2026年物理人工智能报告（英文版）

PhysicalAI

Takinghuman-robotcollaborationtothenextlevel

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Tableofcontents

04

Whoshouldread

thisreportandwhy

36

PhysicalAIisagame-changerforindustry

56

ThegrowingimperativetoadoptphysicalAI

08

24

WhyphysicalAIis

ataninflectionpoint

Executivesummary

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

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Conclusion

70

96

105

ReseaΓchmethodology

ScalingphysicalAIgoes

beyondtechnology,spanningsafety,cybersecurity,

Recommendations:

Acceleratingthe

physicalAIrevolution

78

Humanoidssetthestage

forgeneral-purposerobotics

regulation,andoperations

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

WhoshouldΓeadthisΓepoΓtandwhy?

Thisreportisintendedforseniorexecutives

shapingtheirorganizations’approachto

roboticsandautomation.Itexamineshow

physicalAIistransformingrobotics–fromthecapabilitiesitenablestothevalueitunlocks,

thetimelinesforadoption,andthebarriersthatmustbeaddressedtoscaledeploymentssafelyandeffectively.Itwillbeparticularlyrelevanttotechnologyandinnovationleaders(including

chieftechnologyofficers,chiefinnovation

officers,chiefdigitalofficers,andheadsofAIorrobotics),aswellasmanufacturing,supply

chain,andlogisticsleadersresponsibleforroboticsstrategyanddeployment.

Asroboticsexpandsintoconsumer-facingandserviceenvironments–suchashealthcare,

retail,hospitality,andentertainment–the

reportisalsorelevanttochiefproductofficers,productstrategists,andexperiencedesign

leaderswhoareshapinginteractionsbetweenpeopleandintelligentmachines.

Inaddition,thereportprovidespractical

guidanceforCROsandsafetyorregulatory

leaderspreparingtheirorganizations

forwiderroboticsadoption–including

implicationsforgovernanceandriskoversight.

Thisreportdrawsonaglobalsurveyof

1,678seniorexecutivesacross15industries,complementedbyin-depthinterviewswithindustryexperts,robotmanufacturers,

foundation-modelstartups,technologyproviders,investors,andacademics.

Pleaseseetheresearchmethodologyattheendofthereportformoredetails.

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

WeextendouΓsinceΓethankstothemanyexpeΓtsfΓomindustΓyandacademiawhoshaΓedtheiΓinsightswithus

DanielaRus

Director,ComputerScienceandArtificialIntelligence

Laboratory(CSAIL),MIT

RebeccaYeung

StrategicAdvisoratDexterityandformerCorporateVicePresidentforOperationsScienceand

AdvancedTechnologyatFedEx

DeepuTalla

VPandGM–Robotics&EdgeAI,NVIDIA

AshutoshSaxena

FounderandCEO,

TorqueAGI

SanjayAggarwal

VenturePartner,

F-PrimeCapital

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

AngeloCangelosi

Co-DirectoroftheManchesterCentreforRoboticsandAI,

UniversityofManchester

NageshPuppala

GeneralManager,RoboticsandPhysicalAI,ClientComputing

Group,IntelCorporation

MiladMalekzadeh

Co-FounderandVicePresidentAI,

NeuraRobotics

PedroZheng

SeniorRegionalSalesManager,

UnitreeRobotics

AntoPatrex

FounderandCEO,

CosmicBrainAI

JimMa

RegionalTechnicalDirector,

UnitreeRobotics

DanielJacker

CEO,ZaiNar

JulienPerrin

COO,Niryo

DirkGeiger

SeniorDirectorandTeamLead–HumanoidRobotics,

InfineonTechnologies

VikiYang

OverseasSalesDirector,

UBTECHRobotics

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

"ThelastdecadeofAIwas

aboutinformation.Thecomingdecadewillbeaboutaction."

PhysicalAItakesAIbeyondscreensintotherealworld–enablingmachinestoperceive,reason,andactautonomously.Thisreportfocuseson

itsapplicationinrobotics,wherephysicalAI

representsafundamentalshift:fromrobots

thatfollowfixed,pre‑programmedpathsto

robotsthatcangeneralizeacrosstasks,perceiveandnavigatecomplexenvironments,make

context-awaredecisions,andadapttoreal-worldvariation.Thisenablesrobotstofunctionin

farmorediverseanddynamicenvironments,

RebeccaYeung

StrategicAdvisoratDexterityandformerCorporateVicePresidentforOperationsScienceandAdvancedTechnologyatFedEx

expandingtheirreachacrossnearlyeverymajorindustryandunlockingsolutionstoproblems

earlierautomationcouldn’taddress.

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

TraditionalroboticsversusroboticspoweredbyphysicalAI:AcompaΓison

Traditionalrobotics

Keyfeatures

PhysicalAI-poweredrobotics

Limitedperception–senseswithoutinterpretation

Perception

Perceivestheenvironmentthroughrich,multi-modalsensing

(vision,depth,touch,audio)andinterpretscomplexenvironments

Worksonlyinstructured*environments(consistent,predictablesettings)

Adaptability

Operatesinunstructured**environments(messy,variable,dynamicsettings),includingpreviouslyunseensituations

Hasnorealautonomy;followspre-programmedinstructions

Autonomy

Makescontext-awaredecisionsinrealtime

Noongoinglearning;behaviorisstaticunlessreprogrammed

Learningcapability

Learnsfromdemonstrations,simulations,andexperience,

improvingperformanceovertimewithoutmanualreprogramming

Designedforasingle,specializedtask

Generalization

Handlesmultiplescenariosonasinglerobot;generalizeslearnedskillstonewtasksandunfamiliarsituations

Robotsoperateindependentlywithnoknowledgesharing

Collectivelearning

Robotsshareskillsa∩dlear∩i∩9sacrossaHeet

Requiresprecise,codedcommands

Natural-languageunderstanding

Understandsnaturallanguageinstructions

Canexecuteassemblyonlyinastrictlyprogrammedmanner;failseasilyifpresentedwithanyslightdeviationfromprogrammedsequence

Example

Capableofadaptingautonomouslytovariationinassemblyprocessandsupportstailoredassemblybyadjustingdynamicallytoeachuniqueproduct

*Structuredenvironments:Environmentswherethelayout,tasks,andconditionsarepredictableandconsistent,allowingrobotstofollowfixedpathsandroutineswithlittlevariation.Examples:assemblylines,controlledwarehouseaisles.

**Unstructuredenvironments:Environmentsthatarevariableandunpredictable,whererobotsmustadapttochangeanduncertainty.Examples:retailfloors,hospitals,farms,constructionsites.

ForamoredetaileddescriptionofphysicalAI,itsapplicationinrobotics,andindicativeindustryusecases,pleaserefertotheAppendix.

Source:CapgeminiResearchInstituteanalysis.

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

TounderstandtheimpactofphysicalAIon

roboticsandthevalueitcanpotentiallyunlock,thisreportdrawsonaglobalsurveyof1,678executivesacross15industries,complementedbyin-depth

interviewswithexpertsacrossthephysicalAI

androboticsecosystem(pleaseseetheresearchmethodologyformoredetails).

PhysicalAIisataninflectionpoint

MultimodalfoundationmodelsaΓeΓedefining

robotintelligencebyenablinggeneralization

acrosstasksandenvironments.Theseadvancesareallowingrobotstoadapttounfamiliarsituations

withouttaskspecificΓepΓogΓamming,extendingdeploymentintounstructuredenvironments

–messy,dynamicsettingsthatearlierrobotic

systemscouldnothandle.Inparallel,advancesinsimulationareshorteningrobottrainingcycles,whileanAl-Γobot-dataflywheelisacceleΓating

improvementwitheveryreal-worlddeployment.Combinedwithfallingcostsofkeyhardware

componentssuchassensors,actuators,andelectricmotors,andcommercialmodelssuchasrobotics-as-a-service(RaaS),theseshifts

areloweringbarrierstoadoption.Atthesametime,demographicandeconomicpressures

–includingagingworkforcesandpersistent

laborshortages–areintensifyingdemandfor

roboticsystemscapableoftakingonrolesthataΓeincΓeasinglyhaΓdtostaff.RecoΓdventuΓe

capitalinvestmentintophysicalAIandroboticsisaddingtothemomentumbehindtheseshifts.

Agame-changeracrossmultipledimensions

PhysicalAImarksastepchangefromearlier

automation.Byenablingrobotstointerpret

context,adaptinrealtime,andoperatein

unstructuredenvironments,physicalAIpromotesthemfrompassivetoolstoactivecollaboratorsintheworkspace–openingthedoortoareimaginedworkenvironment,inwhichhumans,robots,

andAIagentsworkintandem.Atthesametime,

physicalAIallowsroboticstoscaleasashared

intelligenceplatform,withlearningandcapabilitiescompoundingacrossdeployments.Indoingso,

physicalAIextendstheagenticparadigmintotherealworld,enablingrobotstoactasembodied

AIagentscapableofplanning,orchestrating,and

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

executingcomplexphysicaltasks.Overtwo-thirds(67%)ofexecutivesviewitasgame-changingfortheirindustryandmostbelieveitwillbecomea

criticaldriverofcompetitiveness.

67%

ofexecutivesviewphysicalAIasgame-changingfortheirindustry

PhysicalAI’svalueismulti-faceted.Executivesexpectthestrongestgainsinproductivity,

e代cie∩cy,a∩dquality,alo∩9side9reater

operatio∩alresilie∩cea∩dHexibilityasadaptiverobotshelporganizationsmanagevolatilityandreco∩fi9ureoperatio∩squickly.PhysicalAIalso

improvesworkplacesafetyandreducesphysicalstrain,asrobotsincreasinglytakeonhazardousandphysicallydemandingtasks.Beyond

operationalimpact,physicalAIisopeningnew

growthavenues:nearlyfourintenexecutives

i∩spectio∩,alo∩9sidesector‑specificapplicatio∩ssuchasdynamicassemblyinmanufacturing,

healthcareandeldercareinthepublicsector,anddisaster-damageassessmentininsurance.

expectnewrevenueopportunities,and60%

believeitwillenableroboticsinareasthatwerepreviouslyimpossibleorimpractical.High-impactusecasesspanhazardousoperations,

micro‑lo9istics,pick‑a∩d‑place,a∩dfield

64%

ofexecutivesbelievephysicalAIwill

becomeacriticaldriverofcompetitiveness

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

ThereisagrowingimperativetoadoptphysicalAI

PhysicalAIadoptioniswellunderway:nearly

eightintenorganizations(79%)arealready

engaging,with27%deployingorscaling,and65%expectingtoreachscalewithinfiveyears.The

primarycatalystsarestructural:laborshortages

(74%)andrisinglaborcosts(69%).Inthenear-

term,growthwillcomefromfamiliar,proven

formfactorsfortask‑specificapplications.As

foundationmodelsmatureandadoptiondeepensacrossindustries,entirelynewcategoriesofrobotsarelikelytoemerge–purpose-builtforvaried

environments,complextasks,andnewmodesofhumancollaboration.Humanoids,despite

substantialinvestment,remainalonger-termbet,askeychallenges–includingtechnicalmaturity

(reliabilityanddexterity),safety,andcost-to-ROIviability–muststillbeaddressed.

Near-termgrowthwillcome

fromestablishedformfactorsfortask-specificapplications;asfoundationmodelsmature,newpurpose-builtrobot

categoriesarelikelytoemerge

ScalingphysicalAIgoesbeyond

technology–italsorequiresbuildingsafety,cybersecurity,regulatory,

andoperationalreadiness

Inpractice,scalingphysicalAIdemandsmore

thanbetteralgorithms–itrequiresrethinking

howsystemsareengineered,secured,governed,andrun.Today’ssystemsdonotyetmeetthe

highreliabilitythresholdsofindustrialandothersafety-criticalsettings,anddexterityremains

limited.Progressisfurtherslowedbydatascarcity–real-worldphysicalinteractiondataisscarceandcostlytoobtain.Tokeeppeopleandassetssafe

whilecapabilitymatures,safetymustbeenforcedthroughdeterministicmechanismsindependent

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

oftheAIlayer.Further,asrobotautonomy

9rows,cybersecurityexposurewide∩s,requiri∩9controlsthatpreventunauthorizedaccessandmanipulation.Regulatoryframeworkslagthe

realitiesofautonomousphysicalaction,leavingu∩resolvedquestio∩saboutaccou∩tabilitya∩dacceptablerisk.Operationally,enterprisesmustpla∩forhardwareco∩strai∩ts,ma∩a9i∩9Heetsatscale,strengtheningdataandAIgovernance,andreskillingworkforces.

Humanoidrobotsinspirestrong

industryconviction–butscaled

deploymentremainsalong-termbet

Twointhreeexecutives(67%)believehumanoidswillultimatelytransformtheirindustry,citingtheirabilitytooperateinhuman-builtenvironments

andtheirpotentialasgeneral-purposesystems;53%arealreadyinvestingorplantoinvest.

However,theconditionsforscalearenotyetinplace.While78%expecttodeployhumanoidsatscaleeventually,averagetimelinesextendtosevenyears,andonly30%seethembecomingviablegeneral-purposeworkerswithinthreetofiveyears.Tech∩olo9yimmaturity,hi9hcosts,uncertainROI,andsafetyconcernsremain

si9∩ifica∩tbarriers,compou∩dedbyasocietal

readinessgap,with62%citingpublicacceptanceasacriticalhurdle.

67%

ofexecutivesbelievehumanoidswillultimatelytransformtheirindustry

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Executivesummary

Recommendations:ActionstounlockthepotentialofphysicalAI

PhysicalAIadoptionisamulti-yearjourney,butthetechnologyismatureenoughtodelivertangible

valuetoday.

FivepΓioΓityactions:

1.Buildunderstanding:DevelopaclearviewofwhatphysicalAIenablestoday–itscapabilities,limits,anddata‑infrastructurerequirements.

2.Startwithconfidence-buildingusecases:

Beginwithfeasible,meaningfulapplicationsthatbuildfamiliarityandconfidence–suchasdull,dirty,ordangeroustasks.

3.Designthroughformexploration:Iteratewithmultipledesignconceptstoassesshowformshapestrust,interaction,andsuitabilityfordifferenttasksandenvironments,ratherthandefaultingtohumanoids.

4.Redesignworkflows:Reworkprocessesforhuman–robotcollaboration,with

clearhandovers,supervision,safety,andescalation.

5.Scaleviaplatforms:Createascalable

architectureforreusablerobotskills

andfleet‑levelorchestration,toenabledisciplinedscalingbeyondisolatedpilots.

Theseactionsmustbeanchoredintrust–throughclearsafety,governance,andhuman-oversight

guardrails–andsupportedbyongoingengagementwiththephysicalAIecosystemastechnologies,

standards,andregulationscontinuetoevolve.

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Smartbet,onlyoption,orboth?BiopharmaR&DturnstoAI.

"PhysicalAImarksashiftfromsystemsthatdescribetheworldto

systemsthatcanactwithinit.Butweshouldstayclear-eyed.Roboticshasalonghistoryofoverpromising,whereearlybreakthroughscreatedexpectationsthetechnologycouldnotyetmeet.Whatisdifferent

todayisnotthehype,buttheconvergenceofAI,data,andengineeringmaturity.Theopportunityisreal,providedwefocusonwhatworksatscale,andgobeyondwhatlooksimpressiveindemos."

PascalBrier

GroupChiefInnovationOfficer,Capgemini

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

PhysicalAI–poweredroboticsinaction

PhysicalAIroboticsystemshavepotentialapplicationsacrosseverymajorindustry.Thefollowingexampleshighlighttheseapplicationsincomplex,dynamic,real-worldenvironments.

FiguΓe1.

ExamplesofphysicalAI–poweredroboticdeployments

IndustriesIllustrativecases

Warehousingandlogistics

Ultra,aUS-basedindustrialAIroboticscompany,haspartneredwithPhysicalIntelligence,aUS-basedstartupdevelopinggeneral-purposeroboticsfoundationmodels,todeployPl’sπ0.6modelonindustΓialΓobotsopeΓatinginlivewaΓehouseenviΓonments.ThemodelhasbeendeployedfoΓ

e-commeΓceoΓdeΓpacking,ataskthathashistoΓicallybeendifficulttoautomateduetolaΓgevaΓiabilityinitemtypes,defoΓmablepackagingmateΓials,andmulti-stepmanipulationthatcausesΓule-basedsystemstofail.Pl’sΓoboticfoundationmodelallowsUltΓa’sΓobotstopeΓceive,Γeason,andadaptinΓealtime.EaΓlydeploymentsshowUltΓa’sΓobotsachievinggainsinΓeal-woΓldautonomouspeΓfoΓmance,demonstΓatinghowphysicalAlcanunlock

waΓehousetaskspΓeviouslyconsideΓednon-automatable.1

FedExispartneringwithUS-basedroboticsstartupDexteritytopilot“superhumanoid”2robotsfortruckloading–oneofthemostcomplexand

physicallydemandingtasksinlogistics,aspaΓcelsvaΓywidelyinsize,shape,andweightandaΓΓiveinunpΓedictablesequences.TheΓobotsautonomouslyinteΓpΓettheincomingmixofpaΓcels,andstackthemintodense,stablewalls.UsingDexteΓity’sFoΓesightwoΓldmodel,theyevaluatehundΓedsof

possibleplacementsfoΓeachiteminmilliseconds,pΓedictinghoweachchoiceaffectstheintegΓityofthestack.ThisenablesΓapidhandlingofiΓΓegulaΓitems一wheΓetΓaditionalautomationstΓuggles一whileincΓeasingthΓoughputandΓeducingphysicalstΓaininhigh-volumeopeΓations.3

Manufacturing

FoxconnispartneringwithIntrinsic,anAlphabet-ownedcompanythatdevelopsAImodelsandsoftwareforrobotics,tohelprealizetheintelligentfactoΓyofthefutuΓe.ThecollaboΓationtaΓgetselectΓonicsassembly一afast-gΓowingsectoΓdΓivenbytheAlboombutstillconstΓainedbyΓigid

automationandmanualpΓocesses.ThepaΓtneΓshipaimstodeliveΓastepchangebyshiftingfΓompΓoduct-specificautomationthatΓequiΓesextensiveΓeengineeΓingacΓosspΓoductgeneΓationstomoΓegeneΓal-puΓposeintelligentΓobotics.lnitially,thecollaboΓationwilluselntΓinsic’sΓoboticsfoundationmodeltofocusoncΓiticalusecasesacΓossassembly,inspection,machinetending,andlogistics.4

Continuedonnextpage

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Industries

Construction

Agriculture

TheconstΓuctionindustΓyfacesmountingpΓessuΓefΓomlaboΓshoΓtagesandincΓeasingdemandfoΓmoΓeefficientandsustainablebuildingmethods,whileincΓeasingconstΓuctingqualityandsafety.Atthesametime,constΓuctionsitesaΓeoneofthemostchallengingenviΓonmentsfoΓautomationdueto

constantlychangingteΓΓain,layouts,andhumanactivity.

AustralianroboticscompanyFBRsHadrianXaddΓessestheseconstΓaintsbyautomatingoneofthemostlaboΓ-intensivetasksinconstΓuction:stΓuctuΓalwallbuilding.HadΓianXisanautonomous,mobileconstΓuctionΓobotthatusesaΓoboticaΓmmountedonavehicleplatfoΓmtoplaceconcΓeteblocks.TheΓobothasbeenpilotedonanactiveconstΓuctionsiteintheUS,andhasdemonstΓatedtheabilitytoconstΓuctstΓuctuΓal,load-beaΓingwallswithinaday.5

BostonDynamicsandFieldAIaΓetacklingadiffeΓentbottleneck:sitemonitoΓingandinspectioninconstΓuctionenviΓonments.ConstΓuctionsitesaΓedifficulttomonitoΓconsistentlyduetochangingconditionsandsafetyΓisks,makingdatacollectionlaboΓ-intensiveandeΓΓoΓ-pΓone.ThepaΓtneΓshipcombinesBostonDynamics’SpotquadΓupedΓobotwithFieldAl’sFieldFoundationModelstoenableautonomousinspection,mapping,andmonitoΓing.AlΓeadydeployed

acΓossmultiplelocations,thesolutionsuppoΓtsfleet-levelautonomyandcooΓdinatedopeΓation,andhasdeliveΓedoveΓ90%Γeductionsininspectionanddocumentationtime,eaΓlieΓissuedetectionthatΓeducesΓewoΓkcosts,andimpΓovedwoΓkeΓsafety.6

Automationisbecomingincreasinglycriticalinagricultureaslaborshortagesintensifyinmanyregions.7HoweveΓ,scalingautomationinagΓicultuΓe

ΓemainschallengingduetothehighlyvaΓiablenatuΓeoffaΓmingenviΓonments一wheΓelighting,teΓΓain,andcΓopvaΓietiesdiffeΓwidelyacΓossfields一

andtheΓelianceonheteΓogeneousfleetsofmachines,includingtΓactoΓs,haΓvesteΓs,andspΓayeΓs.TorqueAGI,aUS-basedstaΓtupbuildingfoundationmodelsfoΓΓoboticautonomy,addΓessestheseconstΓaintswithphysics-infoΓmedAlfoundationmodelsthatcanhandledensefoliage,iΓΓegulaΓplant

geometΓy,andmultimodalpeΓception,whileopeΓatingacΓossdiffeΓentmachines.ToΓqueAGliscollaboΓatingwithJohnDeeretoadvanceAlfoundationmodelsforthenextgenerationofintelligentagriculturalrobots.8

Illustrativecases

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Industries

Illustrativecases

Healthcare/eldercare

Wandercraft,aFranceandUS-basedroboticscompany,isdevelopingAIpoweredmedicalexoskeletonsthatenablepeoplewithspinalcordinjuries,

stroke,andotherseveremobilityimpairmentstostandandwalk.ItslatestdevicethePersonalExoskeletoniscurrentlyinclinicaltrialsandisdesignedforeverydayindoorandoutdooruse.ThedeviceusesAIforbalanceandmovement,adaptingcontinuouslyinrealtimetosupportstablewalkingacrossvariedsurfacessuchasconcrete,carpet,andtile.9

ElliQ,anAI-poweredcompanionrobotforolderadultsdevelopedbyIntuitionRobotics,isbeingintroducedtoJapanthroughapartnershipwith

JapanesetradingcompanyKanematsuCorp.ThecollaborationtargetsJapansrapidlyagingpopulationandtheresultingshortageofcaregiversandnursinghomestaff.ElliQproactivelysupportsolderadultswitheverydayneeds,includinghealthmanagement,preventivecare,communication,

monitoring,andsocialandcognitiveactivities.10

Energy

AI-enabledrobotsfromUS-basedLuminousRoboticswereusedtohelpinstallnearly500,000solarpanelsatENGIEs250MWsolarfarminVictoria,Australia.LuminoussLUMIrobotsautonomouslyliftandplacepanelsontomountingstructuresusingAI-drivenpick-and-placesystems,while

humancrewscompletefinalfastening.Thisreducesheavymanuallabor,improvessafety,andincreasesefficiency.Therobotsdemonstratedahighdegreeofflexibility,operatingeffectivelyacrossarangeofweatherconditions.Morebroadly,automatingsolarconstructionisexpectedtolowercostsandspeedupconstruction,enablinglargerscalesolardevelopments,whilereducingtheneedforhumanlaborinremoteandinhospitable

outdoorenvironments.11

Sources:Informationcompiledfrompubliclyavailablesecondarysources.

AprofessorofroboticsataUK-baseduniversitysays:"Traditionalrobotsareoptimizedtoexecutepredefinedmotions,withlimitedunderstandingofintentorrealworld

impact.PhysicalAIfundamentallychangesthisbyenablingrobotstoperceivetheirsurroundingsandreasonaboutcontext.Indoingso,itopensupproblemdomainsthathaveresistedautomationfordecades–preciselybecausetheyrequireunderstanding,notjustexecution.”

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Evolutionofroboticintelligence

Theevolutionofintelligenceinrobotics(ataglance)

Late2010s–early2020s

Domain-specificLearning-enabled

intelligentrobotsrobotics

Early–mid2000s

1990s

2010s

Pre-programmedautonomy

Today

1960s–1980s

Sensor-aware

robots

PhysicalAIera

Hard-codedautomation

Robotsbegintolearnfromexperienceandsimulation

Robotsasadaptive,autonomoussystems

Robotsexecutefixed,

pre-programmedmotions,

butwithnoawarenessof

theirenvironment

Robotsgainlimitedawareness

oftheirsurroundings,but

decision-makingisstill

rule-based

Robotsoptimizedfornarrow

tasks(enabledbyadvancesin

AIandperception)

Robotsoperateindependently

incontrolledsettings(enabled

bysensingcombinedwith

onboardsoftware)

•ASIMO(2000):Honda’shumanoid

ΓobotdemonstΓatedwalking,balance,andbasicinteΓaction

•EaΓlyseΓviceΓobotsinΓeseaΓchanddefense

•Kivarobots(laterAmazonRobotics):AutonomouswaΓehouselogistics

•CollaboΓativeΓobots(cobots)onfactoΓyflooΓs

•SuΓgicalandinspectionΓobotsinhealthcaΓeandinfΓastΓuctuΓe

•Vision-guidedindustΓialΓobots

•FoΓce-contΓolledaΓmsfoΓpΓecisionassembly

•EaΓlymobileΓobotsinwaΓehousesandlabs

•DeepleaΓningfoΓpeΓceptionandmanipulation

•Cloud-connectedΓobotfleets

•Sim-to-ΓealtΓainingpipelines

•Roboticfoundationmodelsenabling

geneΓalizationacΓosstasksandtheabilitytoopeΓateinunstΓuctuΓedenviΓonments

•Multi-ΓobotandAlagentoΓchestΓation

•RapidpΓogΓessinhumanoidandgeneΓal-puΓposeΓobots

Keymilestones

•Unimate(1961):ThefiΓstindustΓialΓobot,deployedonGeneΓalMotoΓs’assemblyline

•EaΓlyindustΓialaΓmsinautomotivemanufactuΓing

•PLC-dΓivenautomationandfixedpΓoductioncells

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Dr.MiladMalekzadeh,Co-FoundeΓandVicePΓesidentAlatNeuΓaRobotics,aGeΓmany-basedΓoboticsfiΓm,says:

“PhysicalAIwillbeusedacrossvirtuallyeverytypeofcontext–fromindustrialsettingstomedical,home,andserviceenvironments.Progressisbeingdrivennotonlybyadvancesinindividualmodels,butincreasinglyby

platformapproachesthatmakeiteasiertocombine,reuse,anddeployintelligenceacrossdifferentrobotsandusecases.”

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PhysicalAI:Takinghuman-robotcollaborationtothenextlevel

Adiscussionwith

DeepuTalla,

VPandGM–Robotics&EdgeAI,NVIDIA

WhatΓecentadvancesaΓedΓivingtheinflectionpointinphysicalAI?

Inthelast12to24months,twotechnologieshavereachedameaningfullevelofmaturity,bringingusintowhatI

wouldcallagoldenageforphysicalAIandrobotics.First,

foundationmodels–nowextendingbeyondlanguageto

visionandaction–makeitpossibletomovebeyondbrittle,task‑specificsystemstowardmoregeneral‑purposerobotbrains.Second,advancesinsimulation.Thegapbetween

simulationandtherealworldhasnarrowedformanyuse

cases,allowingrobotstobetrainedandtestedextensivelyinsimulation.Thisisordersofmagnitudefaster,safer,andcheaperthanreal-worldtesting,compressingdevelopmentcyclesthatoncetookyearsintohoursorevenminutes.

WhydoesphysicalAIΓepΓesentsuchasignificanteconomicoppoΓtunity?

Themajorityofglobaleconomicactivityistiedtophysicalindustries.