2005 IEEE International Workshop on Robots and Human Interactive Communication
Biologically Inspired Models and Hardware for Emotive Facial Expressions* Do Hyoung Kim, Hui Sung Lee, Myung Jin Chung Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Deajeon, Republic of Korea, 305-701 E-mail: {bborog, eins}@cheonji.kaist.ac.kr,
[email protected] Abstract –Socially intelligent robots are no longer an interesting topic of science fiction. In the robotics community, there is a growing interest in building personal robots, or in buidling robots that share the same workspace with humans. Natural interaction between humans and robots is therefore essential. To interact socially with humans, a robot must be able to do more than simply gather information about its surroundings; it must be able to express its states or emotions so that humans believe it has beliefs, desires, and intentions of its own. In the last decade, many researchers have focused on generating emotive facial expressions, which are known as the best cues for conveying the robot’s state, intentions, feelings and emotions. This paper gives a brief overview of current robotic systems with emotive facial expressions and introduces the basic models and hardware of two different types of facial robotic systems. Index Terms – robot head, emotive facial expression, emotional space, human-robot interaction.
I. INTRODUCTION The human face has long been considered a representation of humans. For instance, according to observations of cognitive psychology, people unconsciously and frequently recognize and identify others from their faces. Many researchers have also emphasized the importance of the human face. For example, Cicero said “Everything is in a face”; Darwin said “The human face is the most complex and versatile of all species”; and Ekman said “The face makes one’s behavior more predictable and understandable to others and improves communication.” Nobody doubts that the human face is a rich and versatile instrument that serves many different functions and conveys the human’s motivational state. Facial gestures, for instance, can communicate information on their own. Moreover, the face serves several biological functions; for instance, we generally close our eyes to protect ourselves from a threatening
*
stimulus, and we close them for longer periods to sleep [1,5,6]. The ideal of the robotic system is for people to interact, play and teach the robot as naturally as they would teach an infant or a very young child. Such interactions provide many different kinds of scaffolding that the robot can potentially use to foster its own learning. As a prerequisite for human-robot interactions, people need to ascribe precocious social intelligence to the robot. However, before people treat the robot as a socially aware being, the robotic system needs to convey subjective internal states such as intentions, beliefs, desires, and feelings. In designing the robot, we therefore need to exploit the natural human tendencies of responding socially to certain types of behavior [1,2]. As a result, facial expressions are critical in a robotic system because they encourage people to treat the robot as an object that can convey internal states, have social intelligence and exploit human-like tendencies. The remainder of this paper is arranged as follows: In Section II, we summarize current works on facial robots with emotive facial expressions. In Section III, we introduce two types of facial robots that were developed in our laboratory. In Section IV, we address the biologically inspired approaches to the development of our facial robots. In Section V, we present the results for our robot’s generation of emotive facial expressions and discuss how the results confirm the validity of the proposed system. Finally, we offer some conclusions and suggestions for further works. II. CURRENT RELATED WORKS There are several projects that focus on the development of robotic faces. Robotic faces are currently classified in terms of their appearance; that is, whether they appear real, mechanical or mascot-like. In brief, this classification is based on the existence and flexibility of the robot’s skin. The real type of robot has flexible skin, the mechanical type has no skin, and the
This research was supported as a Brain Neuro-Informatics Research Program, which is sponsored by the Ministry of Commerce, Industry and Energy in the Republic of Korea.
0-7803-9275-2/05/$20.00 ©2005 IEEE.
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mascot type has hard skin. Note that although there are many other valuable robotic faces in the world, we could not discuss all robots in this paper because space is limited. In the real type of robot, there are two representative robotic faces: namely, Saya and Leonardo. Researchers at the Science University of Tokyo developed Saya, which is a human-like robotic face. The robotic face, which typically resembles a Japanese woman, has hair, teeth, silicone skin, and a large number of control points. Each control point is mapped to a facial action unit (AU) of a human face. The facial AUs characterize how each facial muscle or combination of facial muscles adjusts the skin and facial features to produce human expressions and facial movements [6,7]. With the aid of a camera mounted in the left eyeball, the robotic face can recognize and produce a predefined set of emotive facial expressions [9,10]. In collaboration with the Stan Winston studio, the researchers of Breazeal’s laboratory at the Massachusetts Institute of Technology developed the quite realistic robot Leonardo. The studio's artistry and expertise of creating life-like animalistic characters was used to enhance socially intelligent robots. Capable of near-human facial expressions, Leonardo has 61 degrees of freedom (DOFs), 32 of which are in the face alone. It also has 61 motors and a small 16 channel motion control module in an extremely small volume. Moreover, it stands at about 2.5 feet tall, and is one of the most complex and expressive robots ever built [3]. With respect to the mechanical looking robot, we must consider the following well-developed robotic faces. In 2004, researchers at Takanishi’s laboratory developed a robot called the Waseda Eye No.4 or WE4, which can communicate naturally with humans by expressing human-like emotions. WE-4 has 59 DOFs, 26 of which are in the face. It also has many sensors which serve as sensory organs that can detect extrinsic stimuli such as visual, auditory, cutaneous and
olfactory stimuli. WE-4 can also make facial expressions by using its eyebrows, lips, jaw and facial color. The eyebrows consist of flexible sponges, and each eyebrow has four DOFs. For the robot’s lips, spindle-shaped springs are used. The lips change their shape by pulling from four directions, and the robot’s jaw, which has one DOF, opens and closes the lips. In addition, red and blue electroluminescence sheets are applied to the cheeks, enabling the robot to express red and pale facial colors [12,13,15]. Before developing Leonardo, Breazeal’s research group at the Massachusetts Institute of Technology developed an expressive anthropomorphic robot called Kismet, which engages people in natural and expressive face-to-face interaction. Kismet perceives a variety of natural social cues from visual and auditory channels, and it delivers social signals to the human caregiver through gaze direction, facial expression, body posture, and vocal babbling. With 15 DOFs, the face of the robot displays a wide assortment of facial expressions which, among other communicative purposes, reflect its emotional state. Kismet’s ears have 2 DOFs each; as a result, Kismet can perk its ears in an interested fashion or fold them back in a manner reminiscent of an angry animal. Kismet can also lower each eyebrow, furrow them in frustration, elevate them for surprise, or slant the inner corner of the brow upwards for sadness. Each eyelid can be opened and closed independently, enabling Kismet to wink or blink its eyes. Kismet also has four lip actuators, one at each corner of the mouth; the lips can therefore be curled upwards for a smile or downwards for a frown. Finally, Kismet’s jaw has a single DOF [1,4]. The mascot-like robot is represented by a facial robot called Pearl, which was developed at Carnegie Mellon University. Focused on robotic technology for the elderly, the goal of this project is to develop robots that can provide a mobile and personal service for elderly people who suffer from chronic disorders. The robot provides a research platform of social interaction
Table 1. An overview of facial robots which are classified by the appearance into three groups: real-apparent, mechanical-apparent and mascotapparent type. Appearance
Real type
Mechanical type
Mascot type
Name
Saya
Leonardo
WE-4
Kismet
Pearl
iCat
Birthplace
The Science University of Tokyo
MIT & Stan Winston Studio
Waseda University
MIT
CMU
Philips
DOFs in the face
18
32
26
15
4 or 5, LED
13, LED
3 axes, 9 bases (arousal, stance, valence)
/
/
Modified facial action unit, affective dimension
/
/
Emotion Coordinate
/
/
3 axes , 7 regions (pleasantness, activation, certainty)
Mapping principle (emotion - facial expression)
Facial action unit
/
Modified facial action unit
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Our control scheme is based on a distributed control method because a commercial RC servo generally has its own position controller. We can therefore easily control the position of the RC servo by feeding to the RC servo a digital signal that has the proper pulse width to indicate the desired position, and by then letting the internal controller operate until the current position of the RC servo reaches the desired position. Two objectives of our development in the control sense are to reduce the jerking motion and to determine the trajectories of the 12 actuators in real time. We therefore used using a high-speed, bell-shaped velocity profile of a position trajectory generator to reduce the magnitude of any jerking motion and to control the 12 actuators in real time. Whenever the target position of an actuator changes drastically, the actuator frequently experiences a severe jerking motion. The jerking motion causes electric noise in the system’s power source, worsens the system’s controller, and breaks down the mechanical components. Our control method for reducing the jerking motion is basically equal to a bell-shaped velocity profile. In developing the bellshaped velocity profile, we used a cosine function because a sinusoidal function is infinitely differentiable. As a result, our control algorithm ensures that we achieve the computation time necessary to control the 12 actuators in real time; for further details, see [17] and [18].
a) b) Fig. 1. Two different-type facial robots. a) A mechanical-apparent facial robot called Ulkni has 12 DOFs (4 DOFs to control the gaze direction, 2 DOFs for the neck and 6 DOFs for facial components, eyelids and lips), b) A mascot-apparent facial robot called Doldori has 10 DOFs (2 DOFs for the eyebrows, 4 DOFs for the eyelids and 4 DOFs for lips).
by using a facial robot. However, because this project is aimed at assisting elderly people, the functions of the robot are focused more on mobility and auditory emotional expressions than on emotive facial expressions [16]. Another mascot-like robot called ICat was developed by Philips. This robot is an experimentation platform for human-robot interaction research. ICat can generate many different facial expressions such as happiness, surprise, anger, and sad needed to make the human-robot interactions social. Unfortunately, there is no deep research of emotion models, relation between emotion and facial expressions, and emotional space [22].
A. Facial robot with the mechanical appearance Our facial robot with the mechanical appearance, Ulkni†, has 4 DOFs for controlling its gaze direction, 2 DOFs for its neck, and 6 DOFs for other expressive facial components, namely the eyelids and lips. According to [1], [2], and [11], the positions of the system’s eyes and neck are important for expressive posture and for gazing toward an object of attention. We designed our system architecture to meet the challenges of real-time visual-signal processing (about 30 Hz) and real-time position control of all actuators (1 KHz) with minimal latencies. Ulkni’s vision system is built around a 2.6 GHz commercial PC. In addition, its motivational and behavioural systems run on a fixed point digital signal processor, TMS320LF2407A, and 12 internal position controllers of commercial radio control (RC) servos. The cameras in the eyes are connected to the PC via the IEEE 1394a interface. Furthermore, an IEEE 1394a serial bus sends all the position commands of the actuators.
B. Facial robot with the mascot appearance In 2004, we developed another facial robot, Doldori ‡ , which looks like a mascot or animated character. First, we determined how many DOFs were needed for a facial robot to express emotions effectively. Because Doldori is a mascot type and has hard skin instead of flexible skin, we based the number of DOFs on the AU [6,7]. According to our research on basic emotional expressions, a robot needs 18 DOFs to display six basic emotional expressions: 2 DOFs for the inner eyebrows, 2 DOFs for the outer eyebrows, 4 DOFs for the eyelids, 1 DOF for the jaw, 1 DOF for each of the upper and lower lips, 2 DOFs for each corner of the lips, 1 DOF for the wrinkle on the nose, and 2 DOFs for the cheeks (see Table 2 and Table 3). However, the external form that we developed had no jaw, cheeks, or lower eyelids. Consequently, to enable Doldori to express emotions effectively under these constraints, we assumed the following: 1) Given that Doldori’s external form is not flexible but solid, it can’t show a wrinkle or move its cheeks. Furthermore, although it has no jaw, the jaw’s motion can be replicated by the motion of the
†
‡
III. TWO TYPES OF FACIAL ROBOTS
Ulkni means a child having a big head in Korean.
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Doldori means a smart person in Korean.
biological observations to make artificial models of human visual action and facial expressions. A. Characteristics of human visual movements When analyzing the human visual system, we first considered what features of human vision were important for the development of a mimic system. According to [1], [2], and [11], the positions of the human’s eyes and neck are important for expressive posture. Therefore, to model a human head (or face), we considered the head and eye movements of humans. Figure 2 shows the range of human’s head-eye motion, while Fig. 2(a) shows the limitation of neck movement in a horizontal plane. According to anthropometric observations, human being can
a)
Table 2. Clues of facial components to express 6 basic emotions [7]. Emotion
Happiness
b) Fig. 2. Action range of human head-eye motion; a) the range of neck movement, b) the optimal range of eye movement and the field of human view.
Sadness
lower lip. For these reasons, we can eliminate 4 DOFs. 2) Given that the motion of a human’s inner and outer eyebrow is upward and downward. We can implement this motion by rotating the eyebrow; it is oriented at a centre point. Thus, we can eliminate 2 DOFs. 3) Given that lip stretching is needed for enhanced emotional expressions but not for basic emotional expressions, the horizontal motion of Doldori’s lips is not essential. Thus, we can eliminate 2 DOFs. In conclusion, we reduced the number of DOFs from 18 to 10. Doldori therefore has 10 DOFs to show emotional expressions. We also used direct current (DC) motors to actuate the essential facial components instead of RC servos. The DC motors are controlled in real time with the aid of a TMS320F2812 digital signal processor that uses ten independent pulse width modulation generators. In addition, we used one electrically programmable logic device to capture the position of the motors. The implemented controller was small enough to be inserted into Doldori’s skin, and Doldori’s emotions were carried by the serial communication from the PC.
Surprise
Anger
Disgust
Fear
IV. BIOLOGICALLY INSPIRED MODELS The development of our robots is based on biological models of humans. We used the following
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Clues - Corners of lips are drawn back and up. - The mouth may or may not be parted, with teeth exposed or not. - A wrinkle runs down from the nose to the outer edge beyond the lip corners. - The cheeks are raised. - The lower eyelid shows wrinkles below it. - The inner corners of the eyebrows are drawn up. - The skin below the eyebrow is triangulated, with the inner corner up. - The upper eyelid inner corner is raised. - The corners of the lips are down or the lip is trembling. - The brows are raised, so that they are curved and high. - The skin below the brow is stretched. - Horizontal wrinkles go across the forehead. - The eyelids are opened - The jaw drops open so that the lips and teeth are parted, but there is no tension or stretching of the mouth - Vertical lines appear between the brows - The lower lid is tensed and may or may not be raised. - The upper lid is tense and may or may not be lowered by the action of the brow. - The eyes have a hard stare and may have a bulging appearance. - The lips are pressed firmly together, with the corners straight or down. - The upper lip is raised. - The lower lip is also raised and pushed up to the upper lip. - The nose is wrinkled. - The cheeks are raised. - Lines show below the lower lid, and the lid is pushed up but not tense. - The brow is lowered, lowering the upper lid. - The brows are raised and drawn together - The wrinkles in the forehead are in the center, not across the entire forehead - The upper eyelid is raised, exposing sclera, and the lower eyelid is tensed and drawn up. - The mouth is open and the lips are either tensed slightly and drawn back or stretched and drawn back.
B. Characteristics of human facial expression As mentioned in the introduction, emotions are related to facial expressions. Many cognitive psychologists have tried to explicate the relationship between emotions and facial expressions. Ekman, for instance, made a facial AU based on muscles of the human face (see Fig. 3). He also defined the basic emotions as happiness, sadness, anger, disgust, fear and surprise. The facial action coding system suggested by Ekman and Friensen shows the relationship between facial AUs and the basic emotions; that is, it shows which combination of facial AUs corresponds to which of the six basic emotions (see Table 3). Ekman and Friensen also summarized clues of facial components to express the six basic emotions [7]. In 1990, Waters and Terzopoulos proposed a computer simulation method of manipulating a fivelayered mass-spring system that combines the dynamic characteristics of human skin with artificial muscle actuators [21]. This approach offers guidelines for achieving the dynamic characteristics of facial components.
Fig. 3. Comparison between facial muscles and facial AUs. In this figure, there are 21 facial AUs corresponding to each facial muscle [6].
normally turn their heads 45° to the left or right without any tension or inconvenience. In addition, they can turn their heads up to a maximum of 55° to the left or right. Figure 2(a) also shows that humans can comfortably move their necks 30° up and down. The limitation of neck’s rotation in the vertical plan is 40° up and 50° down. Figure 2(b) shows the field of human view when the head and the eyes are fixed. In the field of view for either the left or right eye, objects are unclear and scattered. However, objects that are watched by two eyes simultaneously seem clear and distinctive [20]. The common field of views for two eyes is called the field of a compound eye view. In Fig. 2b, the field of a compound eye view ranges from 62° to the left to 62° to the right. Figure 2(b) also shows that the optimal eye movement is from 15° to the left to 15° to the right. The limitation of eye movement in the vertical plane is 25° up to 30° down [20]. We also considered the dynamic characteristics of the head and eye motion of humans. The maximum velocity of head motion is 100° to 200° per second. The maximum velocity of eye motion is approximately 600° to 1000° per second.
V. EMOTIVE FACIAL EXPRESSIONS OF OUR ROBOTS To enable our robots to make artificial facial expressions, we let them make various movements such as wrinkles. However, the robots can’t do this without actuators to move the desired facial components. As a result, we adapted several clues of each facial expression for our robots. We chose what the robots could express directly or indirectly and applied the selected clues to the robots. The movements of facial components of two developed robots, Ulkni and Doldori, are shown in Fig. 4. In Ulkni, the rotational movement and blinking of the eyelids are used instead of eyebrow’s movements. Ulkni’s eyelids can droop and it can also squint and close its eyes. In addition, Ulkni can smile thanks to the curvature of its lips. In Doldori, inner and outer
Table 3. The relationship between emotion and facial AUs [6]. Emotion
Ekman & Friensen (2002)
Pantic & Rothkrantz (1999)
Friensen & Ekman (1984)
Happiness
12/13, 6+11+12/13
6, 12+16+(25/26), 12+(25/26), 12
12, 6+12, 7+12
Sadness
1+4, 1+4+15/17
1, 1+4, 1+7, 1+4+7, 15, 15+(25/26), 17
1, 1+4
Anger
4, 4+7+17+23
2, 4, 7, 23+17, 23+26, 23+17+26, 24, 24+17, 24+26, 24+17+26, 10+16+(25/26), 16+(25/26)
4+5
Disgust
9, 4+6+9+10+17+22
10+17+(25/26), 9+17+(25/26), 10, 10+17, 10+(25/26), 9, 9+17, 9+(25/26)
9, 10
Fear
1+5+25/26
1+5, 1+4+5, 1+4+5+7, 1+5+7, 5+7, 20, 20+(25/26)
1+2+4, 20
Surprise
1+2, 1+2+5
1+2, 5, 27, 26
1+2+5(low), 1+2+26, 1+2+5(low)+26
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implemented with downward motion of lower lip. Table 4 summarizes the relationship between Ekman’s AUs and facial component’s movements of the two developed robots. As we can expect, we can observe that Doldori has richer facial expression than Ulkni especially in the lower face. Because Ulkni doesn’t have eyebrows and lip corners contrary to Doldori, Ulkni can’t express the emotion produced by using eyebrows and lip corners. We enabled our robots to artificially produce emotive facial expressions. Figure 5 shows six facial expressions of the two types of facial robots. In Ulkni, the two facial expressions for anger and disgust are identical because of the limited DOFs of the lips. In addition, some of Ulkni’s facial expressions seem unnatural because Ulkni has only 6 DOFs for facial expression. In Doldori, the number of DOFs for facial expression is 10. As a result, Doldori’s facial expressions are clearer and more distinctive than those of Ulkni.
c d
e f
a)
ཿ ྀ
ཱྀ
VI. CONCLUSIONS AND FURTHER WORKS
ྂ ྄
ྃ
b) Fig. 4. Moving facial components of Ulkni and Doldori respectively. a) In Ulkni, rotational movements of the eyelids, c, display the emotion instead of eyebrows. Ulkni’s eyelids can droop and it can also squint and close its eyes, d. Ulkni can smile thanks to the curvature of its lips, e and f. b) Inner brow raiser and outer brow raiser can be displayed with rotating brow of Doldori, ཿ. Brow lower can be replaced with brow’s rotating, ཿ SG and inner lid’s downward moving, ཱྀ. Doldori can implement upper lid raiser, lid droop, slit, and closed eye with up and down motion of inner lids, ཱྀS and outer lids, ྀ. The motion of jaw drop or mouth stretch can be implemented with downward motion of lower lip, ྃ.
eyebrow raisers can be displayed with rotating its eyebrows. Doldori can also implement upper eyelid raiser, eyelid droop, slit, and closed eye with up and down motion of inner eyelids and outer eyelids. The motion of jaw drop or mouth stretch can be Table 4. The relationship between Ekman’s AUs and facial components of our developed robots. Ekman’s AU
Ulkni
1, 2
/
Doldori ཿ
4
c
ཿ+ཱྀ
5, 41, 42, 43, 45
d
ྀ+ཱྀ
10
e
ྂ
12, 15, 20
/
྄
24
e+f
ྂ+ྃ
16, 26, 27
f
ྃ
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We classified current facial robots in terms of their appearance, and we developed two types of facial robots for social interaction with humans. The development of these two robots, namely Ulkni and Doldori, is based on biological analysis, especially analysis of the human face. The robots consequently behave like humans with respect to head and eye movements and facial expressions. This biologically inspired point of view enhances the capabilities of the system. As a result, the robots can observe information from their surroundings similarly to humans, and humans can easily semantically and intuitively perceive the motor actions of the robots, regardless of what the robot intends. How accurately a robot’s expressions mirror the expressions of humans is a topic that needs more extensive study. In addition, evaluations of the facial expressions generated by our robots have provided useful input on how to improve the strength and clarity of robotic expressions [1]. Although we don’t discuss the emotional space in this paper, many studies on facial robots have been developed on the basis of the 2-D or 3-D emotional space for generating emotive facial expressions. The dimension of the space for facial expressions has been assumed to be the same as the dimension of the emotional space. Consequently, the dynamics of expressions are somewhat heuristic and dependant on researchers. Emotional expressions may not be expressions and may not be related to emotions in any simple way [19]. For example, even if we know that blended emotions exist, the expressions of blended emotions are not clearly defined. Many researchers
transitions from the trajectory in the emotional space. We also assume, but can’t be sure, that all the axes in emotional space are orthogonal and independent in the sense of cognitive psychology. We deduce therefore that emotional space might be wrong in the sense of emotional transitions or blended expressions. REFERENCES
a)
b) Fig. 5. Six facial expressions in Ulkni and Doldori respectively. In sequence from top-left to bottom-right, there are happiness, sadness, anger, disgust, fear and surprise. a) By the limited lip’s DOFs, two facial expressions, anger and disgust, are identical in Ulkni. And some facial expressions look unnatural because Ulkni has only 6 DOFs for facial expression. b) In Doldori, the number of DOFs for facial expression, 10 DOFs, is assigned more than Ulkni. Therefore, each facial expression looks more distinctive and clear.
have defined some basic postures of expressions and made blended expressions by linearly combining the basic postures. This method is reasonable but not representative of the blended expressions. Many blended expressions in human face are formed by composing a significant part of the basic expressions rather than by linear combination [6,7]. Blended expressions of happiness and sadness, for example, are composed of the lower part of happiness and the upper part of sadness. The blend of surprise and fear is composed of the lower part of fear and the upper part of surprise. That is, blended expressions are not mixed expressions. For these reasons, old assumptions need to be critically scrutinized and new ideas encouraged and pursued [19]. Emotional space and emotional bases should be reconsidered. If we assume that the present 2-D or 3-D emotional space and emotional bases are correct, we must obtain the transitions between two expressions, such as happiness and anger, by linear combination in the emotional space. Unfortunately, we can’t get those
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