物理人工智能

Physical

AITakinghuman-robotcollaborationtothenextlevelTableofcontents

24Whyphysical

AIisat

aninflectionpoint56Thegrowingimperativetoadoptphysical

AI36Physical

AIisagame-changerfor

industry04Whoshouldreadthisreportand

why08ExecutivesummaryPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute2026278Humanoidssetthestagefor

general-purpose

robotics7096Scalingphysical

AIgoesbeyondtechnology,spanning

safety,cybersecurity,regulation,and

operations105ReseaΓchmethodologyRecommendations:Acceleratingthephysical

AIrevolution104ConclusionPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute20263Thisreportisintendedforseniorexecutivesshaping

their

organizations’approach

toroboticsandautomation.Itexamineshowphysical

AIistransformingrobotics

–fromthe

capabilitiesitenablestothevalueitunlocks,thetimelinesforadoption,andthebarriersthat

mustbeaddressedtoscaledeploymentssafely

andeffectively.Itwillbeparticularlyrelevantto

technologyandinnovationleaders(includingchieftechnologyofficers,chiefinnovationofficers,chiefdigitalofficers,andheadsof

AI

orrobotics),aswellasmanufacturing,supplychain,andlogisticsleadersresponsiblefor

roboticsstrategyanddeployment.Asroboticsexpandsintoconsumer-facingand

serviceenvironments

–suchashealthcare,retail,hospitality,andentertainment

–thereportisalsorelevanttochiefproductofficers,

productstrategists,andexperiencedesignleaderswhoareshapinginteractionsbetween

peopleandintelligentmachines.Inaddition,thereportprovidespracticalguidanceforCROsandsafetyorregulatoryleaderspreparingtheirorganizationsforwiderroboticsadoption

–includingimplicationsforgovernanceandriskoversight.Thisreportdrawsonaglobalsurveyof1,678seniorexecutivesacross15industries,

complementedbyin-depthinterviewswith

industryexperts,robotmanufacturers,foundation-modelstartups,technology

providers,investors,andacademics.Pleaseseetheresearchmethodologyatthe

endofthereportformoredetails.WhoshouldΓeadthis

ΓepoΓtandwhy?PhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute20264WeextendouΓsinceΓethankstothemanyexpeΓtsfΓom

industΓyandacademia

whoshaΓedtheiΓinsights

withusRebeccaYeungStrategicAdvisoratDexterityand

formerCorporateVicePresident

forOperationsScienceandAdvancedTechnologyatFedExAshutoshSaxenaFounderandCEO,TorqueAGIDaniela

RusDirector,ComputerScience

andArtificialIntelligenceLaboratory(CSAIL),MITSanjayAggarwalVenturePartner,F-PrimeCapitalDeepuTallaVPandGM–Robotics&

Edge

AI,NVIDIAPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute20265AngeloCangelosiCo-DirectoroftheManchesterCentreforRoboticsandAI,UniversityofManchesterNageshPuppalaGeneralManager,Roboticsand

PhysicalAI,ClientComputingGroup,Intel

CorporationPedroZhengSeniorRegionalSalesManager,UnitreeRoboticsMiladMalekzadehCo-FounderandVicePresidentAI,NeuraRoboticsDirkGeigerSenior

Director

and

TeamLead–HumanoidRobotics,InfineonTechnologiesJim

MaRegionalTechnicalDirector,UnitreeRoboticsVikiYangOverseasSalesDirector,UBTECHRoboticsJulien

PerrinCOO,NiryoAntoPatrexFounderandCEO,CosmicBrainAIDaniel

JackerCEO,

ZaiNarPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute20266Physical

AItakes

AIbeyond

screensintotherealworld

–

enablingmachinestoperceive,reason,and

act

autonomously.

This

report

focuses

onits

application

in

robotics,

where

physical

AIrepresents

a

fundamental

shift:from

robotsthat

follow

fixed,pre‑programmed

paths

torobotsthat

can

generalize

acrosstasks,perceiveandnavigate

complex

environments,makecontext-aware

decisions,

and

adapttoreal-worldvariation.

This

enablesrobotstofunctioninfar

more

diverse

and

dynamic

environments,expandingtheirreach

acrossnearlyeverymajorindustry

and

unlocking

solutions

to

problemsearlier

automation

couldn’t

address.Executivesummary"Thelastdecadeof

AIwasabout

information.

Thecoming

decadewillbeaboutaction."Rebecca

YeungStrategic

Advisor

atDexterity

andformer

Corporate

VicePresidentfor

Operations

Science

and

Advanced

Technology

at

FedExPhysical

AI:Taking

human-robot

collaboration

to

the

next

levelCapgemini

Research

Institute20267TraditionalroboticsKeyfeaturesPhysicalAI-poweredroboticsLimitedperception

–

senses

withoutinterpretation

PerceptionPerceivestheenvironmentthroughrich,multi-modalsensing(vision,depth,touch,audio)andinterpretscomplexenvironmentsWorksonlyin

structured*environments(consistent,predictable

settings)

AdaptabilityOperatesinunstructured**environments(messy,variable,dynamicsettings),includingpreviouslyunseensituationsHasnorealautonomy;followspre-programmedinstructions

AutonomyMakescontext-awaredecisionsinrealtimeNoongoinglearning;behavioris

staticunlessreprogrammed

LearningcapabilityLearnsfromdemonstrations,simulations,andexperience,improvingperformanceovertimewithoutmanualreprogrammingDesignedforasingle,specializedtask

GeneralizationHandlesmultiplescenariosonasinglerobot;generalizeslearned

skillstonewtasksandunfamiliarsituationsRobotsoperateindependentlywithnoknowledgesharing

CollectivelearningRobots

share

skills

a∩d

lear∩i∩9s

across

a

HeetRequiresprecise,codedcommandsNatural-languageunderstandingUnderstandsnaturallanguageinstructionsCanexecuteassembly

only

ina

strictly

programmed

manner;

failseasily

if

presentedwithany

slight

deviation

from

programmedsequenceExampleCapableof

adaptingautonomously

tovariationinassembly

processandsupports

tailored

assembly

by

adjustingdynamically

toeachunique

productExecutivesummary*Structuredenvironments:Environments

wherethelayout,tasks,andconditionsarepredictableandconsistent,allowingrobotstofollowfixedpathsandroutines

withlittle

variation.Examples:assemblylines,controlled

warehouseaisles.**Unstructuredenvironments:Environmentsthatare

variableandunpredictable,

whererobotsmustadapttochangeanduncertainty.Examples:retailfloors,hospitals,farms,constructionsites.Foramoredetaileddescriptionofphysical

AI,itsapplicationinrobotics,andindicativeindustryusecases,pleaserefertothe

Appendix.Traditionalroboticsversusroboticspoweredbyphysical

AI:AcompaΓisonPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute2026Source:CapgeminiResearchInstituteanalysis.8Tounderstandtheimpactofphysical

AIonroboticsandthe

valueitcanpotentiallyunlock,thisreportdrawsonaglobal

surveyof1,678executivesacross15industries,complementedbyin-depthinterviews

withexperts

acrossthephysical

AIand

robotics

ecosystem(please

see

the

researchmethodologyformoredetails).Physical

AIisat

aninflectionpointMultimodalfoundationmodelsaΓeΓedefiningrobotintelligencebyenablinggeneralizationacross

tasks

and

environments.

These

advances

areallowingrobotstoadapttounfamiliar

situationswithouttaskspecificΓepΓogΓamming,extendingdeployment

into

unstructured

environments–messy,dynamic

settingsthatearlierroboticAgame-changeracrossmultipledimensionsPhysical

AImarksa

stepchangefromearlierautomation.Byenablingrobotstointerpretcontext,adaptinrealtime,andoperateinunstructured

environments,physical

AI

promotesthemfrompassivetoolstoactivecollaboratorsinthe

workspace

–openingthedoortoareimaginedworkenvironment,inwhichhumans,robots,and

AI

agents

work

in

tandem.

At

the

same

time,physical

AIallowsroboticsto

scaleasasharedintelligence

platform,

with

learning

and

capabilitiescompoundingacrossdeployments.Indoingso,physical

AIextendstheagenticparadigmintothereal

world,enablingrobotstoactasembodiedAI

agents

capable

of

planning,orchestrating,andsystemscouldnothandle.Inparallel,advancesinsimulationare

shorteningrobottrainingcycles,whilean

Al-Γobot-dataflywheelisacceleΓatingimprovement

with

every

real-world

deployment.Combined

withfallingcostsofkeyhardwarecomponents

such

as

sensors,actuators,andelectric

motors,and

commercial

modelssuchasrobotics-as-a-service(RaaS),these

shiftsare

lowering

barriers

to

adoption.

At

the

sametime,demographicandeconomicpressures–includingaging

workforcesandpersistentlaborshortages

–areintensifyingdemandforrobotic

systemscapableoftakingonrolesthat

aΓeincΓeasinglyhaΓdtostaff.RecoΓd

ventuΓecapitalinvestmentintophysical

AIandroboticsisaddingtothemomentumbehindthese

shifts.ExecutivesummaryPhysicalAI:Takinghuman-robotcollaborationtothenextlevelCapgeminiResearchInstitute20269PhysicalAI:Taking

human-robot

collaboration

to

the

next

level64%Physical

AI’s

value

is

multi-faceted.

Executivesexpect

the

strongest

gains

in

productivity,e代cie∩cy,

a∩d

quality,

alo∩9side

9reateroperatio∩al

resilie∩ce

a∩d

Hexibility

as

adaptiverobots

help

organizations

manage

volatility

and

reco∩fi9ure

operatio∩s

quickly.

Physical

AI

alsoimproves

workplace

safety

and

reduces

physical

strain,

as

robots

increasingly

take

on

hazardous

and

physically

demanding

tasks.

Beyondoperational

impact,

physical

AI

is

opening

newgrowth

avenues:

nearly

four

in

ten

executivesexpect

new

revenue

opportunities,

and

60%believe

it

will

enable

robotics

in

areas

that

werepreviously

impossible

or

impractical.

High-impact

use

cases

span

hazardous

operations,micro‑lo9istics,

pick‑a∩d‑place,

a∩d

fieldexecuting

complex

physical

tasks.

Over

two-thirds

(67%)

of

executives

view

it

as

game-changing

for

their

industry

and

most

believe

it

will

become

acritical

driver

of

competitiveness.67%of

executives

believe

physical

AI

willbecome

a

critical

driver

of

competitivenessi∩spectio∩,

alo∩9side

sector‑specific

applicatio∩s

such

as

dynamic

assembly

in

manufacturing,healthcare

and

eldercare

in

the

public

sector,

and

disaster-damage

assessment

in

insurance.Executivesummaryof

executives

view

physical

AI

as

game-changing

for

their

industryCapgemini

Research

Institute

202610Thereisa

growingimperativetoadoptphysical

AIPhysical

AI

adoption

is

wellunderway:nearlyeight

inten

organizations(79%)

are

alreadyengaging,

with

27%

deploying

or

scaling,

and65%

expectingtoreach

scale

withinfive

years.

Theprimary

catalysts

are

structural:labor

shortages(74%)

andrisinglabor

costs

(69%).

In

the

near-term,

growth

will

comefromfamiliar,provenformfactorsfortask‑specific

applications.

Asfoundation

models

mature

and

adoption

deepensacross

industries,

entirelynew

categories

of

robots

arelikelyto

emerge

–

purpose-built

for

variedenvironments,

complextasks,

andnewmodesof

human

collaboration.

Humanoids,

despiteScaling

physical

AI

goes

beyondtechnology

–italsorequiresbuilding

safety,cybersecurity,regulatory,andoperationalreadinessInpractice,

scalingphysical

AI

demandsmorethanbetter

algorithms

–

itrequiresrethinkinghow

systems

are

engineered,

secured,

governed,

andrun.

Today’s

systems

donot

yetmeet

thehighreliability

thresholds

of

industrial

and

othersafety-critical

settings,

and

dexterityremainslimited.Progress

isfurther

slowedby

data

scarcity

–real-worldphysical

interaction

data

is

scarce

and

costlyto

obtain.

Tokeeppeople

and

assets

safewhile

capabilitymatures,

safetymustbe

enforcedthrough

deterministicmechanisms

independentExecutivesummaryNear-termgrowth

willcomefromestablishedformfactors

fortask-specificapplications;

asfoundationmodelsmature,

newpurpose-builtrobotcategoriesarelikelyto

emergesubstantial

investment,remain

alonger-termbet,

askey

challenges

–

includingtechnicalmaturity(reliability

and

dexterity),

safety,

and

cost-to-ROI

viability

–must

stillbe

addressed.Physical

AI:Taking

human-robot

collaboration

to

the

next

levelCapgemini

Research

Institute202611Physical

AI:Taking

human-robot

collaboration

to

the

next

levelsi9∩ifica∩tbarriers,

compou∩dedby

a

societalreadiness

gap,

with

62%

citingpublic

acceptanceas

a

criticalhurdle.67%Humanoidrobotsinspirestrongindustryconviction

–butscaleddeploymentremainsalong-termbetTwo

inthree

executives(67%)believe

humanoidswill

ultimately

transform

their

industry,

citing

theirability

to

operate

inhuman-built

environmentsandtheirpotential

as

general-purpose

systems;

53%

are

already

investing

orplanto

invest.However,the

conditionsfor

scale

arenot

yet

in

place.

While

78%

expectto

deployhumanoids

at

scale

eventually,

averagetimelines

extendto

seven

years,

and

only

30%

seethembecomingviable

general-purpose

workers

within

three

to

five

years.

Tech∩olo9y

immaturity,hi9h

costs,

uncertainROI,

and

safety

concernsremainofthe

AIlayer.

Further,

as

robot

autonomy9rows,

cybersecurity

exposure

wide∩s,requiri∩9

controls

thatprevent

unauthorized

access

and

manipulation.Regulatoryframeworkslag

therealities

of

autonomousphysical

action,leaving

u∩resolved

questio∩s

about

accou∩tability

a∩d

acceptablerisk.

Operationally,

enterprisesmust

pla∩forhardware

co∩strai∩ts,ma∩a9i∩9

Heets

at

scale,

strengthening

data

and

AI

governance,

andreskilling

workforces.Executivesummaryof

executivesbelievehumanoids

willultimately

transform

their

industryCapgemini

Research

Institute202612Recommendations:

Actionstounlock

thepotentialofphysical

AIPhysical

AI

adoption

is

amulti-year

journey,butthetechnology

ismature

enough

to

deliver

tangiblevalue

today.FivepΓioΓity

actions:1.

Buildunderstanding:Develop

a

clear

view

of

what

physical

AI

enables

today

–

its

capabilities,limits,

and

data‑infrastructurerequirements.2.Start

withconfidence-buildingusecases:Begin

withfeasible,meaningful

applications

thatbuildfamiliarity

and

confidence

–

such

as

dull,

dirty,

or

dangeroustasks.3.

Designthroughformexploration:Iterate

withmultiple

design

concepts

to

assesshowform

shapes

trust,

interaction,

and

suitabilityfor

different

tasks

and

environments,ratherthan

defaulting

tohumanoids.4.

Redesign

workflows:Reworkprocessesfor

human–robot

collaboration,

withclearhandovers,

supervision,

safety,

and

escalation.5.Scale

viaplatforms:Create

a

scalablearchitectureforreusable

robot

skillsandfleet‑levelorchestration,

to

enable

disciplined

scalingbeyond

isolatedpilots.ExecutivesummaryThese

actionsmustbe

anchored

intrust

–

through

clear

safety,

governance,

andhuman-oversightguardrails

–

and

supportedby

ongoing

engagement

with

thephysical

AI

ecosystem

astechnologies,standards,

andregulations

continueto

evolve.Physical

AI:Taking

human-robot

collaboration

to

the

next

levelCapgemini

Research

Institute202613"Physical

AI

marksashift

fromsystemsthatdescribetheworldtosystemsthatcanactwithinit.Butweshould

stayclear-eyed.Robotics

hasalonghistoryof

overpromising,

whereearly

breakthroughscreated

expectationsthetechnologycould

not

yet

meet.

What

isdifferenttoday

isnotthehype,buttheconvergenceof

AI,data,and

engineering

maturity.

Theopportunity

isreal,providedwe

focuson

whatworksat

scale,and

gobeyondwhatlooksimpressiveindemos."PascalBrierGroupChiefInnovationOfficer,CapgeminiSmartbet,onlyoption,orboth?BiopharmaR&Dturns

toAI.CapgeminiResearchInstitute202614FiguΓe

1.ExamplesofphysicalAI–poweredroboticdeploymentsIndustries

IllustrativecasesWarehousingandlogisticsUltra,aUS-basedindustrial

AIroboticscompany,haspartneredwithPhysicalIntelligence,aUS-basedstartupdevelopinggeneral-purposerobotics

foundationmodels,todeployPl’sπ0.6modelonindustΓialΓobotsopeΓatinginlivewaΓehouseenviΓonments.Themodelhasbeendeployed

foΓe-commeΓceoΓdeΓpacking,ataskthathashistoΓicallybeendifficulttoautomateduetolaΓgevaΓiabilityinitemtypes,defoΓmablepackagingmateΓials,

andmulti-stepmanipulationthatcausesΓule-basedsystemstofail.Pl’sΓobotic

foundation

modelallows

UltΓa’s

Γobots

to

peΓceive,

Γeason,andadaptin

Γealtime.EaΓlydeploymentsshowUltΓa’sΓobotsachievinggainsinΓeal-woΓldautonomouspeΓfoΓmance,demonstΓatinghowphysical

AlcanunlockwaΓehousetaskspΓeviouslyconsideΓednon-automatable.1FedExispartneringwithUS-basedroboticsstartupDexteritytopilot“superhumanoid”2

robotsfortruckloading

–oneofthemostcomplexandphysicallydemandingtasksinlogistics,aspaΓcelsvaΓywidelyinsize,shape,andweightandaΓΓiveinunpΓedictablesequences.TheΓobotsautonomouslyinteΓpΓettheincomingmixofpaΓcels,andstackthemintodense,stablewalls.UsingDexteΓity’sFoΓesightwoΓldmodel,theyevaluatehundΓedsofpossibleplacementsfoΓeachiteminmilliseconds,pΓedictinghoweachchoiceaffectstheintegΓityofthestack.ThisenablesΓapidhandlingofiΓΓegulaΓ

items

一

wheΓetΓaditionalautomationstΓuggles

一

whileincΓeasingthΓoughputandΓeducingphysicalstΓaininhigh-volumeopeΓations.3ManufacturingFoxconnispartneringwithIntrinsic,an

Alphabet-ownedcompanythatdevelops

AImodelsandsoftwareforrobotics,tohelprealizetheintelligent

factoΓyofthefutuΓe.ThecollaboΓationtaΓgetselectΓonicsassembly

一afast-gΓowingsectoΓdΓivenbythe

AlboombutstillconstΓainedbyΓigidautomationandmanualpΓocesses.ThepaΓtneΓshipaimstodeliveΓastepchangebyshiftingfΓompΓoduct-specificautomationthatΓequiΓes

extensiveΓeengineeΓingacΓosspΓoductgeneΓationstomoΓegeneΓal-puΓposeintelligentΓobotics.lnitially,thecollaboΓationwilluselntΓinsic’s

ΓoboticsfoundationmodeltofocusoncΓiticalusecasesacΓossassembly,inspection,machinetending,andlogistics.4Physical

AI–poweredroboticsinactionPhysical

AIroboticsystemshavepotentialapplicationsacrosseverymajorindustry.Thefollowingexampleshighlighttheseapplicationsincomplex,dynamic,

real-worldenvironments.PhysicalAI:Takinghuman-robotcollaborationtothenextlevelContinuedonnext

pageCapgeminiResearchInstitute202615Automationisbecomingincreasinglycriticalinagricultureaslaborshortagesintensifyinmanyregions.7

HoweveΓ,scalingautomationinagΓicultuΓeΓemainschallengingduetothehighlyvaΓiablenatuΓeoffaΓmingenviΓonments

一wheΓelighting,teΓΓain,andcΓopvaΓietiesdiffeΓ

widelyacΓossfields

一andtheΓelianceonheteΓogeneousfleetsofmachines,includingtΓactoΓs,haΓvesteΓs,andspΓayeΓs.

TorqueAGI,aUS-basedstaΓtupbuildingfoundation

modelsfoΓΓoboticautonomy,addΓessestheseconstΓaintswithphysics-infoΓmed

Alfoundationmodelsthatcanhandledensefoliage,iΓΓegulaΓplantgeometΓy,andmultimodalpeΓception,whileopeΓatingacΓossdiffeΓentmachines.

ToΓqueAGliscollaboΓatingwith

JohnDeeretoadvance

Alfoundationmodelsforthenextgenerationofintelligentagriculturalrobots.8TheconstΓuctionindustΓyfacesmountingpΓessuΓefΓomlaboΓshoΓtagesandincΓeasingdemandfoΓmoΓeefficientandsustainablebuildingmethods,

while

incΓeasingconstΓuctingqualityand

safety.

Atthe

sametime,constΓuctionsitesaΓeoneofthemostchallengingenviΓonmentsfoΓ

automation

duetoconstantlychangingteΓΓain,layouts,andhumanactivity.AustralianroboticscompanyFBRsHadrian

XaddΓessestheseconstΓaintsbyautomatingoneofthemostlaboΓ-intensivetasksinconstΓuction:stΓuctuΓal

wall

building.HadΓian

Xisanautonomous,mobileconstΓuctionΓobotthatusesaΓoboticaΓmmountedona

vehicleplatfoΓmtoplaceconcΓeteblocks.

TheΓobot

hasbeenpilotedonanactiveconstΓuctionsiteintheUS,andhasdemonstΓatedtheabilitytoconstΓuct

stΓuctuΓal,load-beaΓing

walls

withina

day.5BostonDynamicsandFieldAIaΓetacklingadiffeΓentbottleneck:

sitemonitoΓingandinspectioninconstΓuctionenviΓonments.ConstΓuction

sitesaΓedifficult

tomonitoΓconsistentlyduetochangingconditionsandsafetyΓisks,makingdatacollectionlaboΓ-intensiveandeΓΓoΓ-pΓone.

ThepaΓtneΓshipcombinesBoston

Dynamics’SpotquadΓupedΓobot

withFieldAl’sFieldFoundationModelstoenableautonomousinspection,mapping,andmonitoΓing.

AlΓeadydeployedacΓossmultiplelocations,thesolutionsuppoΓtsfleet-levelautonomyandcooΓdinatedopeΓation,andhasdeliveΓedoveΓ90%Γeductionsininspection

and

documentationtime,eaΓlieΓissuedetectionthatΓeducesΓewoΓkcosts,andimpΓoved

woΓkeΓsafety.6ConstructionAgricultureIllustrativecasesIndustriesPhysicalAI:Takinghuman-robotcollaborationtothenextlevelContinuedonnext

pageCapgeminiResearchInstitute202616IndustriesIllustrativecasesHealthcare/eldercareWandercraft,

aFrance

andUS-basedroboticscompany,isdeveloping

AIpoweredmedicalexoskeletonsthatenablepeople

with

spinalcordinjuries,stroke,

andother

severemobilityimpairmentsto

stand

and

walk.ItslatestdevicethePersonalExoskeletoniscurrentlyinclinicaltrials

andisdesigned

for

everydayindoor

and

outdooruse.

The

deviceuses

AIforbalance

andmovement,

adapting

continuouslyinrealtimeto

support

stable

walking

across

varied

surfaces

such

asconcrete,carpet,

andtile.9ElliQ,

an

AI-powered

companionrobotfor

older

adults

developedbyIntuitionRobotics,isbeingintroducedto

Japanthrough

apartnership

withJapanesetrading

companyKanematsuCorp.

Thecollaborationtargets

Japan

srapidly

agingpopulation

andtheresulting

shortageof

caregivers

and

nursinghome

staff.ElliQproactively

supports

older

adults

with

everydayneeds,includinghealthmanagement,preventive

care,

communication,monitoring,

and

social

and

cognitive

activities.10EnergyAI-enabled

robots

from

US-based

Luminous

Robotics

were

used

to

help

install

nearly500,000

solar

panels

at

ENGIE

s250MW

solar

farm

in

Victoria,

Australia.Luminous

sLUMIrobots

autonomouslylift

andplacepanelsontomounting

structuresusing

AI-drivenpick-and-place

systems,

whilehumancrewscompletefinalfastening.

Thisreducesheavymanuallabor,improves

safety,

andincreasesefficiency.

Therobotsdemonstrated

ahigh

degree

offlexibility,

operating

effectively

across

arange

of

weather

conditions.Morebroadly,

automating

solar

constructionis

expectedtolower

costs

and

speedup

construction,

enablinglarger

scale

solar

developments,

whilereducingtheneedforhumanlaborinremote

andinhospitableoutdoor

environments.11Sources:Information

compiled

from

publicly

available

secondary

sources.Aprofessor

ofrobotics

at

aUK-baseduniversity

says:

"Traditional

robots

areoptimized

toexecute

predefined

motions,withlimited

understandingof

intent

or

realworldimpact.Physical

AI

fundamentally

changesthis

by

enabling

robotsto

perceivetheir

surroundings

and

reasonabout

context.Indoing

so,it

opensup

problemdomainsthat

have

resisted

automation

for

decades

–

precisely

becausethey

requireunderstanding,notjust

execution.

”Physical

AI:Taking

human-robot

collaboration

to

the

next

levelCapgemini

Research

Institute202617EvolutionofroboticintelligenceThe

evolution

ofintelligenceinrobotics(at

aglance)•Unimate(1961):ThefiΓstindustΓialΓobot,deployed

onGeneΓalMotoΓs’

assemblyline•

EaΓlyindustΓialaΓmsin

automotivemanufactuΓing•

PLC-dΓiven

automation

andfixedpΓoductioncells•

Robotic

foundation

models

enablinggeneΓalization

acΓosstasks

andthe

abilitytoopeΓateinunstΓuctuΓed

enviΓonments•

Multi-Γobot

and

Al

agentoΓchestΓation•

RapidpΓogΓessinhumanoidand