全球物理智能与具身机器人报告2025-2026英文

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GlobalPhysicalIntelligenceandEmbodiedRoboticsReport

GlobalPhysicalIntelligenceandEmbodiedRoboticsReport

（2025-2026）

WorldLaureatesAssociationJuly,2026

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Content

Preface 2

PartI:GlobalEvolutionofPhysicalModels 4

Chapter2:CompetitiveLandscapeandInfrastructure 11

PartII:GlobalEvolutionofEmbodiedRobotics 16

Chapter3:DeepCouplingofRobotsandPhysicalModels 16

Chapter4:EmbodiedRoboticsIndustryandCompetition 23

Chapter5:ScenarioDeploymentandPhysicalModelEmpowerment.30

Chapter6:GlobalGovernance,Standards,andEthics 39

Chapter7:ChallengesandBottlenecks 47

Chapter8:FutureTrendsandStrategicRecommendations 56

Conclusion 63

References 64

Preface

Theyear2026marksthedawnofanewerainPhysicalAI,drivenbythe

resonanceofcarbonandsilicon.Globally,artificialintelligencehasofficially

navigatedintothedeep-waterzoneofEmbodiedAI,usheringintheinaugural

yearofmasscommercialization.Foralongtime,AIhasreliedheavilyon“data-driven”statisticalpatternmatching,achievingbrilliantsuccesseswithinvirtualdomainssuchastextandimagery.However,asintelligentagentsattemptto

crossthedigitaldivideintothedissipative,non-linearphysicalworld,traditional“black-box”modelshaverevealedaglaringlackofphysicalintuitionanda

tendencyforgeneralizationcollapsewhenconfrontedwithextremephysicalphenomenalikenon-rigiddeformations,multiphasefluids,andcontactnon-linearities.

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GlobalHumanoidRobotShipments:HistoricalDataandProjections(2023-2030)

(Source:ComprehensivecalculationbyCCIDResearchInstitute)

~50,000units:

[2025-2026Year-on-YearGrowthRate:178%-2026]

84-87%:

China'sShipmentShareinGlobalTotal

$20,000-$35,000:

AverageBOMCostofRobotsin2026

Calculatedbasedonthe2025baseofapproximately18,000units

Figuresfrommultiplesources:84.7%-86.9%

Aroughly65%declinecomparedto2023

ShipmentVolume(10,000Units)

—DarkBlueLine:GlobalShipments(GGII/CCIDStatisticalCaliber)

PurpleLine:GlobalShipments(OmdiaStatisticalCaliber)

---DashedLine:ForecastData(2026E-2030E)

DataNote:

2023-2025figuresareofficiallyreleaseddata;2026E-2030Ediscrepanciesacrossinstitutions,seeSection4.1fordetailson

datainconsistencies.

Revision:

Theover700%YoYgrowthratestatedintheprefaceis

20

caliberisrecommended.

Source:

CCIDResearchInstitute(2026);Omdia(2025);GGI(2026);MorganStanley(2025)

inconsistentwiththe18.000unit2025base.Cal²ulatedonthe

YoYgrowthrateofapproximately178%.Aunifiedstatistical

200

120

100

2026E

Year

2025

2029E

2030E

2028E

2023

2024

40

Marketdataindicatesthatglobalhumanoidrobotshipmentswillreach

approximately50,000unitsin2026(synthesizedfromGGllandCCIDConsultingdata),representinga178%year-over-yearsurgefromabout18,000unitsin

2025.Chinesemanufacturerscontinuetodominatetheglobalsupplyside,

capturingamarketshareofroughly84-87%(varyingslightlyacrossdifferent

statisticalmetrics).TheoverallscaleoftheglobalembodiedAlvaluechain

continuestoexpand.ThehardwareBillofMaterials(BOM)costhasdroppedtoarangeof20,000to35,000-anearly65%reductioncomparedto2023-hittingthecriticalthresholdforlarge-scalecommercialdeployment.

ThisreporthasbeencompiledbytheWorldLaureatesAssociation(WLA),

bringingtogetherthecollectivewisdomoftheglobalvanguardacrossacademiaandindustry.Adoptingaglobalized,first-principlespanoramicperspective,wewilldeeplydissectthedynamicevolutionofthreecoretechnicalroutes:

traditionalnumericalsimulation,neuralphysics,andgenerativeworldmodels.Wewillsystematicallyanalyzethetrinityvalueofworldmodelsas"training

grounds,predictors,andconstrainers,"whiledirectlyconfrontingfoundationalbottleneckssuchasunmodeledreal-worlddynamics,computationallatency,anddatascarcity.Byfocusingonfourcriticaldimensions-technicalevolution,marketlandscape,applicationdeployment,andregulatorypolicy—thisreportaimstoprovideacomprehensivereferenceframeworkforindustryleaders,

academia,andpolicymakersalike.

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PartI:GlobalEvolutionofPhysicalModels

Inartificialintelligence’sjourneytowardArtificialGeneralIntelligence(AGI),

EmbodiedAIhasbeenuniversallyrecognizedasoneofthemostcritical

breakthroughs.Thereasonhumanscanfreelynavigatecomplexphysical

environments,manipulateobjects,andpredicttheconsequencesoftheir

actionsisthatourmindspossessahighlyrobustinternal“physicsengine.”

Ratherthanpreciselysolvingpartialdifferentialequations,thisengine

instantaneouslyprovidesintuitivepredictionsregardinggravity,inertia,friction,andcollisions.Thiscognitivecapacity,derivedfromabodyembeddedwithintheworld,ispreciselywhatcurrentLargeLanguageModels(LLMs)andvisionmodelslackmost.

Basedonthiscoreunderstandingandadoptingapanoramicperspectiveof

globaltechnologicaladvancement,thispapersystematicallymapsthe

evolutionarytrajectoryofphysicalmodelsfromvirtualsimulationtoworld

simulation.Itdeeplydissectsthecurrentstatusandfuturetrendsofthreemajortechnologicalpathways:traditionalsimulationengines,neuralphysics,and

generativeworldmodels.Furthermore,itexploresthecorevalueof“World

Models”—actingasthe“priorphysicalintuition”ofEmbodiedAI—in

reinforcementlearning,taskplanning,andtheconstructionofsafetyboundaries.AsglobalcompetitioninEmbodiedAIreachesafeverpitch,thecomprehensionandmasteryoftheunderlyingenginesofphysicalmodelshavebecomeakey

benchmarkformeasuringthecorecompetitivenessofanationoranenterpriseinthisstrategicdomain.

ConnotationandParadigmEvolution

TounderstandthecorepositionofphysicalmodelsinEmbodiedAI,onemustfirstclarifytheirdefinitionandtakeacomprehensiveviewofthefundamentalparadigmshiftstheyhaveundergoneattheintersectionofcomputerscienceandartificialintelligence.

DigitalIntelligenceoperatesonlanguageandsymbols,withitscapability

boundariesdeterminedbythesemanticcoverageoftrainingdata.Physical

Intelligence,bycontrast,mustcompleteclosed-loopperception,reasoning,andactionwithinarealworldcharacterizedbycontinuoustime,three-dimensional

space,andmulti-physicscoupling.Itsrelianceon“causalmodels”ratherthan“statisticalcorrelations”inherentlyimposesamuchhighertechnicalthreshold.

Between2023and2026,thisparadigmshiftexperiencedthreedistinct

milestoneinflectionpoints:first,thereasoningcapabilitiesofLargeLanguageModels(LLMs)weretransferredtotherobottask-planninglayerviaVision-

Language-Action(VLA)architectures;second,DifferentiablePhysicsEnginesallowedgradientsignalstobackpropagatethroughsimulatedphysicalfields,

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seamlesslyconnectingperception-controlloops;third,GenerativeWorldModelsbeganactingasdataflywheels,generatingsynthetictrainingdataatan

exponentialratetobridgethecriticalscarcityofreal-worlddata.

However,withinthecontextofEmbodiedAI,thedefinitionofaphysicalmodelhasexpandedfrom“thenumericalsolvingofknownphysicallaws”to“the

computationalrepresentationofthedynamicalevolutionarylawsofanunknownphysicalworld.”ThephysicalmodelsrequiredbyEmbodiedAIarenolonger

merelyone-way,passivesimulators;instead,theyaredynamicreasoning

systemsdeeplyintegratedwithanagent’sperception,decision-making,andcontrolloops.Theyencompassthreecoredimensions:

However,thelimitationsofthisparadigmareequallyprofound.Thefirstisthe“curseofdimensionality”andcomputationalbottlenecks:whenfacingcomplexscenariosinvolvinghighdegreesoffreedom,multi-phasefluids,orsoft-body

contact,thecomputationalcomplexityofnumericalsolvingescalates

exponentially,failingtomeetthereal-timecontroldemandsofEmbodiedAI

(whichtypicallyrequireresponsespeedsof100Hzto1kHz).ThesecondistheRealityGap:traditionalsimulationsassumeparameters(suchasfriction

coefficientsandcontactstiffness)areconstantanduniform,whereastherealworldisrifewithmicroscopicuncertaintiesandnoise.Consequently,roboticpoliciestrainedinsimulationoftenfailimmediatelywhendeployedintherealworld—theclassicSim-to-Realdilemma.

Thisrepresentsthetechnicalleapoccurringrightnow.ItembedsphysicalFirstPrinciples(differentiablephysics,conservationlawconstraints)asoptimizationconditionsintohigh-capacitygenerativeAIarchitectures(suchasDiffusion

ModelsandautoregressiveTransformers).RepresentativetechnologiesincludeSora(attemptsatvideophysicssimulation),PhysiLLM(Physics-InformedLargeLanguageModels),PINNs(Physics-InformedNeuralNetworks),UniSim

(UniversalSimulator),Agibot’sGE-Sim2.0,andGoogleDeepMind’sGenie3.

Thethirdtierofvalue—actingasa“constrainer”—deservesacuteattentionin

currentsafetydiscussionssurroundingEmbodiedAI.Asrobotssteadily

transitionoutoflaboratoriesandenterindustrialassemblylinesandhuman

livingspaces,ensuringthattheirbehaviorconsistentlyconformstofundamentalphysicallawsandsafetystandardshasbecomeanurgent,unavoidable

challenge.Here,theworldmodelassumestheroleofan“embeddedphysical

constrainer.”Anidealworldmodeliscapableofnotonlypredictingfuturestatesbutalsoinherentlyenforcingphysicalconsistency—anyworldevolutionary

trajectorygeneratedbythemodelmustsatisfycorephysicallawssuchasmassandenergyconservation,causality,andcontactmechanics.Furthermore,the

“constrainer”functionsasasafetyguardrailforroboticpolicies.Whenarobotimaginesthefutureworldstateofacertainaction,the“constrainer”

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automaticallycheckswhetherthisimaginationviolatesphysicalsafetyboundaries,adjustingorrejectinghazardoustrajectoriesaccordingly.

1.2TechnologicalRoadmapPanorama

Currently,academiaandindustryworldwideareexploringtheconstructionofphysicalmodelsacrossmultipledimensions.Dependingontheunderlying

technologyfoundation,optimizationtargets,andcomputationalarchitectures,globaltechnologicalroadmapsforphysicalmodelscanbeclassifiedintothreeprimaryschoolsofthought:traditionalnumericalsimulationanddifferentiable

physicsengines;neuralphysics(PINNsandGraphNeuralNetworks);andlarge-model-basedgenerativephysicalworldsimulators.FueledbyEmbodiedAl,

thesethreepathwaysaremovingawayfromisolationandtowardcross-disciplinaryintegration.

ComparativeAnalysisofPhysicalAITechnicalParadigms

Evaluation

Dimension

TraditionalNumerical

TraditionalNumericalSimulation

PINN/GNN

(Physics-InformedNeuralNetworks)

GenerativeWorldModel

GenerativeWorldModel

PhysicalFidelity

PhysicalFidelity

High

Based

error

onfirstprinciples,controllable

Mid

AffectedbytrainingdataandPDEconstraintquality

Lower

Implicitphysics,relyingondatadistributioncoverage

InferenceSpeed

InferenceSpeed

High-precisionsimulation

LowusuallytakesminutestohoursMid

Inferencephaseisfastsolving,trainingcostishigh

High

End-to-endneuralnetworkinference,millisecondlevel

0ODGeneralization

(Out-of-DistributionGeneralization)

HighPhysicalequationscanextrapolatewithinknownphysicaldomain

PDEconstraintsprovidepartialgeneralizationguarantee

Out-of-distributiontrainingLimitedperformanceusuallydrops

significantly

DataRequirements

DataRequirements

LowRequiresnolarge-scalelabeleddata

Requiresasmallamountofobservationdata+physicalconstraints

High

Requireslarge-scale,

diverse,interactivedata

Interpretablity

Interpretability

High

?

Eachtermintheequationshasaclearphysicalmeaning

Mid

?

Networkweightsare

ambiguous,residualsareinterpretable

Low

Black-boxmodel,dificulttotracephysicalreasons

Integration

HighSimulationengineinterfaceisMidRequiresdualexpertiseinPDEStandardizedAPl,friendly

IntegrationComplexity

※complex,intensivecomputingresources

崇knowledge+MLengineeringLowfordownstreamtasks

TypicalSystems

TypicalSystems

ANSYS,MuJoCo(ClassicalMode),FEniCS

DeepXDE,PhysicsX,Taichi(Differential)

Genie3,GE-Sim2.0,DreamerV3

PrimaryUseCases

StructuralMechanics,FluidSim-to-RealTransfer,MaterialRobotStrategyTraining,Data

PrimaryUseCases

Dynamics,AerospaceValidationPropertyPredictionAugmentation,Planning

Notes:Theratingisaqualitativecomprehensvesessment,specificperformancevariesbytask,modelsize,anddataquaity;tisnotanabsoluteranking.Diferentiable

PhysicalEngine(e.,Taichi,ISacSim)belongtothefusionofphysicsinformednouranetworksantraditionalsimulation,havingcharacteisticsintermediatebetweenthetwo.Source:SynthesizedfromWLAReport(2025),DeepMindTechnicalReports,andPINNLiteratureSurvey(Raissietal,2019;Karniadakisetal.,2021).

Thecorecharacteristicofthetraditionalphysicalsimulationengineroadmapis“explicitphysicalmodeling”—simulatingthemotionevolutionofphysical

systemsunderappliedforces/torquesandconstraintconditionsviaanalyticaldifferentialequationsandnumericalsolvingalgorithms.ThisremainsthemostmatureandwidelyadoptedtechnicalsolutioninthefieldofEmbodiedAI.

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MuJoCo(Multi-JointdynamicswithContact)wasoriginallydevelopedby

ProfessorEmoTodorovattheUniversityofWashington,lateracquiredby

DeepMind,andopen-sourcedin2022.MuJoCo’scoreadvantageslieinits

exceptionalcontactdynamicssolverandthenumericalstabilityofmulti-joint

systems.Forroboticarmgrasping,leggedlocomotioncontrol,andlow-level

controlresearch,MuJoCoiswidelyregardedasthestandard“lightweight,

stable,andeasy-to-tune”choice—offeringlightweightmodels,transparent

parameters,shorttuningpaths,andrichbaselineswithintheresearch

community.MuJoCo’spositioninacademiaisnearlyunshakable,longservingasthe“goldstandard”forbaselinecomparisonsinpapers.ItsmodeldefinitionformatbasedonMJCF/URDFisclearandconcise,backedbyahighly

sophisticatedresearchreplicationecosystem.Furthermore,MuJoConow

supportsMJX—anXLA-basedimplementationthatcanrunonNVIDIA/AMDGPUsaswellasApplesilicon,partiallyclosingthegapwiththeIsaacseriesregardingGPUparallelacceleration.

Physics-InformedNeuralNetworks(PINNs)

IntroducedbyGeorgeKarniadakisetal.,thecoreconceptofPINNsistoembedphysicalequations(suchasBrownianmotion,heatconduction,andfluid

equations)directlyintothelossfunctionofaneuralnetworkasregularizationterms:

L(θ)=L_data(θ)+λ·L_physics(θ)

L_data(θ)=(1/N_u)·Σᵢ₌₁ᴺᵘ‖u_θ(xᵢ,tᵢ)−ûᵢ‖²L_physics(θ)=(1/N_f)·Σⱼ₌₁ᴺᶠ‖N[u_θ](xⱼ,tⱼ)‖²

Bulletisanopen-sourcephysicssimulationengine.ItsPythonwrapper,PyBullet,hasbecomethestartingpointandbenchmarkplatformformanyacademic

researchprojectsduetoitsentirelyopen-sourceandfreenature,lowentry

barrier,andcomprehensivefeatureset.Bulletprovidescorefunctionalitiessuchasrigidbodydynamics,collisiondetection,andjointconstraints,andsupportsContinuousCollisionDetection(CCD),makingitmorestableforfast-moving

objects.Withamoderatephysicalaccuracy,itsatisfiesthefoundationalneedsofmostmanipulationandmotioncontrolresearch.Thecorevalueof

Bullet/PyBulletliesinitsdemocratizationofcontribution—itsfree,open-sourcenaturehasallowedcountlessresource-constrainedlaboratoriestoconduct

roboticssimulationresearch.

PhysXisNVIDIA’sGPU-acceleratedphysicsengine.Itsparallelcollision

detectionandGPUsolverscansupportphysicalsimulationsofthousandsof

parallelenvironmentsonasingleGPU.BuiltuponPhysX,NVIDIAIsaacSimisacompleteroboticssimulationplatformencompassingaphysicsengine,high-

fidelityrendering(poweredbyNVIDIARTXtechnology),sensorsimulation,

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ROS/ROS2integration,anddigitaltwinconstruction.ThecorecompetitivenessofIsaacSimliesinits“full-stack”nature—fromphysicalsimulationtovisual

rendering,andfromsensoremulationtopolicydeployment,theentire

developmentworkflowcanbecompletedwithinaunifiedplatform.In2026,NVIDIAfurtherexpandeditsecosystembyintroducingIsaacLab-Arena,anopen-sourceframeworkdedicatedspecificallytopolicyevaluation.

GoogleDeepMind'sGenieseriesstandsasalandmarkrepresentativeofthe

worldmodeltrajectory.TheiterationreleasedinAugust2025achieveda

qualitativeleap—itbecameDeepMind'sfirstworldmodeltosupportreal-time

interaction,capableofgeneratinginteractiveenvironmentsat720presolution

and24framespersecond,withsceneconsistencyenduringforseveralminutes.ThetechnicalcoreofGeniesolvedthedualchallengeof"dynamicgeneration+continuousinteraction”:forreal-timeresponsiveness,themodellinksbackto

theentirehistoricaltrajectoryduringframe-by-framegeneration;forlong-termconsistency,itovercametheerroraccumulationinherentinframe-by-frame

generation,extendingvisualmemoryuptooneminute.Unlikemethods

dependentonexplicit3DrepresentationslikeNeRFsor3DGaussianSplatting,Geniegeneratesscenesframe-by-frameentirelyfromtextdescriptionsanduseractionswithoutanypredefinedmodels.

FRONTIERROBOTICWORLDMODELPLATFORMCOMPARISON(2026E)

(Note:Projectedtechnologylandscapeforillustrativecomparison)

CORETECHNOLOGICAL

FOUNDATIONKEYADVANTAGES

Algorithmicfrontiersandclosed-loopperception-to-

Action-drivenphysicalevolutionenginesand

open-sourceplatform

Databasedon2026ecosystemprojeclions

STRATEGICFOCUS

DepartingfromthegenerativevideopathwaysofGenieandGE-Sim,

BeingBeyond'sBeing-H0.7(releasedinApril2026)forgedacompletelydifferenttechnologicalpath.Thismodelabandonedhigh-compute,high-latencyvideo

generativeschemesinfavorofan“implicitspacereasoning”approachthat

closelymirrorshumanphysicalintuition—directlyexecutingjudgmentsonfuture

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statesandactionoutcomesinternally.ThecoreinnovationofBeing-H0.7liesinits“LatentWorld-ActionModel”architecturedesign,whichcutstrainingcoststolessthan1%ofcomparablegenerativemodels.Trainedon200,000hoursof

egocentrichumanvideos,Being-H0.7ledacrosssixauthoritativeinternationalbenchmarks,becomingthefirstuniversalworldmodeltosimultaneouslycoversevencriticaldimensions,includingcross-proprioception,cross-scene,fluids,deformableobjects,physicallaws,andcontextualreasoning.

1.3WorldModels

Whenphysicalmodelsevolvetothestageof“generativeworldsimulation,”theirrolewithinEmbodiedAIarchitecturesundergoesaqualitativemetamorphosis—theytransformintoanagent’sWorldModel.Ifaphysicalsimulationengineis

the“calculator”ofEmbodiedAI,thenaworldmodelisits“intuition.”This“priorphysicalintuition”empowersanagent,whenfacingunfamiliarenvironmentsandnewtasks,tobypasstediousreal-worldtrial-and-error.Instead,itcandirectly

“imagine”potentialcoursesofactionandtheirconsequencesinternally,makingoptimaldecisionsbasedonthosementalsimulations.

Theconceptofa“WorldModel”wasoriginallyproposedinthe1990sbyAGIpioneerJürgenSchmidhuber,andlaterpopularizedin2018byDavidHaandJürgenSchmidhuberintheirseminalpaperWorldModels.Inrecentyears,the“AutonomousMachineIntelligenceArchitecture”championedbyMeta’sChiefAIScientistYannLeCunhaspropelledworldmodelsfirmlyintotheindustry

spotlight.

YannLeCun’sCorePhilosophy:Thereasonhumansarevastlysmarterthan

existingreinforcementlearningAIsisthatwerarelylearnthroughfataltrial-and-error.Wedonotjumpoffacliffjusttotestgravity,becauseourinternalworld

modelhasalreadysimulatedthelethalconsequencesofthatactioninourminds.ForEmbodiedAItoachievegenerality,itmustpossesssuchaworldmodel

capableof“common-sensereasoning”and“actionrehearsal”withinasafelatentspace.

Aworldmodelcanserveasthe“intuitionengine”forEmbodiedAIbecauseit

possessesthreecorecapabilities:counterfactualreasoning,action-conditionedprediction,andphysicalconsistencyconstraints.

Theintegrationofworldmodelswithreinforcementlearningisfundamentally

reshapingtheRLtrainingparadigm.ThecoretraitoftraditionalRL(especially

model-freeRL)is“learningbytrial-and-error”—aprocessnotoriouslylowin

sampleefficiencyandexceptionallydifficulttoapplyinrobotics,wherethecostofasinglefallisprohibitive.

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TheworldmodelsrequiredbyEmbodiedAImustbeaction-conditioned.Thatistosay,thenextframeoftheworldstategeneratedbythemodelmustalter

deterministicallyandinstrictaccordancewithphysicalconservationlawsbasedontheprecisecontrolvectorsinputtedbytheagent.Itcannotsimplylook

visuallypleasing,asisacceptableinstandardvideogeneration.Currently,toptierresearchinstitutionsgloballyaretacklingthischallenge.Bydeeplyaligninganembodiedrobot’sactiontokenswiththefoundationalsemantictokensof

Vision-LanguageLargeModels,anewstrainof“UniversalPhysicalWorldModels”—infusedwiththebloodofFirstPrinciples—israpidlyemerging.

Despiteimpressiveprogressoverthepasttwoyears,worldmodelsremainaconsiderabledistanceawayfromtheultimategoalof“replacingrealphysicalexperiments.”Theprimarytechnicalchallengescurrentlyfacingworldmodelsareconcentratedinthreemajordirections:

Non-RigidPhysicalModelingisanotherfrontierawaitingabreakthrough.Most

mainstreamsimulationenginesandamajorityofcurrentworldmodelsdefaulttorigidbodyphysicsassumptions.However,therealworldisfilledwithnon-rigidphysicalphenomenasuchasdeformablebodies,fluids,andcloth.Themulti-

scale,high-dimensional,andnon-linearcharacteristicsofthesephenomena

poseseverechallengestobothtraditionalsimulationmethodsand

contemporarydeeplearningapproaches.Theindustryisseekingbreakthroughsviatwomainavenues:ononehand,hybridparadigmmodels(PINNs+

traditionalsimulation)attempttolowerthedatarequirementsofnon-rigid

modelingviaphysicalpriors;ontheotherhand,generativemodelstrainedonmassivevideodatasetsattempttolearnnon-rigiddynamicsdirectlyfromhigh-dimensionalobservations.

Beyondthesetechnicalhurdles,worldmodelsconfrontengineeringchallengesincludingdataacquisitionandquality,Sim-to-Realtransferefficiency,model

inferencelatency,safetyverification,andinterpretability.Resolvingthese

bottlenecksdemandsnotonlyalgorithmicbreakthroughsbutalsomulti-

dimensionalprogressacrosscomputeinfrastructure,datacollectionstandards,evaluationframeworks,andindustrialsynergy.

1.4ModelSystems

DeepMindGenie3:Supportsthegenerationof3Dinteractivescenesfrom2Dimageprompts,featuringanembeddedlearning-basedphysicalprediction

modeltogeneratepolicytrainingenvironmentsforroboticmanipulationtasks.

MetaV-JEPA2(VideoJointEmbeddingPredictiveArchitecture):Learns

structured,abstractrepresentationsoftheworldbypredictingtheembeddedrepresentationsofvideosequencesratherthanpixel-levelreconstructions,

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yieldingcomputationalefficienciessignificantlyhigherthanpixel-generationmodels.

Thecommercialvalueofworldmodelsactingasdataflywheelsisrapidly

materializing.Throughhybridtrainingutilizing“asmallamountofrealdata+alargevolumeofsyntheticworld-modeldata,”leadingmanufacturershave

successfullyelevatedSim-to-Realprecisionmanipulationtasksuccessrates

fromapproximately42%in2023toaround85%–88%in2026,while

substantiallydrivingdownthecostsassociatedwithreal-worlddatacollection.

Conclusion

Reflectingontheevolutionaryjourneyofphysicalmodelsfromtraditional

simulationenginestoworldsimulatorsrevealsaclearascendingtrajectory:

transformingfromamere“computationaltool”into“cognitiveinfrastructure.”

TraditionalsimulationengineslikeMuJoCo,Bullet,andPhysXprovidedreliablefoundationalphysicscomputationcapabilitiesforEmbodiedAIresearch,and

theyremainirreplaceable“industrystandards”forroboticcontrolstudiestothisday.Data-drivenworldmodelshavepropelledphysicalsimulationfrom

“manuallydesignedequations”to“autonomouslearningoflawsfromdata,”unlockingvastspacesforinfinitesceneexpansionandcontinuouscapabilityevolution.Hybridparadigms—suchasPINNsanddifferentiablesimulation—bridgedata-drivenmethodsandphysicalpriors,huntingforanoptimal

equilibriumamongfidelity,efficiency,andgeneralization.

Theconvergenceandsynergyofthesethreetechnologicalroadmapsaredrivingphysicalmodelstounprecedentedheights.Agibot’sphilosophyofa“physical

evolutionengine”viaGE-Sim2.0,NVIDIA’sunifiedsimulationarchitectureviaOmniverse,andTesla’sshared-braindesignforFSDandOptimusallpointinasingulardirection:physicalmodelsareshiftingfroma“supportingtool”in

EmbodiedAIR&Dtoitsirreplaceable“underlyingengine.”

Chapter2:CompetitiveLandscapeandInfrastructure

Asthe“underlyingengine”ofEmbodiedAI,theadvancementofphysical

modelsisshapednotonlybyalgorithmicbreakthroughsbut,moredeeply,by

nationalstrategies,industrialcapital,open-sourceecosystems,and

infrastructure.Enteringthe2025–2026period,theglobalcompetitivelandscapeinp