2025年物理AI：赋能工业运营新时代白皮书（英文版）-世界经济论坛波士顿咨询

Incollaborationwith

BostonConsultingGroup

PhysicalAI:PoweringtheNewAgeofIndustrialOperations

WHITEPAPER

SEPTEMBER2025

Images:GettyImages,Midjourney

Contents

Foreword3

Executivesummary4

Introduction5

1

What’snew:Breakthroughsinintelligentrobotics6

1.1Technologicalbreakthroughsredefiningroboticcapabilities6

1.2Enhancedcapabilitiesenablingend-to-endautomation7

1.3Limitationsyettoberesolved10

2Whereitisworking:Frontierapplications11

2.1Revolutionizingthemanufacturingvaluechain12

2.2Spotlightonthepioneers–transformationjourneys13

ofearlyadopters

3

Howitscales:Technologyplatformsandpartnerships1

6

3.1ThenewphysicalAItechnologystack1

6

3.2Strategicpartnershipsareessential1

7

4Wholeadsit:Empoweringthenewindustrialworkforce18

4.1Atargetpictureforroboticsandworkforcedevelopment18

4.2Ashiftinskillsandroles18

4.3Thenewworkforceimperatives2

0

Conclusion:Timeforaction2

1

Contributors2

2

Endnotes2

5

Disclaimer

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©2025WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformation

storageandretrievalsystem.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations2

PhysicalAI:PoweringtheNewAgeofIndustrialOperations3

September2025

PhysicalAI:PoweringtheNewAgeofIndustrialOperations

Foreword

KivaAllgood

ManagingDirector,

WorldEconomicForum

DanielKuepper

ManagingDirectorandSeniorPartner,BostonConsultingGroup(BCG)

Amidmountingglobalpressures–fromeconomicvolatilityandgeopoliticaldisruptiontogrowing

supply-chaincomplexity,andlabourandtalent

shortages–industrialoperationsareenteringa

transformativenewphase.Whilethesechallengesarenotnew,heighteneduncertaintyhassignificantlyintensifiedtheirimpact,forcingafundamental

rethinkofhowworkisorganized,executedandscaled.

Atthisinflectionpoint,aneweraofindustrial

automationisemerging–poweredbyphysical

AI.Theseintelligentroboticsystemscombine

perception,reasoningandaction,enablingalevelofautonomyandadaptabilitythatmarksacriticaljunctureinindustrialautomation.Bybridgingthedigitalandphysicalrealms,physicalAIpromisestoreimaginehowindustrialsystemsfunction–fromfactoryfloorstosupplychains.

AsphysicalAIbecomesincreasinglyviableand

strategicallyessential,industryleadersareseekingadeeperunderstandingofhowtomakeuse

oftheseinnovationsforsustainable,long-termcompetitiveness.Atsuchapivotalmoment,

thiswhitepaper–developedthroughtheWorld

EconomicForum’sNextFrontierofOperations

initiativeincollaborationwithBostonConsultingGroup–buildsonatraditionofstrategicforesightandmultistakeholderengagementtochartaboldpathforward.

Theinsightspresentedheredrawonthecollectiveexperienceofglobalmanufacturers,robotics

innovatorsandleadingacademicexperts.

Groundedinreal-worldusecasesand,moreimportantly,thetransformationjourneystheyrepresent,thepaperexploreshowphysicalAIisreshapingoperations,enablingnewformsofhuman–machinecollaborationandunlockingproductivityatscale.

Butthistransformationisnotsolelyabout

technology.Italsorequirestheindustrialworkforcetobeequippedwithnewskillstocollaboratewith

intelligentsystemsandtakeonemergingroles.Weinviteallstakeholders–includingmanufacturers,

policy-makers,researchersandtechnologists–toengagewiththisagenda.Together,throughbold

andcoordinatedaction,wecanshapeafuturein

whichintelligentautomationdrivesinclusive,resilientandsustainableindustrialgrowth.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations4

Executivesummary

Technologicalbreakthroughsarepushingtheboundariesofautomation–tasksthatwereoncetoovariableorcost-prohibitivetoautomatearenowbothtechnically

feasibleandeconomicallyviable.

Althoughtraditionalindustrialrobotsare

foundationaltoautomation,theyhavelongbeen

constrainedbylimitedadaptabilityandhigh

integrationcosts.Today,theworldisenteringa

newageofroboticsdefinedbyintelligenceand

flexibilitypoweredbytheconvergenceofadvancedhardware,artificialintelligence(AI)andvision

systems.Together,theseadvancesareunlockingthenextfrontierofrobotics.

Approachessuchastrainingmethods

(reinforcementlearning,imitationlearning)and

multimodalfoundationmodels1forrobotics,as

wellasdexteroushardwarecomponents(e.g.softgrippers,tactilesensors)areenablingrobotsto

handlevariability,reasonincontextandadaptin

realtime.Simplifieddeployment,suchasthroughvirtualtrainingandintuitiveinterfaces,issignificantlyreducingtime-to-valueandexpandingaccessibilitytosmall-andmid-sizedmanufacturersand

logisticsproviders.Throughoutthispaper,theterm“manufacturers”isusedasashorthandtorefertobothmanufacturersandlogisticsproviders.

Suchadvancesleadtothreefoundationalroboticssystemsthatwillcoexistinthefutureofindustrialoperations,togetherformingalayeredautomationstrategy.Thesesystemsarecomplementary,eachsuitedtospecificcombinationsoftaskcomplexity,variabilityandvolume.

–Rule-basedrobotics,deliveringunmatchedspeedandprecisioninstructured,repetitivetasks(e.g.automotivewelding)

–Training-basedrobotics,masteringvariabletasksviareinforcementlearningorimitationlearning(e.g.adaptivekitting)

–Context-basedrobotics,capableofzero-

shotlearning2andexecutioninunpredictableprocessesandnewenvironments(e.g.robotreceives,reasonsandactsoninstructionsvianaturallanguage)

Automationisexpandingopportunitiesacrosstheentireindustrialvaluechain.Earlyadoptersare

alreadyachievingsignificantresults.Forexample,Amazon,operatingtheworld’slargestrobotics

fleet,hasdemonstratedhowtheintegrationof

mobilerobots,AI-basedsortationandgenerativeAI-guidedmanipulatorscanimprovefulfilment

centreperformance.Byorchestratingthese

autonomoussystems,next-generationfacilities

haverealized25%fasterdelivery,30%moreskilledrolesanda25%boostinefficiency.3Similarly,

FoxconnappliedAI-poweredroboticsanddigitaltwinsimulationtoautomatehigh-precisiontaskssuchasscrewtighteningandcableinsertion,

previouslyconsideredtoocomplexforautomation.Throughreal-timeadaptiveforcecontroland

simulation-baseddeployment,itcutdeploymenttimeby40%andreducedoperationalcosts

by15%.

However,realizingsuchoutcomesatscale

demandsmorethancutting-edgetechnology.

Itrequiresafuture-readyautomation

strategythatincorporatesbothtechnicalandorganizationalfoundations:

–EmbeddingtheemergingAItechnologystackintotheexistingindustrialtoolchainandforgingecosystempartnershipsacrossrobotics,AI

andmanufacturingtoensureinteroperability,scalabilityandcontinuousinnovation

–Workforcetransformationthroughreskilling

andupskillingtoenablehuman–machine

collaboration,andprepareworkersforemergingrolessuchasrobotsupervisors,AItrainersandsystemoptimizers

Manufacturerswhoactnowandembedroboticsasastrategicassetwillleadthenextphaseof

industrialcompetitiveness–shapingafutureinwhichintelligentautomationbecomesa

cornerstoneofsustainablegrowth,workforceempowermentandsystemicresilience.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations5

Introduction

Nolonger

confinedto

isolatedefficiencygains,robotics

isemergingasastrategicenablerofresilienceandcompetitiveness.

Manufacturersmustembraceintelligentroboticsnow.

Manufacturerstodayfindthemselvesata

crossroads.Persistentlabourshortages,escalatingcostpressuresandfragileglobalsupplychains–

amplifiedbygeopoliticalandmarketuncertainty–areconvergingtothreatenproductivity,profitabilityandresilience.Atthesametime,growing

consumerexpectationsforspeed,customizationandsustainabilitydemandastep-changein

operationalflexibility.

Theseintensifyingpressuresareacceleratingthe

searchfortransformativeinnovationsthrough

frontiertechnologies.Attheforefrontisone

undergoingprofoundtransformation:robotics.Nolongerconfinedtoisolatedefficiencygains,roboticsisemergingasastrategicenablerofresilience

andcompetitiveness.Roboticsisenteringanewera–inwhichintelligenceallowsforautonomy,andphysicalAIredefineswhatmachines,andbyextensionhumans,arecapableof.

Then:Roboticsforthefew.Inflexible.Static

Sincetheirinitialdeploymentinthe1960s,industrialrobotshavereshapedmanufacturing.Theyplayedapivotalroleinsectorssuchasautomotiveand

electronics,wherehigh-volume,standardized

productionjustifiedtheinvestment.However,

adoptionremainedlimitedtolargeenterpriseswithhighlystandardizedproductionprocesses.Smallandmid-sizedmanufacturers,aswellasthose

withvariableoperations,wereleftbehindduetoprohibitivecost,complexityandinflexibility.

Nowandnext:IntelligentroboticsfortheIntelligentAge

Butthisischanging.Roboticsisevolvinginto

intelligentsystems–capableoflearning,adapting

andactingautonomously.Thisshiftmarksapivotalmomentinthehistoryofautomation,drivenby

theconvergenceofroboticshardware,AIandvisionsystems.

Today,roboticsisscaling–andfast.By2023,

morethan4millionindustrialrobotshadbeen

installedglobally.4Atthesametime,advances

inroboticssoftwareandhardwareareenabling

broadercapabilities–rangingfromdexterous

manipulationtoautonomousnavigation–and

significantlyreducingtheengineeringeffortrequiredfordeployment.Innovationsareacceleratingin

response.Start-upactivitiesandinvestments

aresurging,drivenbythepromiseofphysicalAI.

Fromfoundationmodelsforrobotics(e.g.SKILD

AI,Covariant,DeepMind,TRI)togeneral-purposerobots(suchasthehumanoidrobotsfromFigure,Neura,BostonDynamicsandApptronik),deliveryintheinnovationpipelineisaccelerating.

Asthepaceofchangeaccelerates,leadersface

asetofcriticalquestions:Whattechnological

breakthroughsaredrivingthisshift?Howisroboticsalreadyreshapingmanufacturingoperations,

workforcerolesandindustrialcompetitiveness?Andhowshouldthetechnologicalandpeoplefoundationsbelaidtoprepareforwhat’snext?

Thiswhitepaperprovidesatimely,in-depth

lookathowtheroboticslandscapeinindustrial

operationsisrapidlyevolving.Itgoesbeyond

surface-leveltrendstoprovidereal-worlduse

cases,andpresentsaforward-lookingvisionof

howphysicalAIcanenableflexible,resilientand

scalableautomation.Withachievableinsightsfor

manufacturers,technologyleadersandpolicy-

makersalike,thepaperaimstoserveasastrategicguidetolead–notfollow–intheIntelligentAge.5

PhysicalAI:PoweringtheNewAgeofIndustrialOperations6

1

What’snew:Breakthroughsinintelligentrobotics

Technologicalbreakthroughsexpandthescopeofautomationtoencompasswhathasbeen

technologicallyunfeasibleoreconomicallyunviable,andsimplifyimplementationtodeliverscalable,end-to-endautomation.

Theroboticslandscapeisundergoingaprofoundtransformationdrivenbyrecentadvances.Thissectionoutlinestheprimarydimensionsofthistransformation,

whichcollectivelymarkaturningpointforindustrialautomation.

1.1

Technologicalbreakthroughs

redefiningroboticcapabilities

Recentinnovationsinsoftwareandhardwarehaveusheredinastep-changeinroboticcapability,

enablingrobotstoperformcomplextasksin

dynamicenvironmentswithsimplerdeployment.AdvancesinAIandcomplexsimulations,enabledbyacceleratedcomputingusinggraphics

processingunits(GPUs),havemadeitfeasibleto

runAImodelsandalgorithmsinrealtime,unlockingnewapplications.ThisAI-basedapproachfocusesonenablingrobotstoperceive,planandactin

complex,real-worldscenarios,effectivelyachievingalevelofphysicalintelligence.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations7

Enhanced

perception

AdvancesinsensorsandAIhavedramaticallyimprovedrobots’abilitytoperceivetheirsurroundings.Affordablehigh-resolutioncameras,lightdetectionandranging(LiDAR)andnext-generationtactilesensors,amongothersensors,giverobotsricherrawinputs,whileadvancedcomputervisionalgorithms(poweredbydeeplearning)

enablevisualperceptionapproachinghuman-levelcapabilities.Robotscannowrecognizeandinterpretcomplexenvironmentsinrealtime–identifyingobjects,recognizingtheir3Dorientationandassessingtheirphysical

properties–essentialprerequisitesfordevelopinganunderstandingofhowtointeractwithobjects.Theseadvancesallowrobotsto“see”andcomprehendanobjectanditsenvironmentwithunprecedentedclarity.

Autonomous

decision-makingandplanning

InnovationsinAIandsoftwarehaveenabledrobotstomakeintelligentdecisionsinrealtime.Insteadofrigid

pre-programming,robotsnowexploitreinforcementlearningandsimulationtolearnbehavioursthroughtrial

anderrorinvirtualenvironments.Advancedsimulators(e.g.high-fidelityphysicssimulators)anddomain

randomizationtechniques(e.g.randomizationofparameterssuchaslightingorfriction)areclosingthe

simulation-to-realitygap,sothatbehaviourslearnedinsimulationtransferseamlesslytorealmachines.Robotsalsoincreasinglybenefitfrompowerfulfoundationmodelsthatintegratevision,languageandaction.These

models,suchasGoogleDeepMind’sGeminiRobotics6andNvidia’sIsaacGR00T,7ingestmultimodalinputs

andgeneratetask-appropriateoutputs–allowingforintuitivehuman–robotinteractionsandsuperiorcontextualunderstanding.Thisenablesrobustworkflowplanning:givenagoal(e.g.unloadingashipment),thesystem

determinesasequencedsetofactions(usetheforklifttounload,cutthebanderole,openthepackages,etc.).

Thisprogressionenablesrobotstoevolvefromexecutingisolatedmotionstoperformingcoherent,multistep

tasks,approachinghuman-leveltaskintuitionandplanningcapabilities.Inessence,robotsareenabledto“think”andplantaskswithalevelofflexibilityandcontext-awarenesspreviouslyunattainable.

>

Dexterous

manipulation

andmobility

Advancesinmaterials,actuatorsandroboticdesignshavegreatlyexpandedwhatrobotscanphysicallydo.

Hardwarebreakthroughs–fromhigh-precisionforce-controlledmotorstosoftroboticgrippers–givemachinesmuchmoredexterityinhandlingobjects.Robotscannowgraspirregularordelicateitemsreliably,ratherthan

beinglimitedtorigid,predefinedmotions.ThisiscomplementedbyAI-drivencontrolsoftwarethatadjustsgripandforceinrealtime.Notably,theincorporationofasenseoftouchthroughmoderntactilesensorsisaprimaryenablerofhuman-leveldexterity,allowingrobotstofinelymanipulateobjectsthroughfeedbackofpressureandslip.Longerbatterylifeissignificantlyincreasingtheuptimeofmobilerobots,supportingmoreautonomous

deploymentsandleadingtoextendedmobility.Moreover,roboticsisnolongerconfinedtotraditionalform

factors.Innovationshaveintroducedquadrupeds,humanoids,mobilemanipulatorsandhybridforms,

broadeningtherangeofindustrialapplicationsandincreasingthescopeoffeasibleautomation.Thesephysicalinnovationsenablerobotsto“act”ontheworldwithfargreaterskillandautonomy.

1.2

Enhancedcapabilitiesenablingend-to-endautomation

Theseenhancedcapabilitiesledtotheevolution

ofroboticsfrom(1)rule-basedroboticsthatare

explicitlyprogrammedto(2)training-basedroboticsthatacquiretheirskillintherealworldandthroughsimulationtrainingto(3)context-basedroboticsperformingtasksautonomouslywithoutexplicit

trainingthroughzero-shotlearning.Advancesinallthreeroboticsystemstransformoperationsandexpandtheautomationscopetotasksthatpreviouslycouldnotbeautomatable.8

Attheheartofthistransformation,however,liesthecoexistenceofallthreefoundational

roboticssystems,eachexpandinginautomationscopeandsophistication.Together,theyformacomplementaryecosystem.Ratherthanreplacingoneanother,theyenablealayeredautomationstrategy,alignedwithoperationalneeds(e.g.

degreesoftaskvariability)andeconomic

considerations.Furthermore,asfactoriesand

warehousesmovetowardsgreaterautomation,

manufacturersandwarehouseoperatorswilldeployamixofroboticsystemsandembodiments–fromautonomousmobilerobots(AMRs)tohumanoids–guidedbytaskrequirements,economicviabilityandprocesscharacteristics.

FIGURE1Expandingtheboundariesofautomation(illustrative)

(Physical)AIconsiderablyincreasestheautomationscopeinindustrialoperations

Automationpotential

Rule-basedrobotics(e.g.AI-supportedcoding)

Training-basedrobotics

(e.g.simulation-based

AItraining)

Context-based

robotics

(e.g.zero-shot

learning)

PredictableprocessesUnpredictableprocessesUnpredictableprocesses

andknownenvironmentandknownenvironmentandnewenvironment

Processcharacteristics

Howtointerpretthischart

Theareaofthechartmapsoutphysicaltasks(e.g.assemblysteps,materialhandling,packaging)withinafactoryorwarehouse.Thesetasksarecategorizedalongtwodimensions:

Processcharacteristics(x-axis)aredefinedby

Automationpotential(y-axis)isindicatedthroughcolourshading:

parameterssuchasobjectposition,orientationandsize,andifthesystemoperatesinaknownornewenvironment.Illustrativetargetstatealongdifferentprocesscharacteristics:

Grey:Tasksalreadyautomatablewithtoday’srule-basedrobotics

→Predictableprocesses:Parametersareeitherconstantorvary

onlywithinatightlycontrolledrange–enablingdeterministic,

repeatableexecutionwithouttheneedforadaptivebehaviour.

Blue:AdditionalscopeunlockedbyphysicalAI●

→Unpredictableprocesses:Parametersvarysignificantlyorcannotbeanticipated.

Navy:Illustrativeshareexpectedtoremainmanualinthenearterm

→Newenvironments:Scenarios,layouts,objectsortasks

outsidetherobot’strainingdistribution(e.g.adifferentfactoryline,unfamiliarpartsoralteredwarehouselayout).

Source:BCG,WorldEconomicForum,expertinterviews.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations8

PhysicalAI:PoweringtheNewAgeofIndustrialOperations9

–Context-basedrobotics,thenewestfrontier,

makesuseofroboticsfoundationmodelsand

zero-shotlearningtoautonomouslyperceive,

reasonandactinunfamiliarscenarios.These

systemsinterprethigh-levelinstructionsand

respondtoreal-worldcomplexitywithoutpriortask-specifictraining,makingthemparticularlyvaluableinunpredictableenvironmentswith

unknownpartsornewenvironments.Roboticsfoundationmodelsformthecognitivecorethatenablescontext-basedgeneral-purposerobots–suchashumanoids–toflexiblyexecute

diversetasksacrossdifferentenvironmentswithoutreprogramming.

Whilethethreesystemtypes–rule-based,

training-basedandcontext-based–formalayeredautomationstrategy,theirboundariesoftenoverlap,andasinglerobotcanuseahybridapproachthatcombinesallthree.Forexample,inacollaborativeassemblycell,arobotmightfollowrule-based

logictoperformtaskswithhighprecision.

Simultaneously,itmonitorsitsenvironmentusingperceptionsystems.Whendeviationsfromthe

expectedworkflowoccur–suchasamissing

partorhumanintervention–therobotswitchestocontext-basedreasoningtointerpretthesituationandresolveitautonomously,beforereturningtoitsrule-basedexecution.

Theprocesscharacteristicsdeterminewhichroboticsystemtouse:

–Rule-basedroboticscontinuestodeliver

unmatchedprecisionandcycle-time

performanceinstructuredenvironmentswithrepetitivetasksandpredictableprocesses.

Thesesystems,ubiquitousinautomotive

bodyshopsandsimilarsettings,remain

indispensableforoperationswhereconsistencyandlowvariabilityareparamount.Ongoing

advancesinprogramminginterfacesand

AI-supportedcoding(suchasSiemens

IndustrialCopilotforgenerativeAI-assisted

programmablelogiccontroller[PLC]

programming)9areextendingtheirapplicabilityandeasingdeploymentchallenges.

–Training-basedroboticsisrisingtoprominenceinmorevariableenvironments.Enabledby

advancedreinforcement-learningalgorithmsandsimulations,theserobotslearnthroughvirtual

andreal-worldexperiences.Thevirtualization

oftrainingsignificantlyreducesdeployment

effort,asrobotscanbetrainedandvalidatedinsimulatedenvironmentsbeforereal-worldrollout,therebyexpandingthescopeofeconomically

viableautomation.Theydemonstrateresilienceintasksinvolvingcontrolledvariation–suchasflexiblepartskittingoradaptivelogistics–andareincreasinglyviableformid-volumeornon-repetitiveproductionwhererule-basedroboticslacksflexibility.

FIGURE2ComparisonoftraditionalandphysicalAI-enabledrobotics

Visionofthedifferencestodayvs.thefuture

Fieldof

automation

Effectiveinpredictabletasksorincontrolledscenarioswith

knownparts

Capableofhandlingunpredictable

scenariosand

unknownparts

(e.g.randombinpicking,flexiblematerialhandling)

Timeto

industrialization

Mid/long

industrializationtime(severalmonths/

weeksforcoding

andimplementation)

Human-machineinteraction

Humancanadaptrobotthrough

interfacesorbyguidingrobot

Implementationprocess

Highandcomplexmanualeffortfor

codingandtraining

Scalability

Limitedscalability

acrosssimilarset-upsorusecases

Today

Scalesflexibly

acrossdiversetasks,environmentsand

robottypes

Requiresrelatively

lessengineeringeffortthroughtrainingandself-learning(upto

70%lesseffort)

Enablesintuitive

controlvianaturallanguage,gesturesorvoicecommands

Future

Accelerated

deploymentvia

few-shot/zero-shotorimitationlearning(upto50%fastertime-to-value)

<Ecoiially>

Tecsloially

Source:BCG,WorldEconomicForum.

PhysicalAI:PoweringtheNewAgeofIndustrialOperations10

Vision–language–action(VLA)modelsarerapidlyevolvingasapromising

path,with

foundationmodelspoisedtounlockgeneralizable

spatialreasoningcapabilities.

Whiletechnologicaladvancesareunlocking

previouslyunachievableapplications,thetrueshiftisnotmerelyinwhatisnowtechnicallyfeasible,

butinwhatiseconomicallyviable.AshighlightedinFigure2,thefutureofintelligentroboticsis

definedbysimplifieddeploymentandmoreintuitivehuman–machineinteraction,enablingreductionsinimplementationtimeandgreaterscalability.

AsphysicalAIsupportsawidervarietyof

operationsandbecomeseasiertodeploy–

requiringfewerspecializedskillsandlesstask-

specificcustomization–automationbecomesviableacrossamuchbroaderrangeofoperations.This

evolutiondoesnotjustenablenewusecases,itredefinestheoveralleconomicsofautomation.

Limitationsyettoberesolved

1.3

Evenasrapidadvancescontinue,datascarcity,3Dspatialintelligenceanddexterityremainchallengestobesolvedinthecomingyears:

–Datascarcity:Largelanguagemodels(LLMs)thrivedbecauseoftheabilitytoscrapelarge

amountsofdatafromtheinternet.TheseLLMshavenowreadmanywebsitesandingested

manybooks.PhysicalAIalsothrivesonhigh-

qualitydata,yetcuratedroboticsdatasets

remainlimitedandcostly,becausetheyhave

tobecollectedintherealworld.Thischallengeisrapidlybeingovercomethroughadvances

insyntheticdatageneration.Photorealistic

renderinganddomainrandomizationhelpto

simulatevaryinglighting,texturesandobject

shapesinvirtualenvironmentstoteachrobotshowtograspitemsindiversereal-world

conditions.Combinedwithopen-sourceeffortsandtheaccelerateddeploymentofreal-worldroboticfleets,thesedevelopmentsaresetto

closethedatagapanddramaticallyenhance

learningefficiency.Forexample,asafirststep,roboticcompaniessuchasSanctuaryAI10

startedwithteleoperation–havinganoperatorcontroloneormorerobots–whilecollecting

data.Thegoalistousethisdatatotrainthe

robotstooperateautonomouslyatalaterpoint.Developerscanusetechnologiesprovided

byNvidiaandothercompaniestoproducenumerousplausiblefutures