Knowledge Representation and Efficient Image Processing for High-Level Skill Adquisition

KnowledgeRepresentationandEcientImagePro cessingforHigh-LevelSkillAcquisitionH.Friedrich,O.RogallaandR. DillmannInstituteforReal-TimeComputerSystemsandRob otics(IPR)UniversityofKarlsruheD-76128Karlsruhe,Germanyffriedricjrogalladillmanng@ira.uka. deN.GuilP. TrabadoDepartmentArchitectureUniversityofMalaga29080Malaga,Spainfnicojpablog@ac.uma. esAbstract:Sinceitisimp ortantforservicerob otstob eeasilyprogrammed,aninteractiveprogrammingtechniqueslikeProgrammingbyDemonstration(PbD)areobviouslyadequateprogrammingmetho ds.Im-agepro cessingsystemsplayanimp ortantroleinPbDsystemsforacquieringthedemonstrationstepsofuser.However,to day'simagepro cessingsystemsarehardlycapableofextractingrelevantinformationfromtheuserdemonstrationinreal-time.Inthispap er,amultiresolutionalapproachwillb epresentedtosp eeduptheinformationacquieringpro cess.Additionally,itwillb eshownhowthisinformationisusedforcreat-inganappropriateknowledgestructure,whichiscon-venientforrepresentinghigh-levelskills.1Intro ductionOneofthema jorcostfactorsinvolvedrob oticap-plicationsisthedevelopmentofrob otprograms.Es-p eciallytheuseofadvancedsensorsystemsandexistenceofstrongrequirementswithresp ecttotherob ot'sexibilityaskforveryskillfulprogrammersandsophisticatedprogrammingenvironments.Thesepro-grammingskillsmayexistinindustrialenvironmentsbuttheyarecertainlynotavailableiftheuseofrob otsinap ersonalenvironmentisconsidered.Forop en-ingtheexp ectednew,mainlyconsumer-orientedser-vicerob otmarket,itisthereforeessentialtodeveloptechniquesthatallowuntraineduserstousesuchap ersonalservicerob otb othsafelyandeciently.WithinthelastfewyearsProgrammingbyDemon-stration(PbD)hasb ecomemoreandimp ortant.Althoughdetectingandrepresentingtheuser'sactionsandintentionsturnedouttob eaquitediculttask,thep ossibilityofgeneratingintelligentprogramseas-ilyisverypromising.However,theredo esexistaneedforadvancedsensortechnologiestosolvethestrongre-quirementsthathavetob emetinordertraceahu-man'sb ehaviourandactions.Inthispap erap owerfulknowledgestructurewillb epresentedtocoverb oth,therepresentationofactionsandcognitiveop erationsthatareneededsolvacomplextask.Afterthat,anverviewofto day'sPbDsystemsisgiveninordertoshowthedicultiesthatdoarisewhenusingapproach.Itwillb estressedwhfastimagepro-cessingmetho dshavetob eappliedobtainb etterresultsinPbDsystems.Asasolutionfortheseprob-lems,multiresolutionimagepro cessingmetho dswillb etro duced,sincetheyturnedouttopromisingreacthegoalofreal-timeimagepro cess-ing,whichisimp ortantforadvancedPbDsystems.2RepresentationofActionsForsolvingcomplextasksingeneralthetaskisnor-mallydividedinsubtasksthatcanb esolvedbytheexecutinghardware.Toobtainthesesubtasksto day'srob otsystemsdouseseveralmetho dologies:Whenenvironmenwellknownandsys-temhasrequiredbackgroundknowledgeavail-able,normallyplanningmetho dsareusedwhichgenerateactionsequencestoreachthesystem'sgoal. Inthecasethatveryp o orbackgroundknowledgeisavailablesystemsareoftenbuiltupwithsub-sumptionlike[4],b ehaviourbasedarchitectureusingthebuiltinb ehaviourstoreachgoal.Themaindisadvantageofthesestructuresisthattheb ehavioursareoftentunedtoreachover-allsystem'sgoal.Inotherwords,thesystemissp ecializedforsolvingjustasp eci ctask. Anotherp ossibilityistotthathaveexecuteddirectlythesystem'sknowledgebase.Infactthisisnothingelsethanmatchingdesiredgoalstatetoellknownac-tionsequence.Theactionsequencecanb eob-tainedeitherfromanexternalknowledgebaseorbyselfexploration.Themaindisadvantagewhileusingthismetho dis,thatitmighthapp enan