ThreeGenerationsof AutomaticallyDesignedRobots

JordanB. Pollack,Hod Lipson,GregoryHornby, andPabloFunes

27thAugust2002

DEMO LaboratoryComputerScienceDept.,

BrandeisUniversity,Waltham,MA 02454,USA,www.demo.cs.brandeis.edupollack@cs.brandeis.edu

Abstract

Thedifficultiesassociatedwith designing,building andcontrollingrobotshave led their developmentto a stasis: applicationsare limited mostly torepetitive taskswith predefinedmoves.Overthelastfew yearswehavebeentrying to addressthischallengethroughanalternativeapproach:Ratherthantrying to controlan existing macine,or createa general-purposerobot,weproposethat both the morphologyand the controller shouldevolve at thesametime. This processcanleadto theautomaticdesignandfabricationofspecialpurposemechanismsandcontrollersthatachieve specificshort-termobjectives.Hereweprovideabrief review of threegenerationsof our recentresearch,underlyingtherobotsshown on thecover of this issue:Automat-ically designedstaticstructures,automaticallydesignedandmanufactureddynamicelectromechanicalsystems,andmodularrobotsautomaticallyde-signedthroughagenerative DNA-lik eencoding.

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1 Introduction

Thehighcostsassociatedwith designing,building andcontrollingrobotshaveled

their developmentto a stasis[28]. Robotsin industryareonly appliedto simple

andhighly repetitivemanufacturingtasks.Eventhoughsophisticatedteleoperated

machineswith sensorsandactuatorshave found importantapplications(explo-

rationof inaccessibleenvironmentsfor example),they leave very little decision,

if atall, to theon-boardsoftware[29].

Thecentralissueaddressedby ourwork is away to getahigherlevel of com-

plex physicalityundercontrolat lowercost.Weseekmorecontrolledandmoving

mechanicalparts,moresensors,morenonlinearinteractingdegreesof freedom

- without entailingboth thehugefixedcostsof humandesignandprogramming

andthevariablecostsin manufactureandoperation.We suggestthat this canbe

achieved only whenrobot designandconstructionarefully automatic.Consid-

ering a robot asan artificial life form, we achieve automaticdesignby evolving

creatures— body andbrain — throughinteractionwith (simulated)reality, and

transferthedesignsinto (real)reality.

Traditionally, robotsaredesignedonahardwarefirst, softwarelastbasis:Me-

chanicalandelectricalengineersdesigncomplex articulatedbodieswith state-of-

the-artsensors,actuatorsandmultiple degreesof freedom.Thenext taskshould

be simply to “write the software”. But humanshave drasticallyunderestimated

animalbrains:looking into naturewe seeanimalbrainsof very high complexity,

controllingbodieswhich have beenselectedby evolution preciselybecausethey

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werecontrollableby thosebrains.We believe thatthecostsof writing intelligent

controllersfor arbitrarymechanicaldevicesaresohigh thatdirectengineeringof

robotswill continueto beaneconomicfailure.

In nature,the body andbrain of a creaturearetightly coupled,the fruit of a

long seriesof smallmutualadaptations— like chickenandegg, neitheronewas

designedfirst. Thereis never a situationin which thehardwarehasno software,

or wherea growth or mutation— beyond the adaptive ability of the brain —

survives.Autonomousrobots,like living creatures,requirea highly sophisticated

correspondencebetweenbrain,bodyandenvironment.

Therefore,we have beenworking to co-evolve both the brain andthe body,

simultaneouslyandcontinuously, from asimplecontrollablemechanismto oneof

sufficientcomplexity for aparticularspecializedtask.Althoughwewerenot first

to proposebrain/bodycoevolution [6, 24, 27], we have beenableto put the idea

in practice.

Over thenext decade,weseethreetechnologiesthatarematuringpastthresh-

old to make possiblea new industryof inexpensive automaticallydesignedma-

chines.Oneis the increasingfidelity of advancedmechanicaldesignsimulation,

stimulatedby profits from theCAD softwareindustry[34]. Thesecondis rapid,

one-off prototypingandmanufacture,which is proceedingfrom 3D plasticlayer-

ing to strongercompositeandmetal(sintering)technology[7]. Thethird is most

centralto Artificial Life, our understandingof the dynamicsof coevolutionary

learningin theself-organizationof complex systems[31, 19,1, 8].

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2 Coevolution

Coevolution, whensuccessful,dynamicallycreatesa seriesof learningenviron-

mentseachslightly morecomplex thanthelast,andaseriesof learnerswhichare

tunedto adaptin thoseenvironments. Sims’ work [33] on body-braincoevolu-

tion andthemorerecentFramstickssimulator[23] demonstratedthat theneural

controllersandsimulatedbodiescouldbecoevolved.Thegoalof our researchin

coevolutionaryroboticsis to replicateandextendresultsfrom virtual simulations

like theseto the reality of computerdesignedand constructedspecial-purpose

machinesthatcanadaptto realenvironments.

We are working on coevolutionary algorithmsto develop control programs

operatingrealisticphysicaldevice simulators,bothcommercial-off-the-shelfand

our own customsimulators,wherewe finish the evolution insidereal embodied

robots. We areultimately interestedin mechanicalstructuresthat have complex

physicalityof moredegreesof freedomthananythingthathaseverbeencontrolled

by humandesignedalgorithms,with lower engineeringcoststhancurrentlypos-

siblebecauseof minimalhumandesigninvolvementin theproduct.

It is not feasiblethatcontrollersfor completestructurescouldbeevolved(in

simulationor otherwise)without first evolving controllersfor simplerconstruc-

tions.Comparedto thetraditionalform of evolutionaryrobotics[9, 5, 26,13,22]

which serially downloadscontrollersinto a given pieceof hardware, it is rela-

tively easyto explore the spaceof body constructionsin simulation. Realistic

simulationis alsocrucial for providing a rich andnonlinearuniverse.However,

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while simulationcreatestheability to explorethespaceof constructionsfar faster

thanreal-world building andevaluationcould,thereremainstheproblemof trans-

fer to realconstructionsandscalingto thehigh complexities usedfor real-world

designs.

3 Results

We describethreegenerationsof work in our lab towardsfully automatedde-

sign andmanufactureof high-parts-countautonomousrobots. The fundamental

methodis evolution insidesimulation,but in simulationsmoreandmorerealistic

so the resultingblueprintsarenot simply visually believable,as in Sims’ work,

but also buildable, either manuallyor automatically. Our first resultsinvolved

automaticallycreatinghigh part-countstructuresthat could be transferredfrom

simulationto the realworld. In thesecondgenerationwe evolvedautomatically

buildabledynamicmachinesthatarenearlyautonomousin boththeir designand

manufacture,usingRapidPrototypingtechnology. Thethird generationbeginsto

addressscaling,by handlinghigh part-countstructuresthroughmodularity. Even

with thesethreedemonstrations,we feel the work is at a very early stage,with

majorissuesonly beginningto beaddressedsuchastheintegrationof sensorsand

automatingthefeedbackfrom “li ve” interactions.

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3.1 Generation 1: Legobots

Thefirst steptowardsourvisionof fully evolvedcreatureswasto demonstratethat

evolving morphologyfor therealworld wasindeedpossible.

In orderto evolveboththemorphologyandbehavior of autonomousmechan-

ical devicesthatcanbebuilt, onemusthaveasimulatorthatoperatesundermany

constraints,and a resultantcontroller that is adaptive enoughto cover the gap

betweenthesimulatedandrealworld.

Featuresof a simulatorfor evolving morphologyare:

� Representation—- shouldcoverauniversalspaceof mechanisms.

� Conservative — becausesimulationis never perfect,it shouldpreserve a

margin of safety.

� Efficient— it shouldbequicker to testin simulationthanthroughphysical

productionandtest.

� Buildable— resultsshouldbeconvertiblefrom asimulationto arealobject.

Oneapproachis to custom-build asimulatorfor modularroboticcomponents,and

thenevolveeithercentralizedor distributedcontrollersfor them.

In advanceof a modularsimulatorwith dynamics,we built a simulatorfor

(static)Lego bricks, andusedsimpleevolutionaryalgorithmsto createcomplex

Legostructures,whichwerethenmanuallyconstructed[10, 11,12].

Our modelconsidersthe union betweentwo bricks asa rigid joint between

thecentersof massof eachone,locatedat thecenterof theactualareaof contact

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Figure1: Photographsof theFAD LegoBridge(Cantilever)andCrane(Triangle).Photographscopyright PabloFunes& JordanPollack,usedby permission.

betweenthem. This joint hasa measurabletorquecapacity:morethana certain

amountof force appliedat a certaindistancefrom the joint will breakthe two

bricksapart.Thefundamentalassumptionof our modelis theidealizationof the

unionof two Legobricksasa rotationaljoint with limited capacity.

The evolutionaryalgorithmreliably builds structuresthat meetfitnessgoals,

exploiting physicalpropertiesimplicit in the simulation. Building the resultsof

theevolutionarysimulation(by hand)demonstratedthepower andpossibilityof

fully automateddesign:thelong bridgeof figure1 shows thatour simplesystem

discoveredthecantilever, while theweight-carryingcraneshowsit discoveredthe

basictriangularsupport.

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3.2 Generation 2: Genetically Organized Lifelike Electrome-

chanics (GOLEM)

The Lego machines,with computergeneratedblueprints,andmanualconstruc-

tion, demonstratedthat the interactionbetweensimulatedphysicsandevolution

leadsto a primitive form of discovery which canbe transferredinto reality. The

next goalis to addsomemotionto thesemachines,andaddresstheissueof manu-

facture.While Legokits havemotioncomponents,thedesignspaceis verybroad

anddifficult to model,andno robotcanmatchthemanualdexterity of a 10 year

old humanin assembly.

We startedwith a whole new processin which robot morphologywascon-

strainedto bebuildableby a commercialoff theshelfrapidprototypingmachine.

We evolve the bodiesandcontrollersin simulationandwereessentiallyableto

replicatethemautomaticallyinto reality [25].

Theserobotsarecomprisedof only linearactuatorsandsigmoidalcontrolneu-

rons embodiedin an arbitrary thermoplasticbody. The entire configurationis

evolved for a particulartaskandselectedindividualsareprintedpre-assembled

(exceptmotors)using3D solidprinting(rapidprototyping)technology, laterto be

recycled into differentforms. In doingso,we establishfor thefirst time a com-

pletephysicalevolution cycle. In this project,the evolutionarydesignapproach

assumestwo mainprinciples:(a) to minimizeinductivebias,wemuststriveto use

the lowestlevel building blockspossible,and(b) we coevolve the body andthe

control,sothatthatthey stimulateandconstraineachother. Weusearbitrarynet-

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worksof linearactuatorsandbarsfor themorphology, andarbitrarynetworksof

sigmoidalneuronsfor thecontrol. Evolution is simulatedstartingwith a soupof

disconnectedelementsandcontinuesoverhundredsof generationsof hundredsof

machines,until creaturesthataresufficiently proficientat thegiventaskemerge.

The simulatorusedin this researchis basedon quasi-staticmotion. The basic

principle is that motion is broken down into a seriesof statically-stableframes

solvedindependently. While quasi-staticmotioncannotdescribehigh-momentum

behavior suchasjumping,it canaccuratelyandrapidly simulatelow-momentum

motion.Thiskind of motionis sufficiently rich for thepurposeof theexperiment

and,moreover, it is simpleto inducein reality sinceall real-timecontrol issues

areeliminated. Several evolution runswerecarriedout for the taskof locomo-

tion. Fitnesswasawardedto machinesaccordingto theabsoluteaveragedistance

traveledoveraspecifiedperiodof neuralactivation.Theevolvedrobotsexhibited

variousmethodsof locomotion,includingcrawling, ratchetingandsomeformsof

pedalism(Figure2). Selectedrobotsarethenreplicatedinto reality: their bodies

arefirst fleshedto accommodatemotorsandjoints,andthencopiedinto material

usingrapid prototypingtechnology. Temperature-controlledprint headextrudes

thermoplasticmateriallayerby layer, so that thearbitrarily evolvedmorphology

emergespre-assembledasa solid three-dimensionalstructurewithout tooling or

humanintervention.Motorsarethensnappedin (manually),andtheevolvedneu-

ral network is activated(Figure3). Therobotsthenperformin reality asthey did

in simulation.

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(a) (b) (c)

(d) (e) (f)

Figure2: (a) A tetrahedralmechanismthat produceshinge-like motion andad-vancesby pushingthe centralbar againstthe floor. (b) Bipedalism:the left andright limbsareadvancedin alternatingthrusts.(c) Movesits two articulatedcom-ponentsto producecrab-like sidewaysmotion. (d) While the uppertwo limbspush,the centralbody is retracted,andvice versa. (e) This simplemechanismusesthe top bar to delicatelyshift balancefrom sideto side,shifting thefrictionpoint to eithersideasit createsoscillatorymotionandadvances.(f) This mech-anismhasanelevatedbody, from which it pushesanactuatordown directly ontothefloor to createratchetingmotion. It hasa few redundantbarsdraggedon thefloor.

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(a) (b) (c)

(d) (e) (f)

Figure3: (a)Fleshedjoints,(b) replicationprogress,(c) pre-assembledrobot(fig-ure2f), (d,e,f)final robotswith assembledmotors

3.3 Generation 3: Modularity Generative Design (Tinkerbots)

While the GOLEM projectdemonstratedvalidity of our approachto automatic

designandmanufacture,themachineswhich wereproducedareobviously fairly

simplecomparedto the kinds of robotsbuildableby teamsof humanengineers.

In fact mostwork in automaticdesignof engineeringproductsusingtechniques

inspiredby biologicalevolution,[21, 3, 17, 10,4, 25] suffersthesamecriticism.

Our third generationstartsto addressthe issueof whetherevolutionaryauto-

matic designtechniquescanattain the higher level of complexity necessaryfor

practicalengineeringprojects. Sincethe searchspacegrows exponentiallywith

thesizeof theproblem,searchalgorithmsthatusea directencodingfor designs

maynot scaleto largedesigns.An alternative to a directencodingis a generative

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specification,which is a grammaticalencodingthat specifieshow to constructa

design,[32] and[2]. Similar to a computerprogram,a generative specification

canallow the definition of re-usablesub-proceduresallowing the designsystem

to scaleto morecomplex designsthancanbeachievedwith adirectencoding.

Ideally an automateddesignsystemwould startwith a library of basicparts

andwould iteratively createnew, morecomplex modules,from onesalreadyin its

library. The principle of modularity is well acceptedasa generalcharacteristic

of design,as it typically promotesdecouplingandreducescomplexity [35]. In

contrastto a designin which every componentis unique,a designbuilt with a

library of standardmodulesis more robust and more adaptable,and enhances

field repair.

Our third generationof automaticallydesignedrobotsfocuson modularde-

sign,andusesL-systemsasthegenotypeevolvedby theevolutionaryalgorithm.

L-systemsareagrammaticalrewriting systemintroducedbyLindenmeyerin 1968

to modelthebiologicaldevelopmentof multicellularorganisms.Rulesareapplied

in parallelto all charactersin thestringjust ascell divisionshappenin parallelin

multicellular organisms.Complex objectsarecreatedby successively replacing

partsof a simple objectby using the set of rewriting rules. Using this system

we have evolved3D staticstructures[15], andlocomotingmechanisms[14, 16],

someof whichareshown in figure4, andandtransferredsuccessfullyinto reality,

asseenin figure5 [14]. Thecreatureon thecover is our first 3D machineusing

thesetechniques.

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Figure4: Examplesof evolved,modularcreatures.

Figure5: Two partsof thelocomotioncycleof a2D,modularlocomotingcreaturein bothsimulationandreality.

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4 Discussion and Conclusion

Can evolutionary and coevolutionary techniquesbe usedin the designof real

robotsas“Artificial Lifeforms?” In thispaperwehavepresentedthreegenerations

of ourwork, eachof whichaddressesoneor moredimensionsof theproblem.We

haveoverviewedresearchin useof simulationsfor handlinghighpart-countstatic

structuresthat arebuildable,dynamicelectromechanicalsystemswith complex

morphologythatcanbebuilt automatically, andgenerativeencodingsasa means

for scalingto complex structures.

The limitations of the work areclearly apparent:thesemachinesdo not yet

have sensors,andarenot really interactingwith their environments. Feedback

from how robotsperformin therealworld is not automaticallyfed-backinto the

simulations,but requirehumansto refinethe simulationsandconstraintson de-

sign. Finally, thereis the questionof how complex a simulatedsystemcanbe,

beforetheerrorsgeneratedby transferto reality areoverwhelming.

We cannotclaim immediatesolutionto theseproblems.In otherwork, how-

ever, we have demonstratedhow coevolution can lead to complex performance

in domainslikegame-playing[31] anddesignof complex algorithmslikesorting

networks[18] andcellularautomata[20]. In ournext generationsof evolvedcrea-

turesweexpectto seesomesensorintegration,andwehavealreadydemonstrated

robot“cultural” evolution, learningfrom interactingin therealenvironment.[36]

Theissueof whetherornotthiskind of artificial life workwill everbepractical

andscaleableis bestrelatedto the history of computerchess. The theory that

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machinescouldplayagamelikechesswasfrom the1920’s,thefirst chessplaying

computerwas built in the mid 1950’s, and maderandomlegal moves. While

proponentsof fundingfor thenew field of AI wereover-optimistic,by theendof

the century, nevertheless,with almostunlimited CPU time at its disposal,Deep

Blue wasableto win a tournamentagainstthe leadinghumanplayer- using80

yearold theory[30].

Perhapsthesmalldemonstrationsof automaticdesignwill lead— with con-

tinueddevelopment,andincreasesin computerspeedandsimulationfidelity, cou-

pledto increasesin basictheoryof coevolutionarydynamics— over time, to the

point wherefully automaticdesignis takenfor granted,muchascomputeraided

designis takenfor grantedin manufacturingindustriestoday.

Our currentresearchmovestowardsthe overall goal via multiple interacting

paths,of simulation,theory, building andtestingin the real world, andapplica-

tions. It is a broad,multidisciplinary long-termendeavor, wherewhat we learn

in onepathaidsthe others. We believe sucha broadendeavor is the only way

to ultimately constructcomplex autonomousmachineswhich caneconomically

justify their own existence.

Acknowledgements

This researchwassupportedover theyearsin partby theNationalScienceFoun-

dation(NSF),theofficeof Naval Research(ONR),andby theDefenseAdvanced

ResearchProjectsAgency (DARPA).

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