【美国安全与新兴技术中心】物理人工智能

February2026

CenterforSecurityandEmergingTechnology|1

ExecutiveSummary

Whiletheworldhasfocuseditsattentionforthelastthreeyearsongenerative

artificialintelligence,chatbots,andnewmodelreleasescomingfromfrontierAIlabs,aquieterrevolutionistakingplacethatmanybelieverepresentsthenextstageinAI

development:thearrivalofPhysicalAI.LiketheiPhone’sintroductionin2007,

AlexNet’svictoryinthe2012ImageNetcompetition,andChatGPT’sreleasein2022,analystsandindustryrepresentativesbelieveasimilarbreakthroughisimminent.

PhysicalAI“letsautonomoussystemslikerobots,self-drivingcars,andsmartspacesperceive,understand,andperformcomplexactionsinthereal(physical)world.

”1

NVIDIAhasdeclared“inthenearfuture,everythingthatmoves,orthatmonitorsthingsthatmove,willbeautonomousroboticsystems.

”2

OpenAIreportedlyre-openedits

roboticsdivisioninearly2025tocapitalizeontheconvergenceofAIandrobotics,

whilestartupsfromShanghaitoSiliconValleybuildingthe“brains”ofrobotsare

raisinghundredsofmillionsofdollars

.3

ElectricvehiclemakersTeslaandXPengareracingtodevelophumanoidrobotsoftheirown

.4

MeanwhileAmazon,whichreportshavingonemillionrobotsinoperationtoday,believes“PhysicalAIisabouttochangeeverythingforrobotics[including]autonomy,manipulation,sortation,andcomputer

vision.

”5

Addingtothisenthusiasm,analystsatMorganStanleyassertthemarketforhumanoidrobotswillgrowfromtensofmillionsofdollarstodaytoreach$5trillionby2050

.6

YettheconvergenceofAIandroboticsissonewthatthefieldlacksasharedname,tosaynothingofamaturetechnologystack.Somecompaniescallthisconvergence

“embodiedAI”whileothersprefer“physicalAI,”“embodiedmachineintelligence,”or“generativephysicalAI.

”7

ItisnotatallclearifthehypearoundAIprogresscan

translateintorobotsfindingtheirwaythroughthephysicalworld:autonomousthree-dimensionalnavigationofdynamicenvironmentsrequiresamaturesoftware,

hardware,anddataecosystemthatsimplydoesnotexistatscaletoday.NVIDIAstatespartoftheproblemplainly:“Largelanguagemodelsareone-dimensional,ableto

predictthenexttoken,inmodeslikelettersorwords.Image-andvideo-generationmodelsaretwo-dimensional,abletopredictthenextpixel.Noneofthesemodelscanunderstandorinterpretthe3Dworld.

”8

TheprimarychallengesfacingPhysicalAIarethesameonesthathavetroubledtheroboticsindustryforgenerations:technologybarriersandeconomicbarriers.Partsoftheroboticssupplychainremainintheirindustrialinfancy,keyhardwaretechnologybreakthroughsremainelusive,andevenrecentadvancesarenotreadyforscalablemanufacturing.Batteries,motors,sensors,andactuatorsevolvefarmoreslowlythan

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algorithmsandsoftware,andscalablemanufacturingrequireslargeamountsof

patientcapital.Inaddition,muchofthesupplychainforroboticscomponentsis

commoditized,andtherelativelyslimmarginsdissuadeinnovativestartupsfrom

competingwithestablishedincumbents.Addingtothesechallenges,eachrobotics

companyispursuingitsownuniqueapproach,meaningthesupplychainof

componentsandpartsremainslargelynon-standardized,hamperingscalabilityand

addingcost.Thegapbetweenimpressivedemonstrationsincontrolledenvironmentsandthepromiseofmillionsofaffordablerobotsactingindependentlyastheynavigatetheworldisenormous.

ThefocusofthispaperisoncharacterizingtheconvergenceofPhysicalAIand

robotics,itsunderlyingsupplychain,andidentifyingcompetitiveadvantagesaswellasconstraints.Thispaperprovidesbackgroundonthetechnologyanddescribesthe

ecosystemandsupplychainofhardwareandsoftwaresupplierssupportingthe

technology.Itthencharacterizescompetitivenessworldwideusingbibliometrics,

patents,investmentdata,andindustryreportstodeterminefirmleadership,

constraints,andbreakthroughsacrossthetechnologyecosystemfromAIfoundationmodelsandsoftwaretohardwarecomponentandrobotmanufacturersaswellasendusers.Itconcludeswithasummaryofdriversandpositivetrends,aswellas

constraintsandlimitingtrendswithaneyetowardsopportunitiespolicymakers

interestedinpromotingthetechindustry’snextbreakthroughmomentcanconsider.

ThispaperbuildsonpreviousCSETresearchlookingattheroboticspatentlandscapetocharacterizecompetitivenessusingCSET’s

MapofScience

andseparateresearch

thatproposedamethodologyforidentifyingandcharacterizinganemerging

technology

.9

Itconcludesbyintroducingatemplatethatcouldbeusedbypolicymakersinterestedinglobalcompetitivenessassessmentofotheremergingtechnologies.

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TableofContents

ExecutiveSummary 1

Introduction 4

ScopingandDefiningtheAI-RoboticsSupplyChain 7

ManifestingPhysicalAI:DescribingTheRoboticsHardwareSupplyChain 7

BuildingSentientSilicon:AIAdvancesandtheRoboticsSoftwareSupplyChain 12

CompetitivenessAssessment:AI-RoboticsConvergence 15

InnovationEcosystemMapping:AI-RoboticsResearch,Patents,andInvestment 15

ETOResearchAlmanac:PublicationsandPatents 15

ETOCountryActivityTracker:InvestmentData 17

CharacterizingRelativeNationalStanding:AI-RoboticsMarketAnalysis 18

AI-RoboticsFoundationModelsandtheSoftwareEcosystem 19

RoboticsHardwareComponents 23

RobotManufacturers 25

RobotDeploymentandEndUsers 26

Conclusion:TechnologyTrendsAssessment 29

Author 31

Acknowledgments 31

Appendix1.TemplateforTechnologyCompetitivenessAssessment:

TechnicalLevel-Setting 32

Endnotes 35

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Introduction

PhysicalAIequipsautonomousmachineswithcognitivereasoningandspatial

knowledge,enablingthemtolearnfromtheirinteractionsandrespondinrealtime

.*

Theseautonomousmachinestakemanydifferentforms,andthefieldisemergingin

nearrealtimesuchthatthefinal,optimalform(s)PhysicalAItakesmaynotyetbe

invented.CurrententhusiasmforPhysicalAIprimarilycentersonintegrationwith

robotics.Ingeneral,humanoid,industrial,andautonomousmobilerobots(AMRs)areemergingasleadinginstantiationsofPhysicalAI

.10

Theserobotsrelyonatechnologyecosystemtosupporttheir“brain”(simulationandvisionsoftware,datascience,

semiconductors,andAImodels(includingLLMsandmultimodalfoundationmodels))aswellastheir“body”(sensors,batteries,(more)semiconductors,actuators,andotherphysicalhardware)

.11

Figure1:NotionalHardwareInputsforaHumanoidRobot

Source:MorganStanleyResearch

.12

*Thisdefinitionisderivedfrom

:/en_US/home/explore/physical-ai.html

.The

termsPhysicalAIandEmbodiedAIarecloselyrelatedandfrequentlyusedinterchangeably.PhysicalAItypicallyreferstoAIsystemsthatcanperceive,reasonabout,andinteractwiththephysicalworld.

EmbodiedAIusuallyrefersmorespecificallytoAIsystemsthathaveaphysicalformor“body”that

allowsthemtodirectlyexperienceandinteractwiththeworld.ThekeydifferenceisthatphysicalAIisabroaderconceptthatincludesAIsystemsthatreasonaboutoraffectthephysicalworld.

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Robotsthatassistwitheverythingfromfactoryautomationtohazardouswork

environmentshavebeenadreamfordecades.Thisdreamhasrepeatedlyrunintotherealitythatcorehardwareandsoftwareinnovationsunderlyingtheroboticsecosystemhavematuredatdifferenttimes.Untilrecently,collectingthedataandcompute

necessarytotrainrobotsystemsinsimulatedenvironmentswasexpensive,training

robotsintherealworldtooktoolong,andsimulation-basedtrainingresultedinrobotsill-equippedforreal-worldsettings

.13

Addingtothesechallenges,nostandardbillofmaterialstobuildrobotsexists,makingformultipleheterogeneousecosystemsof

componentsandsuppliers,eachpursuingtheirownpreferredsolution.

ArecentconvergenceofAIadvancesandimprovementsintheunderlyinghardware

supplychainthatsupportsroboticsaccountsforthegrowingsensethata

breakthroughinPhysicalAIisimminent.Thischangeinsentimenthasencouraged

largeinvestmentstoaccelerateAIandroboticsconvergence,bothwithinlargepubliccompaniesandtheventurecapitalandstartupcommunities

.14

Thesumofthese

advancessuggesttheemergenceofapositivefeedbackloopwherebybetterAI

modelsimproverobotcapabilities,improvedprospectsfordeploymentofAI-

empoweredrobotsattractsinvestment,increasedinvestmentallowsstartupsand

establishedfirmstomatureandscalehardwareproduction,andbetterhardware

allowsforimproveddatacollectionthatcanfeedbackintoenhancedAIimprovementsthatoptimizerobotperformance.Intheory,thisfeedbacklooppromisesanewscalinglawsimilartoMoore’sLawinthesemiconductorindustry:consistentimprovementinrobotcapabilityandperformanceatsteadytodecliningcosts

.15

PolicymakerinterestinAIandAIapplicationsremainhigh.Thisinterestismotivatedbyeconomicandsocialfactorsaswellasnationalsecurity.WithinthecontextofPhysicalAI,AI-roboticsconvergenceoffersapotentialsolutiontoloominglaborshortages,

especiallyinmanufacturing,logistics,andhealthcareindustries.Likewise,the

economicappealofarobotworkforcethatcanworkcontinuouslyandconsistently

withalevelofprecisionthatexceedshumanabilityholdscleareconomicappealformanufacturers.Additionally,robotscanworkindangerousenvironmentsunsafeforhumans,promisingtoreducethethousandsoffataloccupationalinjuriesthathappenintheUnitedStatesaloneannually

.16

Finally,militaryinterestinroboticapplicationshaspersistedfordecadesandremainshigh,particularlyintheUnitedStatesand

China

.17

YetdespitethefavorableconvergenceofAI-drivenbreakthroughsandintensepublicinterest,theroboticsrevolutionhasnotarrivedontime,andfurtherdelaysseem

inevitable.Newindustrialrobotinstallationsdeclinedworldwidefrom2023-24in

spiteofhoped-forprogressthatautomationwouldalleviateimpendingmanufacturing

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laborshortagesintheU.S.,China,andelsewhere

.18

Industrialrobotswell-suitedto

workwithsolidphysicalobjects(boxes,carparts,consumerelectronics)fail

spectacularlywhenpresentedwithsoftandstretchyobjectscommonintextile

manufacturing

.19

Humanoidrobotsin2026cancompletehalfmarathons,dancein

festivals,andwalkwithahuman-likegait,buttheystruggletoindependentlynavigatedownastreet,cannothandletasksrequiringdexterity,andrarelyexceedhuman

performanceinwarehousesettings

.20

Robotscontinuetodobestwhendesigned,trained,anddeployedfordiscretetasksandfailwhenforcedtoadapt.ItisnotclearhowmuchtheintroductionofAIcanandwillresolvethesepersistentchallenges.

Againstthebackdropofthesetechnicalheadwinds,economiccompetitionoverAIingeneralisaddingurgencytoAI-roboticsprogressspecifically.Chinesefirmsare

leveragingadiverse,flexible,andscalablemanufacturingbasetotaketheleadinthesupplyofkeyroboticshardwarecomponents,developingawidevarietyofrobots

domestically,andleadingtheworldinrobotinstallations.U.S.firmslikeNVIDIAandMetaarereleasingopen-sourceAI-roboticsmodelsinthehopesthattheycanseedaninnovationecosystemwiththeirpreferredsoftware(muchlikeNVIDIAaccomplishedwithitsGPUsandCUDAtoolkit),andU.S.investmentinroboticsstartupsissurging.

FirmsinAsiaandEuropecontrolthesupplyofkeyhardwarecomponentsandsoftwareand,insomecases,continuetoenjoyfirst-moveradvantagesasroboticsendusers.

ThispaperbeginsbymappingtheroboticssupplychainthatsupportsPhysicalAI.

“ManifestingPhysicalAI”and“BuildingSentientSilicon”describethehardwareand

softwaresupplychainsunderlyingAI-roboticsconvergenceandsummarizerecent

breakthroughs.Next,thepaperpresentsacompetitivenessassessmentofAI-roboticsconvergencefromtwoperspectives.First,usingresourcesfromCSET’s

Emerging

TechnologyObservatory,

bibliometrics,investmentdata,andpatentfilingsare

analyzedtoidentifycountriesandcompaniesleadinginAI-roboticsconvergence

.21

Second,thespecificsegmentsofthesupplychainintroducedearlierareevaluatedwithaneyetowardcompany-andcountry-levelleadership.Thissectionexpandsona2024RANDmethodologyandcombinesitwithpreviousCSETresearchaswellasCSET

bibliometricresourcestoquantitativelyandqualitativelyassessleadership,breakthroughs,roadblocksandsignalsofPhysicalAIemergence

.22

ScopingandDefiningtheAI-RoboticsSupplyChain

ThissectionpresentsthebasicindustrymappingofthePhysicalAIecosystem,

focusingonhardwareandsoftwareinparticular.Thefirstsection(“Manifesting

PhysicalAI”)defineswhatarobotis(andisnot),introducesthedifferentformsrobotscurrentlytake,describesthecorehardwaresubcomponentsonwhichalldifferent

varietiesofrobotsrely,andsummarizesrecentcomponentbreakthroughsthathavecontributedtothecurrententhusiasmaroundAIandrobotics.Thesecondsection

(“BuildingSentientSilicon”)describesthevariousinstantiationsofsoftwarethat

supportAI-roboticsconvergence.Itdescribesbreakthroughsinlargelanguagemodels,multimodalmodels,reinforcementlearning,andsimulation-to-realitytransferthat

havecontributedtothecurrententhusiasmforAI.Inaddition,itdescribesthevariouslayersofcomputeunderlyingthesesoftwareadvances(akeyenablingpartofthis

supplychain)aswellastheroleofreal-worldandsyntheticdataandroboticssimulationandoperatingsystemsoftware.

ManifestingPhysicalAI:DescribingtheRoboticsHardwareSupplyChain

Theleadinginternationalroboticsassociationdefinesarobotas“anactuated

mechanismprogrammableintwoormoreaxeswithadegreeofautonomy,moving

withinitsenvironment,toperformintendedtasks.

”23

Helpfully,italsoexplains

specificallywhattheydonotconsiderarobot:software,drones,voiceassistants,

autonomouscars,ATMs,andsmartwashingmachinesarenotconsideredrobots

.*

InthecontextofPhysicalAI,robotsareoftenthoughtofasautonomousmobilerobots

(AMRs),manipulatorarmsorhumanoids

.24

Therearedozensofothertypesofrobots,includingquadruped(dog-like)robots,medicalrobots(designedtoassistwithsurgery,forexample),andcollaborativerobots(or“cobots,”)thatworkwithahuman-in-the-

loop,typicallyinindustrialenvironments.Ingeneral,allrobotstypicallyconsistoffivecorehardwaresystems:

-Structuralcomponentsprovidethephysicalframework,support,andprotectionforallothersystems.Forhumanoidrobots,asthenameimplies,thesestructuralcomponentsareanthropomorphicandgenerallyaredesignedtoprovidejoint,weightdistribution,movement,andmanipulationthatmimicshumanform.

-Actuationsystemsgenerateandcontrolphysicalmovement.Forindustrial

robotics,thesesystemsrelyonprecisionmotorsthatcanoperaterepeatedlyat

*Thereisroomfordisagreementaboutwhatconstitutesarobotbaseduponthisdefinition,butforthesakeofaccuratelyrepresentingthedataIFRcollects,thispaperusestheIFRdefinitionwhenreferringtostatisticsonroboticsadoptioncitedinsubsequentsections.

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highspeeds,manipulatingheavyloads.Forhumanoidrobots,thesesystemscanrequiremoreprecisionandlessload-bearingability.

-Powersystemsprovideanddistributeenergytoallsystems.Industrialrobotsareusuallyconnectedtoafixedpowersupply,whilehumanoidrobotsand

AMRsrelyonbatteries.

-Computingsystemsprocesssensordataandcontrolrobotbehavior,with

humanoidrobotshavingthemostcomplexcomputestackandindustrialrobotsamongtheleastcompute-intensive.

-Sensorsystemsperceivetheenvironmentinwhichtherobotoperatesaswellasitsinternalstateandincludecameras,sensorsfordepthperception,

torque/force,jointposition,andLiDAR.

Theroboticshardwaresupplychainsuffersfromalackofstandardization,which

hamperseconomiesofscaleforcomponentsupplierswhoareinsteadforcedtosupplyhundredsorthousands(atmost)ofdifferentproductstomeetdemand.Thisinturn

limitsthecomponentsuppliermarginsandtheirabilitytoreinvestinfutureadvances.AsTable1shows,whileallrobotssharethesefivetypesofcomponentsingeneral,thespecificationtowhicheachofthesecomponentsisrated,theiroperating

environment,andintendedendusenecessitateahighlevelofcustomization.

Additionally,morecomplexrobotsinherentlyrequiremorematerials,gears,mechanisms,andcompute,allofwhichdriveupcosts.

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Table1:HardwareBillofMaterialsforThreeTypesofPhysicalAI-PoweredRobots

HumanoidRobot

IndustrialRobot

AutonomousMobile

Robot

Example

Physical

Structure

oFull-bodyframewith~20-30degreesoffreedom

oAnthropomorphicproportionsandjointdesigns

oLightweightmaterials(carbonfiber,aluminumalloys)

oHeavy-dutyfixedbase

oRigidarmlinks(4-7segments)

oIndustrialsteel,aluminum

oWheeled-baseplatform

oPayloadstructure

oProtective

bumpers/housing

Actuators

o20-30+high-performanceelectricmotors/actuators

oCustomgearboxeswithhightorquedensity

oSpecializedjointmechanisms(serieselastic,directdrive)

o6-7high-precisionservomotors

oIndustrialgearboxes

oBrakingsystems

o2-4drivemotorswithwheels

oOptionalliftmechanism

Power

oHigh-densitybatterypacks(2-4kWhtypical)

oPowerdistributionsystem

oFixedpowersupply(typicallynobattery)

oBatterypack(1-3kWhtypical)

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oThermalmanagementsystem

oElectricalcabinet

oCharginginterface

Compute

oHigh-performanceonboardcomputer(oftenmultiple)

oGPU/TPUforAIinference

oMultiplemicrocontrollersforlow-levelcontrol

oIndustrialcontroller

oSafetyPLC

oTeachpendantinterface

oNavigationcomputer

oMotorcontrollers

oFleetmanagementinterface

Sensors

oStereo/depthcameras(2-4)

oForce/torquesensorsatkeyjoints

oIMUsforbalance

oTactilesensorsforhands/grippers

oMicrophonesforaudioinput

oEncodersateachjoint

oLimitedexternalsensing

oOptionalvisionsystemforguidance

oLiDARfornavigation(1-2units)

oCamerasforobjectdetection

oProximitysensors

oWheelencoders

Cost

Range

$50-$500k

$20-150k

$15-80k

Source:Forthecompletelistofdatasourcesforthistable,pleaserefertotheendnote

.25

Thetechnologyforeachofthesehardwarecomponentsmaturedatdifferenttimes,thoughrecentbreakthroughsacrossthehardwarestackareaddingtoindustryandinvestorenthusiasm(Table2).Actuatorshavegottenmorepowerfulandaccurate,sensorsaredecreasingincostandincreasinginquality,commercialoff-the-shelfmicroelectronicscontainmorethanenoughcomputeformostcurrentrobots,andbatterytechnologycontinuestoimprove.Recentresearchfromfinancialanalystssuggeststhatforhumanoidrobots,onlycertainsensors(6Dtorquesensors,tactilesensors)andactuatingcomponents(planetaryrollerscrews)currentlylackmassproducibilitysuchthattheypresentabottlenecktooverallhumanoidrobotics

manufacturingscaling

.26

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Table2:BreakthroughsandAdvancesHardwareforPhysicalAIandLeadingComponentSuppliers

ImprovedActuators:Morepower-dense,responsive,andaffordableelectricmotorsandhydraulic

systems.

SensorTechnology:Bettercameras,depthsensors,tactilesensors,andinertialmeasurementunits(IMUs)atlowercosts.

ComputeIntegration:More

powerfulonboardcomputing

enablingreal-timedecision-making.

BatteryTechnology:Improvementsinenergydensityaremakinglongeroperationtimespractical,especiallyforhumanoidrobotsandAMRs.

MaterialsScience:Advanced

lightweight,strongmaterialsforrobotconstruction.

Source:Forthecompletelistofdatasourcesforthistable,pleaserefertotheendnote

.27

Thisisanoptimisticviewofthehardwaresupplychain.Otherrecentresearchhas

observedthatimitationofhand-likemovementsanddexterousmanipulationin

humanoidrobotsremainsquitedifficultduetobothhardwareandsoftware

constraints

.28

Moregenerally,thehighest-performingrobotscontinuetobethose

whosehardwareandsoftwarestackisoptimizedforspecifictaskssuchassorting

boxesormovingpalletsinawarehouse.GeneralizableAI-poweredrobotscapableof,forexample,workingonanautomotiveassemblylineinonemomentandpivotingtosortthroughabucketofhundredsofmismatchedscrewsthenext,remainaspirational.Theseconstraintswillbediscussedingreaterdetailbelow.

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BuildingSentientSilicon:AIAdvancesandtheRoboticsSoftwareSupplyChain

Atthesametimethehardwarestackhasmatured,recentadvancesinAIhavechangedhowrobotsaretrained,howtheylearn,howtheyinterpretreal-worldfeedback,and

thesourcesofdataonwhichrobotscan“learn,”addingtooverallenthusiasmthatAIisacceleratingthearrivalofabreakthroughmomentforrobotics.AsNVIDIA’sCEO

recentlyputit,“weneedtobuildtheAItobuildtherobots.

”29

Specifically,advancesinLargeLanguageModels,MultimodalFoundationModels,ReinforcementLearning(RL),andSimulation-to-Reality(Sim2Real)Transfersuggestthatroboticreasoning,

perception,skillacquisition,andtrainingarepoisedforcategoricalimprovement.Thesoftwaresupplychainthatsupportsroboticsdevelopmentlackscleardefinitionand

theoreticallycouldextendallthewayupstreamtotheinstructionsetarchitecturesandelectronicdesignautomationtoolsusedbychipdesignerstooptimizetheperformanceofcomputeforrobotics.ThissectiontakesanarrowerviewandfocusesonthespecificAIalgorithmicbreakthroughsthathaveoccurredrecentlytosupportAI-robotics

convergenceaswellastheunderlyinglayerofcomputethatenablesthesebreakthroughs.

Withinthecontextofroboticssoftwarefortrainingandinference,LLMsprovidehigh-levelreasoning,multimodalmodelshandleperceptionandobjectunderstanding,RLenablesskillacquisition,andsim2realmakestrainingfeasibleatscale(Table3)

.30

Together,theycreateatechnicalecosystemwhererobotscanunderstandwhat

humanswant(LLMs),perceivetherelevantaspectsoftheirenvironment(multimodalmodels),learnhowtoaccomplishtasksefficiently(RL),anddosousingrelatively

inexpensivesimulateddata(sim2real)insteadofexpensivereal-worldtraining.As

successivebreakthroughshaveoccurredinrecentyearsacrosseachoftheseareas,

researchersbelievethedayisfastapproachingwhengeneral-purposePhysicalAI

systemsmayfinallybecommerciallyviableacrossmultipledomainsratherthanjustinstructuredindustrialsettings.

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Table3:BreakthroughsandAdvancesinSoftware/AlgorithmsforPhysicalA

I31

LargeLanguageModels(LLMs):SuccessivegenerationsofLLMsdemonstratedthatfoundationmodelscouldserveasreasoningenginesforrobots,enablingthemtounderstandverbalhumaninstructionsinrealtime,decomposecomplextasks

intosimplersteps,andadaptplanswhenencounteringobstacles.

MultimodalFoundationModels:Systemsthatintegratevision,language,and

reasoningprovideamoreholisticunderstandingofphysicalenvironments.Thesemodelsenablerobotstoseeobjects,understandwhattheyare,reasonabouttheirproperties,andmanipulatethemappropriately.Forexample,arobotcanvisuallyidentifyanunfamiliartool,understanditspurposefromitsvisualfeatures,and

determinehowtouseit—allwithoutpriorspecifictrainingonthatexacttool.

ReinforcementLearning:ModernRL(e.g.,offlineRL,model-basedRL)approachescanlearnfromdiversedatasetsofrobotexperiencesandusepredictivemodelstoimagineoutcomesbeforetakingactions,reducingtheamountofreal-worlddata

neededforrobotstomasterskillslikemanipulationandlocomotion.

Sim-to-realtransfer:Bettersimulationtoolsandtransferlearningtechniques

bridgedthegapbetweensimulatedtrainingandreal-worlddeployment,

addressingafundamentaleconomicchallengeinrobotics:thecostandtime

requiredtocollectreal-worldtrainingdata.Byshiftingmosttrainingtosimulation,developerscaniteratequickly,testindiversescenarios,andtraindata-hungry

deeplearningmodelswithouttheconstraintsofphysicaltrainingenvironments.

Source:Forthecompletelistofdatasourcesforthistable,pleaserefertotheendnote

.32

Importantly,alloftheaforementionedadvancesarecontingentonanincreasingly

maturesupplychainofcompute(includingGPUs,CPUs,andMCUs,amongmanyotherkinds)onwhichthissoftwareruns.Inessence,thesoftwarestackreliesonone

computertotraintheAI,onecomputertodeploytheAI,andanothercomputertoserveasadigitaltwin(wheretheAIcangotopractice/employitstraining)

.33

Whilethisisasimplisticrepresentationoftheresourcesrequired,itillustratesthethreekeydomainsinthesoftwaresupplychainandhowinterconnectedtheyare:

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-ComputeforTraining:Fortraining,cutting-edgeAImodelsleveragedistributedcomputingclusterswiththousandsofspecializedaccelerators(e.g.,NVIDIA

GPUsandcustomASICslikeAlphabet’sTPUs)thatenableefficientprocessingofpetabytesofmultimodaldatausingtechniqueslikemodelparallelismandpipelineparallelism.

-ComputeforInference:Deploymentonroboticsplatformsemploysa

heterogeneouscomputingarchitecturewithenergy-efficientedgeprocessors(likeNVIDIAJetsonOrinorotherspecializedAIaccelerators)thathandlereal-timeinference,sensorfusion,andcontrollo