FutureDirectionsWorkshopon

EmbodiedIntelligence

May19–20,2024

JoshuaBongard,UniversityofVermontKyujinCho,SeoulNationalUniversity

Yong-LaePark,SeoulNationalUniversityRobertShepherd,CornellUniversity

Preparedby:

KateKlemic,VirginiaTechAppliedResearchCorporation

SithiraRatnayaka,VirginiaTechAppliedResearchCorporation

FutureDirectionsWorkshopseries

WorkshopsponsoredbytheBasicResearchOffice,OfficeoftheUnderSecretaryofDefenseforResearch&Engineering

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Contents

Prefaceiii

ExecutiveSummary1

Introduction5

ResearchChallenges7

ResearchOpportunities12

Conclusion17

Bibliography18

AppendixI–WorkshopAttendees19

AppendixII–WorkshopAgendaandProspectus27

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Innovationisthekey

tothefuture,butbasicresearchisthekeytofutureinnovation.

–JeromeIsaacFriedman,

NobelPrizeRecipient(1990)

Preface

Overthepastcentury,scienceandtechnologyhasbrought

remarkablenewcapabilitiestoallsectorsoftheeconomy;

fromtelecommunications,energy,andelectronicstomedicine,transportationanddefense.Technologiesthatwerefantasy

decadesago,suchastheinternetandmobiledevices,now

informthewaywelive,work,andinteractwithourenvironment.Keytothistechnologicalprogressisthecapacityoftheglobalbasicresearchcommunitytocreatenewknowledgeandto

developnewinsightsinscience,technology,andengineering.Understandingthetrajectoriesofthisfundamentalresearch,

withinthecontextofglobalchallenges,empowersstakeholderstoidentifyandseizepotentialopportunities.

TheFutureDirectionsWorkshopseries,sponsoredbytheBasicResearchOfficeoftheOfficeoftheUnderSecretaryofDefenseforResearchandEngineering(OUSD(R&E)),seekstoexamine

emergingresearchandengineeringareasthataremostlikelytotransformfuturetechnologycapabilities.Theseworkshopsgatherdistinguishedacademicresearchersfromaroundtheglobe

toengageinaninteractivedialogueaboutthepromisesand

challengesofeachemergingbasicresearchareaandhowtheycouldimpactfuturecapabilities.Chairedbyleadersinthefield,theseworkshopsencourageunfetteredconsiderationsoftheprospectsoffundamentalscienceareasfromthemosttalentedmindsintheresearchcommunity.

ReportsfromtheFutureDirectionWorkshopseriescapture

thesediscussionsandthereforeplayavitalroleinthediscussionofbasicresearchpriorities.Ineachreport,participantsare

challengedtoaddressthefollowingimportantquestions:

?Howwilltheresearchimpactscienceandtechnologycapabilitiesofthefuture?

?Whatisthetrajectoryofscientificachievementoverthenextfewdecades?

?Whatarethemostfundamentalchallengestoprogress?

ThisreportistheproductofaworkshopheldMay19-20,2024

inSeoul,SouthKoreaonthefutureofEmbodiedIntelligence

research,asanessentialandcriticalaspectoffutureroboticsthatareagileandenduring,aswellasdamagetolerant.ItisintendedasaresourcetotheS&Tcommunityincludingthebroader

federalfundingcommunity,federallaboratories,domesticindustrialbase,andacademia.

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ExecutiveSummary

EmbodiedIntelligence(EI)isarapidlyevolvingfieldthatseekstoaddressnewideasaboutthenatureofmachineintelligence.EIblursthelinesbetweenArtificialandPhysicalIntelligence(AIandPI,respectively);itcreatesadiffuseinterfacebetweenartificial

andnaturalcomponentsofasystem.EIaimstoincorporate

intomachinesthemultimodalandmultiscaleadaptation

observedinnaturalorganisms,forawhollynewapproachto

robotictechnology,allowingafuturefilledwithautonomous,

useful,andsafemachines.Consideraworldinwhichevery

machineismorphologicallyandneurologicallyunique.Such

technologieswouldbeimmunetounintentionalsurprise(novelenvironments)orintentionalsurprise(adversarialattacks)

becausenotwomachineswouldshareacommonAchillesheel.Imaginemachinesthat,whencleavedintwo,formtwosmalleryetdistinctversionsoftheoriginalmachine.Imaginemachinesthatcandevolveintoswarmsofindependentcomponentsandreformintoaphysicalunityondemand.Considerautonomousmachinesinwhichthereisnocleardistinctionbetweencontrol,actuation,sensation,communication,computation,andpower,renderingsuchmachinesimmunetocompletefailureofanyonesub-system.Thesemachinesmightalsoincorporatelivingandnon-livingcomponents,furthercombiningthebestofthebioticandabioticworldsandblurringthedistinctionbetween“us”

(humans)and“them”(machines).

Pasteffortsinembodiedintelligencesciencehaveproceeded

withlittleinteractionbetweenthebroadfieldsinwhichR&D

waspursued.Inthepresent,therearemanyeffortssurroundingtheintegrationofnewAIandmachines,leadingtoaneedto

integratethebrainandbodyofthesesystems.Biologicalsystemshaveservedasinspirationformanyofthemodernapplications

andforthesesystems.Thereisanopportunitywithinembodiedintelligencetocauseresponsesintermediatetopre-flex,reflex,andcentralizeddecisionmaking.

Anoctopustentacleprovidesagoodexampleofahigher

level,yetlocalizedObserve,Orient,Decide,Act(OODA)loop.Whilethewholeoctopushasacentralizedbrain,italsohasa

largenumberofneuronsinitstentacles.Evenwhenseparatedfromthebody,thesuckersofatentacleareabletosensethe

chemicalenvironment,locallydecideifanobjectisfoodornot,graspitifitisfoodandpullittowardsitsconceptofwhereitsbeakislocated.Sensing,actuation,computation,andenergyisdistributedthrougheverymm3ofanoctopus’sflesh;aliving,autonomousmaterialsystem.

WhileAIexcelsathandlinglargeamountsofdata,theunderlyingstatisticalprocessoflearningisnotconducivetocausaland

abstractreasoning.Attemptstocreatesuchcapabilitywithin

thatframeworkhavegenerallynotyieldedconsistentlyaccurateresults,andthislikelyrelatestothedifferencebetween

howengineered(AI)andnaturalorganismslearn.Theseare

fundamentalquestions:whatislearningand,toadeeperlevel,whatisintelligence?

Advancesinthesefieldscouldleadtofurtherintegration

betweenhumansandmachines,creatingnewecosystemsinwhichallcanco-exist.

TheFutureDirectionsWorkshoponEmbodiedIntelligence

washeldon19-20May2024inSeoul,SouthKoreatoexamine

theprospectsforapplyingnewapproaches,theories,and

toolsinbasicresearchtoenablethesecapabilitiesoverthe

next10-20years.Itgathered28researchersfromavarietyof

fields,includingsoftrobotics,motioncontrol,biomechanics,

mechanicalengineering,controltheory,systemsbiology,physics,mathematics,computerscience,andbioethics.Theworkshop

includedresearchersfromtheRepublicofKorea(ROK)andthe

UnitedStates(US)andservedasafoundationforcollaborationinthefieldbetweenthetwocountries.

Theworkshopwasorganizedforhighlyinteractivesmallgroupdiscussionswithwhole-groupsynthesisonthechallenges,

opportunitiesandtrajectoryofresearchacrossthreepillarsofembodiedintelligence:perception,motion,andadaptation.

ResearchChallenges

ParticipantsidentifiedthefollowingchallengesforeachtechnicalpillarofEIandidentifiedthekeyresearchareastorealizethe

envisionedfutureofembodiedintelligence.

Perception

Theabilityformachinestosensetheirsurroundings/environmentandgleaninformationfromithasbeenexploredutilizingseveralmethods.Severalsensingmodalitieshaveemergedthatinterfacethebodywiththeenvironment(exteroception)andprovide

moredetailedknowledgeaboutthebody’sstateinternally

(proprioception).Perceptiongoesbeyondthisbyincorporatingsensessuchasolfactory(smell,ortheabilitytodetectchemicalinformation)andnociception(theabilitytodetectharmful

environmentalstimuli),whicharemoreexoticmethodologiesthatcanbeutilizedforenvironmentalnavigation.

ThemainresearchchallengesforPerceptionarelinkedtothe

fundamentalquestionssurroundingsensingandtheability

toinferinformationfromsensors.Forallnaturalorganisms,

knowledgerepresentationishighlydependentonthesensory

modes,andtheirprocessingandfusion.Thus,learningcannot

bedissociatedfromthesensorsusedtoacquireinformation.

Inaddition,usingsensingintheartificialworldrevolvesaroundvision,whileinthenaturalworld,aplethoraofothermethodsareused.ThemainPerceptionresearchchallengesinclude:

Sensitivity:Increasingthesignaltonoiseratiobylocalizingsignalsofinterestandamplifyingisadynamicand

computationallychallengingprocessthathasthepotentialtoincreaseagilityandenergyefficiencyifperformedattheembodiedlevel.

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Innervation:Multiplexingmanysensorsandlayingthem

outsensiblyinsideacomplexstructureisamanufacturing

challengethathasthepotentialtogreatlyincreaseperceptivecapabilitieswiththeabilitytolocalizeallsensingandactuation.

Encoding:Achievinghighinformationthroughputbysiftingthroughlargedataamountseffectivelybyleveragingopticalmodes,biologicalspiking,etc.isadatachallengethathasthepotentialtoprovidemassivedataratesforinformationfusionwithoutexplodingthepracticalwiringandassemblyrequirementstosensinghardware.

Motion

Traversingandnavigatingtheenvironmentisastapleofanysystem/body.Thisfeatisdonedirectlybylocomotion,orby

changingtheenvironmenttosuittheneedsofthesystem/

body.Thedegreesoffreedom(DOF)aredirectlycorrelatedtothecomplexityofthesystem,butcanchangeoveritslifetime,providingincreasedadaptabilityformovement.

ThemainresearchchallengesforMotionarelinkedtoward

thecurrenttechnologicaltrendstodominatetheenvironmentratherthanleveragingit.Beingabletoutilizeiteffectivelywillgreatlyincreasetheenergyefficiencyandsynergyofthebodyandtheenvironment.Themainmotionresearchchallengesarelistedbelow:

Agility:Increasingresponsivenessandpower,without

increasingDOF,willneedtobedonebysupplyingpoweranddatatoactuators;mimickingnature’sbottom-upapproachofself-assemblyallowsforfarmorearchitecturalcomplexity.

Endurance:Withstandingmanycyclesofuse,orusing

lessenergyforoperationsbybeingefficient,willneedthe

utilizationofmultifunctionalenergystorageandtransduction,highenergydensityfuels,storageandreleaseofelastic

energy,andcenterofmassadjustmentsduringlocomotion.

Growth:Changingtotheenvironment(orchangingthe

environment)byadding,subtracting,orchangingdimensions,bodysegments,and/orDOFwithincreasedabilitytoutilize

energywillbeamajorchallengeforthemachine’sbody.

Adaptation

Thenaturalworldhassolvedmanydesignproblemsvia

evolution.Artificialsystemscanbeimbuedwiththiscapability

byutilizingawidevarietyofcomputationaltechniquesdesignedtooptimallymodifythebodytotheenvironment.Anadditionalfeaturetobeexploredistheuseofcollectiveadaptation,in

whichmanybodiesactasawholetoperformspecifictasks,andthuscanbechangedtobetterfittheirenvironment.

ThemainresearchchallengesforAdaptationarecentered

aroundtheco-designofbrainandthebody.Whilenatural

systemsuseevolution,artificialonesadaptfromcentralized

computation.Efficientmanagementofenergyexpenditurealsowillbechallenging,buttakingadvantageofmaterialsscience

andadditivemanufacturingmayamelioratetheseengineeringcontradictions.Themainadaptationresearchchallengesarelistedbelow:

Learning:Logiclinkswillneedtobeincreasedbased

uponnewexperiencesandwillextendbeyondtraditionalneuralplasticitytothebodiesofrobots.Thebodiesas

wellasthebrainsoffuturerobotsmaylearnhowbestto

detectco-occurringfeaturesofexternalchallenges(or

internalchallenges,suchasinjury),andpreparethemselvesmorphologicallyandneurologicallytohandlethose

challengeswhenthere-occur.

Language:Verbalclaimscouldbedemonstratedphysicallyasaself-correctingmechanismforconfabulation;thistask

coulduseLargeLanguageModels(LLMs)asasupplement,butnotasoleuse–astheysufferfromhallucinationsandthegenerationofnon-factualverbalstatements.

Control:ByaddingDOF(andreducingthediscreet

boundariesbetweenthebodyandtheenvironment),the

controlofthesystemswillbeachallenge;therewillbe

kinematicredundancyforsystemswithtoomanyDOFfor

theirtasks(butaddingtheappropriateDOFwillallowfor

moreflexibility);thisinefficiencywillneedtobeaddressedbyselectivelyremovingDOF(oraddingmore).

ATapestryofChallenges

Perception,Motion,andAdaptationareinterdependenttopics

thatwillrequireconcurrentresearchefforts.Subjectssuchas

informationdensitywillneedtobeaddressedutilizingallthree

tobeeffective:Perceptiontoamplifyorfilterdata,Adaptation

tounderstandtheresultinginformation,andMotiontoadjust

forit.Indeed,organismschangebasedontheirenvironments

utilizingallthreeofthese.Inbiology,organismsfocusonrelevantstimuliutilizingsensingorgansanddevelopbehavioralresponseswhichfilteroutunimportantinputs.Theyrespondbasedon

theorganisms’needs,baseduponitsinternalstateandduetolimitedattention/energy.Theyleveragethepastexperiencesoftheorganismvialearningandmemory,whichleadstoinnateresponsesandreflexeswhichhelpsaveenergyandassistin

remodelingandgrowthoftheorganism.

Theinterdependentnatureofartificialsystemsalsorequires

featureintegration.Thesystemfirstselectsthefeaturesthatit

deemsuseful,thenextractsthem(ortheirinformation).Inordertousethenewfeatures,thesystemwillthenregularizethe

newlyacquiredfeaturesandoptimizealgorithmstoreproducethefeaturesforusebythesystem.Unlikebiologicalsystems,

however,theseprocessesconsumelargeamountsofenergyandaresubjectedtosignificantlatency.

Totackletheseinterconnectedresearchchallenges,aconcertedeffortmustbemadetofostercollaborationandcommunicationamongresearchersindiversefields.Increasingknowledge

transferbetweengroupsofresearcherswithdefinedtaxonomyandcommonlanguageisafirststeptothisgoal.Concerted

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Testing,Evaluation,andValidation(TEV)willalsobeparamounttorealizingthisobjective.Transdisciplinaryresearch,whichincludesmaterialsscience,manufacturing,computerscience,mechanicalengineering,andEIdesignwillneedtobewoventogether.

ResearchOpportunities

Asengineeringadvancesproduceevermoresophisticated

artificialsystems,therearetremendousresearchopportunitiestolearnfrombiologicalones.Indeed,organismalbiologyalreadyshowstheabilitytofocusonrelevantstimuli,respondbasedonneeds,andleveragepastexperiences.Thesesystemscanalso

bestudiedtoobservetheirabilitytoidentifyandintegratenewfeaturesfromtheenvironment,perhapsrevealingkeyinsightstobeabletotranslatesuchfeaturestosyntheticsystems.Withtheadvancementsofotherfields,thereexistsmanyopportunities

forexcitingdevelopmentsandresearchtobeconductedinthefieldofembodiedintelligence.Someoftheseincludeadditivemanufacturing,neuromorphiccomputing,biohybridrobotics,autonomousmaterialsystems,andelectrochemistry.

ResearchTrajectory

Theworkshopparticipantsdevelopedatrajectoryforthe

researchopportunitiesidentifiedforthefieldofembodied

intelligencewithavisionforthe5-,10-,and20-yearhorizons.

Five-yeargoals

Intheimmediatefuture,EIwillaugmentexistingrobot

architectures.Theserobots,equippedwithsimplecontrolloops

informedbyanalogsensingandprocessinglayers,commanding

actuators(e.g.,continuum,compliant,standard)willbecapable

ofreducedenergyexpenditureduringmobilitytasksormore

dextrousperformanceinassemblytasksforexample.These

robotsmayfeature,asanexample,endoskeletalstructureswith

softactuatorsandskins,mediatingreconfigurabilitybasedontaskrequirements.Theuseofcompliantmanipulatorsandsoftskinswillimprovetheiragilityandendurancecomparedtonon-EIsystems.

KeyGoals:

?Developconsensusmetricsforenergyconsumptionduringstatetransitions(e.g.,trottingtocantoring),aswellasagility(e.g.,accelerationandturnradius).

?Establishfoundationalcontrolstrategiesusinglogicalbasisfunctionsforcoordinationoftasks.

Overthenextdecade,EIisexpectedtoleveragepriorresultsinanalogsense-act-respondfunctionstoproduceasetoflowlevelrobotsthatdemonstratetheseprincipleswithspecific

functions,akintoorgansor“polyps”seeninbiology(Figure5).Theresultsmaybeakintoreconfigurablesystemsofmodulesmediatedbyanalogcomputationallayersthatcanconfigure

for(asanexample)externaldexterityor(anotherexample)

internaloperationalefficiencyforexistingtasks.Importantly,thedevelopmentofbasisfunctionsforthesetofmoduleswillplayacriticalroleinthisphase,allowingrobotstobedynamically

assembledanddisassembledinresponsetoenvironmentalortaskchanges.

KeyGoals:

?Enumerationofagilityandendurancerequirementsfor

generalpurposerobotics(thesenumbersshouldbearrivedatbeyondjustEIcommunity)

?Defineasetoflow-levelEImodulesthataddresstherequirementsforagilityandendurance

?Algorithmsdevelopedthatprovidethebasisfor

coordinationbetweenthesemodules(digitalandanalogsolutions)

Long-Term(20Years)

Inthelongterm,EIresearcherswillunderstandhowtobestleveragelivingandsyntheticapproachestobuildlow-level

EImodules.Thebasisfunctionstocoordinatethelow-level

biohybridrobotstoautonomouslyassembleanddisassemble

themselvesintomorecomplex,high-levelrobotswillbeknown.Thesehigh-levelrobotsaremoresophisticated,capable

ofperformingcomplextasksandadaptingtochanging

environments.Thissynthesiswillenablethedevelopmentof

general-purposerobotscapableofgrowth,reconfiguration,andcontinuousadaptation.Logicalbasisfunctions(e.g.,autonomousmaterialcomputation)(Yamadaetal.,2022)willbefullyintegratedintotherobot’sarchitecture,enablingseamlesscoordination

acrossmultiplerobotsinvariousenvironments.Inadditiontothecoordinationoflow-levelrobots,wealsoanticipateautonomouscoordinationbetweenmultiple(anddifferent)high-levelrobots.

KeyGoals:

?Developautonomousmaterialsystems(AMS)thatallowforindependentsensinganddynamicreconfiguration.

?Implementneuron-basedcomputingforaccelerated

adaptationandcoordinationoflargerobotassemblies.

?Advancemultiplexedhigh-DOFactuatorarraystosupportsophisticatedmotionandstructuralintegrityduring

assemblyanddisassembly.

?Robustapproachestomaintaininglifeinrealworld

environments,aswellasmediatingtheirinterfacewithartifices.

?CommunicationprotocolsinadditiontoRFandvisualspectrumsignaling,suchasacousticandchemical.

OpportunitiestoAchievetheseGoals

Thisworkshopreportoutlinestheopportunitiesandapath

forwardforresearchinthefieldofembodiedintelligence.

OneaspectistheutilizationofDOF,bothtomanipulate

andunderstandthelimitations,thatwillbeintegraltothe

advancementofthefield.Challengesofmanufacturingand

computationalefficiencymustbeaddressedalongsidelong-termtestingprotocolsandenergyconsiderations.Aconcertedeffortmustbemadetobringtogetherthecommunityto

addressthesechallengesthroughinterdisciplinaryresearch

andcollaboration.Improvingcommunicationandidea-sharingwithinthecommunityisimperativeforthefutureofthisfield.Theparticipantsemphasizedtheimportanceofthefollowingtechnologyareas:

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MaterialsandManufacturing:Advancesinmaterialsand

manufacturingwillenablerobotstobedesignedwithmore

heterogeneousmaterialswhichreduceandeventuallyeliminatetheneedforsubsystems.AutonomousMaterialSystemswill

allowforahighdegreeofadaptabilityinrobots,withreducedcostinmanufacturing.

AdaptationandComputation:Advancesincomputational

hardwarewillenablehyper-efficientcomputationsystemsthat

integratesseamlesslywithphysicalsubstrates,enablingmore

efficientandadaptivebehaviors.Thesesystemswilloperate

beyondcurrentdigitalcommunicationsandmemoryandratheruseanalogandbioticcomputationwithenhancedresponse

speedsand/orreducedpowerconsumption.Asmorecomplex

LargeLanguageModels(LLMs)aredevelopedandintegrated

withotherinteractionmodes,thecommunicationbetween

Perception,Adaptation,andMotiondomainswillbecomemore

efficientandcapable,allowingforhighercomplexityandcompute.

ApplicationFocusAreas

Thereexistmanyapplicationsforthesenewtechnologies.Focusareasfortheseuse-casesinclude:

DailyLifeandLaborReplacement:Societywillthriveinanewerainwhichroboticassistancereduceshumanwork,addressinglaborshortagesandremovingriskfromhumanworkers.

HealthcareandRobotics:Affordablesoftrobotsforpatientcarewillallowforpreciseandenhancedpatientrecovery,withhard

exoskeletonsutilizedforrehabilitationandemergencyresponse.

AdvancedTask-SpecificRobots:Low-costrobotswillbeavailableforuniquetasks,whichwillbesimplerbutmoreeffectivethancurrentrobots.

AcceleratingtheField

Theparticipantsdiscussedmeansforacceleratingthefield.Theynotethatanincreasedfocusonpartnershipswithindustrywill

yieldmoreefficientandviableadvances.Keyenablersinclude:

CollaborationandCommunity:Interdisciplinarycollaborationbetweenrobotics,biology,AI/MLwillneedtodeveloptolaythefoundationforubiquitoususeofrobotsinsociety.Trainingprogramswillalsoneedtomirrorthesecollaborations,with

holisticandcomprehensivelearningandteachingofthenextgenerationofresearchers.

MetricsandEvaluation:standardizedtestingandassessment

willbenecessarytostreamlinetheadvancementsinthefield.ADARPARoboticsChallengeforEmbodiedIntelligence,for

example,wouldpushthefrontiersofroboticsbypromoting

integrationofEmbodiedIntelligencewithinexistingrobots.

Successfulprojectsthatdisplayedtruemasteryofperception,motion,andadaptationwithlowenergyexpenditureswouldbecrucialtodriveforthfutureEmbodiedIntelligenceresearchanddevelopment.

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Introduction

TheideathatthebodyandbrainareseparatedisanassumptionaboutmachineintelligencethatwasformedinthedistantpastbutcontinuestoconstrainhowweapproachAIandrobottechnologydevelopmenttoday.Thisreportisanattempttoformulatea

newviewofembodiedintelligence,freeofpriorassumptions,

topromotestepchangesinrobotics.Weanticipateprogress

inthisdomainwilldramaticallyimproveagility,endurance,anddamagetoleranceinourautomatedmachinery.Wenotethatinthisemergingfield,theterminologyusedtolabelitisconfusing:

ArtificialIntelligence,PhysicalIntelligence,EmbodiedIntelligence,aswellasseveralotherphrasesareusedsynonymouslyand

sometimesantonymously.Toaidinreadingthisreport,wemakeabriefattemptatclarifyingsomeofthemoreimportantterms:

?ArtificialIntelligence(AI)isusedtodescribealgorithms

represented,typically,insoftwarethatprovidesoutput(e.g.,recommendationsoractuations)basedoninputs(e.g.,

instrumentedmeasurementsorhumansuggestions).

?PhysicalIntelligence(PI),isusedforrobotsthathave

AIembeddedinfirmwareandoperatinglocallyon

autonomoushardwareaswellasasynonymforEmbodiedIntelligence.Fortheformerdefinition,PItendstoassume

athermodynamicallyclosedmachine(themassandenergyavailabletothemachinecomefromwithin).

?EmbodiedIntelligence(EI)isusedtodescribesystems

thatblurthelinesbetweenthemachine’sbodyandthe

environmentinwhichitisinteracting;ultimately,itwillbeananalogapproachtointeractingwiththeworld.EIenvisionsthermodynamicallyopenmachinesthatcanincorporatenewmassandenergytorecoverorexpandtheircapability.

EmbodiedIntelligenceblurstheinterfacebetweenmachineandenvironment,andbetweentheboundariesofinternal

componentsormodules.Inexternalinteractionswiththe

environment,forexample,EIsystemsallowthegravelbelowarobot’sfoottochangetheshapeofthefoot—storing

energy,addingstability,andbecomingpartofthemachinefor

millisecondspriortorelease.Aprojectileimpactingasurface

maypartiallyandreversiblyimbeditselfintothevolume,the

newlyformedobjectcanmakeadecisionwhethertoacceptor

rejectthenewformatthespeedofsound.Achemicalspraymaychangethemacromolecularorientationofthesurface,changingitsopticalandmechanicalproperties,displayingawarningto

humanteamsandchangingthetrajectoryofarobotawayor

towardsthesource.Internaltoamachine,multipleinteracting

low-levelroboticsubsystemscouldsynthesizemorecomplex

autonomy;thisfunctionisseenbiologicallyinzootic