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    Adaptive Behavior

    http://adb.sagepub.com/content/8/1/49The online version of this article can be foun d at:

    DOI: 10.1177/1059712300008001042000 8: 49Adaptive Behavior

    Cynthia Breazeal and Brian ScassellatiInfant-like Social Interactions between a Robot and a Human Caregiver

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    Infant-like Social Interactions between aRobot and a Human Caregiver.

    CYNTHIA BREAZEAL AND BRIAN SCASSELLATIMassachusetts Institute of Technology ArtificialIntelligenceLab

    From birth, human infants are immersed in a social environment that allows them to learn byleveragingthe skills and capabilitiesof their caregivers. A critical pre-cursor to this type of sociallearningis the abilityto maintain interaction levels that are neither overwhelmingnor under-stim-ulating. In this paper, we present a mechanism for an autonomous robot to regulatethe intensi-ty of its social interactions with a human. Similar to the feedback from infant to caregiver,therobot uses expressivedisplaysto modulate the interaction intensity. This mechanism is integrat-ed within a generalframework that combines perception,attention, drives, emotions, behaviorselection, and motor acts. We present a specificimplementationof this architecture that enablesthe robot to react appropriatelyto both social stimuli (faces)and non-social stimuli (movingtoys)while maintaininga suitable interaction intensity. We present results from both face-to-face inter-actions and interactions mediated througha toy.Note: This paper was submitted in June, 1998.

    1 INTRODUCTION

    Social robotics has generallyconcentrated on thebehavior of groups of robots performingbehaviorssuch as flocking,foragingor dispersion (Balch &

    Arkin, 1994;Mataric,1995)or on pairedrobot-robotinteractions (Billard & Dautenhahn, 1997). Ourwork focuses not on robot-robot interactions, butrather on the construction of robots that engage inmeaningfulsocial exchangeswith humans. Bydoingso, it is possibleto have a sociallysophisticatedhumanassist the robot in acquiringmore complexcommuni-cation skills and in learningthe meaningthese actshave for others. The interactions with the caregivercan bootstrapthe robots capabilities. By leveragingthe skills and abilities of a benevolent caregiver,it ispossibleto alleviate many of the normal difficulties ofrobot learning,such as sparse reinforcement, uncon-strained task complexity,and unstructured environ-ments.

    Our approachis inspiredbythe way infants learnto communicate with adults. An infants emotionsand drives playan importantrole in generatingmean-ingfulinteractions with the caregiver(Bullowa,1979).These interactions constitute learningepisodesfornew behaviors. In, particular, the infant is stronglybiased to learn communication skills that result in

    having the caregiver satisfy the infants drives(Halliday,1975). The infants emotional responsesprovideimportantcues which the caregiveruses toassess how to satiate the infants drives, and how tocarefullyregulatethe complexityof the interaction.The former is critical for the infant to learn how itsactions influence the

    caregiver,and the later is critical

    for establishingand maintaininga suitable learningenvironment for the infant.

    A critical pre-cursor to this type of social learningis the ability to maintain interaction levels that areneither overwhelmingnor under-stimulating. In thispaper, we present a mechanism for an autonomousrobot to regulatethe intensity of its social interactionswith a human. This mechanism is the first stage of a

    long-termendeavor to enable social learningbetweenthe robot and a human caregiverand is integratedwithin a generalframework that combines perception,attention, drives, emotions,behavior arbitration, andmotor acts (Breazeal, 1998). We concentrate on thedesignspecificationof the perceptualand motivation-al systems because of the critical role theyserve in thisdynamicprocess for infants. Other work in progressfocuses on the construction of shared attention sys-tems that allow the infant and the caregiverto groundlearningin perceptual episodes (Scassellati, 1996,1998c).The specificsof the learningalgorithmshave

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    yet to be addressed.We do not claim that this system models infant

    development.However, the designis heavilyinspiredbythe role motivations and facial expressionsplay insocial interaction between infants and adults.

    Regulatinginteraction intensity is a critical skill forthis kind of social learningbecause it helpsthe care-givertune her actions so that theyare appropriateforthe infant. For our purposes, the context for learninginvolvessocial exchangeswhere the robot learns howto manipulatethe caregiverinto satisfyingits internaldrives. Ultimately,the communication skills targetedfor robot learningare those exhibitedbyinfants, suchas turn taking, shared attention, and pre-linguisticvocalizationsexhibitingshared meaningwith the care-giver.

    Thispaper

    isorganized

    as follows: first we discussthe numerous roles motivations play in natural sys-tems-particularlyas theyapplyto behavior selection,regulatingthe intensity of social interactions, andlearningin a social context. Next we describe a robotcalled Kismet that has been designedand built to pro-vide emotional feedback to the caregiverthroughfacial expressions. We then present a framework forthe designof the behavior engine, which integratesperception,motivation (drives and emotions),atten-tion, behavior, and motor skills (expressiveor taskbased). Particular detail is providedfor the designofthe perceptual and motivational systems. After weillustrate these ideas with a specificimplementationon a physicalrobot, we present the results of someearlyexperimentsin which a human engages the robotin face-to-face social exchanges.

    2 THE ROLE OF MOTIVATIONS IN

    SOCIAL INTERACTION

    Motivations,which encompass drives, emotions, andpain,playseveral importantroles for both arbitratingand learningbehavior. We are interested in how theyinfluence behavior selection, regulatesocial interac-tions, and promote learningin a social context.Behavior Selection

    2.1 Behavior Selection

    Much of the work in motivation theoryin ethologyisintended to explainhow animals engage in appropri-

    ate behaviors at the appropriatetime to promote sur-vival (Lorenz, 1973; Tinbergen, 1951). Internaldrives influence which behavior the animal pursues.Furthermore,the same sensory stimulus may result invery different behavior dependingon the intensity of

    the drives. For example,a

    dogwill responddifferent-ly to a bone when it is hungrythan when it is fleeingfrom danger.

    It is also well acceptedthat animals learn thingsthat facilitate the achievement of biologicallysignifi-cant goals. Motivations providean impetusfor thislearning. In particular, the motivational system pro-vides a reinforcement signalthat guideswhat the ani-mal learns and in what context. When an animal hasa strong drive that it is trying to satisfy, it is primedtolearn behaviors that directlyact to satiate that drive.For this reason, it is much easier to train a

    hungryani-

    mal than a satiated one with a food reward (Lorenz,1973).

    For a robot, an importantfunction of the motiva-tion system is to regulatebehavior selection so that theobservable behavior appears coherent, appropriatelypersistent,and relevant giventhe internal state of therobot and the external state of the environment. Theresponsibilityfor this function falls largelyunder thedrive system of the robot. Other work in autonomous

    agent research has used drives in a similar manner(Arkin, 1988; Maes, 1992; McFarland & Bosser1993; Steels 1995). Drives are also necessary forestablishingthe context for learningas well as provid-ing a reinforcingsignal.Blumberg(1996)used moti-vations (called internal variables)in this way to imple-ment operant conditioningso that a human usercould teach an animated dognew tricks.

    2.2 RegulatingInteraction

    An infantsmotivations are vital to regulatingsocialinteractions with the caregiver(Kaye,1979). Soonafter birth, an infant is able to displaya wide varietyof facial expressions(Trevarthen,1979). As such, theinfant respondsto events in the world with expressivecues that can be read, interpreted,and acted upon.The caregiverinterprets them as indicators of theinfants internal state (how he or she feels and why),and acts to promote the infants well being(Chappell& Sander, 1979; Tronick, Als, and Adamson, 1979).For example,when the infant appears content the

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    caregivertends to maintain the current level of inter-action, but when the infant appears disinterested thecaregiverintensifies or changesthe interaction to tryto re-engage the infant. In this manner, the infant can

    regulatethe intensity of interaction by displaying

    appropriateemotivecues.

    The caregiverinstinctivelyreads the infants expressivesignalsand acts to main-tain a level of interaction suitable for him.

    An importantfunction for a robots motivationalsystem is not onlyto establish appropriateinteractionswith the caregiver,but also to regulatethe intensitysothat the robot is neither overwhelmed nor under-stim-ulated. When designedproperly,the intensity of therobots expressionsprovideappropriatecues for thecaregiverto increase the intensity of the interaction,decrease the intensity, or maintain it at the currentlevel.

    Bydoingso, both

    partiescan

    modifytheir own

    behavior and the behavior of the other to maintain theintensity of interaction that the robot requires.

    2.3 Learningin a Social Context

    The use of emotional expressionsand gestures facili-tates and biases learning during social exchanges.Caregiverstake an active role in shapingand guidinghow and what infants learn by means of scaffolding.

    As the word implies,the caregiverprovidesa support-ive framework for the infant by manipulatingtheinfants interactions with the environment to fosternovel abilities. Commonly, scaffoldinginvolvesreducingdistractions, markingthe tasks critical attrib-utes, reducingthe number of degreesof freedom inthe target task, providingongoingreinforcementthroughexpressivedisplaysof face and voice, orenablingthe infant to experiencethe outcome of asequence of activities before the infant is cognitivelyor physicallyable to attain it for himself or herself(Wood, Bruner, and Ross, 1976). The emotive cuesthat the adult receives duringsocial exchangesserve asfeedback so that the adult can adjustthe nature andintensity of the structured learningepisodeto main-tain a suitable learningenvironment in which theinfant is neither bored nor overwhelmed.

    In addition, during early interactions with thecaregiver,an infants motivations and emotional dis-playsare critical in establishingthe foundational con-text for learningepisodes(Halliday,1975). An infantdisplaysa wide assortment of emotive cues such as

    coos, smiles,waves, and kicksduringearlyface-to-faceexchanges.Duringthe first month, the infants basicneeds, emotions, and emotive expressionsare amongthe few things the adult thinks theyshare in common.Consequently,the caregiver imparts a consistent

    meaningto

    the infantsexpressivegesturesand

    expres-sions, interpreting them as meaningfulresponses andas indications of the infantsinternal state.

    Curiously,experimentsbyKaye(1979)argue thatthe caregiveractuallysuppliesmost if not all of themeaning to the exchangewhen the infant is veryyoung. The infant does not know the significancethat expressiveacts have for the adult, nor how to usethem to evoke specificresponses. However, becausethe adult assumes the infant shares the same meaningsfor emotive acts, this consistencyallowsthe infant todiscover what sorts of activities will

    get specificresponses. Routine science of a predictablenaturecan be built up, which serve as the basis of learningepisodes(Newson, 1979). Furthermore,theyprovidea context of mutual expectations. For example,earlycries of an infant elicit various care-givingresponses,dependingupon how the adult initially interpretsthese cries and how the infant responds. The infantand the adult converge over time on specificmeaningsfor different kinds of cries. The infant comes gradu-ally to differentiate his or her cries (i.e., cries of dis-tress, cries for attention, cries of pain, cries of fear) inorder to elicit different responses from the caregiver.The adult reinforces the shared meaning of the criesby respondingin consistent ways to these variants.Evidence of this differentiation is providedby thedevelopmentof unique communication protocolsthat differ from those of other adult-infant pairs(Bullowa,1979).

    Combiningthese ideas, a robot can be biased tolearn how its emotive acts influence the caregiver inorder to satisfyits own drives. Toward this end, weendow the robot with a motivational system that

    works to maintain its drives within homeostaticbounds and a set of emotive expressionsanalogoustothe types of emotive expressionsthat human infantsdisplay. These capabilitiesallow the caregiver toobserve the robots emotive expressionsand interpretthem as reflections of the robots internal drives. Thehuman can then act appropriately.This interactionestablishes the routine necessary for the robot to learn(eventually)how its emotive acts influence the behav-

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    ior of the caregiver,and how these acts ultimatelyserve to satiate the robots own drives.

    This section has arguedthat motivations shouldplay a significantrole in determiningthe robotsbehavior,how it interacts with the caregiver,and what

    itcan

    learn duringsocial exchanges.With these longterm challengesin mind, an importantpre-requisitefunction for the robots motivational system is not

    only to establish appropriate interactions with thehuman, but also to regulatethe interaction intensityso that the robot can learn without being over-whelmed or under-stimulated. When designedprop-erly, the interaction among the robots drives, emo-tions, and expressionsprovideappropriatecues for thecaregiver so that he or she knows whether to changethe activityitself or to modifyits intensity. Bydoingso, both

    partiescan

    modifyboth their own behavior

    and the behavior of the other in order to maintain aninteraction from which the robot can learn from anduse to satisfyits drives.

    3 ROBOT HARDWARE

    To explorethese ideas, we have constructed a robotwith capabilitiesfor emotive facial expressions,shownin Figure1. The robot, called Kismet, consists of an

    Figure1. Kismet with toys. Kismet has an active sterio visionsystem with color CCD cameras mounted inside the eyeballs.There are also a varietyof facial features which givethe robotits expressivecapabilities

    active stereo vision system (described in Scassellati,1998a)and a set of facial features for emotive expres-sion. Currently,these facial features include eyebrows(eachwith two degrees-of-freedom:lift and arch), ears(each with two degrees-of-freedom:lift and rotate),

    eyelids(eachwithone

    degreeof freedom: open/close),and a mouth (with one degree of freedom:open/close).The robot is able to show expressions

    Anger Calm

    Disgust Fear

    Happiness Interest

    Sadness Sleep

    Surprise TirednessFigure 2. Static extremes of Kismets facial expressions.During operation,the 11degrees-of-freedomfor the ears, eye-brows, mouth and eyelidsvary continuouslywith the currentemotional state of the robot.

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    analogousto anger, fatigue,fear, disgust,excitement,happiness, interest, sadness, and surprise(showninFigure2).

    Similar to other active vision systems (Coombs,1992; Sharkey, Murray, Vandevelde, Reid &McLauchlan, 1993), there are three degreesof free-dom ;each eye has an independentvertical axis of rota-tion (pan)and the eyes share a joint horizontal axis ofrotation (tilt). Each eyeballhas a color CCD camerawith a 5.6 mm focal lengthlens. Althoughthis limitsthe field of view, most social interactions require ahighacuity central area to capture the details of face-to-face interaction. Infants have poor visual acuity,which restricts their visual attention to about two feetaway - typicallythe distance to their mothers facewhen the infant is being held (Goldstein, 1989).This choice of camera is a balance between the need

    for highresolution and the need for a wide low-acuityfield of view.

    The active vision platformis attached to a parallelnetwork of digital signal processors (TexasInstruments TMS320C40), as shown in Figure 3.The DSP network serves as the sensory processingengineand implementsthe bulk of the robots per-ceptionand attention systems. Each node in the net-

    work contains one processor with the optionfor morespecializedhardware for capturingimages,performingconvolutions quickly,or transmitting imagesto aVGA display. Nodes may be connected with arbitrarybi-directional hardware connections, and distantnodes may communicate throughvirtual connections.Each camera is attached to its own frame grabber,which can transmit capturedimagesto connectednodes.

    A pair of Motorola 68332-based micro-controllersare also connected to the robot. One controllerimplementsthe motor system for driving the robotssfacial motors. The second controller implementsthemotivational system and the behavior system. Thisnode receives pre-processed perceptualinformationfrom the DSP network througha dual-portedRAM,and converts this information into a behavior-specificpercept which is then fed into the rest of the behaviorengine.

    4 A FRAMEWORK FOR DESIGNINGBEHAVIOR ENGINES

    A framework for how the motivational system influ-ences behavior is shown in Figure4. The organization

    Figure3. Computationalhardware utilized byKismet. A network of digital signal processors acts as the sensory processingengineand implementsthe perceptionsystem, the attention system, and part of the motor system. This network is attached totwo 68332-based micro-controllers that implementthe motivation system, the behavior system, and the remainder of the motorsystem.

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    Figure4. A &amework for designingbehavior engines. Fivesystems interact to enable the robot to behave coherently.Theperception system extracts salient features &om the world.The motivation system maintains internal state in the form ofdrives and emotions. The attention system determines salien-cy based upon perceptionand motivation. The behavior sys-tem selects a set of coherent actions. The motor system real-izes these behaviors as facial expressionsand other motorskills.

    andoperation

    of this framework isheavily

    influenced

    by concepts from cognitiveand developmentalpsy-chologyand ethology,as well as the applicationsofthese fields to robotics as outlined byBrooks,Ferrell,Irie, Marjanovic,Scassellati,and Williamson (1998).The system architecture is an elaborated version of thearchitecture of Breazeal (1998), and consists of fivesubsystems:the perception, motivation, attention,behavior,and motor systems. The perceptionsystemextracts salient features from the world. The motiva-tion system maintains internal state in the form ofdrives and emotions.22 The attention system deter-mines saliencybased upon perceptionand motivation.The behavior system implementsvarious types ofbehaviors as conceptualizedby Tinbergen( 1951 )andLorenz (1973). The motor system realizes thesebehaviors as facial expressionsand other motor skills.

    The overall system is implementedas an agent-based architecture similar to those of Blumberg(1996), Brooks (1986), Maes (1992), and Minsky(1988). For this implementation,the basic computa-

    tional process is modeled as a transducer. Each drive,emotion, behavior, percept, and facial expressionismodeled as a separate transducer process specificallytailored for its role in the overall system architecture.The activation energy x of a transducer is computed

    bythe

    equation:

    where i,are inputs,w}are weights, bis the bias, and nis the number of inputs. Weightsare derived empiri-cally,and can be either positiveor negative;a positiveweightcorrespondsto an excitatoryconnection and anegativeweightcorrespondsto an inhibitory connec-tion. The process is active when its activation levelexceeds an activation threshold. When active, theprocess may performsome specialcomputation,sendoutput messages to connected processes, spreadsomeof its activation energy to connected units, and/orexpress itself throughbehavior.

    4.1 The PerceptionSystem

    The responsibilityof the perceptionsystem is to con-vert raw sensory stimuli into meaningfulinformationto guidebehavior. For this system, visual imagesareprocessedfor both social stimuli (faces)and non-socialstimuli (motion). These processedimagesresult in afaceperceptand a non-facepercept, each of which ismodeled by a transducer. The intensity values foreach percept are used to guidethe robots behavior -the robot respondsin a manner to keepthe faceandnon-faceperceptswithin a desired intensityrange.

    4.2 The Motivation System

    The motivation system consists of two related subsys-tems, one that implementsdrives and a second thatimplementsemotions and expressivestates. The drivesserve as an internal representationof the robotsagen-da, while the emotions and expressivestates reflect howwell the robot is achievingthat agenda.

    Motivations establish the nature of a creature bydefiningits needs and influencinghow and when itacts to satisfythem. The &dquo;nature&dquo;of this robot is tolearn in a social environment. All drives,emotions,andbehaviors are organizedsuch that the robot is in a state

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    of homeostatic balancewhen it is functioningadeptlyand is in an environment that affords highlearningpotential. This entails that the robot be motivated toengage in appropriateinteractions with its environ-ment (includingthe caregiver)and that it is neitherunder-whelmed nor overwhelmed

    bythese interac-

    tions.

    4.3 Drives

    The robots drives serve three purposes. First, theyinfluence behavior selection bypreferentiallypassingactivation to some behaviors over others. Second,they influence the emotive state of the robot bypass-ing activation energy to the emotion processes. Sincethe robots expressionsreflect its emotive state, thedrives indirectlycontrol the expressivecues the robotdisplaysto the caregiver.Third, theyprovidea learn-ing context which the robot could use to learn skillsthat satiate its drives.

    The designof the robots drives subsystemis heav-ily inspiredby ethologicalviews (Lorenz, 1973;Tinbergen, 1951). One distinguishingfeature ofdrives is their temporallycyclicbehavior. That is, adrive will tend to increase in intensity until it is satiat-ed, at which point it will decrease below a thresholdlevel onlyto beginincreasingagain. For instance, ananimalshungerlevel or need to sleepfollowsa cycli-cal pattern. Another distinguishingfeature of drives istheir homeostatic nature. For animals to survive, theymust maintain a varietyof critical parameters (suchastemperature, energy level, amount of fluids, etc.)within a bounded range. Similarly,the drives of therobot changein intensityto reflect the ongoingneedsof the robot and the urgency for tendingto them.There is a desiredoperationalpoint for each drive andan acceptablebounds of operationaround that point.We call this range the homeostatic regime. As longasa drive is within the homeostatic regime,the robots

    &dquo;needs&dquo;are beingadequatelymet.For this robot, each drive is modeled as a separateprocess with a temporalinput to implementits cyclicbehavior. The activation energy of each drive rangesbetween two extremes, where the magnitudeof thedrive represents its intensity. For a givendrive level, alargepositive magnitudecorrespondsto beingunder-stimulated bythe environment, whereas a largenega-tive magnitudecorrespondsto beingover-stimulated

    by the environment. In general,each drive is parti-tioned into three regimes:an underwhelmed regime,an overwhelmed regime,and a homeostatic regime.

    4.4 Emotions and ExpressiveStates

    The emotions of the robot serve two functions. First,theyinfluence the emotive expressionof the robot bypassingactivation energy to motor processes. Second,theyplay an importantrole in regulatingface-to-faceexchangeswith the caregiver. The drives play animportantrole in establishingthe emotional state ofthe robot, which is reflected by its facial expression,hence emotions play an importantrole in communi-cating the state of the robots &dquo;needs&dquo;to the caregiverand the urgency for tendingto them. It is importantthat the caregiver find these expressivestates com-pelling. Certainly,the importanceof emotionalexpressionfor believable interactions with artificialsystems has alreadybeen arguedbyBates, Loyall,andReilly(1992), and byCassell (1994). Emotions alsoplay an importantrole in learningduringface-to-faceexchangeswith the caregiver,but we leave the detailsof this to another paper.

    The organizationand operation of the emotionsubsystemis stronglyinspiredbyvarious theories ofemotions in humans (Ekman & Davidson, 1994;Izard, 1993), and most closelyresembles the frame-work presentedbyVelasquez(1996), as opposedtothe cognitive assessment systems of Elliot (1992),Ortony,Clore, and Collins (1988), or Reilly(1996).Kismet has several emotion processes. Althoughtheyare quite different from emotions in humans, they aredesigned to be roughanalogs- especiallywithrespect to the accompanyingfacial expressions. Assuch,each emotion is distinct from the others and con-sists of a familyof similar emotional states, which aregradedin intensity. For instance, the emotion happi-ness can range from beingcontent (a baseline activa-tion level) to ecstatic (a highactivation level).Numerically,the activation level of each emotioncan range between zero and an empiricallydeterminedintegervalue. Althoughthe emotions are alwaysactive,their intensity must exceed a threshold level beforethey are expressedexternally. Above threshold levels,the correspondingfacial expressionreflects the level ofactivation of the emotion. Once an emotion rises aboveits activation threshold, it decaysover time toward the

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    baseline level (unless it continues to receive excitatoryinputs from other processes or events). Hence, unlikedrives, emotions have an intense expressionfollowed bya fleetingnature. Ongoingevents that maintain theactivation level slightlyabove thresholdcorrespondtomoods in this

    implementation.For the robot, its drives

    are a main contributor to its ongoing mood.Temperamentsare established bysettingthe gain andbias terms of the emotion transducers. Blends of emo-tions occur when several compatibleemotions areexpressedsimultaneously.To avoid havingconflictingemotions active at the same time, mutuallyinhibitoryconnections exist between conflictingemotions.

    4.5 The Attention System

    The attention system acts to direct computationalandbehavioral resources toward salient stimuli. In anenvironment sufficientlycomplexfor interestinglearning,perceptual processinginvariablyresults inmany potentialtarget stimuli. In order to determinewhere to assign resources, the attention system mustcombine raw sensory saliencywith motivational influ-ences. Raw saliencycues are equivalentto the &dquo;pop-out&dquo;effects studied by Triesman(1986), such as colorintensity, motion, and orientation for visual stimuliand intensity and pitch for auditory stimuli. Themotivational

    system biasesthe

    selection process,but

    does not alter the underlyingraw saliencyof a stimu-lus (Neidenthal& Kitayama,1994). For example,ifthe robot has become bored, it may be more sensitiveto visual motion (whichmay indicate somethingthatwould engage the robot) and less sensitive to orienta-tion effects (whichare likely to be static backgroundfeatures).

    To build a believablecreature, the attention systemmust also implementhabituation effects. Infantsrespondstronglyto novel stimuli, but soon habituateand respondless as familiarityincreases (Carey&Gelman, 1991). Habituation acts both to keeptheinfant from beingcontinuallyfascinatedwith any sin-gle object and to force the caregiverto continuallyengage the infant with slightlynew and interestinginteractions. For a robot, a habituation mechanismremoves the effects of highly salient backgroundobjects,and placesrequirementson the caregiver tomaintain interaction with slightlynovel stimulation.

    4.6 The Behavior System

    Borrowingfrom the behavioralorganizationtheoriesof Lorenz (1973)and Tinbergen( 1951 ),driveswithinthe robots motivation system cannot satiate them-selves. Theybecome satiated whenever the robot isable to evoke the correspondingconsummatorybehavior. For example,eating satiates an animalshungerdrive and sleepingsatiates its fatiguedrive. Atany point in time, the robot is motivated to engage inbehaviors that maintain the drives within their home-ostatic regime.Whenever a drive moves away from itsdesired operationpoint, the robot becomes predis-posedto engage in behaviors that serve to satiate thatdrive. As the drive activation level increases, it passesmore of its activation energy to the correspondingconsummatory behavior. As longas the consumma-

    tory behavior is active, the intensity of the drive isreduced toward the homeostatic regime. As the inten-sity approachesthe homeostatic regime, the drivebecomes satiated, and the amount of activation ener-

    gy passedto the consummatory behavior decreasesuntil the behavior is eventuallyreleased.

    For each consummatory behavior, there may alsobe one or more affiliated appetitivebehaviors. Eachappetitivebehavior can be viewed as a behavioralstrategy for bringingthe robot to a state where it candirectlyactivate the desired consummatory behavior.For

    instance,a

    givendrive

    may stronglypotentiateits

    consummatory behavior but environmental circum-stances may prevent the behavior from becomingactive. In this case, the robot may be able to activatean affiliated appetitive behavior instead, which willeventuallyallow the consummatory behavior to beactivated.

    In this implementation,every behavior is modeledas a separate goal-directedprocess. In general,bothinternal and external factors are used to computewhether or not a behavior should be activated. Themost significantinputs come from the associateddriveand from the environment. The activation level ofeach behavior can range between zero and an empiri-callydetermined integervalue. When a consummato-ry behavior is active, its output acts to reduce the acti-vation energy of the associateddrive. When an appet-itive behavior is active, it serves to bringthe robot intoan environmental state suitable for activatingthe affil-iated consummatory behavior.

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    4.7 The Motor System

    The motor system incorporatesboth motor skills,such as smooth pursuit tracking,as well as expressivemotor acts, such as wigglingthe ears or loweringthebrow. Each

    expressivemotor act is linked to a corre-

    spondingemotion. The motor system also blendsmultiplefacial postures to reflect the set of currentlyactive emotions. The robots facial expressionsare sim-ilar to human facial expressions(Ekman & Friesen,1978), and the robots ears move analogouslyto howdogs move their ears to express motivational state(Milani, 1986). The motor system is also responsiblefor implementingemotional &dquo;overlays&dquo;over the taskbased motor skills. These overlaysare importantforconveyingexpressivenessthrough posture - forinstance, the robot can look to a givenobjectwhileconveyingapprehensionor deliberateness bythe wayit moves its neck and eye motors as well as its facialmotors.

    This section has presenteda broad overview of thearchitectural framework of this system. The followingsections describe the designdetails of each of these fivesystems in greater detail. Specificsof the implemen-tation were chosen to make Kismet an &dquo;infantinfor-mavore&dquo;,3that is, to define the robots nature so thatit is driven to learn in a social context. This architec-ture is designedto enable the robot to influence thebehavior of the caregiverin order to maintain aninteraction of suitable intensity so that the robot canlearn and satisfyits drives.

    5 DESIGN OF THE PERCEPTUAL SYSTEM

    Human infants discriminate readilybetween socialstimuli (faces,voices, etc.) and salient nonsocial stim-uli (brightly colored objects, loud noises, largemotion, etc.) (Aslin, 1987). The perceptualsystemhas been designedto discriminate a subset of bothsocial and non-social stimuli from visual images. As asocial stimulus detector, we have implementeda facedetector based on illumination-invariant imagefea-tures which operates at 20-30 Hz. We further relyonvisual motion detection both to supplementthe accu-racy of the face detector and as an indicator of thepresence of a salient non-social stimulus.

    5.1 PerceivingMotion

    The robot detects motion bycomputingthe differ-ence between consecutive imageswithin a local field.

    A region-growingtechniqueis then used to identifycontiguousblocks of motion within the differenceimage. The boundingbox of the five largestmotionblocks are providedthroughdual-portedRAM to themotivation system.

    The motion detection process receives a digitized128x128 image. Incomingimagesare stored in a ringof three frame buffers; one buffer holds the current

    imageIo, one buffer holds the previousimageI,, anda third buffer receives new input. The absolute valueof the difference between the grayscalevalues in eachimageis thresholded to providea raw motion image:

    The raw motion imageis then filtered with a 3x3Gaussian function (standarddeviation of 2 pixels)inorder to filter high-frequencynoise.

    The filtered imageis then segmentedinto bound-ingboxes of contiguousmotion. The algorithmscansthe filtered image,markingall locations that passthreshold with an identifyingtag. Locations inherittags from adjacentlocations througha regiongrow-and-mergeprocedure(Horn, 1986). Once all loca-tions above threshold have been

    tagged,the

    tagsare

    sorted based on their frequency. The boundingboxand centroid of each taggedregionare computed,anddata on the top five tags are sent to the motivationalsystem.

    5.2 PerceivingFaces

    The face detection algorithmused here was initiallyimplementedas part of a developmentalprogram forbuildingsocial skills based on detection of signalsofshared attention such as eye direction, pointingges-tures, and head position (Scassellati,1998b). In thatwork, our choice of a face detection algorithmwasbased on two criteria. First, it must be a relativelysimplecomputationthat can be performedin real-time. Second,the techniquemust performwell undersocial conditions, that is, in an unstructured environ-ment where people are most likely to be lookingdirectly at the robot. Based on these criteria, we

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    selected the ratio templateapproachdescribed bySinha (1994). Because these criteria are also applica-ble to the task specificationsfor providingperceptualinput for the social and motivational models dis-cussed in this paper, we elected to use the same algo-rithm.

    The ratio templatealgorithmwas designedtodetect frontal views of faces under varyinglightingconditions, and is an extension of classical templateapproaches(Sinha, 1996). While other techniqueshandle rotational invariants more accurately(Sung&Poggio,1994)or providebetter accuracy at the cost ofgreater computation(Rowley,Baluja,and Kanade,1995; Turk & Pentland, 1991), the simplicityof theratio templatealgorithmallows us to operate in real-time while detectingfaces that are likelyto be engagedin social interactions. Sinha (1994)has also demon-strated that ratio templatesoffer multiple levels ofbiologicalplausibility;templatescan be either hand-coded (as an innate structure) or learned adaptivelyfrom qualitativeenvironmental conditions.

    A ratio templateis composedof regionsand rela-tions, as shown in Figure5. For each target locationin the grayscaleimage,a templatecomparisonis per-formed usinga specialset of comparisonrules. The

    Figure5. A 14 pixel by 16 pixel ratio templatefor face detec-tion. The templateis composed of 16 regions(the gray boxes)and 23 relations (shown by arrows). Essential relations areshown as solid arrows while confirmingrelations are shown asdashed arrows. Adapted from Sinha (1996).

    templateis overlaid on a 14x16 grayscaleimagepatchat a potentialface location. For each region, we com-pute the average grayscalevalue of the imageareaunderneath that region. Each relation is a compari-son between two regions,for example,between the&dquo;leftforehead&dquo;

    regionand the

    &dquo;lefttemple&dquo;region. A

    relation is satisfied if the ratio of the average grayscalevalue of the first region to the average grayscalevalueof the second regionexceeds a constant value (in ourcase, 1.1).

    This ratio allows us to compare the intensities ofregionswithout relyingon the absolute intensity of anarea. In Figure5, each arrow indicates a relation, withthe head of the arrow denotingthe second region(thedenominator of the ratio). This templatecapitalizeson illumination-invariant observations. For example,the eyes tend to be darker than the

    surroundingface,

    and the nose is generally brighterthan its surround.We have adaptedthe ratio template algorithmtoprocess video streams. In doingso, we additionallyrequirethe absolutedifferencebetween the regionstoexceed a noise threshold, in order to eliminate falsepositiveresponses for small, noisy grayscalevalues.Figure6 shows a sampleimageprocessedbythe facedetection algorithm.

    The ratio templatealgorithmcan detect faces atmultiple scales. Multiplenodes of the parallelnet-work run the same algorithmon different sized inputimages,but without changingthe size of the template.

    Figure6. An exampleface in a cluttered environment. The128x128 grayscaleimage was capturedby the active visionsystem and then processedby the pre-filteringand ratio tem-plate detection routines. One face was found within theimage,and is shown outlined.

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    This allows the system to respondmore quickly tofaces that are closer to the robot, since closer faces aredetected in smaller imageswhich require less compu-tation. With this hardware platform,a 64x64 imageand a 14x 16 templatecan be used to detect faceswith-in

    approximatelythree to six feet of the robot. The

    same size templatecan be used on a 128x128 imagetofind faces within approximatelytwelve feet of therobot.

    5.3 Improving the Speedof Face Detection

    To improvethe speedof the ratio templatealgorithm,we have implementedtwo optimizations: an early-abort scheme and a motion-based pre-filter. Theearly-abort scheme decreases processing time byrejectingpotentialface locations as soon as possible.Usinga post-hocanalysisof ten minutes of video feed,relations were classified as either essential (solidarrowsin Figure5) or confirming(dashedarrows). Face loca-tions alwayssatisfied ten of the eleven essential rela-tions and seven of the twelve confirmingrelations. Byexaminingthe essential relations first, we can reject alocation as a potentialface as soon as two or more ofthe essential relations have failed. This early-abortmechanism increases the speedof our computationbya factor of 4, without any observable decrease in per-formance. The pre-filteringtechniquedecreasespro-cessingtime byevaluatingonlythe locations that arelikely to contain a face. Usingthe motion detectionroutines described earlier, the algorithmlooks formovingobjectsthat are the same size as the face tem-plate. A location is evaluated by the ratio templatealgorithmonlyif it has had motion within the last fiveframes (movingfaces), if it contained a verified facewithin the last five frames (stationaryfaces), or if ithad not been checked for faces within the last threeseconds (facesnear the noise threshold). This filteringtechniqueincreased the speedby a factor of five to

    eight,dependingon

    the imagesize. The combinationof these two techniquesallows our face detection tooperate at 20-30 Hz.

    5.4 Evaluation of Ratio Templates

    The ratio template algorithmwas evaluated on bothstatic imagesand real-time video streams. As a meas-urement of the illumination invariance, we ran the

    algorithmon a test set of static face imagesfirst usedbyTurk and Pentland ( 1991 ). The database containsimagesfor 16 subjects, each photographedunderthree different lightingconditions: with the primarylight source at 90 degrees,45 degrees,and head-on.

    Figure7 shows

    imagesfrom two

    subjectsunder each

    lighting condition. The ratio templatealgorithmdetected 34 of the 48 test faces. While this staticdetection rate (71 %)is considerablylower than otherface detection schemes (Rowleyet al., 1995; Turk &Pentland, 1991), this result is a poor indicator of theperformanceof the algorithmin a complete,behavingsystem (Scassellati, 1998b). By utilizing a pair oflearned sensory-motor mappings,this system wascapableof saccadingto faces and extractinghighreso-lution imagesof the eye on 94% of trials (see Figure8). Additionally, the overall behavior of the system

    Figure7. Six of the static test imagesfrom Turk and Pentland(1991)used to evaluate the ratio templateface detector. Eachface appears in the test set with three lighting conditions,head-on (top), from 45 degrees(middle),and from 90 degrees(bottom).The ratio templatecorrectly detected 71% of thefaces in the static imagedatabase, includingeach of these facesexcept for the middle imagefrom the first column. However,these conditions were more severe than the average environ-mental stimuli (see text).

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    Figure 8. Six detected faces. Only faces of a single scale(roughlywithin four feet of the robot) are shown here

    corrected for trials where the first saccade missed thetarget. The system performsbest when the subjectisfacingthe robot and attemptingto be noticed, whichare the conditions that we expect for social interac-tions.

    6 DESIGN OF THE MOTIVATION SYSTEM

    The robotsmotivational system is composedof two

    Figure9. Implementationof the behavior engineframeworkused in the experimentspresentedhere. There are two per-cepts, resulting from face-like stimuli and non-face stimuli.The motivation system contains three drives (fatigue,social,and stimulation) and eight emotion and expressivestates(anger, disgust, happiness,interest, fear, sadness, and tiredness) each of which can be expressedthroughthe motor sys-tem. These percepts and motivations influence the selectionof the three behaviors (sleep,play, and socialize).

    inter-related subsystems.One subsystemimplementsthe robots drives, another implementsits emotionsand expressivestates. Figure9 shows the current sys-tem implementationfor the entire behavior engine.

    6.1 The DrivesSubsystem

    For an animal,adequatelysatisfyingits drives is para-mount to survival. Similarly,for the robot, maintain-ing all its drives within their homeostatic regimeis anever-ending,all-important process. Currently, therobot has three basic drives: a social drive, a stimulationdrive, and a fatiguedrive.

    One drive is to be social, that is, to be in the pres-ence of peopleand to be stimulated bypeople.Thisis importantfor biasingthe robot to learn in a socialcontext. On the underwhelmed extreme the robot is

    lonely;it is predisposedto act in ways to establishface-to-face contact with people. If left unsatiated,this drive will continue to intensifytoward the lonelyend of the spectrum. On the overwhelmed extreme,the robot is asocial; it is predisposedto act in ways toavoid face-to-face contact. The robot tends towardthe asocial end of the spectrum when a person is over-

    stimulatingthe robot. This may occur when a personis movingtoo much or is too close to the camera.

    Another drive is to be stimulated,where the stim-ulation can either be generatedexternallybythe envi-ronment or internallythroughspontaneous self-play.On the underwhelmed end of this spectrum, the crea-ture is bored. This occurs if the robot has been inac-tive or unstimulated over a periodof time. On theoverwhelmed part of the spectrum, the robot is con-fused. This occurs when the robot receives morestimulation than it can effectivelyassimilate, and pre-disposesthe robot to reduce its interaction with theenvironment, perhapsbyclosingits eyes or turningitshead away from the stimulus. In the future, this drivewill also be relevant for learning;this drive will tend

    toward the bored end of the spectrum if the currentinteraction becomes very predictablefor the robot.This will bias the robot to engage in new kinds ofactivities and encourage the caregiverto challengetherobot with new interactions.

    The fatiguedrive is unlike the others in that itspurpose is to allow the robot to shut out the externalworld instead of trying to regulateits interaction withit. While the robot is active, it receives continual

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    stimulation from the environment. As time passesthis drive approachesthe exhausted end of the spec-trum. Once the intensity level exceeds a certainthreshold, it is time for the robot to &dquo;sleep&dquo;.In thefuture, this will be the time for the robot to consoli-date its learned

    anticipatorymodels and

    integratethem with the rest of the internal control structure.While the robot &dquo;sleeps&dquo;,all drives return to theirhomeostatic regime.

    6.2 The Emotions and ExpressiveStates Subsystem

    So far, there are a total of eightemotions and expres-sive states implementedin this system, each as a sepa-rate transducer process. The overall framework of theemotion system shares strong commonalitywith thatof Velasquez(1996), althoughits function is specifi-callytargetedfor social exchangesand learning. Therobot has analogs of five primaryemotions inhumans: anger, disgust,fear, happiness,and sadness.The robot also has three expressivestates that do notcorrespondto human emotions, but do play animportantrole in human learningand social interac-tion : surprise,interest, and excitement. Manyexper-iments in developmentalpsychologyhave shown thatinfants show surprisewhen witnessingan unexpectedor novel outcome to a familiar event (Carey &Gelman, 1991). Furthermore,caregiversuse theirinfants displayof excitement or interest as cues toregulatetheir interaction with them (Wood et al.,1976).

    In humans, four factors serve to elicit emotions:neurochemical,sensory-motor, motivational, andcognitive factors (Izard, 1993). In this system,emphasishas been placed on how drives and otheremotions contribute to a givenemotionslevel of activa-tion. The influence from other emotions serve to pre-vent conflictingemotions from becomingactive at thesame time. To implementthis, conflictingemotionshave mutuallyinhibitory connections between them.For instance, inhibitoryconnections exist between theemotions happinessand sadness, between disgustandhappiness,and between happinessand anger.

    For a givendrive, each regimepotentiates a differ-ent emotion and hence a different facial expression. Ingeneral,when a drive is in its homeostatic regime,itpotentiates positiveemotions such as happinessor

    interest. The accompanyingexpressiontells the care-giverthat the interaction is goingwell and the robotis poisedto playand learn. When a drive is not with-in the homeostatic regime, negativeemotions arepotentiated(such as anger, disgust,or sadness)which

    producessignsof distress on the robots face. The par-

    ticular sign of distress providesthe caregiverwithadditional cues as to what is wrong and how he or she

    might correct for it. For example,overwhelmingsocial stimuli (suchas a rapidlymovingface)producesignsof disgust- an asocial response. In contrast,overwhelmingnonsocial stimuli (such as a rapidlymovingball) producesignsof fear.

    Note that the same sort of interaction can have a

    very different effect on the robot dependingon thedrive context. For instance, playingwith the robotwhile all drives are within the homeostatic regimeelic-its the emotion happiness.The expressionof this emo-tion tells the caregiverthat playingwith the robot is anappropriate interaction to be havingat this time.However, if the fatiguedrive is deepinto the exhaust-ed end of the spectrum, then playingwith the robotactuallyprevents the robot from goingto sleep. As aresult, the fatiguedrive continues to increase in inten-sity. When highenough,the fatiguedrive beginstopotentiate the emotion anger. The caregivermayinterpret the expressionof this emotion as the robotacting &dquo;cranky&dquo;because it is &dquo;tired&dquo;.In the extremecase, fatiguemay potentiateanger so stronglythat therobot displayssigns of fury. The caregivermay con-strue this as the robot throwing a &dquo;tantrum&dquo;.Normally,the caregiverwould desist before this pointand allow the sleepbehavior to be activated.

    Importantnear-term extensions to this subsysteminclude addinga varietyof sensory-motor elicitors sothe robot can respondemotionallyto various percep-tual stimuli. For instance, the robot should showimmediate displeasureto very intense stimuli, showinterest to particularlysalient stimuli, and show sur-

    priseto suddenlyappearingstimuli.7 DESIGN OF THE ATTENTION SYSTEM

    The current implementationhas a very simplisticattention mechanism. To limit the computationalrequirements,the robot processes only the mostsalient face stimulus (whichis the target location thatgives the best quantitative match to the ratio tem-

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    plate)and the five most salient motion stimuli (whichare the five largestcontiguousregionsof motion). Allother output from these perceptualprocesses is sup-pressed. Note that this attention process does notcurrentlylimit the computationalrequirementsof

    perception,nor

    does itaccount

    for habituation effectsor for influences from the motivational system.However, this simplisticsystem does limit the com-putation necessary for behavior selection. A morecomplexattention system that incorporateshabitua-tion, influences from the motivational system, andadditional sensory inputs is currentlyunder construc-tion.

    8 DESIGN OF THE BEHAVIOR SYSTEM

    For each drive there is anaccompanying

    consumma-

    tory behavior. Ideally,this behavior becomes activewhen the drive enters the under-whelmed regimeandremains active until it returns to the homeostaticregime. The three consummatory behaviors are thesocialize,play, and sleepbehaviors.

    The socialize behavior acts to move the social drivetoward the asocial end of the spectrum. It is potenti-ated more stronglyas the social drive approachesthelonely end of the spectrum. Its activation levelincreases above threshold when the robot can engagein social interaction with a person, that is, when itobtains a face stimulus at a reasonable activation level.The behavior remains active for as longas this inter-action is maintained. Onlywhen the behavior isactive does it act to reduce the intensity of the drive.When the interaction is of suitable intensity,the driveapproachesthe homeostatic regime and remainsthere. When the interaction is too intense, the drivewill pass the homeostatic regimeand move into theasocial regime.

    The play behavior acts to move the stimulationdrive toward the confused end of the spectrum. It is

    potentiated more stronglyas the stimulation driveapproachesthe bored end of the spectrum. The acti-vation level increases above threshold when the robotcan engage in some sort of stimulatinginteraction, inthis case, byobservinga non-face objectthat movesgently. It remains active for as longas the robot main-tains the interaction. While active it continues tomove the drive toward the confused end of the spec-trum. If the interaction is of appropriateintensity,the

    drive will remain in the homeostaticregime.. :The sleepbehavior acts to satiate the fatiguedrive.

    When the fatiguedrive reaches a specifiedlevel, thesleepbehavior activates and remains active until thefatiguedrive is restored to the homeostatic regime.

    Sleepalsoserves a

    specialfunctionto reset

    the motiva-tion system. When active, it not only restores thefatiguedrive to the homeostatic regime,but all theother drives as well. If any drive moves far from itshomeostatic regime,the robot displaysstronger andstronger signsof distress, which eventuallyculminatesin extreme anger if left uncorrected. This expressivedisplayis a strong sign to the caregiver to interveneand help the robot. If the caregiverfails to act appro-priatelyand the drive reaches an extreme, a protectivemechanism activates and the robot eliminates externalstimulation

    by activatingthe

    sleepbehavior. This

    extreme self-regulationmethod allows the robot torestore all its drives byitself. Once all the drives havebeen restored, the behavior is released and the robotbecomes active. A similar behavior is observed ininfants; when they are in extreme distress, they mayfall into a disturbed sleep(Bullowa,1979).

    In the simplestcase, each drive and its satiatingbehavior are connected as shown in Figures10, 11,and 12. Both the drive and the behavior are modeledas transducers where the output is simplythe currentactivation energy. As shown, the output of a drive isan excitatoryinput of its associatedbehavior. Hence,as the drive grows in intensity, it potentiates the acti-vation level of that behavior more and more. Whenthe activation level rises above threshold, the behaviorbecomes active and is expressedthroughthe robotsactions. As the robot performsthese motor acts, theoutput of the behavior inhibits the drive, reducingitsintensity level. As the drives intensity decreases, itpotentiates the behavior less and less. Finally,whenthe drive is restored to the homeostatic regime,theactivation level of the behavior falls below threshold

    and is deactivated.Two of the three consummatory behaviorscannot

    be activated by the intensity of their associated drivealone. Instead, theyrequire a specialsort of environ-mental interaction in order to become active. Forinstance, socialize cannot become active without theparticipation of a person. (Analogouscases hold forplay.) Furthermore,it is possiblefor these behaviors tobecome active bythe environment alone if the inter-

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    Figure10. Portions of the behavior engineactive duringthenon face stimuli experiments.Non face stimuli activate theplaybehavior, which is potentiatedbythe stimulation drive.The stimulation drive acts upon the emotion processes of fear,sadness, anger, and interest. ,

    action is strong enough.This has an importantcon-sequence for regulatingthe intensityof interaction.For example,if the intensity of the stimulus is toointense, the drive may move into the overwhelmedregime. In this case, the drive is no longerpotentiat-ing the consummatory behavior, the environmental

    Figure11. Portions of the behavior engineactive duringtheface stimuli experiments. Face stimuli activate the socializebehavior,which is potentiatedbythe social drive. The socialdrive acts upon the emotion processes of disgust,anger, sad-ness, happiness,and interest.

    Figure12. Portions of the behavior engineactive in the over-stimulation experiments. Both face and non-face stimuliinhibit the sleepbehavior,which is potentiatedbythe fatiguedrive. The fatigue drive acts upon the emotion processes ofinterest, tiredness, and anger.

    inputalone is strong enoughto keepit active. Whenthe drive enters the overwhelmed regime,the systemis stronglymotivated to act to stop the stimulation.For instance, if the caregiveris interacting with therobot too intensely,the social drive may move into theasocial regime. When this occurs, the robot displaysan

    expressionof displeasure,which isa cue

    for thecaregiverto stop.

    9 DESIGN OF THE MOTOR SYSTEM

    Our current system designhas incorporatedexpres-sive motor actions for each emotion. Additionally,wehave implementedthe hardware and software controlfor various motor skills, such as smooth pursuittrack-ing and saccadic eye movement (Scassellati,1998a),but have yet to incorporatethese skills into the behav-ior

    engine.Each of the eleven degreesof freedom for the facialfeatures is controlled by a low-level transducerprocesses that controls both the positionand velocity.Mid-level coordinated motion processes control com-

    plex movements of matched facial features such aswigglingboth ears or archingboth brows inward.High-levelface expressionprocesses direct all facialfeatures to show a particularexpression.For eachexpression,the facial features move toward a charac-

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    teristic configuration,with the speedand magnitudedependingon the intensity of the emotion evokingtheexpression.In general,the more intense the expres-sion, the quickerand further the facial features move.Blended expressionsare computedbytakinga weight-ed average of the facial configurationscorrespondingto each evoked emotion. In general,expressiveactsmay modifythe task based motor skills (suchas look-ing at a particularobject)and overall postures (eyeand neck position) to convey different emotionalstates, but this has yet to be implemented.

    10 EXPERIMENTS AND RESULTS

    A series of experimentswas performedwith the robotusing the behavior engineshown in Figure9. Thetotal

    systemconsists of

    three drives (fatigue,social,andstimulation), three behaviors(sleep,socialize,and play),two visually-basedpercepts (face and non-face),fiveemotions (anger,disgust,fear, happiness,sadness),twoexpressivestates (tirednessand interest), and their cor-respondingfacial expressions. More detailedschematics for the stimulation circuit, the social cir-cuit, and the fatiguecircuit are shown in Figures10,11, and 12 respectively.

    Each experimentinvolved a human interactingwith the robot either throughdirect face-to-face inter-action,

    bywavinga hand at the robot, or

    usinga toy

    to playwith the robot.44 The toys were a small plushblack and white cow and an orange plastic slinky(shownin Figure1). The perceptualsystem classifiesthese interactions into two classes: face stimuli andnon-face stimuli. The face detection routine classifiesboth the human face and the face of the plushcow asface stimuli, while the wavinghand and the slinkyareclassified as non-face stimuli. Additionally, themotion generatedbythe object givesa rating of thestimulus intensity. The robots facial expressionsreflect its ongoingmotivational state (i.e., its mood)and providesthe human with visual cues as to how tomodifythe interaction to keepthe robots driveswith-in homeostatic ranges.

    In general,as long as all the robots drives remainwithin their homeostatic ranges, the robot displaysinterest. This cues the human that the interaction isof appropriateintensity. If the human engages therobot in face-to-face contact while its drives are with-in their homeostatic regimes,the robot displayshap-

    piness. However, once any drive leaves its homeostat-ic range, the robots interest and happinesswane as itgrows increasinglydistressed. As this occurs, therobots expressionbecomes more distressed. Thisvisual cue tells the human that all is not well with the

    robot, and signalswhether the human should switchthe type of stimulus as well as whether the intensity ofinteraction should be intensified, diminished, ormaintained at its current level.

    For all of these experiments,data was recordedonline in real-time during interactions between ahuman and the robot. Figures13 through18 plottheactivation levels (A) of the appropriate emotions,drives,behaviors,and perceptsas a function of time (t).Emotions are alwaysplotted togetherwith activationlevels rangingfrom 0 to 2000. Percepts,behaviors,and

    drivesare often

    plotted together.Perceptsandbehav-

    iors have activation levels that also range from 0 to2000, with highervalues indicatingstronger stimulior higherpotentiation respectively.Drives have acti-vations ranging from -2000 (the over-whelmedextreme)to 2000 (the under-whelmed extreme).

    10.1 Non-Face Stimuli Experiments

    Figures13 and 14 illustrate the influence of the stim-ulation drive on the robots motivational and behav-ioral state when interacting with a salient non-facestimulus. The activation level of the robots playbehavior cannot exceed the activation thresholdunless the human interacts with the robot with suffi-cient intensity;low intensity interaction will not trig-ger the playbehavior even if highlypotentiatedbythestimulation drive. If the interaction is intense, eventoo intense, the robotsplay behavior remains activeuntil the human either stops the activity,or the robottakes action to end it.

    For the wavinghand experiment(seeFigure13), alack of interaction before the start of the run (t s 0)

    placesthe robot in the sadnessemotional state. Thestimulation drivelies in the bored end of the spectrumfor activations Asttm> 400. Duringthe interval 5 s t s25, a wavinghand moves gentlyback and forth, stim-ulatingthe robot within the acceptableintensity range(400 s Anonftce s1600). This causes the stimulationdrive to diminish until it resides within the homeo-static range, and a look of interest appears on therobots face. Duringthe interval 25 z t z 45, the

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    Figure13. Experimentalresults for Kismet interactingwith a person waving.The top panelshows the activation levels of theemotion

    processes involvedin

    this experimentas a

    function oftime.

    The bottom panelshows the activation levels of the drives,behaviors, and percepts relevant to this experiment.While the person continues to wave at a reasonable intensity, the robotexpresses interest. When the stimulus intensity becomes too intense, the robot beginsto express fear.

    stimulus maintains a desirable intensity level, thedrive remains in the homeostatic regime, and therobot maintains interest. During the interval45 s t s 70, the hand stimulus intensifies to large,sweepingmotions (A,,,n-f,,, 21600),which overwhelmthe robot. This changecauses the stimulation drive tomigrate toward the overwhelmed end of the spec-trum. As the drive approachesthe overwhelmedextreme, the robots face displaysan intensifyingexpressionof fear. Around t = 75 the robot looks &dquo;ter-rified&dquo;(Afear >1500). The experimenterrespondsbyremainingstill until the robotsexpressionof fear dis-sipates, and then resumes the stimulation within theacceptablerange. Consequently,the stimulation drivereturns to the homeostatic regimeand the robot dis-playsinterest again. For the remainder of the run

    (t z 105), the experimenterstopswaving.Because therobot is under-stimulated the stimulation drive movesinto the bored end of the spectrum and an expressionof sadnessreappears on the robots face.

    The slinkyexperiment(Figure14) was conductedin a similar fashion. As in the previouscase, the robotis placed into a bored state before the experimentbegins. Att = 5 the robot is shown small slinkymotions whichcorrespondto an acceptableintensity. Occasionallythe slinkymotion is too intense (t = 30 and t = 35),but on average the motion is acceptable. As a result,the stimulation drive is restored to the homeostaticregimeand the robot looks &dquo;interested&dquo;.Duringtheinterval 75 s t s 105, the experimentermoves theslinkyin largesweepingmotions which are too vigor-

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    Figure14. Experimentalresults for Kismet interactingwith a toy slinky. While the slinkycontinues to move at a reasonable

    intensity, the robot expresses interest. When the stimulus intensity becomes too great, the robot beginsto express fear, whicheventuallyleads to anger.

    ous for the robot. Consequentlythe drive moves farinto the overwhelmed regime. When the drive activa-tion drops too low (Amm< -1600), the expressionanger is blended with the intensifyingexpressionfear.

    At t = 105, the experimenterstops the slinkymotioncompletelyand allows the distressed expressionstodiminish. The experimenterthen resumes smallslinkymotions, the drive returns to the homeostaticregime,and the robot appears &dquo;interested&dquo;again. Forthe remainder of the trial (t z 150), the slinkymotionceases, the lack of stimulation causes the drive to moveback into the under-whelmed regime,and an expres-sion of sadnessreturns to the robots face.

    10.2 Face Stimuli Experiments

    Figures15 and 16 illustrate the influence of the socialdrive on the robotsmotivational and behavioral state

    when interactingwith a face stimulus. The robotssocialize behavior cannot become active unless ahuman interacts with the robot with sufficient inten-sity ; low intensity interaction will not trigger thesocialize behavior even if highlypotentiated by thesocial drive. While the face stimulus intensityexceedsthis base threshold (Ajcez 400), the robots socializebehavior remains active until either the human or therobot terminates the interaction.

    Figure15 shows the interaction of the robot witha human face stimulus. Before the run begins,therobot is not shown any faces so that the socialdriveliesin the lonelyregimeand the robot displaysan expres-sion of sadness. At t = 10 the experimentermakesface-to-face contact with the robot. Duringthe inter-val 10 s t s 58, the face stimulus is within the desiredintensity range. This correspondsto small head

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    Figure15. Experimentalresults for Kismet interactingwith a personsface. When the face is present, the robot expresses inter-est and happiness.When the face begins

    to move too

    violently,the robot beginsto

    express disgust,which

    eventuallyleadsto

    anger. Note that the robot reacts differentlyto a social stimulus (in this case, a face) than to the previousnon-social stimuli.

    motions, much like those made when engaginga per-son in conversation. As a result, the social drive movesto the homeostatic regime,and a blend of the expres-sions interest and happinessappears on the robots face.Duringthe interval 60 s t S 90, the experimenterbeginsto sway back and forth vigorouslyin front ofthe robot. This results in a face stimulus of over-

    whelmingintensity,which forces the

    social driveinto

    the asocial regime. As the drive intensifies toward avalue of -1800, the expressiondisgustappears on therobotsface, which grows in intensity and is eventual-ly blended with anger. Duringthe interval 90 s t s115, the experimenterturns her back on the robot, sothat no face is detected bythe robot. This allows thedrive to recover back to the homeostatic regimeandthe robot againshows the expressioninterest. From115 s t s135, the experimenterre-engages the robot

    in face-to-face interaction of acceptableintensity,andthe robot respondswith the expressionof happiness.From 135 s t s 170, the experimenterturns awayfrom the robot, which causes the drive to return to thelonelyregimeand to displaysadness. For t z 170, theexperimenterre-engages the robot in face-to-face con-tact, which leaves the robot expressinginterest and

    happinessat the conclusion of the run.

    Figure16 shows the interaction of the robot withthe plushtoy cow. Because the face detector triggerson the cows face, the cow is treated as a social stimu-lus and therebyinfluences the social drive. This exper-imental run followed the same format as that for thehuman face stimulus. The run beginswith the socialdrive within the lonelyregimeand the robot express-ing sadness. At t = 5, the experimentershows therobot the cows face and moves the cow in small gen-

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    Figure16. Experimentalresults for Kismet interactingwith a toy stuffed animal. The perceptualsystem recognizesthe face ofthe toy, and the stimulus is classified as a social object. When the face is present, the robot expresses interest and happiness.When the face beginsto move too violently,the robot beginsto express disgust,which eventuallyleads to anger.

    tle motions. This results in a stimulus of acceptableintensitywhich restores the drive to the homeostaticregime. As a result the robot expresses interest andhappiness.Duringthe interval 50 s t s 78, the exper-imenter beginsswingingthe cow quicklyin front ofthe robots face. Because the stimulus is too intense,the drive moves into the asocial regimeand the robots

    expressionof disgust intensifies until eventuallyblended with anger. At t = 78, the experimenterremoves the cow from the robots visual field andallows the drive to return to the homeostatic regime.From 98 s t s 118, the cows face is shown to therobot again which maintains the drive within thehomeostatic regimeand the robot displaysinterest andhappiness.Duringthe interval 118 s t s 145, thecows backside is shown to the robot. The lack of aface stimulus causes the social drive to return to the

    lonelyregime.For the remainder of the run (t z 145),the cow is turned to face the robot and the drive isrestored to the homeostatic regime. The run endswith the robot expressinghappinessand interest.

    10.3 Sleepand Over-Stimulation Experiments

    As discussedearlier, infants fall into a disturbed sleepwhen put into an extremelyanxious state for a pro-longedtime. Similarlyfor the robot, if the interactionis overwhelmingfor long periodsof time, the sleepbehavior becomes active. Figure17 shows one exam-pleof this effect. As the social drive moves toward anextreme, the robot first expresses signsof disgust,even-tually blendingwith increasinglyintense signs ofanger. When no relief is encountered and the socialdrive reaches an extreme (t = 30), the sleepbehavior

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    Figure17. Further experimentalresults for Kismet interacting with a toy staffed animal. In this case, the experimentercon-tinues to stimulate the robot bymovingthe stuffed animal even after the robot displaysboth disgustand anger. The sleepbehav-ior is then activated as an extreme measure to block out stimulation. The sleep behavior restores the drives and emotions tohomeostatic levels before allowingthe robot to become active.

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    Figure18. Experimentalresults for long-terminteractions of the fatigue drive and the sleepbehavior. The fatiguedrive con-tinues to increase until it reaches an activation level that potentiates the sleepbehavior. If there is no other stimulation, this

    will allow the robot to activate the sleep behavior.

    becomes active. This resets the motivational state byrestoringall drives to their homeostatic ranges. Oncethe drives have been restored, the sleepbehavior is sup-pressedand the robot becomes active again.

    Figure 18 illustrates the influence of the fatiguedrive on the robots motivational and behavioral statewhen interactingwith a human. Over time, the

    fatiguedrive increases toward the exhausted end of thespectrum. As the robots level of fatigueincreases, therobot displaysstronger expressionsof tiredness. Att = 95, the activation of the fatiguedrive becomes suf-ficient to activate the sleepbehavior without externalstimulation. The sleepbehavior remains active untilall drives are restored to their homeostatic ranges.Once this occurs, the activation level of the sleepbehavior decaysuntil the behavior is no longeractive.This experimentalso shows what happensif a human

    continues to interact with the robot when the fatiguedrive is high (t = 215). The sleepbehavior cannotbecome active while a person interacts with the robotbecause the playbehavior remains active (note themutuallyinhibitoryconnections in Figure12). If thefatiguedrive exceeds threshold and the sleepbehavior isnot active, the robot begins to express anger.Eventuallythe activation of the emotion anger reachesan intense level (Aanger= 1800),and the robot appears&dquo;enraged&dquo;.The human persistswith the interactionand the robots fatiguelevel reaches near maximum.Emergencyactions are taken bythe robot to force anend to the interaction; the sleepbehavior becomesactive until the drives are restored.

    These experimentalresults characterize the robotsbehavior when interacting with a human. Theydemonstrate how the robotsemotive cues are used to

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    regulatethe nature and intensity of the interaction,and how the nature of the interaction influences therobots behavior. The result is an ongoing&dquo;dance&dquo;between robot and human aimed at maintainingtherobots driveswithin homeostatic bounds. If the robot

    and human are goodpartners, the robot expressesinterest and happinessmost of the time. These expres-sions indicate that the interaction is of appropriateintensity for learning.

    11 SUMMARY

    We have presented a framework (heavilyinspiredfrom work in ethology,psychology,and cognitivedevelopment)for designingbehavior engines forautonomous robots specificallygeared to regulatesocial interaction between naive robots and sophisti-cated humans. We have shown how the percepts,drives, emotions, behaviors, and facial expressionsinfluence each other to establish and maintain socialinteractions that can provide suitable learningepisodesin which the robot is proficientyet slightlychallenged,and where the robot is neither under-stimulated nor over-stimulated. With a specificimplementation,we demonstrated how the systemengages in a mutuallyregulatoryinteraction with ahuman while distinguishingbetween stimuli that canbe influenced

    socially(faces)and those that cannot

    (motion).The specificsof learningin a social context (what

    is learned and how it is learned)were not addressed inthis paper. That is the subjectof future work,whichwill include tuning and adjustingthis earlymotiva-tion system to appropriatelyregulatethe intensity ofinteraction to benefit the learningprocess. Additionalareas of future investigationinclude the implementa-tion of a selective attention mechanisms, additionalmotor skills, such as smooth pursuit trackingand sac-cadic eye movement, and vocalization capabilities.We will also investigateadditional perceptualcapabil-ities includingdetectingfacial gestures, emotive cuesof the caregiverfrom visual and auditorydata streams,and attention markers such as eye direction andpointinggestures. We are continuingto laythe foun-dation upon which the learningof earlycommunica-tion skills (turn taking,shared attention, vocalizationshavingshared meaning)can take place.

    NOTES

    1For example,at one month the infant has a visualacuitybetween 20/400 and 20/600.

    2 As a convention, we will use italics to distinguishparts of the architecture of this particularsystemfrom the generaluses of those words. In this case,"drives" refers to the particular computationalprocesses that are active in the system, while "drives"refers to the generaluses of that word.

    3 A term Dan Dennett mentioned to us duringcon-versation.

    4For all of these experiments,the human subjectwasfamiliar with the motivations and facial expressionsgeneratedbythe robot.

    ACKNOWLEDGMENTS

    Supportfor this researchwas providedbya MURIgrant under the Office of Naval Research,contractN00014-95-1-0600. The motivational system was

    designedand largelyimplementedduringthe firstauthors visiting appointmentat the Santa FeInstitute. The second author was additionallysup-ported by a National Science and EngineeringGraduate Fellowship from the United StatesDepartmentof Defense.

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