Control Ar chitectureand Experiment of A SituatedRobot SystemforInteracti ve Assembly

JianweiZhangzhang@techfak.uni-bielefeld.de

Facultyof Technology, Universityof Bielefeld,33501Bielefeld,Germany

Alois Knollknoll@informatik.tu-muenchen.deTechnicalUniversityof Munich,

81667Munich,Germany

Abstract

We presentthe developmentof andexperimentwith a robotsystemshowing cognitive capabilitiesof childrenof threetofour years. We focuson two topics: assemblyby two handsandunderstandinghumaninstructionsin naturallanguageasapreconditionfor assemblysystemsbeingperceivedby humansas“intelligent”. A typical applicationof sucha systemis in-teractive assembly. A humancommunicatorsharinga viewof theassemblyscenariowith therobot instructsthe latterbyspeakingto it in the sameway that he would communicatewith a child. His instructionscanbe under-specified,incom-pleteand/orcontext-dependent.

After introducing the general purposeof our project, wepresentthe hardwareandsoftwarecomponentsof our robotsnecessaryfor interactive assemblytasks. The control archi-tectureof the robot systemwith two stationaryrobot armsisdiscussed.We thendescribethefunctionalitiesof theinstruc-tion understanding,planningandexecutionlevels.Theimple-mentationsof a layered-learningmethodology, memoriesandmonitoringfunctionsarebriefly introduced.Finally, we out-line a list of futureresearchtopicsfor extendingoursystem.

1 Intr oduction

Human-beingsinteractwith eachotherin a multimodalway.By reviewing thehistoryof robotics,themodalitiesof human-robot interactioncan be classifiedinto threelevels: explicitlevel, implicit level, and inter-humanlike level. With the en-hancementof robot intelligenceand advanceof humanper-ception,human-robotinteractioncanbe developednaturallyandinter-humanlike. A usercaninstructarobotby usingnat-ural language(NL), gestureandgazeinformationin the wayhecommunicateswith ahumanpartner. Technologiesleadingtowardssucha naturalinteractionwith robotswill contributeto the extensionof robotic applicationsto all human-in-the-loopsystemssuchasservicerobots,medicalrobots,entertain-mentrobots,softwarerobots,etc. In mechatronicapplications,the “machine intelligencequotient” (MIQ) can be enhancedsothatuntrainedpersonscanusesuchhigh functionaldeviceseasily. For building a robotsystemwhich understandshumannaturalinstructions,arobotcontrolarchitecturewhichenables

multimodalinput,globalmemoryaccessandfaultmonitoringbecomesacentraltopic.

2 Somerelevant work

Onechallengeof theresearchprogramfor roboticsis to auto-matetheprocessof multisensorsupportedassemblyby gradu-ally enablingtherobotandsensorsystemto carryout theindi-vidualstepsin amoreandmoreautonomousfashion.Thetyp-ical hierarchicalRCSarchitecturefor realizingsuchsystemswas explainedin detailsin [1]. However, a fully automaticassemblyunderdiverseuncertainconditionscanrarelybere-alizedwithoutany failure.Severalprojectsoncommunicativeagentsrealizedwith realrobotshavebeenreported,e.g.[8]. Intheprojectsdescribedin [2] and[10], naturallanguageinter-faceswereusedasthe“front-end” of anautonomousrobot. Ifconstrainednaturallanguageis usedto realisea limited num-ber of robot operations,specialstepscan be taken, e.g. byonly recongizingnounsin an instructionandlisting the pos-sibleactionsbasedon a pre-definedknowledgedatabase[11].In the SAIL project[10], level-basedAA-learning combinedwith attention-selectionandreinforcementsignalswasintro-ducedto let a mobile robot learn to navigate and to recog-nize humanfacesandsimplespeechinputs. In [7], the mainsystemarchitectureswerecompared,andanobject-basedap-proachwasproposedto helpmanagethecomplexity of intelli-gentmachinedevelopment.In theCogproject[3], thesensoryandmotorsystemsof a humanoidrobotandtheimplementedactivesensingandsocialbehaviorswerestudied.

To overcomethe limitations of this approach,the conceptof the “Artificial Communicator”was developed,which webriefly outlinein thesequel.

3 The Communicator Approach

If the natureof assemblytaskscannotbe fully predicted,itbecomesinevitable to decomposetheminto moreelementaryactions.Ideally, theactionsspecifiedareatomicin suchawaythat they alwaysrefer to only onestepin theassemblyof ob-jectsor aggregates,i.e. they refer to only oneobject that isto beassembledwith anotherobjector collectionthereof(ag-

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gregates).Theentiretyof asystemthattransformssuitablein-structions� into suchactionsis calledanartificial communica-tor (AC). It consistsof sensorsubsystems,NL processing,cog-nitive integrationandtheroboticactors.Fromtheinstructor’spointof view theAC shouldresembleahumancommunicator(HC) ascloselyaspossible[6]. The AC mustbe seamlesslyintegrated into the handling/manipulationprocess.More im-portantly, it mustbesituated, whichmeansthatthesituationalcontext (i.e. thestateof theAC andits environment)of a cer-tainNL (andfurthermodalities)input is alwaysconsideredforits interpretation.The processof interpretation,in turn, maydependon thehistoryof utterancesup to acertainpoint in theconversation.It may be helpful, for example,to clearlystatethegoalof theassemblybeforeproceedingwith a descriptionof theatomicactions.Thereare,however, situationsin whichsucha “stepwiserefinement”is counter-productive,e.g. if thefinal goalcannotbeeasilydescribed.Studiesbasedon obser-vationsof childrenperformingassemblytaskshave proventobe useful in developingpossibleinterpretationcontrol flows.From an engineeringperspective, the two approachescanbelikenedto openloopcontrol (Front-EndApproach)andclosedloop control (IncrementalApproach)with thehumaninstruc-tor beingpartof theclosedloop.

Theresearchdescribedin thefollowing sectionsis embeddedinto a larger interdisciplinaryresearchproject aiming at thedevelopmentof ACsfor variouspurposesandinvolving scien-tistsfrom thefieldsof computerlinguistics,cognitive linguis-tics,computerscienceandelectricalengineering.

4 The SituatedArtificial Communicator

Thereis ampleevidencethatthereexistsastronglink betweenhumanmotorskill andcognitive development(e.g. [5]). Ourabilities of emulation,mentalmodelingandplanningof mo-tion arecentralto humanintelligence[4] and,by the way, apreconditionfor anticipation,but they alsocritically dependon the experiencewe make with our own body dynamicsaswe plasticallyadaptour body’s shapeto theenvironment.Asabasicscenario,theassemblyprocedureof atoy aircraft(con-structedwith “Baufix” parts,seeFig.1) wasselected.Wehavebeendevelopinga two-armroboticsystemto modelandreal-ize humansensorimotorskills for performingassemblytasksandto facilitatehumaninteractionwith languageandgestures.This robotic systemserves as the major test-bedof the on-going interdisciplinaryresearchprogramof the projectSFB1

360“SituatedArtificial Communicators”at theUniversityofBielefeld [13]. A numberof partsmustbe recognized,ma-nipulatedand built togetherto constructthe model aircraft.Within theframework of theSFB,in eachof thesesteps,ahu-mancommunicatorinstructstherobot,which impliesthat theinteractionbetweenthemplaysanimportantrole in thewholeprocess.

1Collaborative researchunit fundedby the DeutscheForschungsgemein-schaft(DFG).

(a) The Baufix constructionparts.

(b) Thegoalaggregate.

Figure 1: Theassemblyof a toy aircraft.

Figure 2: The two-arm multisensorrobot systemfor dialogue-guidedassembly.

The physical set-upof this systemconsistsof the followingcomponents(Fig. 2):

(i) Two 6 d.o.f. PUMA-260 manipulatorsare installedover-headin astationaryassemblycell. Oneachwrist of themanip-ulator, a pneumaticjaw-gripper with integratedforce/torquesensorand“self-viewing” hand-eye system(local sensors)ismounted.

(ii) Two cameraswith controllablezoom,auto-focusandaper-tureprovide themainvision function. Their tasksareto build2D/3D world models,to supervisegrossmotion of the robotaswell asto tracethehandandviewing directionof thehumaninstructor.

(iii) A microphoneand loudspeakers are connectedwith astandardvoicerecognitionsystem,IBM ViaVoice, to recognizethehumanspeechinstructionsandto synthesizethegeneratedspeechoutput.

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5 Control Ar chitecture

As thebackboneof anintelligentsystem,thecontrolarchitec-tureof a complex technicalsystemdescribesthefunctionalityof individual modulesand the interplay betweenthem. WedevelopedaninteractivehierarchicalarchitectureaccordingtoFig. 3. A HC is closelyinvolved in the wholeassemblypro-cess.

5.1 High-level functionsThe systemand the HC interactthroughnaturalspeechandwith handgestures.First,aninstructionis spokento therobotsystemand recognizedwith the ViaVoice speechengine. Inthecurrentsystem,ViaVoicerecognizesonly sentences,whichthe grammarwe developedallows. In practice,hundredsofgrammarrulescanbe used. If the recognitionsucceeds,theresultsareforwardedto thespeechrecognition/understandingmodule.

By their verynature,humaninstructionsaresituated,ambigu-ous,andfrequentlyincomplete. In mostcases,however, thesemanticanalysisof suchutteranceswill resultin sensibleop-erations.An exampleis thecommand“Grasptheleft screw”.The systemhasto identify the operation(grasp), the objectfor this operation(screw), andthesituatedspecificationof theobjects(left).

With the help of a handgesturethe operatorcanfurther dis-ambiguatetheobject.Thesystemmaythenusethegeometricknowledgeof theworld to identify theright object.Othersit-uatedexamplesare:“ Insertin theholeabove”, “Screw thebaron thedownsidein thesamewayason theupside”, “Put thatthere”, “Rotateslightly further to the right”, “Do it again”,etc.

The outputof the analysisis thenverified to checkif the in-tendedoperationcan be carriedout. If in doubt, the robotagentasksfor furtherspecificationsor it hastheright to pickan objectby itself. Oncethe properoperationis determined,it is given to the coordinationmoduleon the next level. Thefinal resulton this level consistsof an ElementaryOperation(EO)andtheobjectsto bemanipulatedwith themanipulation-relevant informationsuchastype,position/orientation,color,pose(standing,lying, etc).

An EO is definedin this systemasan operationwhich doesnot needany furtheractionplanning.Typical EOsare:grasp,place, insert into, put on, screw, regrasp, alignment(for anillustration seeFig. 4). The robustnessof theseoperationsmainly dependson thequalityof thedifferentskills.

5.2 Planning tasksOn the planninglevel, an assemblytask of the toy aircraft,or of sub-aggregates,is decomposedinto a sequenceof EOs.Thefinal decisionaboutthemotionsequencedependson theinstructionsof the humanuseraswell asthe generatedplan.Theplanningmoduleshouldnotonlybeabletounderstandthehumaninstructions,but alsoto learnfrom thehumanguidance

(a) Graspa screw (b) Regrasp

(c) Placeanaggregate (d) Putapartin

(e)Screw (f) Alignment

Figure 4: Examplesof elementaryoperations.

andimprove its planningabilitiesgradually.

Theplanningmoduleon theschedulinglevel receivesanEOfrom the instruction understanding. By referencingthe ac-tion memory, theplanningmodulechoosesthecorrespondingbasicprimitive sequencefor the operation.This sequenceisa script of basicprimitives for implementingthe given EO.The taskhereincludesplanningof thenecessarytrajectories,choosingtheright robot(s)andbasicexceptionhandling.

Sequencesareexecutedby thesequencer, whichactivatesdif-ferentskills on thenext executionlevel. Theplanningmodulealso receivesan event report that is generatedby the execu-tion level. If the event is a failure detection,the monitoringmoduleis informed.In thenormaloperations,themonitoringmoduleupdatesthe actionmemory. It alsodetectsthe eventfailures. If it is found that the robot canre-dothe operation,the planningmodulewill try again. Otherwise,the monitor-ing modulesendsa requestto the dialog moduleto ask thehumancommunicatorhow to handlethe exceptionandwaitsfor an instruction. After the executionof eachoperation,theknowledge baseis updated.

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5.3 Execution levelThe sequencingmoduleon the schedulinglevel usesthe as-semblyskills providedby theexecutionlevel to performa se-quence. The complexity of the skills can rangefrom open-ing the handto collision-freecontrol of the two armsto themeetingpoint. Advancedskills arecomposedof oneor morebasicskills. Generally, threedifferentkindsof skills areclassi-fied: (i) Motor skills: Openandclosegripper; Drive joint to;Drive arm to; Rotategripper; Move arm in approach direc-tion; Movecamera, etc. (ii) Sensorskills: Get joint; Getpo-sition in world; Getforcein approach direction;Gettorques;Check if a specificpositionis reachable; Take a camera pic-ture; Detectobject; Detectmoving robot; Track an object,etc. (iii) Sensorimotorskills: Force-guardedmotion; Vision-guidedgrossmovementto a goal position;Visual servoingofthegripper to optimalgraspingposition,etc.

5.4 Layered-learningLearningtheinterplayof perception,positioningandmanipu-lation aswell asbasiccognitive capabilitiesis thefoundationof a smoothexecutionof a commandsequenceof a humaninstructor. If a commandrefers to an EO, the disambigua-tion of the instructionbasedon multimodal input is the keyprocess.Theautonomoussensor-basedexecutionof thesein-structionsrequiresadaptive,multi-sensorbasedskills with anunderstandingof acertainamountof linguistic labels.If com-plex instructionsareused,however, the robot systemshouldpossesscapabilitiesof skill fusion, sequencegenerationandplanning. It is expectedto generatethe sameresult after arepeatedinstructioneven if the situationhaschanged. Thelayered-learningapproachis the schemeto meet this chal-lenge.

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Layered-learningis a hierarchicalself-improving approachtorealize� multimodalrobotcontrol, in particularadaptive, mul-tisensorbasedskills. Under this concept,tasksare decom-posedfrom highto low level. Realsituatedsensorandactuatorsignalsare locatedon the lowest level. Both self-supervisedandreinforcementlearninghave beenappliedto theB-splinemodel[12] to realizemostof thesensorimotorskills. Throughtask-orientedlearningthelinguistic termsto describetheper-ceivedsituationsaswell asrobotmotionsaregenerated.Skillsfor manipulationandassemblyareacquiredby learningonthislevel usinganeuro-fuzzymodel.Furthermore,thelearningre-sultson thelower levelsserve asthebasisof thehigherlevelssuchasEOs,sequences,strategies,planningandfurthercog-nitivecapabilities.

To learntheoperationsequencesautomaticallyfor two arms,wedevelopedamethodfor learningcooperativetasks.If asin-glerobotis unableto graspanobjectin acertainorientation,itcanonly continuewith thehelpof otherrobots.Thegraspingcanbe realizedby a sequenceof cooperative operationsthatre-orientthe object. Several sequencesareneededto handlethedifferentsituationsin which anobjectis not graspablefortherobot. It is shown thatadistributedlearningmethodbasedonaMarkov decisionprocessis ableto learnthesequencesforthe involved robots,a masterrobot that needsto graspandahelpingrobot thatsupportsit with there-orientation.A novelstate-actiongraphis usedto storethereinforcementvaluesofthelearningprocess.

5.5 MemoriesTo describethe knowledge base,both semanticand proce-dureknowledgeareused.In our currentimplementationsuchknowledgeis still hard-coded.It canbeviewedaslong-term-memoryto a certaindegree,which will beextendedby learn-ing approachesin our future researchactivities. Short-term-memoriesexist in perceptionmodules,which are usedforscenerecognition,dialogpreparationandaction(sensorimotorfunctions). Learningof anotherimportanttype of memories,theepisodicmemory, is preliminarily studiedin theassemblyscenarios.

Accordingto theempiricalinvestigations,theepisodicmem-ory representsoneof the most importantcomponentsof hu-man intelligence. The reminding,mentalsimulationaswellasplanningusetheepisodicmemoryasthebasis.Thediversemultisensordatawith largebandwidthof therobotsuchasvi-sionsystem,joint angles,positions,forceprofilesetc.,cannotbesavedin their roughformatfor arbitrarily longtime. There-fore,codingapproachesbasedonappearancesandfeaturesaresuggested[9] for summarizingandgeneralizingexperiencesfrom thesuccessfullyperformedoperations.Themultisensortrajectoriesandthemotorsignalsareusedfor “grounding” thelearnedoperationsequences.

5.6 MonitoringMonitoringplaysanimportantrole to makeanintelligentsys-tem robust. It is also usedfrequentlyby a human-beingin

manipulationandspeaking,especiallyin a new environmentor for a new task. Monitoring and eventually re-planningfor repairingresult in the non-linearityof the understanding-planning-execution cycle, but they representone essentialfunctionin thecognitive architectureof a robot.Furthermore,it is meaningfulto addadiagnosisfunctionwhichcanprovidehypothesesaboutthereasonsof diversefailures.

Theunexpectedeventsduring the robotactioncanbe for ex-ample:A forceexceedsa definedthreshold;A camera detectsno object; Singularity; Collision; etc. If suchan event hap-pens,it is reportedto theplanninglevel.

6 Dialogueand AssemblyResults

Oneexampleto build the “elevator control” aggregateof theaircraft out of threeelementaryobjectsby carrying out dia-logueswasstudied.Theobjectswerelaid outonthetable,andthereweremany moreobjectspositionedin arbitraryorderonthetablethannecessary. TheHC hada completeimagein hismindof whattheassemblysequenceshouldbe.Alternatively,hecouldhave usedtheassemblydrawingsin theconstructionkit’s instructionsandtranslatedtheminto NL.

After theAC findingout if all objectsarepresentandaftergo-ing throughanoptionalobjectnamingproceduretheHC inputfirst triggerstheactionplanner, whichdecideswhichobjecttograspandwhich robot to use. Sincethe HC did not specifyeitherof theseparameters,bothareselectedaccordingto theprinciple of economy. In this case,they areso chosenastominimize robot motion. The motion plannerthencomputesa trajectory, which is passedto the robots. Sincethereareenoughboltsavailable,theAC issuesits standardrequestforinputoncethebolt is pickedup.

HC input resultsin theotherrobotpicking up theslat. Beforethis mayhappen,however, it hasto beclearedup, which slatto take. This involvesthe incorporationof thegesturerecog-niser. Then the screwing is triggered,involving the peg-in-holemodulementionedabove followedby thescrewing mod-ule. For reasonsof spacethesubsequentstepsof thedialoguehave to omittedhere;they show how errorhandlingandmanyotheroperationscanbe performed– mostof which humansare not awareof when they expect machinesdo do “what Imean”.Fig. 5 shows two typical objectsthatcanbebuilt withthesetupasdevelopedup to now.

7 Futur e Work

Amongmany topicsto beexplored,someimportantonescanbelistedasfollows:

� The long-term-memoryis learnedfrom theshort-term-memory so that symbols, sequences,namesand at-tributesareanchoredin therealsensor/actuatorworld.

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1

3

2

6

4 5

Figure5: Sampleaggregatesmadeby our interactiveassemblysys-tem.

� Methodsneedto bedevelopedfor increasingthecapa-bility and quality of reinforcementsignalsand fitnessevaluationof the learningsystem. Active sensingandactivemanipulationcanfind their applicationsfor thesepurposes.

� To enablethe arbitrarytransitionbetweendigital mea-surementsandconcepts,symbolicsparsecoding,gran-ular computing,fuzzysetsandroughsetswill beinves-tigatedandintegrated.

� Action sequenceslearnedon thebasisof verbalandvi-sualinstructionsandsummarizationneedto bebuilt intoanappropriaterepresentationsothatthey canbegener-alizedfor analogor evennew tasks.

� Learningon thehigherlevel shouldbeconductedto se-lect actionstrategiesandto generateintelligentdialogs.Thiswill needthetight integrationof morecomponentsandmoreknowledgeshown in Fig. 3.

� More functionssuchasamotivationor creationmoduleneedto be addedin the architectureso that the robot’sinitiativescanbeusedinsteadof passive acceptanceofinstructions.

Acknowledgment

This researchis supportedundergrantSFB360by DFG, theGermanResearchCouncil.

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