCASSELLATI
Figure5. A ratiotemplate for facedetection . Thetemplate iscomposed of 16regions (thegrayboxes ) and23relations (shownbyarrows ). The systemconvergesto an averageof lessthan one pixel of error per saccadeafter 2000 trials (1.5 hours). More information on this technique can be found in Marjanovic, Scassellati , and Williamson (1996). Becausehumans are rarely motionless, after the active vision system has saccaded to the face, we first verify the location of the face in the peripheral image. The face and eye locations from the template in the peripheral cameraare then mapped into foveal camera coordinates using a second learned mapping. The mapping from foveal to peripheral pixel locations can be seenas an attempt to find both the difference in scalesbetween the imagesand the difference in pixel offset. In other words, we need to estimatefour parameters: the row and column scalefactor that we must apply to the foveal image to match the scaleof the peripheral image, and the row and column offset that must be applied to the foveal image within the peripheral image. This mapping can be learned in two steps. First, the scalefactors are estimated using active vision techniques: while
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 159
Figure
Once
a
face
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is
and
image
Additional
in
head
in
capable
achieve
gaze
a
a
image
subimage
.
within
is
whenever
grayscale
thirty
into
of
to
left
subjects
pupil
gaze
,
recognize
system
to
distal
location
.
take
but
The
hundred
Gaze
of
.
that
from
measure
foveal
typical
seen
accurately
Implementing
angle
faces
to
eye
ratio
extracted
seven
the
,
the
environment
one
must
of
learned
face
world
position
subskills
the
foveal
from
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on
to
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location
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shows
1998b
,
and
saccade
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.
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-
eye
Scassellati
tecting
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an
In
.
on
faces
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tained
,
is
correlated
the
real
robot
offsets
the
7a
to
dynamic
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e
processing
figure
variety
a
~
routines
saccading
a
of
speed
rates
has
of
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of
ter
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position
constant
mapping
detection
image
finding
system
the
flow
find
image
for
face
for
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.
this
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map
a
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and
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,
.
of
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and
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to
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been
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The
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(
,
foveal
the
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6 :
has
distal
the
gazing
of
detecting
eye
following
:
object
,
caregiver
.
at
you
and
motor
and
,
the
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,
contact
extracting
require
of
routines
gaze
for
angle
requires
we
angle
of
the
gaze
skills
three
,
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extrapolating
the
alternating
is
a
noted
between
the
generalization
of
in
the
preceding
de
-
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 161
Figure 8. Proposeddevelopmentalprogressionof gazefollowing adaptedfrom Butterworth (1991) .
162SCASSELLATI ing to the right (see figure 8) . Over the next three months , their accuracy in creases so that they can roughly determine the angle of gaze. At nine months , the child will track from the caregiver 's eyes along the angle of gaze until a salient object is encountered . Even if the actual object of attention is further along the angle of gaze, the child is somehow "stuck " on the first object encoun tered along that path . Butterworth labels this the "ecological " mechanism of joint visual attention , since it is the nature of the environment itself that com pletes the action . It is not until twelve months that the child will reliably attend to the distal object regardless of its order in the scan path . This "geometric " stage indicates that the infant successfully can determine not only the angle of gaze but also the vergence . However , even at this stage, infants will only exhibit gaze following if the distal object is within their field of view . They will not turn to look behind them , even if the angle of gaze from the caregiver would warrant such an action . Around eighteen months , the infant begins to enter a "representational " stage in which it will follow gaze angles outside its own field of view , that is, it somehow represents the angle of gaze and the presence of objects out side its own view . At six months, infantsshowsensitivityonly to the sidethat the caregiveris gazing. At nine months, infantsshowa particularstrategyof scanningalongthe line of gazefor salientobjects. By one year, the child can recognizethe vergenceof the caregiver 's eyesto localizethe distal target, but will not orient if that object is outside the field of view until eighteen monthsof age. Implementing
this progression for a robotic system provides a simple means
of bootstrapping behaviors . The capabilities used in detecting and maintaining eye contact can be extended to provide a rough angle of gaze. By tracking along this angle of gaze, and watching for objects that have salient color , intensity , or motion , our robot can mimic the ecological strategy. From an ecological mecha nism , we can refine the algorithms for determining gaze and add mechanisms for determining vergence . A rough geometric strategy can then be implemented , and later refined through feedback from the caregiver. A representational strategy requires the ability to maintain
information
on salient objects that are out -
side of the field of view including
information
on their appearance , location ,
size, and salient properties . The implementation of this strategy requires us to make assumptions about the important properties of objects that must be in cluded in a representational structure , a topic beyond the scope of this chapter .
Implementing
Imperative Pointing
Implementing imperative pointing is accomplished by implementing the more generic task of reaching to a visual target. Children passthrough a developmental progression of reaching skills (Diamond 1990). The first stage in this progression appearsaround the fifth month and is characterizedby a very stereotyped reach that always initiates from a position close to the child 's eyes and
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 163
~
\
~
~
\ " ---~ ~ \
)
Image
.,~,~, Correlation '""""""
~ii' Motion D etectlon .
"":",, '; ".-"""N
Figure 9. Reachingto a visual target is theproduct of two subskills:foveating a target and generatinga ballistic reachfrom that eyeposition. Imagecorrelationcanb~ usedto train a saccade mapthat transformsretinalcoordinates into gaze coordinates (eyepositions).This saccade mapcanthen be usedin conjunctionwith motion detection to train a ballisticmapthat transformsgazecoordinates into a ballisticreach.
moves ballistically along an angle of gaze directly toward the target object. Should the infant miss with the first attempt, the arm is withdrawn to the starting position and the attempt is repeated. To achievethis stageof reaching on our robotic system, we have utilized the foveation behavior to train the arm to reach (Marjanovic , Scassellati , Williamson 1996). To reach to a visual target, the robot must learn the mapping from retinal image coordinates x = (x, y) to the head-centered gazecoordinates of the eyemotors e = (pan, tilt ) and then to the coordinatesof the arm motors a = (a 0 ... a 5) (seefigure 9). The saccademap S: x ~ e relatespositions in the cameraimage with the motor commands necessaryto foveate the eye at that location. Our task then becomesto learn the ballistic movement mapping headcentered coordinates e to arm-centered coordinates a . To simplify the dimensionality problems involved in controlling a six degree-of-freedom arm, arm positions are specifiedas a linear combination of basisposture primitives. The ballistic mapping B: e ~ a is constructed by an on-line learning algorithm that comparesmotor command signalswith visual motion feedbackclues to localize the arm in visual space. Once the saccademap has been trained, we can utilize that mapping to generateerror signalsfor attempted reaches(seefigure 10). By tracking the moving arm, we can obtain its final position in image coordinates. The vector from the tip of the arm in the image to the center of the image is the visual error signal, which can be converted into an error in gazecoordinates using the saccademapping. The gazecoordinates can then be used to train a forward and inverse model of the ballistic map using a distal supervised
164 SCASSELLATI
Oncea visualtargetis foveated , the gazecoordinatesaretransformedinto a ballisticreachby the ballisticmap. By observingthe positionof the movinghand, we canobtaina reachingerror signalin imagecoordinates , which canbe convertedbackinto gazecoordinatesusingthe saccade map.
learning technique ceeds as follows : 1 . Locate
( Jordan
a visual
Rumelhart
using
the eye position
the learned
to a ballistic
4 . As the arm moves , use motion
, which
6 . Withdraw This
learning
tured complex
algorithm
operates
" environment
kinematics
. This three
The
task
tion
of
new
initial
Declarative
of recognizing the
to locate
the error
can be used to train
approximately
Implementing
learning
trial
pro -
signal
the ballistic
from
the ballistic
map .
the end of the arm . image
coordinates
into
gaze
map .
the arm , and repeat .
" real - world
within
single
saccade map . reach using
detection
5 . Use the saccade map to convert positions
1992 ) . A
target .
2 . Saccade to that target 3 . Convert
and
geometric stimulus
technique hours
. Instead
, in using
real
time
explicit
, and world
successfully
trains
of self - supervised
training
in
an
unstruc
-
coordinates
a reaching
or
behavior
.
Pointing
a declarative and
continually without
pointing
representational of extrapolating
gesture mechanisms from
the
can
be seen as the for
vector
gaze
applica
-
to
a
following
formed
by the
an -
166 SCASSELLATI wise, the position of the attentional marker over time representsthe motion of the face stimulus. The motion of the attentional marker for a fixed-duration window is classifiedinto one of three static classes : a yesclass, a no class, and a no-motion class. Two metrics are used to classifythe motion , the cumulative sum of the displacements between frames (the relative displacement over the time window ) and the cumulative sum of the absolute values of the displacements (the total distance traveled by the marker). If the horizontal total trip distance exceedsa threshold (indicating some motion ), and if the horizontal cumulative displacement is below a threshold (indicating that the motion was back and forth around a mean), and if the horizontal total distanceexceedsthe vertical total distance, then we classifythe motion as part of the no class. Otherwise, if the vertical cumulative total trip distance exceedsa threshold (indicating some motion ), and if the vertical cumulative displacement is below a threshold (indicating that the motion was up and down around a mean), then we classifythe motion as part of the yesclass. All other motion types default to the no-motion class. These simple classesthen drive fixed-action patterns for moving the head and eyesin a yes or no nodding motion . While this is a very simple form of imita tion , it is highly selective. Merely producing horizontal or vertical movement is not sufficient for the head to mimic the action- the movement must come from a facelike object.
Future Work The implementation of Baron-Cohen's model is still work in progress. All of the basic sensory-motor skills have been demonstrated. The robot can move its eyes in many humanlike ways, including saccades , vergence, tracking, and maintaining fixation through vestibulo-ocular and opto-kinetic reflexes. Orientation with the neck to maximize eye range has been implemented, as well as coordinated arm pointing . Perceptualcomponents of EDDand SAMhave also been constructed; the robot can detect and foveate faces to obtain high-resolution images of eyes. These initial results are incomplete, but have provided encouraging evidence that the technical problems faced by an implementation of this nature are with in our grasp. Cog's perceptual systemshave been successfulat finding facesand eyesin real-time, and in real-world environments. Simple social behaviors, such as eye-neck orientation and head-nod imitation , have been easyto interpret by human observerswho have found their interactions with the robot to be both believableand entertaining. Our future work will focus on the construction and implementation of the remainder of the EDD, 10, and SAMmodules from Baron-Cohen's model. From an engineering perspective, this approach has already succeededin providing
INVESTIGATING MODELS OFSOCIAL DEVELOPMENT 167 adaptive solutions to classicalproblems in behavior integration , space-variant perception, and the integration of multiple sensoryand motor modalities. From a scientific perspective, we are optimistic that when completed, this implementation will provide new insights and evaluation methods for models of social development. Note 1. The originalvideoclipsareavailableat www.ai.mit.edu/projects / cog/.
Epilog BarbaraWebb andThomas R. Consi
chapters collected in this book cover a highly diverse range of researchendeavors- from insect neurobiology to human cognitive development and from mechanical design considerations to high-level learning algorithms . Nevertheless the participants in the AAAI symposium from which this book is derived felt a strong senseof common ground. This lay, not in the subject matter per se, but in the approach and methodology adopted to addresstheseapparently diverse topics. "Biorobotics" was seenas the true intersection of biology and robotics: where one systemaddressedand answeredquestions in both fields. At the meeting we held extensiveand productive discussions of key issuesfor this methodology. For example, how closely should the biology be representedin the robot model? What advantagesaccrue in building physical rather than simulation models and what technological problems get in the way? What evaluation methods are most appropriate? What systemslook promising for application of the approach in the near future? What are the longer-term possibilities for scaling up? In this epilog we will attempt to summarize and synthesize some of the outcomes of these discussions. It should be stressedthat all the authors in this book (and the other AAAI symposium participants) have contributed points to the discussionwe present.
Copying Biology While a great deal has been said over the last twenty yearsor so about the possible technological benefits of copying biology, what is common to the research reported here is that a seriousattempt has been made to do so. Rather than simply being "inspired" by somevaguely understood notions of how animal systems work , thesestudies identify specific, well studied animal systemsand attempt to build truly representativemodels of them. Much of this researchinvolves direct collaboration between biologists and robotic engineers. A thorough knowledge
170 WEBBANDCONS ! of the relevant biological data is taken to be necessary , so that the risk of misinterpretation is minimized . Having said this , it is important to add that biorobotics does not simply consist in taking an example from biology and directly translating it in hardware and software. While the animal servesas a "default" point of referencein design decisions, it is rare for any biological system to be sufficiently well studied that all the answersneededto build the systemare directly available. Indeed, for a number of the systemsdiscussedhere there are substantial gaps in the data. For example, almost nothing may be known about the neural underpinnings of a behavior, or measurementsof sensory or motor capabilities may not available, or a number of hypothetical control mechanisms may be consistentwith the data. A problem alwaysto be solved is how to go from the results ofneuroethologi cal researchto specifications for robotic implementation . First it needs to be clear what are the current hypothesesin the field: both explicit and implicit . Are there already mathematical or computer models of the system, and if so what is the robot model intended to add? There are then a variety of different ways in which the implementation can be constrained: by behavioral evidence, by physiological evidence or neural plausibility of the mechanisms, by the information processingproblem posed, by the environment, and by the physical limitations of sensoryand motor capacities. Obviously it is useful if extensiveinformation is available, but a complete story from the biology is not necessarybefore robot building can make a contribution . Indeed, building a robot can be extremely productive in determining critical areasneeding further biological research. The other major consideration is how to determine which aspects of the known biology should be included in the robot model. What details are crucial and what are irrelevant? One conclusion of the AAAI symposium was that no a priori judgment (e.g. that a low-level model of neural processesis always better; or that exact replication of a sensing device is needed before any conclusions about processingcan be made) could be universally applied. It needsto be determined for each different example with referenceto current understanding and current questions that the systemcan hope to answer. This may be easierto do when the system is intended as a means to test a very specific hypothesis, but more difficult when the systemis a more open-ended project intended to achieve a biological level of competencein some task or tasks. This determination also applies to the correctnessof the choice to build a physical model in the first place, which is linked to assumptionsabout the importance of the physical plant and the environment in understanding behavior, and the need to close the loop between sensingand action. Making a "realistic" model is not necessarilyin the senseof highly accuratedetail, but rather in the senseof addressingthe essential aspectsof the problem. Two kinds of (unavoidable) abstraction can be identified: in isolating the problem to be tackled, e.g. taking the animal behavior out of the context of survival and propagation of the species; and in making the mapping betweenthe animal and the availabletechnology to implement the model.
EPILOG 171 Certain biological systemsmight be particularly fruitful to explore with current technology. Criteria for identifying such systemsinclude the following . The animal must be a good "model" systemfor a classof behavior. The phenomenon must have been well investigated in biology and be "ripe" for more rigorous modeling. There must be relatively clear evaluation criteria (measurementand experimental protocols) for the successof the model. Finally, the behavior must have some inherent fascination for the researcherto be willing to take the time to obtain nontrivial training in another field (biology or engineering). Our meeting came up with a number of suggestionsfor future work , i.e. biological systemsthat seem ready for a biorobotics approach (some of these have in fact already begun to be studied). Specific biological systemsand phenomena include: escapebehaviors, nematodes, electrolocation in fish, bird song, jumping spider trajectory planning , magnetic sensing in birds and other animals, and construction behaviors such as bird nests and spider webs. More abstract phenomena for which biological examplescan be found include: object invariance, extracting energy from the environment (e.g. an oil -spill eating robot), complex multimodal systems (e.g. body stabilization via optical and vestibular inputs), long distance navigation, the use of biological signal processing and control strategiesto compensatefor noisy sensorsand sloppy actuators, error correction, self-repair, and reproduction.
PhysicalImplementations The previous section encouragedrobotics researchersto embracedetails of biology; this section encouragesbiological researchersto embrace the nitty -gritty of building real robotic systemsrather than limiting themselvesto computational, mathematical or box-and-arrow models. Not that "biorobotics" is alwaysor only the correct approach for model building - other methods are sometimes more appropriate. Rather, biorobotics is particularly useful in situations when it is suspected that the complexity of the stimulus is important and simulations cannot adequatelyrepresentthat complexity. It is also useful where it is desired to criti cally addressperceptual problems in context of actions, or motor problems in context of the plant. Counter to what seemsexpectedby some biologists, robots can be very useful evenwhere we have a rather limited notion of how the animal performs the behavior of interest. In these situations biorobotics is a rapid, yet stringent, way of evaluating many alternative hypotheses. It is especially useful for eliminating ideasthat are physically unreasonable, e.g. the animal cannot use a particular algorithm without certain data that is, in fact, unobtainable given its sensinglimitations or the environmental constraints. Why go to the trouble of building real robotic systemsto test biological or cognitive hypotheses? What emergesagain and again in the literature is that the
172WEBB AND CONSI physical implementations raised critical, central issuesthat had not been previously anticipated or addressed . To make a robotic system work you really have to solve the full problem, which may be more difficult than realized (e.g. underwater odor plume tracking, chapter 2). On the other hand, it may be discovered that certain problems can be solved more simply than expectedby novel, often noncomputational , solutions that exploit the physical constraints (e.g. the cricket ear, chapter 1). However , one should bear in mind that physical implementations are strongly constrained by current technology and that these constraints, though sometimes similar to those faced by animals, are by no means guaranteed to be so. Limitations in technology are a major issue in biorobotics . Compared to their biological counterparts, sensors are typically more bulky and more limited in dynamic range. Linear actuators of the size and form factor of real muscles are simply not available. Robots have almost no ability to extract energy from their surroundings, as animals do, and thus have operational times that are constrained by the amount of energy they can carry. Finally there is the ever-present reliability problem , which grows rapidly with the complexity (i .e. number of components, size of the software) of the robot . Limits in mi croprocessorsare mostly in their interfaces to sensorsand actuators and not in their computational abilities. Our wish-list of new technology for biorobotics includes : muscle-like actuators (motor , housing , transmission , power and sensing in one cheap package), tough , compliant , and conformable materials for skin and joint coverings, sensor arrays that can be easily embedded in a biorobot 's skin, improved chemical sensors, better power sources (from biolo gy-derived ideas for low -power consumption and energy conserving strategies), and parallel processor architectures tailored for easy implementation of neural-like computational strategies.
Experimental Approach The preceding considerations- both engineering and biological justifications for the biorobotics approach- feed into the third major issue: evaluation. In strict robotics terms this is an important consideration not alwayssufficiently attended to, that is, the necessityto evaluatehow well the robot can perform a task. This becomesincreasingly important when claims are made regarding the advantages of adopting biologically basedapproaches. For example, biological sensingstrategies are often claimed to be more robust but how can this be demonstrated?This is equally important with regard to the biological understanding that can be derived from a robot experiment. Given that technology constrainsa model's accuracy, it is important to consider how this affects the experimental data and the conclusions drawn from that data. It can be hard to pinpoint the causeof a fail-
EPILOG 173 ure to replicate a desired behavior: is it a weaknessin the theory itself, or in the physical match betweenthe animal and the robot, or simply a failure of the robot builders to be clever enough in their implementation? On the other hand, success might be due to nonbiological featuresof the model. The high biological FIdelity of biorobots confers the advantagethat the experimental methods developed by biologists can be directly applied to biorobotic experiments . Indeed , a good practical yardstick of biological adequacy is whether or not the biorobot can be put in the same experimental apparatus as used for the animal. The use of the sameexperimental methods permits a rigorous test of biological relevance in that it enables verifiable predictions to be made on the behavior of the animal under study. Another advantage of the biorobotic approach is the ability to perform experiments that explore the performance of the model in situations beyond its original design space. This feature is most important to evaluating the utility of the biological idea in nonbiological applications. To summarize- the biorobotic method should be carried out in the same stagesas other forms of modeling. First is the identification of the target phenomenon and the specific question that is to be resolved. Next is the development of a mapping between the biological and robotic systemsusing appropriate representations that include physical as well as symbolic analogs. Having built the model, it is tested in the appropriate ways, often through the useof the sameexperimental apparatusas that used for parallel biological studies. The results of the experiments are then analyzed and biological predictions are made for further biological and robotic explorations. The key to this methodology is the continuous back and forth between the biological and robotic systemsduring both the developmental and the experimental stagesof the investigation. Note that the robot is not an endpoint in the study: a well-evaluatedhypothesis is the goal. It is a mistake to consider the robot as finished product in the sense of, for example, an automobile. Rather, a good biorobot is a dynamic breadboard for testing biological ideas, devicesand software are added and subtracted as dictated by the needsof the experiments.
The Challenge
of Multidisciplinary
Research
Severalpractical issueswere raised by the participants with regard to the problems inherent in working in a novel interdisciplinary field. First is the communication difficulties often experiencedbetween collaborating biologists and engineers. Both fields have their own particular language, point of view, and priorities, and a willingness to understand and appreciatethesedivergencesis important . Second, effort should be devoted to clearly establishingthe nature of the collaboration (researchpriorities, publication authorship, etc.) to ensure that no
174 WEBBANDCONSI member of the team feelsleft out or exploited. Third is the challengeof training students. To work with this methodology, biology studentswill need to get some engineering training; and engineering students will need a reasonablegrounding in biology; neither typically occurs in current standard degreeprograms. Encouragingly, there do seemto be more "cross-over" programs being introduced. Funding of biorobotics is currently easier to achieve from engineering resources, which can sometimesundermine the biological relevancewith the focus being placed on immediate technological results. A more long-term view- that really understanding how biological systemswork , rather than just superficially taking inspiration from them, will have multiple payoffs- needsto be brought to the attention of funding bodies. Finally, we consideredavenuesof publication for biorobotics research. There is especially a need to go beyond the specialist forums, such as the AAAI symposium, and present biorobotics researchto the larger biological and engineering communities. Thus we considered it inappropriate to attempt to establish a journal or conference series, which would only isolate this areafrom the main streamsfrom which it draws its strength and justification . On the other hand it would be useful to have a forum for communi cation by which the relevant researchthat currently appearsin highly diverse places can be brought to attention of workers in the field . Internet-based resourcesseemthe most appropriate mechanism for this kind of interdisciplinary networking . A website and mailing list resulting from the AAAI symposium can be found at: http ://www.ai.mit .edu/ .--scaz/biorobotics/
Scaling the Achievements So far biorobotics has been most successfulin the study of single sensorimotor systems or " isolated " motor control , particularly in invertebrates . Can biorobotics scale up to explore more complex systems? The chapters in this book suggestthat this is possible. First, there are severalobvious ways in which existing systemsmight be combined (e.g. taxis and walking) and such collaborative efforts were one outcome of the meeting. Second, implicit even in some of the invertebrate systemsdiscussedwas the issue of limits of simple "reactivity" and need for more complex notions of behavioral control . Third , is the explicit idea that sensoryand motor complexity in robots are neededasdriving forces to make us face the real problems solved by animal systems(chapter 6). Finally, the last two chapters in this book have provided examplesof the successfulapplication of biorobotics to researchwhere the aim is to investigate mammalian and human systems. Our general feeling was one of confidence that, in synergywith developments in biology and robot technology, the methodology of biorobotics will have a significant role in future researchin exploring and understanding a wide range of animal behaviors.
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Index Acheta domestica , 5
ALV
aerial minirobot , with visual scanning
American
sensor , 67 - 83
elementary motion detector
robot features, target tracking and , 80 - 81
features , visual inertial
feed -
back system , 79 - 80
scanning sensorfeatures, 71- 76 scanning sensorfeatures, compo-
, of , 24 - 26
analog robot, ALV model and discussionof findings, 60- 61 experimentswith , 55- 59 homing precision of, 61 overview
of , 50 - 52
AN (auditory neurons), in
nents , 72 - 73
crickets / cricket
scanning sensorfeatures, impact of on output
,
robots , 8- 10
angleof gaze , developmentof gazefollowing and, 159- 162 animals
variable angular speed, 68- 71 variable angular speed, formula for , 69
as benchmark
for robot
performance, vii complexity of relationship to envi.
variable angular speed, photoreceptor
model
for , 68 - 69
variable angular speed, processing photoreceptor signals, 69- 71 algorithms biological categorizationand, 124 control strategiesand, 113 face recognition, 157 gazefollowing , 162
learning, 164, 173 RoboLobster experiments, 27- 29, 33 - 35
In -
lobster
environment
and distance
for Artificial
chemo - sensors of , 23 - 24
76 - 79
contrast 73 - 76
Association
( 1998 ) , xi American
robot features, dynamical modeling of twin -propeller robot,
visual fixation
vec -
telligence (AAAI ) Symposium
(EMD ), 71- 72
robot
model . see average landmark
tor (AL V) model
ronment
.
.
, VIII
navigation strategiesof, 37 using biorobotics to experience world
of , 21
animats , 107 - 120
adding complex sensoryinput to (type 1) , 108 , 109
adding internal recurrent connections (type 2) , 108 , 109
adding motor systems(type 3), 110 , 111
arguments supporting study of
198 INDEX complex recurrent systems, 111 - 112
Braitenberg -type, 108 about
control
solutions of com -
plex kinematics, 120 examplecontrolling all geometrically possiblesolutions, 115- 119 examplecontrolling eighteen degrees of freedom , 112 - 115
massivelyparallel architecture with recurrent connections (type 4) ,
useby ethologists , 107 ants, navigation strategies, 37 arthropods, biorobotics and, x artificial intelligence, early research , x
Asperger's syndrome, 146 associativehoming , of insects, 62. see alsohoming auditory neurons (AN ), in crickets / cricket
robots , 8- 10
.
autIsm
tools for , 152
joint attention and, 146- 147 Autism Diagnostic Interview, 152 Autism Diagnostic Observation Schedule , 152
average landmark vector (ALV) mod -
el. seealsoanalog robot insect navigation and, 37- 38 navigation models, 43- 44 Baron - Cohen future
, 115 - 119
animats,eighteendegrees of freedom , 112 - 115 brain
and , 110
local positive feedbackand, 113 - 114
using body models for, 116 biological research applications of biorobots, 21, 171
determiningfocusof roboticmod-
110 , 111
evaluation
control
animats, all geometrically possible
conclusions
on
behavior
model
work
with , 166
joint attention and, 148 BC joints , cockroaches climbing and, 93 modeling Blaberusand, 95- 97 bees
navigation strategiesof, 37, 39 - 40 , 41
eling applications, 170 early researchusing robots, x- xi multidisciplinary challengesof biorobotics
physical demandsof robotic modeling, 171- 172 role of biorobotics
in , 34 ,
169 - 170
biology emulating in robot design, 87 observing cockroaches, 87- 94
observingcockroaches , climbing, 93
observing cockroaches, horizontal walking, 90- 92 observing cockroaches, techniques for , 87 - 90
observing cockroaches, turning , 92 - 93
observing cockroaches, walking speed, 92 using robotics to copy, 169- 170 visual categorization and, 123 - 124 biomimetic
robots , vii
bio-mimetic scaling, of RoboLobster, 27 - 28
biomorphic robots, vii biorobotics. seealsorobotic modeling
advantages vs. computersimula.
visual dead reckoning of, 60
, 173 - 174
tlon
.
, IX
INDEX199 applications
of
emulating
biology
evaluation
issues
Grasso
on
history
of
as
,
21
,
,
,
-
of
,
of
demands
animal
tionships
of
-
,
of
,
171
-
for
169
-
using
rela
,
,
research
stages
as
techniques
Blaberus
-
,
,
94
.
coxa
(
see
BC
climbing
and
modeling
,
,
cockroaches
and
architectures
and
,
11
-
93
Blaberus
bottleneck
model
cockroaches
joints
,
,
95
-
brain
97
struc
chemo-tropotaxis, 31- 32 child development research, 146 climbing , of cockroaches, 93 , 87 - 89
biological observationsof, 87- 94 biological observationsof, changes in walking speed, 92 biological observationsof, climb ing, 93 biological observationsof, hori zontal walking, 90- 92 biological observationsof, techniques for observing, 87- 90
,
173
also
)
other
-
biologicalobservations of, turning,
0
92 - 93
brain
behavior
control
complex
function
motor
early
on
,
of
systems
research
brain
,
and
emulation
110
,
-
type
disembodied
animat
,
of selected features , 87
leg joints of, 89 Cog. seealsosocial development model (Cog)
112
x
Braitenberg
hardware
108
of , 155
illustration
Ca
tagiyph
is
insects
,
.
see
also
in
of
using
visual
of
of
homing
celestial
,
cameras
.
see
39
,
37
-
search on , 145
38
compass
di
-
complex recurrent systems animats (type 4) , 110 , 111
of
,
124
for
using animats to understand, 120 using humanoid robots for re-
53
49
resolution
categorization
,
,
by
cues
rection
visual
of
model (Darwin V )
habitat
strategies
use
-
homing
of ) 154
cognition systems. seealsosocial development model (Cog); visual system
modeling
navigation
123
visual
robotic
experiments
CCD
, 27 - 28
, 26 - 27
cockroaches
172
34
170
same
ing
172
and
biological
lobster , 23 - 24
of RoboLobster
,
environment
limitations
using
of American
174
viii
technological
ture
,
chemotaxis
studying
V, 130 - 131
chemo - sensors
174
physical
device , 126
22 - 23
and
challenges
-
, Darwin
Cephalopods, 112 chemo-orientation , of marine species,
22
achievements
173
, 81
mobile
cell activation
biology
multidisciplinary
-
170
173
169
measuring
body
NOMAD
xi
intersection
robotics
-
-
,
aerial minirobot
viii
169
in
ix
-
172
issues
,
vii
of
also
visual
,
arguments for researching,
60
stimuli
object
,
selectivity
111 - 112
conclusions
, 120
deterrents to study of, 112
200 INDEX complex sensoryinput , animats (type
results
,
1), 108 , 109 computer
11
simulation
,
107 system ,
(
of
,
coxa
-
,
11
phonotaxis
frequency
,
3
preferences
trochan
changes
ter
in
fern
in
ur
)
joints
,
and
speed
walking
cybernetics
of plumetrackingby lobsters , 24,
-
walking
horizontal
visual models , 125
,
,
,
cockroaches
of insect navigation, 41 of neuronal
rate
5
CTF
control
syllable
sound
cricket
song
of animat plus its environment,
to
of
cricket
for
turning
studies
model , 44
of decentralized 114
selectivity
16
results
of adjacent photosensors, 70 ofALV
-
,
ix
-
and
and
early
,
research
90
,
-
on
92
92
robots
x
26 - 27
of snapshotmodel, 42- 43 , 46 vs . biorobotics
, viii - ix
Darwin
v
vs. physical modeling, 5- 6
vs. syntheticmodeling, 37
control
, aerial minirobot
Darwin
(
dead
,
control
results, recognition and choice, 17
,
fast
-
,
165
165
-
166
152
and
,
of
110
limbs
and
,
redundant
possibilities
116
depressors
)
in
,
cockroaches
walking
speed
walking
motor
and
neurons
disembodied
,
brain
,
and
,
,
92
91
89
early
research
on
,
x
distance
output
73
Ds
-
(
,
slow
)
in
,
,
cockroaches
walking
speed
walking
motor
and
neuron
dynamic
memory
dynamic
modeling
matics
minirobot
depressors
horizontal
as
aerial
76
changes
implications of study, 17- 19 4- 5
of
as
7- 8
overviewof cricketphonotaxis ,
-
166
164
116
horizontal
cuit , 8 - 10
implementation , robot's ears, 6- 7 implementation , robot's neurons,
151
chain
-
changes
implementation , 3- 10 implementation , experimental implementation, generalfeatures, 8 implementation , phonotaxis cir-
,
control
on
-
,
freedom
impact
(
164
and
of
114
Df
variables , 10
,
and
controlling
work , 19 - 20
integration
systems
of
advantages of roboticmodeling, 5- 6
path
pointing
behavior
changesin walking speedand, 92 horizontal walking and, 90- 92 cricket phonotaxis, robotic modeling
see
following
degrees
model
)
mimicry
of cockroaches
system
.
overview
coxa-trochanter-femur (CTF ) joints ,
for further
visual
)
reckoning
declarative
cortex . see visual cortex
directions
see
reckoning
gaze
. see behavior
.
V
dead
constant speedscanning, 67 contrast output 73 - 76
(
,
94
,
,
in
,
-
95
and
,
,
92
91
89
animats
cockroach
,
108
kine
-
INDEX201 ears, robot phonotaxis, 6- 7
face detection
, 156 - 157 , 160
ratio templates and, 157- 158
EDD (eye-direction detector ) . seealso
steps in , 156
eye co n tact
in Baron - Cohen maintenance 149
model , 148
of eye contact
and ,
electromyograms(EMGs) observing motor activity of cock-
fastdepressors (Of), cockroaches changes in walkingspeedand, 92 horizontalwalkingand, 91 as motor
replacing central controller with ,
roaches with , 89
113 - 114
walking speedand, 92 EMD (elementary motion detector), of aerial minirobot
visual inertial
feedback
aerial minirobot
, 71 - 72
EMGs (electromyograms) observing motor activity of cock-
enVironment
lobster , 24 - 26
complexity of relationship of animals to , viii
system of
, 79 - 80
femur-tibia (FT ) joints , of cockroaches
changesin walking speedand, 92 horizontal walking and, 90- 92
roaches with , 89
walking speedand, 92 engineering, multidisciplinary challengesof biorobotics and, 173- 174 of American
neurons , 89
feedback
footfall
patterns , of cockroaches
, 88
form recognition. seeobject selectivity frequency preferencesin female crickets, 5 selectivity of robotic ears, 7 FT (femur-tibia) joints , of cockroaches
simulating animat plus its environment
, 107
ethologists, useof robots by, 107 experimental approach, evaluation and , 172 - 173
external compass, Sahabot2 (snapshot model ) , 47- 50 eye contact , 156 - 159 autism
shared attention module (SAM )
and , 156 - 157
finding eyeimage, 157- 159, 160 significancein animals and humans , 149
work on location of pupil and position
gazefollowing , 159- 162 declarativepointing and, 164- 165 developmentalprocessof, 160 - 162
and , 147
face detection
changesin walking speedand, 92 horizontal walking and, 90- 92
of head , 159
and , 149 skills necessary for , 159 - 160 . I
Gryllus bimaculatus, 5, 7 Gryllus campestris ,5 gyroscopes , yaw stabilization 79
eye-direction detector (ED D ) . seealso eye co n tact
in Baron - Cohen maintenance 149
hardware
model , 148
of eye contact
and ,
Cog, 155 Robot111 , 98- 100
and , 68 ,
202 INDEX Sahabot
humanoid
2 , 46 - 47
Hebbian-type learning mechanism,
robots . see also social devel -
opment model (Cog) advantagesand applications of,
141
153 - 154
Hexapod robot, with cockroach kine-
motivationsfor developing , 145
matics , 87 - 105
biologicalobservations of cock-
using to investigatehuman intelli -
roaches , 87 - 94
gence , 145 - 146
biological observationsof cock-
work
roaches , changes in walking
hypotheses
of Ichiro
Kato on , x
of chemo - orientation
speed, 92 biological observationsof cockroaches, climbing , 93 biological observationsof cockroaches, horizontal walking,
mechanisms
,
26
roboticmodelingand, 170 using biorobotics to test, 21
90 - 92
biological observationsof cockroaches, techniquesfor observing, 87- 90 biological observationsof cockroaches, turning , 92- 93 conclusion
of work
on , 103 - 105
construction of robot's physical plant, 98- 100 design of posture controller, 100 - 103
data , 94 - 95
robot design basedon biological observation
in Baron - Cohen
model , 148
declarativepointing and, 151 .
.
Image processIng AL V model and , 64 Sahabot
2 and , 47 , 49
imperative pointing , 162- 164 overview
of , 151
reachingskillsand, 162- 163 robotic implementation of, 163 - 164
dynamicmodelingof biological observation
ID (intentionality detector)
, 95 - 98
high-speedvideo, biological observation with , 88 H omarus americanus . see American lobster
homing . seealsovisual homing of insects, robotic modeling as basic navigational ability, 62 local and associative , 62
in snapshotmodel, 42
inferior temporalcortex, modeling, 141 - 142 . see also visual cortex
insects, visual homing . seevisual homing of insects intentionality detector (ID ) in Baron - Cohen
model , 148
declarativepointing and, 151 intrinsic connections, temporal lobe, 142
potential function of, 137- 140 translation invariance and object selectivity and, 137 invariance. seeperceptual invariance
honeybees , visualdeadreckoningof, 60
human intelligence, using humanoid robots
for research on , 145
joint attention, 146- 1S3 declarativepointing and, 164- 166
INDEX203
decomposing
into
skills
,
147
-
subset
of
decomposition
based
tion
,
eye
148
micro - air vehicle . see aerial minirobot
social
-
on
observa
-
robots , viii
152
contact
,
with visual scanning sensor micro-environment exploration
148
and
,
156
-
mimicry systems, declarativepointing
159
and , 165 - 166 gaze
following
and
history
of
,
159
research
imperative
on
,
pointing
robotic
-
162
146
-
and
,
implementation
147
162
of
MMC . seemean of multiple computa-
tion (MM C ) MN (motor neurons )
164
,
in cockroaches 152
-
, 89
153
in crickets / cricket social
learning
and
,
motion theoretical
decomposition
sensors
Baron
-
, 82
model
motivations (
robots , 10 , 19
146
Cohen
)
,
, animats
, 108
148
motor neurons (MN ) in cockroaches
, 89
in crickets / cricket Kato
,
Ichiro
( on
humanoid
robots
)
,
motor Khepera
miniature
mobile
robots
robots , 10 , 19
x
systems . see also animats ; Hexa -
,
pod robot, with cockroach kinematmodeling
cricket
phonotaxis
with
,
.
6
lCS kinematics
,
cockroach
,
94
-
95
animats (type 3) , 110 , 111
closing loop with sensorysystems, 170 lobster
chemotaxis
.
see
RoboLobster
Darwin
.
expenmen
V, 130
ts
understanding cognition and, 120 local
homing
,
of
insects
,
62
.
see
also
homing
Loeb
,
Jacques
,
navigation models, of insects
ix
average landmark vector (AL V ) model , 37 - 38 , 43 - 44 magneto
resistive
marine
sensor
species
22
-
men
23
,
.
see
-
snapshotmodel, 38, 41- 43
73
orientation
of
RoboLobster
,
experi
nervous system, Darwin v, 128- 130
-
motor
Institute
MIT
)
,
of
current
visual system , 129 architecture
connections
complex
of
,
for
-
to
-
three
4
)
.
119
kinematic
problems
-
neuralprocesses , modelingcricket phonotaxis and, 3- 4 neural spiking, 6, 7- 8 neuroethology, European school of, x neuromuscular activity, of cockroach-
see
computation
116
applying
117
type
re
systems
multiple
)
with
(
recurrent
MMC
of , 128
taste system , 129
Technology
xi
parallel
mean
system , 130
schematic
massively
(
-
72
ts
Massachusetts
(
chemo
also
,
,
es, 89
neurons, auditory neurons (AN ),
118
-
joint
planar
arm
,
117
8 - 10
204 INDEX neurons , motor neurons (MN ) in cockroaches
, 89
in crickets / cricket
robots , 10 , 19
neurons, robot phonotaxis four - neuron
navigation strategiesof ants, 39 testing mechanismsof, 60 perceptual invariance examplesof, 125
model , 8- 10
membrane potential and, 8 neural spiking and, 6, 7- 8 responseto syllable rate, 11, 13 - 16
turning toward sound and, 11- 12 neurons , visual
factors in , 123 visual cortex
and , 124
performance measures , RoboLobster experiments, 32- 33 phonotaxis, 3. seealsocricket phono-
taxis, roboticmodeling photoreceptormodel
development mechanismseffecting, 124
of aerial minirobot
, 68 - 69
processingsignals, 69- 71
linking connectionsand, 124- 125
plume-tracking. seeodor plumes,
visual stimuli
tracking by lobsters pointing , as mechanism of joint atten-
NOMAD
and , 123
mobile device , Darwin V,
126 - 131 functions
tion , 151 of , 127 - 129
use of CCD cameraby, 126
objectrecognition,biological, 140 - 141
object selectivity, 140- 142 in Darwin V experiments, 132 - 137
development of visual recognition, 132
intrinsic connections of temporal lobe and , 137 - 140
position sensors, 82 posture controller 100 - 103
, Robot
III ,
primary visual area(VAp), Darwin v, 126 .
prImates eye contact
in , 149
gazefollowingby, 149 pointing behavior and, 151 pulse scanning, 67 pupil detection, 159
role in biological object recognition , 141 0 ckham 's razo r
applying to selection of algorithms for RoboLobster experiments, 34
using in algorithm selection, 28 Octopus, 112
odor plumes, trackingby lobsters , 23 - 24 , 28 - 29
. . paSSIve motIon paradigmJ I body models and, 116 path integration (dead reckoning)
ratio templates, face detection and, 157 - 158
reaching skills development of, 162- 163 pointing behavior and, 151 recognition and choice, in cricket robot , 17 - 18
recurrent connections , animats (type 2) , 108 , 109
RoboLobster experiments, 21- 35 algorithm selection, 33- 35 bio-mimetic scaling of RoboLobster , 27 - 28
INDEX 205 charting RoboLobster againstlobster paths, 30- 31 chemo - sensors of American
lob -
ster , 23 - 24
external
as component
of work
chemotaxis
on lobster
of American
lobster ,
evidencesupporting multiple odor tracking strategies, 29 issueswith performance measures , 32 - 33
reasonsfor using a robot to study chemotactic strategies, 26- 27 testing chemo-tropotaxis, 31- 32 testing tropotaxis, 28- 29 I , 96
robotic modeling. seealsobiorobotics advantagesfor modeling social skill development, 153- 154 biological emulation in design, 87 considerationsin application of, 170
II , 96 cockroach
conclusion
kinematics
of work
of cockroaches
, 95 - 98
design of posture controller, 100 - 103
control
advantagesof using physical modeling vs. simulation, 5- 6 studies of , 3 sensory systems . see also aerial
minirobot ; cricket phonotaxis; RoboLobster experiments; visual homing of insects closing loop with motor systems, 170
sensoryarray of analog robot, 50 - 52 sensory array of Sahabot
2 , 46 - 47
using robots for study of, 27 cockroaches
changesin walking speedand, 92
horizontalwalkingand, 91- 92 shared attention module (SAM ) model , 148
declarativepointing and, 151 gazefollowing and, 149 slow depressors(Os), cockroaches changesin walking speedand, 92 horizontal walking and, 91 as motor
neuron , 89
slow extensor of the tibia (SETi ) , of
robot phonotaxis. seecricket phono-
taxis, roboticmodeling "Robots and Biology: Developing Connections
V, 126 sensorimotor
in Baron - Cohen
on , 103 - 105
construction of robot's physical plant, 98- 100 design basedon biological observation
module
(SAM )
sharedattention . seejoint attention
Robot III . seealsoHexapod robot, with
, 45 - 46
SETi (slow extensor of the tibia ) ,
copying biology, 169- 170 physical demandsof, 171- 172 useof animats by ethologists, 107 Robot
modifications
SAM . see shared attention
secondaryvisual area(VAs), Darwin
24 - 26
Robot
compass , 47 - 50
hardware , 46 - - 47
image processingcapability, 47, 49
, 22 - 23
design challenge, integrating appropriate constraints, 29- 31 environment
Sahabot2 (snapshotmodel), 45- 50 discussionof findings, 60 experiments, 53- 55
" , xi
cockroaches
changesin walking speedand, 92 horizontal walking and, 91- 92 snapshotmodel. seealsoSahabot2
206INDEX (snapshotmodel) navigation models, 41- 43 navigation models, of insects, 38 social development model (Cog). see social development model (Cog) advantagesof robotic modeling of social skill development,
syllable repetition interval (SRI), in cricket
song , 5
synaptic rules, Darwin V, 130- 131 synthetic modeling. seerobotic model.
mg of animal
behavior , 62
of visualhomingstrategies of in-
153 - 154
sects , 60
applications of humanoid robots, 145 - 146
vs . computer
simulation
, 37
syntheticneuralmodeling, 125
implementation , declarativepoint ing, 164- 166 implementation , eyecontact,
systematicsearch, navigation strategies of ants , 39
156 - 159
implementation , gazefollowing , 159 - 162
target tracking and visual fixation , aerial minirobot
implementation , imperative point ing, 162- 164
modelingjoint attention, 146 - 153
modelingjoint attention, decomposing social skills, 147- 148
modelingjoint attention, decomposition basedon observation, 148 - 152
modelingjoint attention, history of research on , 146 - 147
modeling joint attention, robotic implementation for, 152- 153 modeling joint attention , theoretical decomposition, 148 robotic hardware of Cog, 155
, 80 - 81 , 82
Tarry II , 114 taste system, Darwin V, 129
technological limitations , of biorobotics Tesla , Nikola
, 172 , ix
TF (trochanter-femur) joints , cockroaches
climbing, 93 modeling Blaberusand, 95, 97 theories
of mind , 146 - 147 , 151
theory-of-mind module (TOMM ), Baron - Cohen
model , 148
trace rule, modeling inferior temporal cortex , 141 - 142 translation
invariance
, Darwin
V ex -
.
penments
social skills
attending to objects of mutual interest (joint attention), 146- 147
biological object recognition and,
role of eye contact
development, 132- 137
in , 149
role of gazefollowingin, 149- 151 sound, cricket robot turning toward, SRI (syllable repetition interval), in song , 5
syllable rate, responsiveness of cricket robot
development , impairingby restricting self-generatedmovement , 135 - 136
11 - 12
cricket
141
to , 13 - 16 , 19
development, patterns revealing, 134
development, testing, 133 in higher vertebrates, 124
INDEX207 intrinsic connections of temporal lobe and , 137 - 140
object selectivity and, 140- 142 trochanter-femur (TF ) joints , cock-
analog robot (ALV model), experiments , 55 - 59
analog robot (ALV model), overview , 50 - 52
experimentalprocedures for mea-
roaches
clim bing, 93 modeling Blaberusand, 95, 97
suring, 39- 40
homingasbasicnavigationalabili-
.
ty , 62 limitations
tropotaxls defined , 28
using RoboLobster to test, 28- 29 (urning , of cockroaches, 92- 93 tWin propeller dynamics, aerial minirobot
, 76 - 79 , 81
and issues in robotic
modelingtechniques , 62- 65 navigation models, 37- 38 navigation models, averagelandmark vector (ALV) model , 43 - 44
navigation models, snapshotmodVAp (primary visual area), Darwin V, 126
el , 41 - 43
Sahabot2 (snapshotmodel),
variable speedscanning, aerial minirobot
, 68 - 71
formula
for , 69
photoreceptor model, 68- 69 processingphotoreceptor signals, 69 - 71
vs. constant speedscanning, 67 VAs (secondaryvisual area), Darwin V, 126
45 - 50
Sahabot2 (snapshotmodel), dis-
cussionof findings, 60 Sahabot2 (snapshotmodel), experiments, 53- 55 Sahabot2 (snapshotmodel), external
compass , 47 - 50
Sahabot2 (snapshotmodel), hardware , 46 - 47
Vehicles , Experimentsin SyntheticPsychology(Braitenberg), x Vineland Adaptive Behavior Scales, 152
Sahabot2 (snapshotmodel), im ageprocessingcapability, 47, 49 Sahabot2 (snapshotmodel), modifications
visual area, primary and secondary, Darwin V, 126 visual cortex model
based on , 123
modeling inferior temporal cortex, 141 - 142
aerial minirobot
feedback
al minirobot
, 79 - 80
, 80 - 81 , 82
visual homing of insects, robotic mod-
eling, 37- 66 analog robot (ALV model), discussion of findings, 60- 61
system , of aeri -
visual landmark navigation, navigation strategiesof ants, 39- 40 visual remote minirobot
perceptual invariance and, 124 visual fixation and target tracking, of
, 45 - 46
visual inertial
control , aerial , 76
visual scanning sensor, aerial minirobot
, 71 - 76
components
of , 72 - 73
impact of contrast and distanceon output
, 73 - 76
visual system model (Darwin V) , 123 - 143
208 INDEX
developmentof Darwin V, 126 discussion, intrinsic organization of temporal cortex, 142 discussion , translation
invariance
andobjectselectivity , 140- 142 implementation of Darwin V, 126 - 131
cortex , 123 - 126 results , 131 - 140 results , development invariant
130 - 131
selective
responses , 132 - 137 results , overall
behavior , 131 - 132
results , potential
implementation of Darwin V, cell activation and synaptic rules,
of translation
and object
trinsic
function
connections
of in -
, 137 - 140
visumotor processes , in aerial minirobot , 67
implementation of Darwin V, nervous system , 129 - 130
implementation of Darwin v, NOMAD
mobile
device ,
126 - 131
overviewof functioningof visual
walking cockroaches cockroaches Tarry
, changes in speed , 92 , horizontal , 90 - 92
II and , 114
