Robovie-IV: A Communication Robot
Interacting with People Daily in an Office
Noriaki Mitsunaga , Takahiro Miyashita , Hiroshi Ishiguro , Kiyoshi Kogure and Norihiro HagitaIntelligent Robotics and Communication Laboratories
Media Information Science Laboratories
Advanced Telecommunication Research Institute International
Keihanna Science City, Japan
mitunaga,miyasita,kogure,hagita @atr.jp
Graduate School of Engineering, Osaka University
Suita City, Japanishiguro@ams.eng.osaka-u.ac.jp


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Abstract— We believe that robots will take an active partin our daily lives in the near future. Once this time arrives,
robots will be expected to have the social skills necessary forinteracting with people in addition to the ability to carry out theirown tasks. We call an interaction that increases familiarity andmakes communication smoother a “social interaction.” We haverecently developed a human-size humanoid, called ”Robovie-IV,”
which will have the ability to interact with people in their daily
lives. This paper discusses the design requirements for humanrobot interaction by a communication robot, then introduces an
overview of Robovie-IV’s hardware and software architectures.
There are also preliminary results of a daily experiment that weare currently conducting with Robovie-IV in our office.

I. I NTRODUCTION
Robots are expected to play a much more active role in ourdaily lives in the near future. Since there are large differencesin social aspects between short-term interaction in a laboratory
and long-term interaction in real life, it is imperative to
conduct experiments with a robot that interacts with people
everyday in their real daily live to develop such robots in the
future.
Jijo-2 [1] was developed as an office-conversant robot that
informs people where a person is, guides them to a room, andso on. Also, a museum guiding robot has been developed [2].
They both interacted with people on a daily basis, and theirinteractions were designed to achieve tasks such as providing
information about a person, guiding a visitor to a room, or
explaining displays. However, communication is not just forachieving tasks but also for building social relationships. For
example, when people chat with others, there may be no explicit intention to exchange information; it is just conversation.
Although such kinds of interaction without explicit intention
might seem unnecessary, we think they increase familiarity
which serves as the basis of smooth communication to achievetasks. This means we will prefer the robot most familiar to us
when encountering two robots that are functionally identical.
We call such interaction that increases familiarity and facilitates communication “social interaction.” Our interest is in
how to realize such interactions in a robot and what makes
an interaction a social one. To investigate these issues it isnecessary to perform experiments in daily life environments.
For the first step of our research, we have recently developed
a human-seized version of Robovie, Robovie-IV. Robovie-IV
features interaction abilities including voice chats, which areintended to be “social interactions.” Using Robovie-IV wehave begun an experiment that in which it interacts with people
in their daily life in our office environment.
This paper is arranged as follows. In Section 2, we discuss
how to implement daily interactions between a human and arobot, and then in Section 3 we discuss the requirements fora robot to carry out everyday communication. In Section 4
we briefly introduce the hardware and software architectureof Robovie-IV. Sections 5 and 6 respectively outline theimplementation of the base software and describe our experiment. Finally, we discuss the results and offer conclusions in
Section 7.
II. DAILY INTERACTION BETWEEN HUMANS AND ROBOTS
Isbell et al. [3] have realized an agent that can participate
in a chat session over the Internet. The agent learns how
to interact in a chat with rewards given by other humanparticipants. Kubota et al. [4], meanwhile, have created anagent that chats with people using a type of corpus basedon exchanged E-mails on a mailing list that discusses drinks.
These agents and the method to build the corpus are free fromvoice recognition, voice synthesis, and so on since electricaltexts are given.
It is still difficult, however, to dictate our daily conversations
and to build a usable corpus by current voice recognition
methods. Also, there are many differences between chats overthe Internet and our daily face-to-face chats. To the best of
our knowledge, there is no corpus presently usable for daily
human-robot conversation. Consequently, we have decided to
implement rule-based interactions between a human and arobot including chats.
There are four modes in human-robot interaction:A) the human makes the first move and does not wait for
the robot’s reaction;B) the human makes the first move and expects a reaction
from the robot;C) the robot makes the first move and does not wait for thehuman’s reaction; andD) the robot makes the first move and expects a reaction
from the human.
Patting a robot’s shoulder or head is an example of interaction
Mode A. In the case where a human passes by without expecting a reaction from the robot, it is not necessary to explicitly
implement interactions. Approaching, touching, talking to, andmaking a gesture to the other are examples of Modes B and D,
while saying ”hello” is an example of interaction Mode C. We
regarded Mode D as most important and mainly implemented
the interactions where the robot actively tries to talk with ahuman when the distances between them are within a certain
range. For Mode B, we mainly implemented reactions in which
a human touched the robot’s skin.
We classify the interactions in Mode D into four types,
according to sensor information:1) interactions in which a robot can continue to playregardless of the sensor values, without making people
feel there something wrong with them;
2) interactions that may comprise reactions that rely only
on simple sensor information;3) interactions that need voice recognition, but misrecognitions do not lead to severe discomfort; and
4) interactions that need voice recognition, and misrecognitions lead to severe discomfort.
Greetings and performances like a dance are examples of
Type1 interactions, and giving responses is an example ofType2 interaction. When a person is drinking something, therobot might ask “What are you drinking?” Without recognizing
the answer, the robot can say “I would like to drink it too,” A
Type2 interaction does not need voice recognition even if thecommunication type is a chat. Both Type3 and 4 interactions
use the result of the voice recognition system; the differences
are their required recognition accuracy. Suppose the robot
says “Let’s play,” and fails to recognize the person’s vocal
response of “no,” and it starts to play. The human may feel itis selfish but this is not serious problem. However, if the voicerecognition fails for a word used as the robot’s response fromthe robot, the human will easily detect the failure.
We are mainly implementing Type2 and 3 interactions
because too many Type1 interactions reduces humans’ interestin the robot, and Type4 interactions disappoint the human if
voice recognition fails. For Type4 interactions, we needed tocarefully construct a voice recognition dictionary in order toreduce the number of failures. To talk about current events,
we prepared fixed-form rule-based chats that included sensing
results, chat histories, dates and times, and information fromthe Internet. For example, how many people the robot met
today, who it met, current weather, and so on. We are incrementally adding interaction rules to Robovie-IV since weincrementally learn what are expected interactions. Currently,
Robovie-IV has 12 Type1, 11 Type2, 49 Type3, and 2 Type4

Fig. 1. From left to right, Robovie-IIS, Robovie-IIF, and haptic interaction
between children and Robovie-IIS.

interactions.
III. R EQUIREMENTS FOR A COMMUNICATION ROBOTWhat capabilities are necessary for a communication robot?
At least the following requirements are needed to achieve
natural and effective human-robot communication. First of all,the robot should be self-contained. Although its computers,
database systems, and even sensors can be outside its body, it
should have mechanisms to perform both verbal and nonverbal
communication by itself. Furthermore, it should be able tomove around with no wires for smooth communication because the distance between a person and a robot cannot beignored when attempting to achieve effective communication.
Also, it should not have an outward appearance that frightens
or discomforts people.
Second is the capability of haptic communication because
haptic communication is as important as vision and voice.
People who are familiar with each other often touch eachother’s hair or hug each other; such haptic interaction reinforces their familiarity. If a communication robot equippedwith tactile sensors over its entire body could have the same
haptic interaction capability as human do, the robot would
become more familiar, thus shortening its communicative
distance from people. To study haptic communication, we havepreviously developed two types of humanoid robots, RobovieIIS and Robovie-IIF, that have tactile sensors embedded in asoft skin that covers the robot’s entire body [5]. These robots
were developed based on Robovie-II [6]. Figure 1 shows
overall views of Robovie-IIS, Robovie-IIF, and a scene ofcommunication with a human. Robovie-IV has tactile sensors
based on the technique we used for these robots.
Third is the locomotion mechanism that can generate involuntary motions. Human motion can be classified into two
types: voluntary and involuntary motion [7]–[9]. Voluntary
motions are a set of motions made to achieve given tasks orintentions. Going to a certain place, moving an arm forward
for a handshake, vocalization to say hello, and reacting to a pat
in order to know who did it are such examples. Involuntary
motions, on the other hand, are a set of incidental motions
such as feedback control arising in response to physical stimuli

IV. T HE HARDWARE IMPLEMENTATION
Figure 2 shows front and side views of Robovie-IV. The
height of the robot is about 1.1 m, which is smaller thanthe Robovie-II/II-S/II-F models (which are 1.2 m high). As
the figure shows, it has two arms each with four dof, one
head with pan, tilt, and roll joints, four spare wheels (passive
casters), two powered wheels, and one jack to enable theinverted pendulum state. Robovie-IV is equipped with twovideo cameras with pan and tilt joints, one camera with anomni-directional mirror, a microphone and speaker, an optical
tag reader (easily detachable, not shown in the figure), and an
RF tag reader (the system is based on the active tag systemcalled Spider by RF Code Inc. [13]). Robovie-IV also features
two laser range sensors in the front and back, two gyros thatsense the same axes (we use two to take the average of their
outputs in order to reduce random noise, and to detect failure
so that the robot does not fall over), and 56 tactile sensors.
Figure 3 displays the arrangement of the sensor elements thatare embedded in the soft skin. The figure clearly shows that
there are tactile sensors under the nose, ears, and in the thumbs
for Robovie-IV to sense contact to those areas.

camera withomni-directional
mirror1 speaker and1 microphone

1 optical
tag reader

2 arms(4dof x2)

1 RF tagreader56 tactile
sensors

2 gyro sensors(1 axis x2)

56 tactile
sensors

2 laser range
sensors

4 spare wheels(passive casters)

1 jack(1dof)
Front view

(diameter of wheels)

2 cameras withpan and tiltneck
(3dof)

200from the environment without prior planning or motivation.
These motions do not correspond to tasks directly but instead
consist of motions that make the robot appear to behave morenaturally. Robovie-III was developed to enable involuntary
motion for a robot [10]. It uses a wheeled inverted pendulum
mechanism for locomotion. Since the inverted pendulum iscontrolled by feedback on its posture, involuntary motion of
the whole body occurs. In addition to effecting involuntary
motion, the wheeled inverted pendulum has a unique feature:
When some external force is applied to the body from its
front, it moves backwards. Since its wheels are controlled tomaintain its posture, it moves backwards to keep the posture
as it tilts backwards due to the applied force. We also adopt
the wheeled inverted pendulum mechanism for Robovie-IV.
Fourth is human recognition. People expect a robot tobe able find and identify them. Many methods for humandetection and identification using images have been proposed;
however, with current computational power and video camera
resolution and viewing angles, conditions under which thosemethods work are still limited. Consequently, we decided touse a laser range sensor to find human leg candidates andan optical/RF tag system for human identification. RobovieIV finds leg candidates using a laser range sensor, verifies theresults with its camera, and identifies the detected human witha tag.
Based on the above discussion, Robovie-IV is designed as arobot 1) whose height is the same as a child’s; 2) whose wholebody is covered with soft, light-colored skin for a soft lookand touch; 3) with many tactile sensors are embedded in thesoft skin; 4) which can move in two modes, one is a normal
wheeled robot mode with passive casters, and the other is theinverted pendulum mode; and 5) which has optical and RF tag
readers and laser range sensors for human identification.

2 wheels
Left side viewFig. 2. Front and left-side views of Robovie-IV. Fitted actuators and sensors
are shown.

Fig. 3. Arrangement of the skin sensor elements. There are 56 sensors in
its soft skin (#16 is not implemented).

Figure 4 shows Robovie-IV’s hardware architecture. There
are four PID motor controllers connected to a PC via RS-232
dedicated for wheels, left and right arms, and neck joints. The
jack and the inverted pendulum are controlled by the motor
controller. Two laser range sensors and optical and RF tag
readers, and cameras with pan/tilt joints are also connected to
the PC via RS-232. Signals from the tactile sensors are fed
to five skin processors. The processed signals are then sent
to the PC via the RS-422 bus. Images from three cameras arecaptured by a frame grabber installed on the PC. A speaker anda microphone are connected to the sound output and input of
the PC. Each controller has a SH2 micro-processor (Renesas
Technology Corp.) and the main computer has a Pentium-M
processor that runs at 2 GHz. The PC can be connected to thesensors via a wireless LAN.
Figure 5 shows the structure of a tactile sensor element
embedded in the soft skin. As the figure illustrates, the soft
skin consists of four layers. The outside layer is made ofthin silicone rubber, and the middle layer is made of thicksilicone rubber. We use these silicone rubber layers to realize
humanlike softness. The inner layer is made of urethane foam,
which has a density lower than that of the silicone rubber; thedensities of the urethane foam and the silicone rubber are 0.03
g/cm and 1.1 g/cm , respectively. The total density of the soft

omni camera

frame grabber

NTSC signals

right arm
controller (SH2)2 cameras with pan/tilt
RS-422right arm (4dof)

connections
via RS-232
Sony EVI protocol

left arm
controller (SH2)usleft arm (4dof)

2bhead controller
(SH2)

2
-4

skin
sensorskin
sensorskin
sensorskin
processing(SH2)skinsensor
sensorprocessing(SH2)
processing(SH2)
processing
processing(SH2)(SH2)

neck (3dof)

V. T HE S OFTWARE I MPLEMENTATION

RS

tactile sensors
tactile sensors
tactile sensors
tactile sensors
tactile sensors

sound in/out

gyros
wheels (2dof)

jack

wheel controller(SH2)

PC base computer(Pentium-M 2[GHz])

speaker2 laser range sensors

microphone

RF tag reader
optical tag reader

Fig. 4. Robovie-IV’s hardware architecture. There are four motor controllers,
five skin processors, and one main computer.

Thin silicone rubber
Piezoelectric film (PVDF)
Thick silicone rubber
Urethane foam

Fig. 5. The skin’s structure, which consistsof four layers. The piezoelectric sensor is inserted between the outer and middle layers.

Fig. 6.
The powered
wheels are connected by ajack to the main body andspare wheels. The figure
shows the inverted pendulum mode.

skin, including all layers, is 0.6 g/cm . Robovie-IV’s tactile
sensor elements are film-type piezoelectric sensors inserted
between the thin and thick silicone rubber layers. These filmtype sensors, consisting of polyvinylidene fluoride (PVDF)
and sputtered silver, output a high voltage proportionate tochanges in applied pressure. Since the middle and the inner
layers deform easily upon human contact with the skin, thesensor layer can easily detect the contact.
Figure 6 illustrates how the powered wheels, the spare
wheels, and the jack are connected. The spare wheels areconnected to the main body directly, while the powered wheels
are connected via the jack. Robovie-IV can select two locomotion modes, with or without spare wheels, by controlling thejack. With the spare wheels, Robovie-IV moves as a normal
robot with two powered wheels, but without them, it moves
in the inverted pendulum mode. We use the wheeled inverted
pendulum controller proposed by Ha and Yuta [11].

Figure 7 presents an overview of Robovie-IV’s softwarearchitecture. The PC’s operating system is Linux, and abox with bold line in the figure indicates a process runningon Linux. There are five processes, robovie4, robobase4,
robomap4, PostgreSQL, and Julian. The processes are connected through named pipes, sockets, and shared memory.
The process robovie4 makes decisions for the robot fromthe internal state, information in the database, and the sensor
information gathered and processed by the other processes.
Processors on the motor controllers control motors as directedby a program called robobase4 running on the PC. Process
robobase4 handles the communication between controllers andsensors while hiding the differences of the protocols fromrobovie4. Process robomap4 is the one for self-localization.
It receives information from the two laser range sensors andodometry from robobase4 and compares this information withthe map of the environment. The estimated position is returned
to robobase4 and sent to robovie4. The PostgreSQL [15] is a
popular database engine. We use the database to store andrecall co-experiences with humans.
The Julian process is that of a grammar-based recognition parser called “Julian.” Julian is a modified version of
Julius [12], which is a high-performance, two-pass largevocabulary continuous speech recognition (LVCSR) decoder
software for speech-related researchers and developers. We
have prepared manually designed deterministic finite automata(DFA) grammar as the language model and use it for the
speech recognition by Julian. The robovie4 process connects
to the Julian process and commands dictionary selection andso on. The recognized words are sent back to robovie4.
The robovie4 process mainly consists of an event handler
written in C++ and tasks written in Lua [14], which is a
scripting language that can be embedded in a C or C++ program. Tasks written in Lua run in parallel by the collaborative
multithreading supported by Lua. There are task threads to do
dialogue, react to tactile events, track a human’s face, track
legs, and so on. The events from sensor values are detectedin the event handler, negating the need to copy an event
detection for each task thread. The event handler in robovie4
continuously,
1) collects sensor values from julian and robobase4;2) detects events;
3) sends events to task threads;4) receives commands from task threads; and5) sends commands to robobase4 as in Fig. 7.
Each of the task thread continuously,
1) waits for events from the event handler;2) reads sensor values sent from julian or robobase4; reads
from and writes to the database; and3) decides the next action.
The dialogue task is responsible for human-robot interactions and controls other task threads. In terms of the fourmodes of human-robot interaction defined earlier, the dialogue
task does not react to Mode A (human-initiated non-waiting)cameracameracameramicrophone
sensors and actuators

JulianSend results of speech
recognition to robovie4

robovie4
Event handler

1

Tasks (written in Lua)
Track face

1.

1
robobase4
Collect sensor values fromsensors and send them torobovie4
Receive commands fromrobovie4 and transfer themto actuators

2.
3.
4.

1

5.

5

Collect sensor
values
Detect events
Send events
Receivecommands
Sendcommands

React for contact3

Track Leg
Adjust parameters
depending on location
Get info.
from the net

Do dialogue4

robomap4

Do dialogue
in charge state

Map matching

Walk

Walk straight
Follow human

PostgreSQL

Database engine.

Fig. 7.
Julian.

Robovie-IV’s software architecture. The PC’s OS is Linux. There are six processes robovie4, robobase4, robomap4, robocam4, PostgreSQL, andinteractions since no reaction is expected. It just records
the event, such as someone patting the robot’s head. Whena Mode B (human-initiated waiting) interaction, such as ahuman approaching the robot, is detected, the task tries totake the initiative and move to the interaction Mode D (robotinitiated waiting) by initiating a dialogue. Although people
may feel that the robot interacted in Mode C (robot-initiatednon-waiting), no Mode C interaction is explicitly implemented.
An interaction in Mode D repeats the following until its
end.
1) The robot speaks and makes a gesture,2) the robot waits for an answer or reaction from thehuman, and3) the robot reacts according to the answer.
The Type1 interactions do not have steps 2) and 3). The Type2interactions do not use the result of Julian but just use the factthat the human utterance ends. The Type3 and 4 interactions
react according to the result of voice recognition. The Type3s
do not repeat a word which the human uttered, while theType4s directly include it in the robot’s answer.

Fig. 8.

Robovie-IV is roaming in the office at ATR’s IRC laboratory.

VI. O BSERVATION OF DAILY HUMAN ROBOT INTERACTION
IN AN OFFICE ENVIRONMENT

We are now conducting a long-term experiment in our officeat ATR’s IRC laboratory. Robovie-IV roams around the office
and interacts in our daily life. In the office there are about 100
people including researchers, engineers, and secretaries. The
total length of the corridor in which Robovie-IV can moveis about 150 meters. We implemented interactions resembling
those of a child in an office who comes along with its parent,
since the height of the Robovie-IV is about 1.1 m, similar
to that of a child. We are incrementally adding interactions
and updating other items of software during the experiment.
Fig. 8 and 9 show a scene in the office and an example ofinteraction.
Basically, Robovie-IV repeats a) moving around, seeking
a human to interact with; and b) interaction with the foundhuman until he or she quits the interaction. Robovie-IV detects
humans by first finding leg candidates from front laser rangesensor values by background subtraction. The laser range

Fig. 9. A scene from the experiment. A person is communicating with
Robovie-IV. The robot is asking if the person likes watching baseball.

sensor measures at the height of an ankle. Figure 10 illustrates
the readings of the range sensor plotted on the floor map.
The circle on the right and the line indicates the position andthe posture of Robovie-IV from self-localization data basedon map matching. Clearly, the walls and cartoon boxes aredetected by the sensor, whereas distant walls are not detected
since the sensor’s maximum range is 4 m. The two small
circles in front of the circle representing the robot indicate
detected legs. The circle on the left of the robot, however, is afalsely detected leg candidate. When a leg candidate is found,

.GIU
4QDQXKG+8(CMGNGI(a) Robovie-IV trying tocommunicate(b) A person patting therobot’s head while passing
by(c) Two people talking toeach other, one toying withthe robot’s nose(d) A person covering therobot’s eyes(e) Robovie-IV moving into
a conversation in progress(f) Robovie-IV interactingwith a person

Fig. 10. A depth image plotted on the floor map by a laser range sensor. A
circle and a line on the right indicate Robovie-IV’s position and posture. Twocircles in front of the robot are detected legs. The larger circle to the left of
the robot is a false detection.

Robovie-IV rotates itself and gazes in the direction of the leg
candidate. If a large skin-colored blob or a large blob in adifferential image is detected by Robovie-IV’s camera, or the
tag reader detects a valid ID, it assumes the candidate to be ahuman leg. During interaction, it tracks the skin-colored blobat the shortest distance in the direction of the leg candidate
and maintains that distance and posture with respect to theleg candidate. The tag reader’s attenuator is adjusted to detecttags within 1.5 m of Robovie-IV.
Once Robovie-IV has detected a human, it will try tointeract with him or her. It will say “Hi” in Japanese and wait
for the response. If it does not detect a response it will begin
to seek another human; otherwise, it will recognize the person
as the interaction partner. Interaction rules are derived fromthe Lua program (functions), gestures, and DFA grammars
for Julian. Currently, there are more than 70 interaction rules
and Lua codes implemented, with more than 5,000 lines ofcode. There are three branches on average on the conditional
branches of Type2, 3, and 4 interactions.
Robovie-IV records the results of interaction to the database.
Examples of results include with whom it interacted, which
rule it used, when the interaction rule started and finishedbeing used, the response from the human, how long the human
moved, how many times it met with a person, and so on. It also
records the number of leg candidates, skin-colored blobs, andIDs detected by the tag reader in a certain interval. RobovieIV uses the data in the database in order not to repeat sameinteraction, to say it has met many people today, and so on.
It also talks about current weather based on the informationfrom the Internet.
Fig. 11 shows some scenes from the daily experiment,(a) Robovie-IV trying to communicate, (b) a person patting
the robot’s head while passing by, (c) two people talking toeach other, one toying with the robot’s nose, (d) a person
covering the robot’s eyes, and (f) Robovie-IV interacting witha person. In scene (e), following its usual human-seeking
behavior, Robovie-IV moved into the middle of a conversationin progress. At this stage, of course, Robovie-IV is unable toFig. 11.

Some scenes from the daily experiment.

meaningfully join such a conversation. Robovie-IV does notalways interact with detected humans as in Fig. 11(a). because
the experimental environment is an office and people are busy
with their jobs. Though the novelty of having Robovie-IV inthe office has worn off, people still notice it out of the cornerof their eye while working, indicating that they are still awareof its presence, and that it is gradually changing its behavior.
Robovie-IV is still under development, but it has become arobot that makes its presence felt by people.
There still remain some technical issues to be solved. One
is the timing to say hello to a person passing by. There arecases that he or she will walk away before Robovie-IV starts
its greeting. The second issue is that there are people whoare not getting used to conversing with Robovie-IV. Julian
does not allow overlapping of human and robot voices without
external sound processing. Also there is a sweet spot in the
microphone where the success rate of recognition increases.

We do not have answers to deal with these issues yet.
Furthermore, there are different feelings toward what a
robot actually does. When Robovie-IV asks, “What are you
drinking?”, even if a person is not drinking, one would feel
it is very strange that the robot does not understand thesituation. The person might feel that is interesting, however,that the robot is making small talk, so he or she answers thequestion. What kinds of interaction increase familiarity andmake communication smoother is still open question. We think
this question will be answered by continuing the experimentand adding more types of interaction.

tactile sensors, it moves by an inverted pendulum mechanismthat causes involuntary motions, and it has sensors for humanrecognition. Finally, we briefly described the current statusof our ongoing daily experiment in our office and discussed
behaviors of the robot and how humans relate to it.
Future work will involve continuing the daily experimentin our office, and incrementally adding interaction rules toRobovie-IV, and analyze the recorded logs to clarify themeaning of “social interaction” in the context of human-robot
interaction.

VII. F UNDAMENTAL ISSUES AND CONCLUSIONS
An interaction robot communicating with people in everyday life is the key to revealing the social relationships ofa robot among people. Long-term everyday interactions arenecessary to observe and analyze the following;
Influence of a robot on people’s sense of time, e.g.
whether a robot’s behavior reminds us of the current time;for example, if the robot establishes a “habitual” routineof behaviors based on the time of day, could it serve to
make people aware of the time?
The relationship between a robot’s task and its preferredsocial interactions, e.g. what kinds of interactions arenecessary for a cleaning robot, a secretary robot, a
guiding robot, or a robot that carries heavy objects intandem with a human;
Preferred types of human-robot dialogues according to
the social relationship and the robot’s task, e.g. whether
simple statements are preferred, colloquial language ispreferred, or new type of language emerges;
The social role of a robot among people, e.g. whether
a robot will be perceived to be “alive” in a way that isdifferent from humans, plants, pets, inanimate objects,
and so on, and what factors contribute to this perception;
What kind of social behaviors would be acceptable todifferent communities.
We believe our daily experiment is the first step to reveal thesefundamental issues.
In this paper, we proposed a basic framework for analyzing
the daily interactions between a human and a robot. We first
discussed the daily interactions between a human and a robot.
Human-robot interaction falls into four modes according to
who initiates the interaction and whether he or she expects areaction from the other. Then, we subdivided one mode of thatinteractions, which a robot initiates and expects a reaction fromthe human, into four types according to the sensor information
the robot uses. We focused on the interactions which require
voice recognition but for which misrecognitions do not leadto severe discomfort.
Next, we explained what is necessary for a communication
robot and introduced the hardware and software architecturesof Robovie-IV. Robovie-IV has been developed as a selfcontained robot that fulfills the requirements of a communication robot. That is, it is as tall as a human child, its
entire body is covered by a soft-skin containing 56 embedded

This research was supported by the Ministry of Internal
Affairs and Communications.

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