CogSci 2012 Workshop

TELEOPERATED ANDROID AS A
TOOL FOR COGNITIVE STUDIES,COMMUNICATION AND ART
August 1, 2012
Sapporo, Japan

The aim of this full-day workshop is to introduce and discuss on current insights
and future usage of teleoperated androids.
Teleoperated androids, robots owning humanlike appearance equipped with semiautonomous teleoperation facility, was first introduce to the world in 2007 with the
public release of Geminoid HI-1. Geminoid is a teleoperated android robot that
resembles existing human being. While androids were designed for studying humannature in general, geminoids was made to study individual aspects as presence or
personality traits, tracing their origins and implementation into robots. Both its
appearance that resembles the source person and its teleoperation functionality
serves in making Geminoid as a research tool. After the release of Geminoid HI-1,
several types of teleoperated androids has been produced: Geminoid F, Geminoid
DK, Telenoid R1/R2 and Elfoid P1. While the Geminoids are after real existing
persons, Telenoid and Elfoid are attempts to represent human beings in theirminimalistic forms; a challenge to see to what extent elements that forms us can beomitted but still able to transfer presence of the teleoperating person.
Since their birth, Geminoids and Telenoids have been used in a variety of domains
throughout the world, from studies in various fields such as in cognitive
psychology / neuroscience, social psychiatry, developmental psychology, robotics,
and human-machine interface to philosophy and art. One example is the android
drama which showed new possibilities on not only on usage for teleoperated
android robots but for artistic representations as well as seeking purity in thenatures of human beings.
The past workshops that concentrated on autonomous humanlike robots andandroids laid a foundation for android science research, a field that integrates thesynthetic approach from robotics with the empirical methodologies of the social
sciences. Participants, coming from engineering and the social, cognitive, andbiological sciences sought fundamental principles underlying cognition andcommunication between individuals.
In this workshop, we will focus on the further enhanced and broadened usage ofteleoperated androids that can provide new means for cognitive science studies,and can bridge the gap between cognitive neuroscience and the behavioral
sciences, as well as philosophy, social science and arts, leading to a new way ofunderstanding human beings.

TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Morning Session

Afternoon Session

09:30-09:40
Hiroshi Ishiguro (Osaka University)
Opening remarks
09:40-10:20
Kazuo Hiraki (University of Tokyo)
Can Humanoids be our Friends I: Yet anotherapproach in cognitive science
10:20-11:00
Shuichi Nishio (ATR)
Transmitting human presence with teleoperated
androids: From proprioceptive transfer to elderlycare

13:30-14:30 Invited Talk
Gerd Gigerenzer(Max Planck Institute for Human Development)Less-is-more: Simple solutions for complex
problems--- Break --11:10-11:40
Ayse Pinar Saygin(University of California, San Diego)
What can the brain tell us about interactions withartificial agents, and vice versa?
11:40-12:20
Shoji Itakura (Kyoto University)
Perception of non-human agents by human infants--- Break --14:40-15:10
Tsutomu Fujinami (JAIST)
Enabling bodily communication between
schoolchildren using a tele-operated humanoid
15:10-15:50
Hiroshi Ishiguro (Osaka University)
Representation of humanlike presence with robotics
avatars
15:50-16:20
Antonio Chella (University of Palermo)
Sing with the Telenoid--- Break --16:30-17:10
Round-table discussion--- Lunch break ---COGSCI 2012 WORKSHOPTELEOPERATED ANDROID AS A TOOL FOR
COGNITIVE STUDIES, COMMUNICATION AND
ART
Organizers
Shuichi Nishio (nishio@ieee.org)
Advanced Telecommunications Research InstituteInternational (ATR)
Hiroshi Ishiguro (ishiguro@sys.es.osaka-u.ac.jp)
Department of Systems Innovation,
Graduate School of Engineering Science,
Osaka University
Page 3TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Kazuo Hiraki

University of Tokyo

Can Humanoids be our Friends I:
Yet another approach in cognitive
science
Abstract:
Progress in cognitive science requires the balance between divergence andconvergence of approaches. As an interdisciplinary and integrated field for scientificunderstanding of human mind, cognitive science has incorporated diversified
approaches such as computer modeling and brain imaging. These approaches
brought us not only new findings but also new problem where the findings ofdifferent approaches are sometime conflicting each other. To resolve the problem,
we need another new approach to converge the conflicting findings. In this talk I
would like to introduce a new approach to investigate human mind. Usinghumanoids we have conducted a series of studies. Here I will focus on the merit of"real" robots to study human mind.

Page 4TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Shuichi Nishio

Advanced Telecommunications ResearchInstitute International (ATR)

Transmitting Human Presencewith Teleoperated Androids:
From proprioceptive transfer toelderly care
Abstract:
Teleoperated androids, robots owning humanlike appearance equipped with semiautonomous teleoperation facility, was first introduce in 2007 with the public releaseof Geminoid HI-1. Both its appearance that resembles the source person and its
teleoperation functionality serves in making Geminoid as a research tool for seeking
the nature of human presence and personality traits, tracing their origins andimplementing into robots. Since the development of the first teleoperated android,
we have been using them in a variety of domains, from studies on basic humannatures to practical applications such as elderly care. In this talk, I will introduce
some of our findings and ongoing projects.

Page 5TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Ayşe Pınar Saygin

University of California, San Diego

What can the Brain Tell us about
Interactions with Artificial Agents,
and Vice Versa?
Abstract:
No longer encountered only in science fiction, artificial agents such as humanoid
robots and interactive animated characters are rapidly becoming participants inmany aspects of social and cultural life. Artificial agents have a range of biomedical,
educational and entertainment applications. In particular, they can enable
telepresence, opening a range of new possibilities for human interaction. For thesetechnologies to succeed however, we need to understand human factors guiding
our interactions with these agents.
In our research we use methods from cognitive neuroscience and neuroimaging to
explore how humans perceive, respond to, and interact with others, including
artificial agents. Not only can we inform the design of new agents by studying
human brain responses in interactions with artificial agents, but studies withartificial agents can improve our understanding of how the human brain enables
some of our most important skills such as action understanding, social cognition,
empathy, and communication. We suggest interdisciplinary collaboration is themost fruitful way to proceed in advancing robotics and animation on one hand, andcognitive science and neuroscience on the other.

Page 6

What can the Brain Tell us about Interactions with Artificial Agentsand Vice Versa?
Ayse Pinar Saygin (saygin@cogsci.ucsd.edu)
Department of Cognitive Science, 9500 Gilman Drive
University of California, San Diego
La Jolla, CA 92093-0515 USA
Abstract
No longer encountered only in science fiction, artificial
agents such as humanoid robots and interactive animatedcharacters are rapidly becoming participants in many aspectsof social and cultural life. Artificial agents have a range of
biomedical, educational and entertainment applications. In
particular, they can enable telepresence, opening a range of
new possibilities for human interaction. For thesetechnologies to succeed however, we need to understandhuman factors guiding our interactions with these agents. In
our research we use methods from cognitive neuroscience andneuroimaging to explore how humans perceive, respond to,and interact with others, including artificial agents. Not only
can we inform the design of new agents by studying humanbrain responses in interactions with artificial agents, but
studies with artificial agents can improve our understandingof how the human brain enables some of our most importantskills such as action understanding, social cognition,
empathy, and communication. We suggest interdisciplinary
collaboration is the most fruitful way to proceed in advancing
robotics and animation on one hand, and cognitive scienceand neuroscience on the other.
Keywords: action perception; uncanny valley; mirror
neurons; biological motion

Introduction
With advances in technology, artificial agents such as robots
are quickly becoming parts of our daily lives (Coradeschi etal., 2006; Ishiguro & Nishio, 2007). These technologies canenable telepresence, opening up new possibilities in humaninteraction that can reduce costs and travel (and associatedcarbon emissions), as well as increase diversity ofparticipation. Thus, research on how humans perceive,
respond to and interact with these agents is increasingly
important (MacDorman & Kahn Jr, 2007; Sanchez-Vives &Slater, 2005; Saygin, Chaminade, Urgen, & Ishiguro, 2011).
In particular, neuroscience and psychology research
exploring human robot interaction (HRI) and telepresence
can make valuable contributions to the development of
future applications (Chaminade & Cheng, 2009; Chaminade& Hodgins, 2006; Saygin et al., 2011). An interdisciplinary
perspective on human-agent interaction is especially
important, since this field will impact issues of publicconcern in the near future, for example in domains such as
education and healthcare (e.g., Billard, Robins, Nadel, &Dautenhahn, 2007; Kanda, Ishiguro, Imai, & Ono, 2004;
Mataric, Tapus, Winstein, & Eriksson, 2009).
Conversely, experiments on the perception of artificial
agents and telepresence can help advance neuroscience,since they can help us explore the functional properties ofbrain areas that subserve social cognition (e.g., Chaminade
et al., 2010; Cross et al., 2011; Gazzola, Rizzolatti, Wicker,& Keysers, 2007; Saygin, Chaminade, Ishiguro, Driver, &Frith, 2012). Using artificial agents and telepresence we cancontrol stimulus properties precisely, or create entities orenvirontments that violate physical realities of the world.
Such manipuations can allow us to test whether particularneural systems or perceptual processes are selective or
sensitive to natural (biological) stimuli or might also
generalize to non-biological (artificial) stimuli.
The goal of our research program is to both improve ourunderstanding of how the human brain enables social
cognition, and to help engineers and designers in developing
interactive agents that are well-suited to their application
domains, as well as to the brains of their creators. In thispaper, I will give an example of a neuroimaging study inwhich we have used artificial agents (humanoid robots) tostudy the human brain. Such interdisciplinary work that canallow us to answer questions about both artificial agents andabout the brain are important as we face a future that
includes interactions with such agents and telepresence.

Action Perception
In primates, the perception of body movements and actions
is supported by network of lateral superior temporal, inferiorparietal and inferior frontal brain areas. Here, we refer tothis network as the action perception system, or APS (Fig.
1). Two of the areas within the APS, (PMC and IPL)
contain mirror neurons in the macaque brain (Rizzolatti &Craighero, 2004). Mirror neurons respond not only when amonkey executes a particular action, but also when it
observes another individual perform the action. For instancea mirror neuron that fires as the monkey cracks a peanut,
can also fire as the monkey observes someone else crack apeanut. It is thought that a similar system underlies action
perception in the human brain (e.g., Grafton, 2009; Iacoboni& Dapretto, 2006; Saygin, 2007; Saygin, Wilson, Hagler,

Figure 1: Schematic of the Action Perception System (APS):
Superior Temporal Sulcus (pSTS), Inferior Parietal Lobule (IPL),
Premotor Cortex (PMC). Adapted from Iacoboni & Dapretto, 2006.

Bates, & Sereno, 2004). Some researchers have argued thatin addition to subserving action processing, the APS helps
in linking “self” and “other”, and thus may constitute a basis
for social cognition (Rizzolatti & Craighero, 2004).
The finding that the visual perception of another entity
automatically engages the observers’ own motor system
indicates that at some levels of the nervous system, simply
seeing another agent automatically engages interaction.
The APS has received intense interest fromneuroscientists in the last decade and a half, and we can now
use the accumulated knowledge in this field to study how
the human brain supports interactions with artificial agentsand telepresence. Conversely research on artificial agent
perception and telepresence can help research on the humanbrain by allowing us to test functional properties of the APS
and other brain areas.
Due to the presence of mirror neurons, the neural activity
in PMC and IPL regions during action perception is often
interpreted within the framework of “simulation”: Avisually perceived body movement is mapped onto theperceiving agent’s sensorimotor neural representations and“an action is understood when its observation causes the
motor system of the observer to ‘resonate” (Rizzolatti,
Fogassi, & Gallese, 2001). But what are the boundary
conditions for ‘resonance’? What kinds of agents or actions
lead to the simulation process? Is human-like appearance
important? Is human-like motion?
On the one hand, we might expect the closer the match
between observed action and observers’ own sensorimotor
representations, the more efficient the simulation will be. In
support for this, the APS is modulated by whether theobserver can in fact perform the seen movement (e.g.,
Calvo-Merino, Grezes, Glaser, Passingham, & Haggard,2006). The appearance of the observed agent may also be
important (e.g., Chaminade, Hodgins, & Kawato, 2007).
On the other hand, human resemblance is not necessarily
always a positive feature in artificial agents. The “uncanny
valley” hypothesis suggests that as a robot is made morehuman-like, the reaction to it becomes more and morepositive, until a point is reached at which the robot becomes
oddly repulsive (Mori, 1970). While this phenomenon iswell known to roboticists and animators, there is only asmall (but growing) body of experimental evidence in favorof or against it (e.g., Cheetham, Suter, & Jancke, 2011; Ho,
MacDorman, & Dwi Pramono, 2008; Lewkowicz &Ghazanfar, 2012; MacDorman & Ishiguro, 2006; Saygin etal., 2012; Seyama & Nagayama, 2007; Steckenfinger &Ghazanfar, 2009; Thompson, Trafton, & McKnight, 2011;
Tinwell, Grimshaw, Nabi, & Williams, 2011). The uncanny
valley not only constitutes a practical challenge for robotics
and telepresence, but also is a puzzling phenomenon to
study from a perceptual and cognitive standpoint.
Robots can have nonbiological appearance and movement
patterns – but at the same time, they can be perceived ascarrying out recognizable actions. Is biological appearance
or biological movement necessary for engaging the human
Action Perception System (APS)? Robots can allow us toask such questions and to test whether particular brain areas
are selective or sensitive to the presence of a human, or an
agent with a humanlike form, or respond regardless of theagent performing the action.

Neuroimaging Study: Perception of Robot andAndroid Actions
There is a small neuroscience literature on the perceptionof artificial agents, including robots (e.g., Gazzola et al.,2007; Oberman, McCleery, Ramachandran, & Pineda, 2007;
Tai, Scherfler, Brooks, Sawamoto, & Castiello, 2004).
Unfortunately, the results are highly inconsistent.
Furthermore, many studies had used toy robots or very
rudimentary industrial robot arms, so the results were notinformative regarding state-of-the-art humanoid robots or
telepresence. Furthermore, the roles of humanlike
appearance or motion were not explored in previous work.
We used neuroimaging (functional Magnetic ResonanceImaging (fMRI)) along with a method called Repetition
Suppression (RS) to overcome limitations of previous work,and studied this question with well-controlled stimuli
developed in by an interdisciplinary team (Saygin,
Chaminade, & Ishiguro, 2010; Saygin et al., 2012).
We performed fMRI as participants viewed video clips ofhuman and robotic agents carrying out recognizable actions.
fMRI is a powerful method that allows imaging the activityof the live human brain non-invasively and has
revolutionized neuroscience, though as with any method,
there are limitations (e.g., no ferromagnetic materials,
limited interactivity).
We used Repliee Q2, an android developed at Osaka
University in collaboration with Kokoro Ltd (Ishiguro,
2006; Ishiguro & Nishio, 2007). Repliee Q2 has a very
human-like appearance (Fig. 2, Android (A)); the robot’s
face was modeled after an adult Japanese female who also
participated in our stimulus development (Fig. 2, Human(H)). Repliee Q2 can make facial expressions, as well aseye, head, upper limb, and torso movements. It has 42
degrees of freedom (d.o.f.) in its movements, with 16 d.o.f.
in the head. With very brief exposure times, Repliee Q2 isoften mistaken for a human being, but more prolonged
exposure and interaction can lead to an uncanny valley
experience (Ishiguro, 2006).

Figure 2: Stills from the videos depicting the three agents (R, A, H)
and the experimental conditions (form and motion) they represent.

Repliee Q2 was videotaped both in its original human-likeappearance (A) and in a modified, more mechanicalappearance (Fig. 2, Robot (R)). For this, we removed as
many of the surface elements as possible in order to reveal
the electronics and mechanics underneath. The silicone
covering the face and hands could not be removed, so weused a custom mask and gloves to cover these areas. The
end result was that the robot’s appearance becamemechanical and nonhuman. However, since the A and R arein fact the same robot, the motion dynamics and kinematics
are the same for these two conditions.
There were thus three agents: human (H), robot withhuman form (A), and robot with nonhuman form (R). H and
A are very close to each other in form, both with humanlike
form, whereas R has nonhuman form. In terms of the
movement, H represents truly biological motion and A and
R are identical, both with mechanical kinematics. Using
fMRI and RS, we explored whether the human brain would
display specialization for human form (similar responses for
A and H, and different for R) or motion (similar responses
for R and A, and differential responses for H). Anotherpossibility was for RS responses not to reflect biologicalform or motion per se, but instead pattern with the uncanny
valley. In this scenario, responses to H and R would be
similar to each other, even though these two agents aredivergent from each other in both form and movement.
The articulators of Repliee Q2 were programmed over
several weeks at Osaka University. The same movements
were videotaped in both appearance conditions (R and A).
The human, the same female adult to whom Repliee Q2 wasdesigned to resemble, was asked to perform the sameactions as she naturally would. All agents were videotapedin the same room and with the same background. A total of
8 actions per actor were used in the experiment (e.g.,
drinking water from a cup, waving hand). 20 adults
participated in the fMRI experiment. Participants had noexperience working with robots. Each was given exactly thesame introduction to the study and the same exposure to thevideos prior to scanning since prior knowledge can affectattitudes to artificial agents differentially (Saygin & Cicekli,2002). Before the experiment, subjects were told that theywould see short video clips of actions by a person, or by two
robots with different appearances and were shown all themovies in the experiment. By the time scanning started,
participants were not uncertain about the robotic identity of
the android.
Scanning was conducted at the Wellcome Trust Centre for
Neuroimaging, in London, UK using a 3T Siemens Allegra
scanner and a standard T2* weighted gradient echo pulse
sequence. During fMRI, subjects viewed the stimuli
projected on a screen in the back of the scanner bore
through a mirror placed inside the head coil. There were
blocks of 12 videos, each preceded by the same video(Repeat) or a different video (Non-repeat), which allowed us
to compute the RS contrast (Non-repeat > Repeat). Every30-seconds, they were presented with a statement about
which they would have to make a True/False judgment (e.g.,“I did not see her wiping the table”). Since the statements
could refer to any video, subjects had to be attentive
throughout the block. Data were analyzed with SPM
software (http://www.fil.ion.ucl.ac.uk/spm).
RS differed considerably between the agents (Fig. 3). All
agents showed RS in temporal cortex near the pSTS. For A,
extensive RS was found in additional regions of temporal,
parietal and frontal cortex (Fig. 3b).
In the left hemisphere, lateral temporal cortex responded
to H and A, but not to R. The specific location of thisactivation corresponds to extrastriate body area (EBA), a
region that responds strongly during the visual perception of
the body and body parts (Peelen, Wiggett, & Downing,2006). Our data showed that robotic appearance can weaken
the RS response in the EBA.
Aside from the EBA, we did not find evidence selective
coding for human form or motion. Instead, for A, whose
form is humanlike, but its motion mechanical, increased
responses were found in a network of cortical areas. Thiswas most pronounced (and statistically significant) in the
IPL, one of the nodes of the APS (Fig. 3b, circled areas).
But why would there be an area of the brain highly
selective for androids? This response pattern brings to mind
the uncanny valley – except, rather than valleys, wemeasured “hills” in the neural responses, in the form of
increased RS. A framework within which to interpret thesedata is the predictive coding account of cortical computation(Friston & Kiebel, 2009; Friston, Mattout, & Kilner, 2011;
Kilner, Friston, & Frith, 2007). Predictive coding is basedon minimizing prediction error among the levels of a
cortical hierarchy (e.g. the APS). More specifically, during
the perception of H and R, there is no conflict between form
and motion of the agent. H appears human and moves like ahuman. R appears mechanical and moves mechanically. For
A on the other hand the agent’s form is humanlike, which
may result in a conflict when the brain attempts to process

Figure 3. Repetition suppression (RS) results for the Human (a),Android (b), and Robot (c). (Non-repeat > Repeat at t>=8.86, p<0.05
with False Discovery Rate (FDR) correction for multiple comparisons,cluster size of at least 30 voxels). Adapted from Saygin et al., 2012.
.

and integrate the movement of the agent with its form. This
conflict leads to the generation of a prediction error, which
is propagated in the network until the predictions of eachnode are minimized. During this process, we can measure
the prediction error in the fMRI responses. It is not possible
from the current data to know the exact neural sources, thedirectionality, and the time course of error propagation, but
it is clear that the cortical network is engaged more strongly
during the perception of A compared with the agents thatlead to less prediction error (R and H). Furthermore, theeffect is largest in parietal cortex, the node of the networkthat links the posterior, visual components of the APS andthe frontal, motor components (Matelli & Luppino, 2001).
In summary, in this interdisciplinary study, we found thata robot with highly humanlike form is processed
differentially compared with a robot with a mechanicalform, or with an actual human. These differences are foundin a network of brain areas, most prominently in parietal
cortex (Saygin et al., 2012). We propose these “hills” in thebrain activity reflect the prediction error that is propagatedin the system. The uncanny valley may thus arise fromprocessing conflicts in the APS, and the resultant error
signals, which can in turn be measured using fMRI.
The study described above constitute only a beginning. In
future work, we can utilize animation in order to modulate
form and motion parameters more precisely (although this is
likely to lead to a decrease in presence (Sanchez-Vives &Slater, 2005)). We will also use other neuroimaging andpsychological methods in addition to, or in conjunction with
fMRI. More time-resolved behavioral and neuroimaging
methods are also important to study the temporal dynamics
of action processing (Saygin & Stadler, 2012; Urgen, Plank,
Ishiguro, Poizner, & Saygin, 2012).

Discussion
Using cognitive neuroscience, we have been able to suggest
an interpretation for the classic anecdotal reports of theuncanny valley hypothesis. While our experiment was notdesigned to explain the uncanny valley, the results suggest an
intriguing link between the phenomenon, and brain
responses in the APS. As shown in Figure 2, the android
condition features a mismatch between form and motion. In
a predictive coding, the android is not predictable: an agent
with that form (human) would typically not movemechanically as Repliee Q2 does. When the nervous system
is presented with this unexpected combination, a propagationof prediction error may occur in the APS. We suggest thisframework may contribute to an explanation for the uncanny
valley and future experiments will test this hypothesis.
Using robotics, we were able to answer questions
regarding the neural basis of action perception. We were able
to test functional properties of human action perception
system (APS), helping shed light on how our brains enablesocial cognition.
Collaboration between cognitive neuroscience androbotics and telepresence research can be a win-win for bothsides. Understanding both the computational and the humanside of human-agent interaction is necessary for developing
successful assistive artificial agents and telepresence systems.

Acknowledgments
This research was supported by the Kavli Institute for Brainand Mind, California Institute of Telecommunications andInformation Technology (Calit2), NSF (CAREER1151805), Marie Curie Intra-European Fellowship, and theWellcome Trust. I would like to thank our coauthors,collaborators and colleagues who have commented on thiswork: Thierry Chaminade, Jon Driver, Chris Frith, Antonia
Hamilton, Hiroshi Ishiguro, Javier Movellan, Takashi
Minato, Marty Sereno; and the coauthors on our EEG and
MEG studies: James Kilner, Marta Kutas, Howard Poizner,
Markus Plank and Burcu Urgen.

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TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Shoji Itakura

Kyoto University
Yuko Okumura

Perception of Non-Human Agentsby Human Infants
Abstract:
How do human infants perceive the nonhuman agents? We report two studies here
concerns to this issue. In study 1, infants faced to the android in the context of stillface paradigm. In this paradigm, infants’ reactions to a sudden 1 min still face
adopted by a social partner in a face-to-face interaction were recorded. Previous
studies had shown that infants demonstrated attempts to re-engage theexperimenter during the still-face episode with human tester. In the case withandroid, however, infants did not show such re-engage action. In study 2, twelvemonth-old infants were shown videos in which a human or a robot gazed at an
object. The results demonstrated that the infants followed the gaze direction ofboth agents, but only human gaze facilitated their object learning: Infants showedenhanced processing of, and preferences for, the target object gazed at by thehuman but not by the robot. Importantly, an extended fixation on a target object
without the orientation of human gaze did not produce these effects. Together,
these findings show the importance of humanness in the gazer, suggesting that
infants may be predisposed to treat humans as privileged sources of information forlearning.

Page 12TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Gerd Gigerenzer

Max Planck Institute for Human
Development
Invited Talk

Less-is-More:
Simple solutions for complex
problems
Abstract:
In worlds of known risks, probability theory typically provides the optimal course ofaction. In uncertain worlds, however, simple heuristics can result in smart solutions
by focusing only on a few cues and ignoring the rest. I illustrate such less-is-more
effects through the recognition heuristic, the gaze heuristic, and lexicographic
heuristics. The heuristics in the “adaptive toolbox” are anchored in the mind andthe environment. They are embodied in the sense that they can exploit capacities of
the human mind (such as recognition memory), which allow judgments to be quick.
They are anchored in the environment in the sense that they can exploit statisticalor social structures (such as signal-to-noise ratio). The study of the ecologicalrationality of heuristics and the bias-variance dilemma provides a general accountto understand why and when less can be more.

Page 13TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Tsutomu Fujinami

Japan Advanced Institute of Science andTechnology
Ryuji Yamazaki, Masaru Nishikawa, Shuichi Nishio

Enabling Bodily Communication
between Schoolchildren using a
Tele-Operated Humanoid
Abstract:
Tele-operated humanoids open a new field of communication within which
participants interact with each other through body movements in addition to voices.
Such a new medium poses a challenge to participants in that they are forced toincorporate humanoids into their communication. They have to find out what they
are and how they can be used for communication. We need to establish ahumanoid literacy just as the Internet literacy was required for everyone tocommunicate with each other through the Internet. As the first attempt to look for
an appropriate use of humanoids in communication, we brought in the telenoid, atele-operated humanoid, to a primary school to test it in a classroom setting. We
recruited a professional dancer, with whom we designed a course of bodily
communication using telenoid. In the classroom, students were first instructed totouch it to feel it as if it were an existence akin to human being. They were thenasked to name it and were induced to dance to animate it. These steps helped
them to attribute a sort of personality to the telenoid. Establishing a communication
channel in which the telenoid was involved was, however, another matter andrequired further trainings to them. The way they communicate with each other was
restricted to hand-clapping and singing. All these instructions and trainings had
finally led them to a usage in which the telenoid functioned as a medium to convey
bodily expressions between them. We report the process in detail and discuss its
implication.
Page 14

TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Hiroshi Ishiguro
Osaka University

Representation of Humanlike
Presence with Robotics Avatars
Abstract:
Geminoid that is an tele-operated android of an existing person can transmit thepresence of the operator to the distant place. The operator recognizes the android
body as his/her own body after talking with someone through the geminoid and has
virtual feeling to be touched when someone touches to the geminoid. However, thegeminoid is not the ideal medium for everybody. For example, elderly people
hesitate to talk with adult humans and adult androids. A question is what is theideal medium for everybody. In order to investigate the ideal medium, we areproposing the minimum design of interactive humanoids. It is called Telenoid. The
geminoid is the perfect copy of an existing person and it is the maximum design ofinteractive humanoids. On the other hand, the minimum design looks like a human
but we cannot know the age and gender. Elderly people like to talk with thetelenoid. In this talk, we discuss the design principles and the effect to theconversation.

Page 15TELEOPERATED ANDROID AS A TOOL FOR COGNITIVE STUDIES, COMMUNICATION AND ART

Antonio Chella

University of Palermo
Haris Dindo, Rosario Sorbello, Shuichi Nishio, Hiroshi Ishiguro

Sing with the Telenoid
Abstract:
We introduce a novel research proposal project aimed to build a robotic setup inwhich the Telenoid learns to improvise jazz singing in a duet with a human singer. In
the proposed application, the Telenoid acts in teleoperated mode during thelearning phase, while it becomes more and more autonomous during the working
phase. A goal of the research is to investigate the essence of humancommunication which is based on gestures and prosody. We will employ anarchitecture for imitation learning that incrementally learns from demonstrations
sequences of internal model activations, based on the idea of coupled forwardinverse internal models for representing musical phrases and the body sequencesof singer movements. Possible applications are in the field of entertainment, where
the Telenoid setup system acts as a musical game like Guitar Hero or Rock Band; inthe field of music learning; for the analysis of mental disorders as Autism, and tobuild a new generation of companion robots for elderly care.

Page 16

Sing with the Telenoid
Antonio Chella (Antonio.Chella@Unipa.IT)
Haris Dindo (Haris.Dindo@Unipa.IT)
Rosario Sorbello(Rosario.Sorbello@Unipa.IT)
University of Palermo, Viale delle Scienze, Palermo, 90128 Italy

Shuichi Nishio (Nishio@Ieee.Org)
Hiroshi Ishiguro Laboratory,
Advanced Telecommunications Research Institute International (ATR), Japan.

Hiroshi Ishiguro (Ishiguro@Sys.Es.Osaka-u.Ac.JP)
Department of Systems Innovation, Graduate School of Engineering Science,
Osaka University, Japan.
Keywords: Computer Music; Embodiment; Emotions;
Imitation learning; Creativity; Human-robot Interaction.

Introduction
We introduce a novel research proposal project at the
RoboticLab of the University of Palermo, aimed to build a
robotic setup in which the Telenoid learns to improvise jazzsinging in a duet with a human singer. In the proposed
application, the Telenoid acts in teleoperated mode during
the learning phase, while it becomes more and moreautonomous during the working phase.
A main goal of the research is to investigate the essenceof human communication which is based on gestures andprosody. This essential interaction is at the basis of music
and of conversation (see Donald 1991).
The adopted setup is based on the Telenoid as a jazz
singer able to improvise in a duet with a human singer (seeFigure 1). This setup is highly challenging: it takes into
account the gestural movements of the singer together with
the Telenoid, the expressions of emotions of the Telenoid
and the singer during music production, and the creative
aspects of music improvisation. It is to be stressed that these
aspects are not predefined by the system designer, but they
are learned according to the framework of imitation
learning.
The software architecture controlling the Telenoid is able
to acquire and to learn the gesture movements of a singer
teacher during music production and to associate them asuitable emotional state. Therefore, the architecture willlearn to associate jazz phrases with suitable gestures andemotional states. During the imitation learning step, thehuman jazz singer will start to move and to improvise on a
fixed musical base with fixed tempo and chords sequences.
Then, the Telenoid acting in a teleoperated mode, will
begin to learn to mimic the human singer in order to learn
the association of gestures, emotions and sound.
During successive steps, the Telenoid will become more
and more autonomous and will be able to generalize fromthe learned music phrases and to improvise by responding in
an appropriate way to the partner singer.

Imitation learning

Music
Gestures

Singer

Emotions

Figure 1: The Telenoid setup.
Embodied systems performing music currently at the stateof the art are the Haile robot (Weinberg & Driscoll 2006)
and the Waseda Flautist Robot (Solis et al. 2006). However,these systems, through effective, do not take into accountthe complex interactions between the two partners of amusical duet and based on gestures, emotional states andproduction of musical phrases.
The Telenoid is an effectively physically embodied agent
and it is perceived as a physical agent by the duet partner. It
is able to sing, to move, and to receive audio and visualfeedbacks by the cameras and the microphones, therefore itis an ideal robot candidate to study the essential basis ofhuman robot interactions (Nishio & Ishiguro 2011).

Jazz Improvisation
Jazz improvisation is an interesting case of study inrelation to creativity: creativity during improvisation isdifferent from other typical models of creativity. In fact, thecreativity process is often studied with regards to the
production of new abstract ideas, as new mathematical or
physical idea after periods of great concentration (Boden
2004). Jazz improvisation is instead a form of immediate
and continuous lively creation process closely connectedwith the external world, made up of musical instruments,

people, moving bodies, environments, audience and othermusicians.
Moreover, jazz improvisation has peculiar features that
set it apart from the traditional classic improvisation(Pressing, 1988): as part of Western classical music,
improvisation is a kind of real time composition with thesame rules and patterns of classic composition. On the otherside, improvisation is based on a specific set of patterns andelements. The melody, the rhythm (the swing), the chord
progressions are some of the issues that need to be analyzed
and studied with stylistic and aesthetic criteria different
from those of Western classical music.
The classical view, often theorized in textbooks of jazz
improvisation (see e.g., Coker, 1964), suggests that during asession, the improviser follows his own musical path largely
made up by a suitable musical sequence of previously
learned patterns. This is a partial view of an effective
improvisation. Undoubtedly, the musician has a repertoireof musical patterns, but is also able to deviate from its pathdepending on the feedback he receives from other musiciansor the audience, for example from the rhythm section or due
to signals of appreciation from the listeners or due to the
movements of the other singers.
Cognitive scientists typically model jazz improvisation
processes by means of formal grammars (see Johnson-Laird
1991). This model appears to be problematic because it does
not explain the complexity of the interaction between theplayer, the rest of the group and the audience.
López-González & Limb (2012) in a recent review
discuss interesting results from neuroscience related withjazz improvisation. Limb & Braun (2008) pointed out thatexpert jazz players enter in a peculiar state of mind during
improvisation, that is similar to dreaming or hypnosis states.
This altered state of mind is in facts characterized by strong
activation of sensorimotor areas and deactivation of
motivation and emotional brain functions. Spontaneousmusical performance therefore happens in absence of selfmonitoring and volitional control.
According to Engen & Keller (2011), expert jazz players
are quite good at recognizing a genuine jazz improvisation
performance with respect to pure rehearsal. Amygdala andsuitable motor areas are more activated during listening of
improvised melodies than to imitated ones. The listenerseems in facts to internally simulate the musicalperformance in order to evaluate its spontaneity.
These results suggest that a spontaneous jazz
improvisation performance do not depends on voluntaryactions but it strongly depends on the personal actionrelated musical experience.

Embodiment and gestures
Embodiment is fundamental to any kind of musicalperformance: according to Krueger (2009), making music isessentially a body activity. Instead, the current research on
algorithmic composition mainly concentrated on expert
systems (see Balaban, Ebcioglu & Laske 1992) or neuralnetworks (see Todd & Loy 1991) in order to represent andto generate music.
Only recently, Leman (2008) discussed at length the main
relevance of embodiment and gestures for computer music.
Notably, Leman reports many evidence concerning the factthat listeners are typically able to associate and reproduce
the main gestures of a player during performance bylistening off line records of the player. Music is therefore
tightly associated with gestures production.
Apart from the previously mentioned Haile and the
Waseda Flautist, an example of embodied systems formusic productions is Roboser (Manzolli & Verschure,2005), a Khepera robot that can move autonomously in anenvironment and generate sound events in real time
according to its internal state and to the sensory input it
receives from the environment. EyesWeb (Camurri et al.
2000) is another system that analyzes body gestures withparticular reference to emotional connotations in order toaccordingly generate sound and music in real time and also
to suitably control robots.
As previously stated, the mentioned systems take into
account only partially the interactions between partner duetsin the framework of imitation learning as in our proposed
setup. In this respects, the Telenoid, is an ideal candidate to
explore the role of embodiment and gestures in music
production.

Emotions and Empathy
The relationships between emotions and music have been
widely analyzed in the literature, suggesting computationalmodels describing the main mechanisms underlying the
evocation of emotions while listening to music (Juslin &Sloboda 2010).
In the case of a live performance as an improvisation, thelink between music and emotions is a deep one: during a
successful performance the player create a tight empathic
relationship between herself and the listeners.
Gabrielsson & Juslin (1996) conducted an empiricalanalysis of the emotional relationship between a musician
and the listeners. According to this analysis, a song arouses
emotions on the basis of its structure: for example, a sadsong is in a minor key, it has a slow rhythm and thedissonances are frequent, while an exciting song is fast,strong, with few dissonances.
The emotional intentions of a musician during a live
performance can be felt by the listener with greater or lesser
effectiveness depending on the song itself. The basic
emotional connotations such as the joy or the sadness areeasier to transmit, while more complex connotation such assolemnity are more difficult to convey. The particular
musical instrument employed has a relevance in thecommunication of emotions, and of course the degree ofachieved empathy depends on the skill of the performer.
This analysis shows that an agent, to make an effective
performance, must be able to convey emotions and to have amodel (even implicit) of them.

The relationships between emotions and music also
concern social aspects of music. Molnar-Szakacs & Overy(2006) among others, analyze data from neuroimaging
suggesting that music, as language, may perform activationsof hierarchical organized motor elements able to generateaffective responses mediated by mirror neurons systems.
Panksepp (1998) speculates on the possible influences ofmusic to the social motivation system of the brain which arein turns strictly linked with the thermoregulatory systems of
the brain. Thus, a sad song may resemble to a cry of a lostchild, it may evoke chills generating sensations of cold andthermoregulatory discomfort therefore promoting a desire of
reunion.
Music has therefore a strong role in generating andmaintaining social bonds; this is an active research field
related with the studies on the origins of music that parallels
the studies on the evolution of the human kind (Wallin et al.
2000).
In the Telenoid setup we will adopt a computationalmodel of emotions that has been previously employed in
Cicerobot (Barone et al. 2008), a mobile robot acting as a
museum guide in indoor and outdoor museums. In
particular, we refer to a neural network model of emotional
learning developed by Balkenius & Moren (2001) able tosummarize and classify the ongoing musical performance ofthe robot by means of six basic emotions proposed by(Ekman 1992): Joy, Sad, Surprise, Anger, Fear and Disgust.
Our model will give the Telenoid the ability todynamically learn the emotional value for musical andgestures stimuli. In turns, this emotional state will drive theTelenoid gestures and singing.
The setup with the Telenoid will therefore allow theemotional system to associate emotional states with musicalphrases and the body gestures during duet singing.

Imitation Learning
By imitation learning we mean the process of achieving theintention hidden in the observation of an action (Chella etal. 2006). In other words, it is not the means that should be
imitated, as in a pure mimicking, but rather the final goals.
Central to the modeling of such intentional actions is theidea that learning by imitation can be efficiently achieved byreenacting one’s own internal models in simulation(Wolpert et al. 2003).
In the Telenoid setup we will employ an architecture forimitation learning that incrementally learns fromdemonstrations sequences of internal model activations(Dindo et al. 2011). It is based on the idea of coupledforward-inverse internal models for representing goaldirected behaviors and the body sequences of movements. A
forward model is, in the musical context, a predictor that,given the state of the system and a command, produces aprediction of the future outcome of the given musical phrase
and the associated gestures and emotional state.
An inverse model, known also as controller in control
theory, produces commands necessary to reach a state as amusical phrase given the present state. Internal models are

powerful constructs able to represent operational knowledgeof an agent and to govern its interaction with theenvironment. While a single internal model is capable ofsolving a single task, it is their sequence that enables thesystem to hierarchically deal with complex task.
The overall behavior therefore emerges by the interactionof incrementally learned skills. Internal models encode
behaviors of the system and an ad hoc module is responsible
for coordinating the activation of the most significant
internal models given the actual state of the system
including its motivations and emotions. In a similar way,learning of complex skills is achieved by composing andcoordinating simple ones.
Learning to sing is initially triggered by domaindependent knowledge stored in a component called
Masterplan. It stores a set of primitive musical phrases,
body gestures and emotional states together with an initialontology, goals and heuristics needed to monitor thelearning progress. By directly interacting with the humansinger the system generates hypotheses of new models.
These will be stored in Masterplan and tested in order toassess their usefulness.
The system learns new models of singing and gestures
and the related emotional states by interacting with thehuman singer. The components of the system continuously
analyze the perceptual data in order to acquire this new
knowledge. Initially the system interacts with the humansinger in a teleoperated way by singing casual phrases andexecuting random movements in order to learn simple
causal relations of the entities of the world and of itself - aprocess common in newborns called babbling. Morecomplex models, encoded as chains of simple models
activations, are learned by interacting and observing thehuman singer during long time sessions.
When acting, the agent needs to decide what to do in aparticular musical situation. It should be noticed that, as
showed by the previous results from neuroscience, jazz
improvisation is not a goal driven activity, but it is a sort of
explorative activity without volition.
An explorative module is therefore responsible of
selecting which internal models to execute given the current
musical, emotional and body situation. Decisions are madereactively and in parallel by exploiting the available
knowledge at present, by taking into account the emotional
state too. Since multiple decisions can be made, wedeveloped a module that anticipates possible choices anduses this information to decide the set of gestures andmusical phrases to be executed in a particular emotional
state.

Conclusions
An application field of the described system is theentertainment, where the system may act as a sort of musicalgame like Guitar Hero or Rock Band, in which the humanplayer is engaged in singing in tune at the right tempo with
the Telenoid. A related application is in the field of music
learning: the system may be employed by jazz singers tolearn and practice jazz songs and to experiment new musicalideas.
An interesting application field is the analysis of mental
disorders. The described system may be employed in orderto study Williams syndrome (Levitin & Bellugi 1998) orAutism disorders (Bhatara et al. 2010).
Finally, an application area may concern elderly care,when a senior person could have the possibility to teach andstore by means of the Telenoid, all the popular songs
learned through his whole life in order to save part of the
popular music cultural heritage.

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