Regulation and Entrainment inHuman-Robot Interaction

Dr. Cynthia BreazealMIT Artificial Intelligence Lab

Cambridge, MA 02139 USAcynthiadai.mit.edu

Abstract:

Newly emerging robotics applications for domestic or entertainment pur-poses are slowly introducing autonomous robots into society at large. Acritical capability of such robots is their ability to interact with humans,and in particular, untrained users. This paper explores the hypothesis thatpeople will intuitively interact with robots in a natural social manner pro-vided the robot can perceive, interpret, and appropriately respond withfamiliar human social cues. Two experiments are presented where naivehuman subjects interact with an anthropomorphic robot. Evidence for mu-tual regulation and entrainment of the interaction is presented, and howthis benefits the interaction as a whole is discussed.

1. IntroductionNew applications for domestic, health care related, or entertainment basedrobots motivate the development of robots that can socially interact with, learnfrom, and cooperate with people. One could argue that because humanoidrobots share a similar morphology with humans, they are well suited for thesepurposes - capable of receiving, interpreting, and reciprocating familiar socialcues in the natural communication modalities of humans.

However, is this the case? Although we can design robots capable ofinteracting with people through facial expression, body posture, gesture, gazedirection, and voice, the robotic analogs of these human capabilities are a crudeapproximation at best given limitations in sensory, motor, and computationalresources. Will humans readily read, interpret, and respond to these cues inan intuitive and beneficial way?

Research in related fields suggests that this is the case for computers [1]and animated conversation agents [2]. The purpose of this paper is to explorethis hypothesis in a robotic media. Several expressive face robots have beenimplemented in Japan, where the focus has been on mechanical engineeringdesign, visual perception, and control. For instance, the robot in the upper leftcorner of figure 1 resembles a young Japanese woman (complete with siliconegel skin, teeth, and hair [5]. The robot's degrees of freedom mirror those ofa human face, and novel actuators have been designed to accomplish this inthe desired form factor. It can recognize six human facial expressions and can

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Figure 1. A sampling of robots designed to interact with people. The far leftpicture shows a realistic face robot designed at the Science University of Tokyo.The middle left picture shows WE-3RII, an expressive face robot developed atWaseda University. The middle right picture shows Robita, an upper-torsorobot also developed at Waseda University to track speaking turns. The farright picture shows our expressive robot, Kismet, developed at MIT. The twoleftmost photos are courtesy of Peter Menzel [8].

mimic them back to the person who displays them. In contrast, the robotshown in the upper right of corner of figure 1 resembles a mechanical cartoon[6]. The robot gives expressive responses to the proximity and intensity of alight source (such as withdrawing and narrowing its eyelids when the light istoo bright). It also responds expressively to a limited number of scents (suchas looking drunk when smelling alcohol, and looking annoyed when smoke isblown in its face). The lower right picture of figure 1, shows an upper-torsohumanoid robot (with an expressionless face) that can direct its gaze to look atthe appropriate person during a conversation by using sound localization andhead pose of the speaker [7].

In contrast, the focus of our research has been to explore dynamic, expres-sive, pre-linguistic, and relatively unconstrained face to face social interactionbetween a human and an anthropomorphic robot called Kismet (see lower rightof figure 1). For the past few years, we have been investigating this question in avariety domains through an assortment. of experiments where naive human sub-jects interact with the robot. This paper summarizes our results with respect totwo areas of study: the communication of affective intent and the dynamics ofproto-dialog between human and robot. In each case we have adapted the the-ory underlying these human competencies to Kismet, and have experimentallystudied how people consequently interact with the robot. Our data suggeststhat naive subjects naturally and intuitively read the robot's social cues andreadily incorporate them into the exchange in interesting and beneficial ways.We discuss evidence of communicative efficacy and entrainment that results inan overall improved quality of interaction.

2. Communication of Affective IntentHuman speech provides a natural and intuitive interface for both communi-cating with humanoid robots as well as for teaching them. Towards this goal,we have explored the question of recognizing affective communicative intentin robot-directed speech. Developmental psycholinguists can tell us quite alot about how preverbal infants achieve this, and how caregivers exploit it to

regulate the infant's behavior. Infant-directed speech is typically quite exag-gerated in the pitch and intensity (often called motherese). Moreover, mother'sintuitively use selective prosodic contours to express different communicativeintentions. Based on a series of cross-linguistic analyses, there appear to beat least four different pitch contours (approval, prohibition, comfort, and at-tentional bids), each associated with a different emotional state [9]. Figure 2illustrates these four prosodic contours.

approval prohibition attention comfort

TTtw,. ?oid bO-ll N baby. Cýou cý yOU NMKdM Oh. ,y

time (ms) time (ms) time (ms) trme (ms)

Figure 2. Fernald's prototypical prosodic contours for approval, attentionalbid, prohibition, and soothing.

Mothers are more likely to use falling pitch contours than rising pitchcontours when soothing a distressed infant [10], to use rising contours to elicitattention and to encourage a response [11], and to use bell shaped contours tomaintain attention once it has been established [12]. Expressions of approvalor praise, such as "Good girl!" are often spoken with an exaggerated rise-fallpitch contour with sustained intensity at the contour's peak. Expressions ofprohibitions or warnings such as "Don't do that!" are spoken with low pitchand high intensity in staccato pitch contours. Fernald suggests that the pitchcontours observed have been designed to directly influence the infant's emotivestate, causing the child to relax or become more vigilant in certain situations,and to either avoid or approach objects that may be unfamiliar [9].

Inspired by these theories, we have implemented a recognizer for distin-guishing the four distinct prosodic patterns that communicate praise, prohibi-tion, attention, and comfort to preverbal infants from neutral speech. We haveintegrated this perceptual ability into our robot's emotion system, thereby al-lowing a human to directly manipulate the robot's affective state which is inturn reflected in the robot's expression.

2.1. The Classifier Implementation

As shown in figure 3, the affective speech recognizer receives robot-directedspeech as input. The speech signal is analyzed by the low-level speech process-ing system, producing time-stamped pitch (Hz), percent periodicity (a measureof how likely a frame is a voiced segment), energy (dB), and phoneme values1

'This auditory processing code is provided by the Spoken Language Systems Group atMIT. For now, the phoneme information is not used in the recognizer.

in real-time. The next module performs filtering and pre-processing to reducethe amount of noise in the data. The pitch value of a frame is simply set to 0 ifthe corresponding percent periodicity indicates that the frame is more likely tocorrespond to unvoiced speech. The resulting pitch and energy data are thenpassed through the feature extractor, which calculates a set of selected fea-tures (Ft to Fn). Finally, based on the trained model, the classifier determineswhether the computed features are derived from an approval, an attentionalbid, a prohibition, soothing speech, or a neutral utterance.

Erma I Approal,Root- AftntiMnl Bid,S0 P Prohbition,

Speech Soothing,Neutral

Figure 3. The spoken affective intent recognizer.

2.1.1. Training the System

Two female adults who frequently interact with Kismet as caregivers wererecorded. The speakers were asked to express all five affective intents (ap-proval, attentional bid, prohibition, comfort, and neutral) during the inter-action. Recordings were made using a wireless microphone, and the outputsignal was sent to the low-level speech processing system running on Linux.For each utterance, this phase produced a 16-bit single clannel, 8 kHz signal(in a . wav format) as well as its corresponding real-time pitch, percent period-icity, energy, and phoneme values. All recordings were performed in Kismet'susual environment to minimize variability of environment-specific noise. Sam-ples containing extremely loud noises (door slanms, etc.) were eliminated, andthe remaining data set were labeled according to the speakers' affective intentsduring the interaction. There were a total of 726 utterances in the final dataset - approximately 145 utterances per class.

2.1.2. Data Preprocessing

The pitch value of a frame was set to 0 if the corresponding percent period-icity was lower than a threshold value. This indicates that the frame is morelikely to correspond to unvoiced speech. Even after this procedure, observa-tion of the resulting pitch contours still indicated the presence of substantialnoise. Specifically, a significant number of errors were discovered in the highpitch value region (above 500 Hz). Therefore, additional preprocessing was per-formed on all pitch data. For each pitch contour, a histogram of ten regions wasconstructed. Using the heuristic that the pitch contour was relatively smooth,it was determined that if only a few pitch values were located in the high regionwhile the rest were much lower (and none resided in between), then the highvalues were likely to be noise. Note that this process did not eliminate highbut smooth pitch contour since pitch values would be distributed evenly across

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Figure 4. Fernald's prototypical prosodic contours found in the preprocesseddata set. Notice the similarity to those shown in figure 2.

nearby regions.

2.1.3. Classificat~ion Method

In all training phases each class of data was modeled using a Gaussian mix-ture model, updated with the EM algorithm and a Kurtosis-based approachfor dynamically deciding the appropriate number of kernels [13]. Due to thelimited set of training data, cross-validation in all classification processes wasp~erformed. Specifically, a subset of data was set aside to train a classifier usingthe remaining data. The classifier's performance was then tested on the held-out test set. This process was repeated 100 times per classifier. The mean andvariance of the percentage of correctly classified test data were calculated toestimate the classifier's performance.

Feature DescriptionF1 Pitch meanF2 Ptich VarianceF3 Maximum PitchF 4 Minimum PitchF5 Pitch RangeF6 Delta Pitch MeanF7 Absolute Delta Pitch MeanF8 Energy MeanF9 Energy VarianceFla Energy RangeF 1l Maximum Energy

F12 Minimum Energy

Table 1. Features extracted in the first-stage classifier. These features aremeasured over the non-zero values throughout the entire utterance. FeatureF6 measures the steepness of the slope of the pitch contour.

2.1.4. Feature Selection

As shown in figure 4, the preprocessed pitch contour in the labeled data resem-bles Fernald's prototypical prosodic contours for approval, attention, prohibi-tion, and comfort/soothing. A set of global pitch and energy related features(see table 1) were used to recognize these proposed patterns. All pitch featureswere measured using only non-zero pitch values. Using this feature set, a se-quential forward feature selection process was applied to construct an optimalclassifier. Each possible feature pair's classification performance was measuredaid sorted from highest to lowest. Successively, a feature pair from the sortedlist was added into the selected feature set to determine the best n features foran optimal classifier. Table 2 shows the results of the classifiers constructedusing the best eight feature pairs. Classification performance increases as morefeatures are added, reaches maximum (78.77 percent) with five features in theset, and levels off above 60 percent with six or more features. It was found thatglobal pitch and energy measures were useful in roughly separating the pro-posed patterns based on arousal (largely distinguished by energy measures) andvalence (largely distinguished by pitch measures). However, further processingwas required to distinguish each of the five classes distinctly.

Accordingly, the classifier consists of several mini-classifiers executing instages. In the beginning stages, the classifier uses global pitch and energy fea-tures to separate some of the classes into pairs (in this case, clusters of soothingalong with low-energy neutral, prohibition along with high-energy neutral, andattention along with approval were formed). These clustered classes were thenpassed to additional classification stages for further refinement. New featureshad to be considered to build these additional classifiers. Using prior informa-tion, a new set of features encoding the shape of the pitch contour was included,which proved useful in further separating the classes.

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Table 2. The performance (the percent correctly classified) is shown for the best

pair-wise set having up to eight features. The pair-wise performance was rankedfor the best seven pairs. As each successive feature was added, performancepeaks with five features (78.8%), but then drops off.

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Figure 5. Feature space of all five classes with respect to energy variance, F9 ,and pitch mean, F1 . There are three distinguishable clusters for prohibition,soothing and neutral, and approval and attention.

To select the best features for the initial classification stage, the sevenfeature pairs listed in table 2 were examined. All feature pairs worked betterin separating prohibition and soothing than other classes. The F1 -F 9 pairgenerates the highest overall performance and the least number of errors inclassifying prohibition. Several observations can be made from the featurespace of this classifier(see figure 5). The prohibition samples are clusteredin the low pitch mean and high energy variance region. The approval andattention classes form a cluster at the high pitch mean and high energy varianceregion. The soothing samples are clustered in the low pitch mean and lowenergy variance region. The neutral samples have low pitch mean and aredivided into two regions in terms of their energy variance values. The neutralsamples with high energy variance are clustered separately from the rest of theclasses (in between prohibition and soothing), while the ones with lower energyvariance are clustered within the soothing class. These findings are consistentwith the proposed prior knowledge. Approval, attention, and prohibition areassociated with high intensity while soothing exhibits much lower intensity.Neutral samples span from low to medium intensity, which makes sense becausethe neutral class includes a wide variety of utterances.

Based on this observation, the first classification stage uses energy-relatedfeatures to classify soothing and low-intensity neutral with from the otherhigher intensity classes (see, figure 6). In the second stage, if the utterancehad a low intensity level, another classifier decides whether it is soothing orneutral. If the utterance exhibited high intensity, the F1 - F 9 pair is used toclassify among prohibition, the approval-attention cluster, and high intensityneutral. An additional stage is required to classify between approval and at-tention if the utterance happened to fall within the approval-attention cluster.

Vaa

Figure 6. The classification stages of the nmulti-stage classifier.

2.1.5. Stage 1: Soothing -- Low-Intensity Neutral versus Everything Else

The first two columns in table 3 show the classification performance of the topfour feature pairs (sorted according to how well each pair classifies soothing and

Feature Pair Perf. Feature Perf.Pair I Mean (%o) Set Mean (%)F9, F1 1 93.0 F9F11 93.0Flo, F11 91.8 FfF9 oF1 F 93.6F2 , F) 91.7 F2F9 F1 oF _ _ 93.3F7, F9 91.3 F2F7 F9 F1 oF11 91.6

Table 3. Classification results in stage 1.

low-intensity neutral against other classes). The last two columns illustratethe classification results as each pair is added sequentially into the feature set.The final classifier was constructed using the best feature set (energy variance,maximum energy, and energy range), with an average performance of 93.6percent.

2.1.6. Stage 2A: Soothing versus Low-Intensity Neutral

Since the global and energy features were not sufficient in separating these twoclasses, new features were introduced into the classifier. Fernald's prototypicalprosodic patterns for soothing suggest looking for a smooth pitch contour ex-hibiting a frequency down-sweep. Visual observations of the neutral samples inthe data set indicated that neutral speech generated flatter and choppier pitchcontours as well as less-modulated energy contours. Based on these postula-tions, a classifier using five features (number of pitch segments, average lengthof pitch segments, minimum length of pitch segments, slope of pitch contour,and energy range) was constructed. The slope of the pitch contour indicatedwhether the contour contained a down-sweep segment. It was calculated byperforming a linear fit on the contour segment starting at the maximum peak.This classifier's average performance is 80.3 percent.

2.1.7. Stage 2B: Approval-Attention versus Prohibition versus High-IntensityNeutral

A combination of pitch mean and energy variance works well in this stage. Theresulting classifier's average performance is 90.0 percent. Based on Fernald'sprototypical prosodic patterns, it was speculated that pitch variance wouldbe a useful feature for distinguishing between prohibition and the approval-attention cluster. Adding pitch variance into the feature set increased theclassifier's average performance to 92.1 percent.

2.1.8. Stage 3: Approval versus Attention

Since the approval class and attention class span the same region in the globalpitch versus energy feature space, prior knowledge (provided by Fernald's pro-totypical prosodic contours) gave the basis to introduce a new feature. Asmentioned above, approvals are characterized by an exaggerated rise-fall pitchcontour. This particular pitch pattern proved useful in distinguishing betweenthe two classes. First, a three-degree polynomial fit was performed on eachpitch segment. Each segment's slope sequence was analyzed for a positive slopefollowed by a negative slope with magnitudes higher than a threshold value.

Class Test Stue Cassiaton Res % CorsstfA.k riwal Alterlm Proh1itlbf oSotbhb Net Claselfied

FIrst PasS A.&rp al 40 27L 9 0 0 4 67.5Affordion 40 11 29 a a a 72.5

Proh0tbon 40 0 0 39 0 1 97..soothing 40 ¶ 0 0 30 9 i5Neuta 4.0 0 0 4 5 31 775

All 20_ 7-Second 64frn¶al 84 04 1i 0 5 MIS . . 1Pass Atesrflttn 17 21 55 0 0 I 1433

M proh40n 1 0 1 78 0 T 3 71 5NMI, 62 3 1 0 53 52 03 IL

All 371 _1_94

Table 4. Overall classification performance.

The longest pitch segment that contributed to the rise-fall pattern (which was0 if the pattern was non-existent) was recorded. This feature, together withpitch variance, was used in the final classifier and generated an average perfor-mance of 70.5 percent. Approval and attention are the most difficult to classifybecause both classes exhibit high pitch and intensity. Although the shape ofthe pitch contour helped to distinguish between the two classes, it is very diffi-cult to achieve high classification performance without looking at the linguisticcontent of the utterance.

2.1.9. Overall Performance

The final classifier was evaluated using a new test set generated by the samefemale speakers, containing 371 utterances. Because each mini-classifier wastrained using different portions of the original database (for the single-stageclassifier), a new data set was gathered to ensure that no mini-classifier stagewas tested on data used to train it. Table 4 shows the resulting classificationperformance and compares it to an instance of the cross-validation results of thebest single-stage five-way classifier obtained using the five features describedin section 2.1.4. Both classifiers perform very well on prohibition utterances.The multi-stage classifier performs significantly better in classifying the diffi-cult classes, i.e., approval versus attention and soothing versus neutral. Thisverifies that the features encoding the shape of the pitch contours (derivedfrom prior knowledge provided by Fernald's prototypical prosodic patterns)were very useful.

It is important to note that both classifiers produce acceptable failuremodes (i.e., strongly valenced intents are incorrectly classified as neutrally va-lenced intents and not as oppositely valenced ones). All classes are sometimesincorrectly classified as neutral. Approval and attentional bids are generallyclassified as one or the other. Approval utterances are occasionally confusedfor soothing and vice versa. Only one prohibition utterance was incorrectlyclassified as an attentional bid, which is acceptable. The single-stage classifiermade one unacceptable error of confusing a neutral utterance as a prohibition.In the multi-stage classifier, some neutral utterances are classified as approval,attention, and soothing. This makes sense because the neutral class covers awide variety of utterances.

3. Integration with the Emotion SystemThe output of the recognizer is integrated into the rest of Kismet's syntheticnervous system as shown in figure 7. The entry point for the classifier's re-sult is at the auditory perceptual system. Here, it is fed into an associatedreleaser process. In general, there are many different kinds of releasers definedfor Kismet, each combining different contributions from a variety of perceptualand motivational systems. Here, I only discuss those releasers related to theinput from the vocal classifier. The output of each vocal affect releaser repre-sents its perceptual contribution to the rest of the SNS. Each releaser combinesthe incoming recognizer signal with contextual information (such as the cur-rent "emotional" state) and computes its level of activation according to themagnitude of its inputs. If its activation passes above threshold, it passes itsoutput on to the emotion system.

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is inspired by the Somatic Marker Hypothesis of [3] where incoming perceptual

Category Arousal Valence Stance TrpicalI I I Expression]

Approval medium high approach pleasedhigh positive

Prohibition low high withdraw sadnegative

Comfort low medium neutral contentpositive

Attention high neutral aproach interest

Neutral neutral neutral neutral calm

Table 5. Table mapping [A, V, S] to classified affective intents. Praise biasesthe robot to be "happy," prohibition biases it to be "sad," comfort evokes a"content, relaxed" state, and attention is "arousing".

information is "tagged" with affective information. Table 5 summarizes howeach vocal affect releaser is somatically tagged.

There are three classes of tags that the affective assessment phase usesto affectively characterize its perceptual, motivational, and behavioral input.Each tag has an associated intensity that scales its contribution to the overallaffective state. The arousal tag, A, specifies how arousing this percept is tothe emotional system. Positive values correspond to a high arousal stimuluswhereas negative values correspond to a low arousal stimulus. The valence tag,V, specifies how good or bad this percept is to the emotional system. Positivevalues correspond to a pleasant stimulus whereas negative values correspondto an unpleasant stimulus. The stance tag, S, specifies how approachablethe percept is. Positive values correspond to advance whereas negative valuescorrespond to retreat. Because there are potentially many different kinds offactors that modulate the robot's affective state (e.g., behaviors, motivations,perceptions), this tagging process converts the myriad of factors into a commoncurrency that can be combined to determine the net affective state.

For Kismet, the [A, V, S] trio is the currency the emotion system usesto determine which emotional response should be active. This occurs in twophases: First, all somatically marked inputs are passed to the emotion elici-tor stage. Each emotion process has an elicitor associated with it that filterseach of the incoming [A, V, S1 contributions. Only those contributions thatsatisfy the [A, V, S] criteria for that emotion process are allowed to contributeto its activation. This filtering is done independently for each class of affectivetag. For instance, a valence contribution with a large negative value will notonly contribute to the sorrow emotion process, but to the fear, anger, anddistress processes as well. Given all these factors, each elicitor computes itsnet [A, V, S] contribution and activation level, and passes them to the associ-ated emotion process within the emotion arbitration subsystem. In the secondstage, the emotion processes within the emotion arbitration subsystem compete

for activation based on their activation level. There is an emotion process foreach of Ekman's six basic emotions [4]. Ekman posits that these six emotionsare innate in humans, and all others are acquired through experience. The"Ekman six" encompass joy, anger, disgust, fear, sorrow, and surprise.

If the activation level of the winning emotion process passes above thresh-old, it is allowed to influence the behavior system and the motor expressionsystem. There are actually two threshold levels, one for expression and one forbehavior. The expression threshold is lower than the behavior threshold; thisallows the facial expression to lead the behavioral response. This enhances thereadability and interpretation of the robot's behavior for the human observer.For instance, given that the caregiver makes an attentional bid, the robot'sface will first exhibit an aroused and interested expression, then the orientingresponse ensues. By staging the response in this manner, the caregiver gets im-mediate expressive feedback that the robot understood her intent. For Kismet,this feedback can come in a combination of facial expression, tone of voice, orposture. The robot's facial expression also sets up the human's expectation ofwhat behavior will soon follow. As a result, the human observing the robotcan see its behavior, in addition to having an understanding of why the robotis behaving in that manner. As I have argued previously, readability is animportant issue for social interaction with humans.

3.1. Affective Intent Experiment

Communicative efficacy has been tested with people very familiar with therobot as well as with naive subjects in multiple languages (French, German,English, Russian, and Indonesian). Female subjects ranging in age from 22 to54 were asked to praise, scold, soothe, and to get the robot's attention. Theywere also asked to signal when they felt the robot "understood" them. Allexchanges were video recorded for later analysis.

Figure 8 illustrates a sample event sequences that occurred during ex-periment sessions of a naive speaker. Each row represents a trial in whichthe subject attempts to communicate an affective intent to Kismet. For eachtrial, we recorded the number of utterances spoken, Kismet's cues, subject'sresponses and comments, as well as changes in prosody, if any.

3.2. Discussion

Recorded events show that subjects in the study made ready use of Kismet'sexpressive feedback to assess when the robot "understood" them. The robot'sexpressive repertoire is quite rich, including both facial expressions and shifts inbody posture. The subjects varied in their sensitivity to the robot's expressivefeedback, but all used facial expression, body posture, or a combination of bothto determine when the utterance had been properly communicated to the robot.All subjects would reiterate their vocalizations with variations about a themeuntil they observed the appropriate change in facial expression. If the wrongfacial expression appeared, they often used strongly exaggerated prosody to"correct" the "misunderstanding". In trial 20-22 of subject S3's experimentsession, she giggled when kismet smiled despite her scolding, commented thatvolume would help, and thus spoke louder in the next trial. In general, the

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instance, several of the subjects reported experiencing a very strong emotionalresponse immediately after "successfully" prohibiting the robot. In these cases,the robot's saddened face and body posture was enough to arouse a strong senseof empathy. The subject would often immediately stop and look to the experi-menter with an anguished expression on her face, claiming to feel "terrible" or"guilty". In this emotional feedback cycle, the robot's own affective responseto the subject's vocalizations evoked a strong and similar emotional responsein the subject as well. This empathic response can be considered to be a formof entrainment.

Another interesting social dynamic we observed involved affective mirror-ing between robot and human. For instance, for another female subject (S2),she issued a medium strength prohibition to the robot, which caused it to dipits head. She responded by lowering her own head and reiterating the pro-hibition, this time a bit more foreboding. This caused the robot to dip itshead even further and look more dejected. The cycle continues to increase inintensity until it bottoms out with both subject and robot having dramaticbody postures and facial expressions that mirror the other. We see a simi-lar pattern for subject S3 while issuing attentional bids. During trials 14 16the subject mirrors the same alert posture as the robot. This technique wasoften employed to modulate the degree to which the strength of the messagewas "communicated" to the robot. This dynamic between robot and human isfurther evidence of entrainment.

4. Proto-DialogAchievement of adult-level conversation with a robot is a long term researchgoal. This involves overcoming challenges both with respect to the content ofthe exchange as well as to the delivery. The dynamics of turn-taking in adultconversation are flexible and robust. Well studied by discourse theorists, hu-mans employ a variety of para-hnguistic social cues, called envelope displays, toregulate the exchange of speaking turns [2]. Given that a robotic implementa-tion is limited by perceptual, motor, and computational resources, could suchcues be useful to regulate the turn-taking of humans and robots?

Kismet's turn-taking skills are supplemented with envelope displays asposited by discourse theorists. These paralinguistic social cues (such as raisingof the brows at the end of a turn, or averting gaze at the start of a turn)are particularly important for Kismet because processing limitations force therobot to take-turns at a slower rate than is typical for human adults. However,humans seem to intuitively read Kismet's cues and use them to regulate therate of exchange at a pace where both partners perform well.

4.1. Envelope Display ExperimentTo investigate Kismet's turn-taking performance during proto-dialogs, we in-vited three naive subjects to interact with Kismet. Subjects ranged in age from12 to 28 years old. Both male and female subjects participated. In each case,each subject was simply asked to carry a "play" conversation with the robot.The exchanges were video recorded for later analysis. The subjects were told

that the robot did not speak or understand English, but would babble to themsomething like an infant.

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W 0 10:40 8:2o-10:40 ft.

Figure 9. The left table shows data illustrating evidence for entrainment ofhuman to robot. The right table summarizes Kismet's turn taking performanceduring proto-dialog with three naive subjects. Significant disturbances aresmall clusters of pauses and interruptions between Kismet and the subjectuntil turn-taking become coordinated again

Often the subjects begin the session by speaking longer phrases and onlyusing the robot's vocal behavior to gauge their speaking turn. They also expectthe robot to respond immediately after they finish talking. Within the firstcouple of exchanges, they may notice that the robot interrupts them, and theybegin to adapt to Kismet's rate. They start to use shorter phrases, wait longerfor the robot to respond, and more carefully watch the robot's turn taking cues.The robot prompts the other for their turn by craning its neck forward, raisingits brows, and looking at the person's face when it's ready for them to speak.It will hold this posture for a few seconds until the person responds. Often,within a second of this display, the subject does so. The robot then leans backto a neutral posture, assumes a neutral expression, and tends to shift its gazeaway from the person. This cue indicates that the robot is about to speak. Therobot typically issues one utterance, but it may issue several. Nonetheless, asthe exchange proceeds, the subjects tends to wait until prompted.

Before the subjects adapt their behavior to the robot's capabilities, therobot is more likely to interrupt them. There tend to be more frequent delaysin the flow of "conversation" where the human prompts the robot again for a

response. Often these "hiccups" in the flow appear in short clusters of mutualinterruptions and pauses (often over 2 to 4 speaking turns) before the turns be-come coordinated and the flow smoothes out. However, by analyzing the videoof these human-robot "conversations", there is evidence that people entrainto the robot (see the table to the left in figure 9). These "hiccups" becomeless frequent. The human and robot are able to carry on longer sequences ofclean turn transitions. At this point the rate of vocal exchange is well matchedto the robot's perceptual limitations. The vocal exchange is reasonably fluid.The table to the right in figure 9 shows that the robot is engaged in a smoothproto-dialog with the human partner the majority of the time (about 82%).

5. Conclusions

Experimental data from two distinct studies suggests that people do use theexpressive cues of an anthropomorphic robot to improve the quality of inter-action between them. Whether the subjects were communicating an affectiveintent to the robot, or engaging it in a play dialog, evidence for using therobot's expressive cues to regulate the interaction and to entrain to the robotwere observed. This has the effect of improving the quality of the interactionas a whole. In the case of communicating affective intent, people used therobot's expressive displays to ensure the correct intent was understood to theappropriate intensity. In the case of proto-conversation, the subjects quicklyused the robot's cues to regulate when they should exchange turns. As theresult, the interaction becomes smoother over time with fewer interruptions orawkward pauses. These results signify that for social interactions with humans,expressive robotic faces are a benefit to both the robot and to the human whointeracts with it.

6. Acknowledgements

Support for this research was provided by ONR and DARPA under MURIN00014-95-1-0600, by DARPA under contract DABT 63-99-1-0012, and byNTT.

References[1] B. Reeves and C. Nass 1996, The Media Equation. CSLI Publications. Stanford,

CA.12] J. Cassell 2000, "Nudge Nudge Wink Wink: Elements of face-to-face conversation

for embodied conversational agents". In: J. Cassell, J. Sullivan, S. Prevost & E.Churchill (eds.) Embodied Conversational Agents, MIT Press, Cambridge, MA.

131 A. Damasio 1994, Descartes Error: Emotion, Reason, and the Htuman Brain, G.P.Putnam's Sonds, New York.

[4] P. Ekman 1992, "Are there basic emotions?", Psychological Review 99(3), pp550-553.

[5] F. Hara 1998, "Personality characterization of animate face robot through inter-active communication with human". In: Proceedings of IARP98. Tsukuba, Japan.pp IV-L.

[6] H. Takanobu, A. Takanishi, S. Hirano, I. Kato, K. Sato, and T. Umctsu 1998,

"Development of humanoid robot heads for natural human-robot communication".In: Proceedings of HUR098. Tokyo, Japan. pp 21 28.

[71 Y. Matsusaka and T. Kobayashi 1999, "Human interface of humanoid robot re-alizing group conununication in real space". In: Proceedings of HUR099. Tokyo,Japan. pp. 188-193.

[8] P. Menzel and F. D'Alusio 2000, Robosapiens. MIT Press.[9] A. Fernald 1985, "Four-month-old Infants Prefer to Listen to Motherese". In In-

fant Behavior and Development, vol 8. pp 181-195.[101 Papousek, M., Papousek, HI., Bornstein, M.H. 1985, The Naturalistic Vocal En-

vironment of Young Infants: On the Significance of Homogeneity and Variabilityin Parental Speech. In: Field,T., Fox, N. (eds.) Social Perception in Infants. Ablex,Norwood NJ. 269-297.

[11] Ferrier, L.J. 1987, Intonation in Discourse: Talk Between 12-month-olds andTheir Mothers. In: K. Nelson(Ed.) Children's language, vol.5. Erlbaum, HillsdaleNJ. 35-60.

[12] Stern, D.N., Spieker, S., MacKain, K. 1982, Intonation Contours as Signals inMaternal Speech to Prelinguistic Infants. Developmental Psychology, 18: 727-735.

[131 Vlassis, N., Likas, A. 1999, A Kurtosis-Based Dynamic Approach to GaussianMixture Modeling. In: IEEE Trans. on Systems, Man, and Cybernetics. Part A:Systems and Humans, Vol. 29: No.4.

[141 C. Breazeal & L. Aryananda 2000, "Recognition of Affective CommunicativeIntent in Robot-Directed Speech". In: Proceedings of the 1st International Confer-ence on Humanoid Robots (Humanoids 2000). Cambridge, MA.

[15] C. Breazeal 2000, "Believability and Readability of Robot Faces". In: Proceedingsof the 8th International Symposium on Intelligent Robotic Systems (SIRS 2000).Reading, UK, 247-256.