International Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101

89

Regular Paper

Utility of Remote Workers’ Video of a Surrogate Telepresence Robot
for Predicting Its Motion1
Kosuke Sasaki† , Zijie Yuan† and Tomoo Inoue‡†

Graduate School of Library, Information and Media Studies, University of Tsukuba, Japan‡
Institute of Library, Information and Media Science of University of Tsukuba, Japan{ksasaki, inoue}@slis.tsukuba.ac.jp

Omnidirectional
cameraController to
get the location

Drive unit of thisrobot

Figure 1: Beam® from Suitable Technologies
Figure 2: The telepresence robot used in this study
Abstract - This study focuses on environments where people and robots coexist and investigates how to support the coexistence of people and robots. This study assumes a situationin which local workers and telepresence surrogate robots operated from remote locations work in the same ofﬁce. To investigate how to support the situation, we examined whether
a person and a robot can pass by each other by using a technology that projects an image of the person who operates therobot onto the robot, which has been proposed to support
physical cooperative work. The experiment results showedthat a person could predict the robot’s direction of travel andpass by looking mainly at the upper body of the remote person. This suggests the effect of projecting a human ﬁgure onto
a robot in an environment where humans and robots coexist.
Keywords: Cooperative work, Physical interaction, Nonverbal cue, Telepresence robot

1 INTRODUCTION
Remote work has recently become increasingly popular.
Nevertheless, face-to-face work remains essential. This study
considers a work environment in which telework and face-toface work are combined. That is, some workers work in anofﬁce while others work at a remote location. In this environment, the remote worker operates robots in the ofﬁce tocommunicate with other workers.
1 This paper is an extended version of the IWIN proceeding [1].
2 Home - Beam, https://suitabletech.com/home (Visited on
Jul 21, 2023)
3 Blue Ocean Robotics - We Create and Commercialize Robots, https://www.blue-ocean-robotics.com/ (Visited on Jul 21, 2023)

Tracking
cameraRoom P

Room Q

Robot B

Worker A

Tracking
cameraWorker B

Tracking
camerasRobot A

Figure 3: An environment of passing each other
The robot assumed for use in this study is a surrogate telepresence robot, such as the Beam Pro from Suitable Technologies (now Blue Ocean Robotics of Denmark), with a mobile
drive unit and a camera or monitor attached to the top of therobot. By using such a telepresence robot, workers in an ofﬁce
can move with the robot and communicate with remote workers via the robot. Such telepresence robots have been used
in ofﬁce environments [2], [3] and in non-ofﬁce environments[4], [5], and various studies have argued for the usefulness of
telepresence robots.
However, previous studies have not paid much attention to
the coexistence between humans and robots. There needs tobe more understanding of what kind of support is possible
when humans and robots freely move through the same space.
This study aims to achieve smooth coexistence between humans and robots. In order to examine what kind of assistanceis available, this study focuses on a situation in which a person and a robot pass by each other in a narrow space as one

ISSN1883-4566 © 2023 - Informatics Society and the authors. All rights reserved.

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K. Sasaki et al. / Utility of Remote Workers' Video of a Surrogate Telepresence Robot for Predicting Its Motionof the situations in which a person and a robot coexist.
In an environment in which humans and robots coexist, humans and robots must behave cooperatively. Many studies
have been conducted to support human-robot coexistence [3],[6]–[8], which is the aim of this research. We must determine
what kind of assistance is needed for people and robots tocoexist.
In cooperative work in a face-to-face environment or a remote environment with ﬁxed displays, it is known that workers can guess their partners’ actions by observing their facial
direction, body movements, and other information, thereby
being able to work without conﬂict or collision [9]–[11]. Additionally, whole-body movements are known to provide theviewer with emotional information such as credibility andpersuasiveness, which is essential to work collaboratively[12], [13]. However, since robots lack such nonverbal information, humans and robots may not pass by each other well.
Inoue and Yuan have proposed a virtual reality (VR) system for collaborative work with physical movement. In thesystem, a human and a telepresence robot (as shown in Fig.
2) operated by another person in a remote place passed byeach other, which is a form of collaborative work. This system shows the feasibility of a human and a telepresence robot
passing by each other in the same workplace [14].
In this study, we examined the method by which Inoueand Yuan initially proposed that a video of a robot operator’swhole body in a remote place is shown to a local worker who
passes by a telepresence robot. We veriﬁed the effect of thismethod in an environment where humans and robots coexist
based on objective and subjective data.
We examined the method by a passing-by task. A worker
and a telepresence robot were placed in the same workspacein the passing-by task. Another worker in another workspace
operated the telepresence robot. In the experiment, we arranged two pairs of a human and a robot in two workspaces(as shown in Fig. 3). Each worker faced a telepresence robotthat moved synchronously with a remote worker and wore ahead-mounted display (HMD) that displayed video capturedby the camera attached to the robot, which was in a remoteplace. The worker could walk and see the remote worker’s
whole body through the HMD.
We analyzed how a worker moved while seeing the remote worker’s whole body during the passing-by task basedon recorded video of the experiment, a questionnaire survey,
and interviews with the workers. The analysis result showsthat a worker passing by a telepresence robot tended to lookat the upper body of the remote worker’s image. Additionally,
workers using the system could predict where the robot wasmoving to [1]. This result suggests that the system displaying the remote worker’s image, especially the worker’s upper
body, onto the robot can support situations in which humans
and robots coexist.

2 RELATED WORK
We investigate a situation in which a human and a telepresence robot pass by each other, regarded as remote cooperative
work between a local worker and a remote worker. In thischapter, we introduce remote cooperative work support.

2.1

Remote Communication Support Using
Flat Fixed Displays

Unlike face-to-face environments, remote environments
lack visual and nonverbal information, such as gaze information and body movements. Since nonverbal information isessential for smooth communication, studies have been conducted on communicating visual and nonverbal information
with each other to facilitate remote collaboration.
Fixed displays are often used to communicate in remote
workspaces. Previous studies have proposed systems thatuse worker gaze information [15], or that project a sharedworkspace or background to give users immersion and the
feeling of being in the same room [16], [17]. Some proposedsystems show images of remote workers on ﬁxed displays.
Methods of overlaying remote persons on the background of
a ﬁxed display [18] or showing a worker’s life-size image on
display [19] enable communication as if the workers are in
the same space.
Other studies have proposed methods of conveying nonverbal information by projecting images of a remote worker
instead of using ﬂat displays [20], [21]. However, methods using ﬂat displays or projections greatly restrict workers’ body
movements, and these methods cannot support remote cooperative work involving spatial movement.

2.2

Remote Communication Support Using
AR and VR Technologies

Systems using VR and augmented reality (AR) technologies with HMDs have also been proposed to solve the problem of movement restriction, including systems that immerse
a worker in the same virtual space with a remote worker [22],[23], systems that immerse a worker by displaying the remote workspace [24], and systems that can switch the image displayed on the HMD to other images captured fromother viewpoint, such as another worker’s point of view ora bird’s-eye view [25]. Furthermore, some systems use surrogate avatars of workers for smooth communication [26]–[29].
These previous studies show that AR and VR technologies
can reduce restrictions on worker movement. However, incollaborative work in a virtual space, workers do not havephysical bodies and cannot interact in real space. Furthermore, using avatars can eliminate the nonverbal informationof workers.

2.3

Remote Communication Support Using
Telepresence Robots

Several studies have investigated telepresence robots, as we
focus on in this study, to support communication in the ofﬁce. A study by Shen et al. investigated the interpersonal
distance between a person and a telepresence robot. American and Indian participants were tested in a situation in which
the participant operated a telepresence robot to approach a
co-worker working in an ofﬁce. The results revealed thatthe worker operating the robot behaved similarly in terms ofculture-speciﬁc distancing as in a face-to-face environment[2]. Myodo et al. investigated whether telepresence robots

International Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101

91

could facilitate informal communication in an ofﬁce setting.
Experiments using a telepresence robot to communicate between a remote supervisor and a subordinate in the ofﬁce revealed that the supervisor’s facial expressions were conveyed
more strongly when the telepresence robot was used [3].
There have been studies using telepresence robots in environments other than the ofﬁce. Newhart and Olson’s study
investigated the use of telepresence robots by children who
cannot attend school due to illness. While they showed thatthe use of telepresence robots in schools could provide learning opportunities for children who are unable to attend school,
they also pointed out that there is room for improvement in
the school environment where telepresence robots are used,such as physical barriers in the school and the operability of
the robots [4]. Another study examined the use of telepresence robots to allow remote users to attend academic conferences. In this study, telepresence robots were arranged
at ACM international conferences such as CHI, CSCW, andUBICOMP/ISWC to observe the reactions of local attendees
and their interactions with the telepresence robots’ in conference activities. As a result, they revealed several insights
about the requirements for telepresence robots and systems,
which varied depending on the characteristics of each conference [5].
Other systems, including ThirdEye which presents bothﬁrst- and third-person perspectives simultaneously to the operator of the telepresence robot [30], and the telepresence
robot system to shop with a remote user [31], have been
proposed. Additionally, robots with higher degrees of freedom, including MeBot and iRIS, which can change facial
orientations and move their arms just like human beings [6],[32]–[35], have also been used in research.
However, these studies have yet to focus primarily onhuman-robot coexistence. In particular, nonverbal information that was claimed to be necessary for cooperative movement in ﬂat ﬁxed display environments, AR, and VR-based
environments is not always conveyed because only what the
robot can reproduce can be communicated to the other person.
Therefore, in this study, we use VR technology to give images of people in remote locations to the telepresence robot.
By showing the remote worker the image of a person as they
are, nonverbal information is communicated.

body through an HMD. Using an HMD removes restrictionsof body movement, and showing the appearance of the remote worker to the local worker prevents them from missing nonverbal information when compared to using avatars[14]. However, their experiment was unrealistic: a worker
only walked 1.0 m to pass by the robot. Furthermore, theevaluation was based on a small number of participants.
Sasaki et al. evaluated the VR system through a more realistic experiment with more participants. They reported theresults of a questionnaire survey and interviews with participants, which revealed that a worker passing by a robot lookedat the human body to predict where the robot was moving to[1]. In this paper, we show results of objective and subjective
data, which support the previous claim that a worker looks at
the upper body when passing a robot for smooth movement.

2.4 Passing by a Telepresence Robot
In general, robots need to ensure the safety of humans [36].
For example, studies have investigated collision avoidance
between humans and autonomous robots by using sensors(e.g., [37]–[39]), predicted emotions from a person’s face totake avoiding action [40], and designed or developed interfaces for human-operated robots [41]–[43]. However, little is
known about situations in which a human passes by a humanoperated robot.
Inoue and Yuan have proposed a VR system for smoothphysical collaboration between a human and a robot and evaluated the system in a situation in which a human passes a surrogate robot-that is, a robot operated by another worker. The
system provided video of the remote worker to a telepresence
robot, and the local worker could see the remote worker’s

3

ENVIRONMENT WHERE A WORKER
AND A TELEPRESENCE ROBOT PASSBY EACH OTHER

In this study, we focus on a situation in which a local
worker and a telepresence robot operated by a remote worker
pass by each other in the same space to investigate how to
support the coexistence of humans and robots. In this section,
we explain the situation in which a worker and a telepresence
robot pass by each other.

3.1

Overview of the Environment

The environment in which a worker and a robot pass byeach other is shown in Fig. 3. Two pairs of a worker and amobile robot equipped with a 360-degree camera (as shownin Fig. 2) were arranged in two separate workspaces. The
robots moved according to the position of the remote workers: for example, when worker A moved forward, robot A
also moved forward. Each robot moved, synchronizing its respective worker’s position, as shown in Fig. 4.
Workers wore HMDs that showed videos captured from thecamera of their respective robots; therefore, worker A could
see the video of worker B in room Q, acquired by robot A’scamera through the HMD, and worker B could see worker A
in room P acquired by robot B’s camera through the HMD.
In this way, an environment was set up in which the local
worker and the telepresence robot operated by the remoteworker passed by each other. Figure 5 shows an image of theworker in the remote location as presented through the HMD.
Originally, to analyze the system’s effectiveness, it was notnecessary to prepare two sets of workspaces as shown in Fig.
3, but only one workspace was needed, with one worker, one
robot, and one remote worker operating the robot. However,by arranging the worker and robot as shown in Fig. 3, twosituations occurred for the two workers, in which the remoteworker is operating the robot in front of each worker, and two
samples can be obtained at the same time per experiment trial.

3.2

Implementation of the Environment

The environment was constructed as shown in Fig. 3. To
eliminate differences between facilities, we set up two rooms

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K. Sasaki et al. / Utility of Remote Workers' Video of a Surrogate Telepresence Robot for Predicting Its Motion

Room Pxx

Robot B

Room Q

Worker BzzWorker A

Robot A

Figure 4: Synchronization between the workers and therobotsAn HMD shows the videoof the remote worker

Room P

Room Q

Worker A

Worker B
Robot B

Robot A

Figure 5: The view in the HMDof the same size and shape, and the robots and sensors inboth rooms were the same. Two tracking cameras were set
up on the diagonal of each workspace to establish the positional standard.
The workers and the robots’ acquired position and orientation information was sent to a PC, which sent commands toeach robot to move, synchronizing the position of the remoteworkers.
We used VIVE™ products from HTC Corporation1 in thisexperiment. The VIVE™ products can obtain position andorientation information by receiving optical lasers from thetracking camera via sensors embedded in the HMD and thecontroller.
The mobile robot shown in Fig. 2 was the iRobot® Create
2 from iRobot Corporation2 . iRobot® Create 2 is a mobile
robot that can rotate in any direction and move forward andbackward. The two wheels on the robot can be commanded
separately, and by making one wheel move slower than theother wheel, the robot can rotate while moving forward.
A height-adjustable tripod for the camera was placed on
top of the mobile robot. A VIVE™ controller was attached tothe tripod. A RICHO3 360-degree camera, the Theta V™ , wasattached to the tip of the tripod to capture ﬁrst-person images
1 VIVE - VR Headsets, Games, and Metaverse Life, https://www.
vive.com/ (Visited on Dec 10, 2022)
2 Coding Robots, Learning Library & STEM Outreach | iRobot Education, https://edu.irobot.com/ (Visited on Dec 10, 2022)
3 Ricoh Global | EMPOWERING DIGITAL WORKPLACES, https://www.ricoh.com/ (Visited on Dec 10, 2022)

to be presented to the remote worker. The 360-degree camera
eliminated the need to move the camera with the worker’s
orientation, allowing the HMD to adjust its ﬁeld of view tothe worker’s small movements.
The images captured by the robot were transmitted to the
HMD using WebRTC technology. This entailed a delay fromwhen the camera acquired the image to when it was displayedon the HMD. This delay was an average of 0.43 seconds(SD: 0.10 seconds) based on the preliminary measurements(N=16).

3.3

Synchronization of Worker and Robot
PositionIn this system, the same systems to match the positionsof the worker and the robot were prepared for the twoworkspaces. Each worker’s position was represented by x
and z coordinates in Unity™4 . After obtaining the workers’
position information, the PC sent the position coordinates ofworker A in the coordinate system of room P to robot A inroom Q, and it sent the position coordinates of worker B in
the coordinate system of room Q to robot B in room P. Eachrobot moved based on the location information of the remoteworker, as shown in Fig. 4.

4

EXPERIMENTThis study focuses on environments where humans androbots physically coexist and aims to support coexistence. In
particular, this study assumes an environment where humans
and robots work in the same ofﬁce. Among the problems thatcan occur when humans and robots coexist in such an environment, this study focuses on situations where a human anda robot pass by each other in a narrow space, such as a corridor or passageway. In order to support the situation, this study
examined the effectiveness of a telepresence robot system developed for real-world cooperative work support [14].

4.1

Passing-by Task

In order to verify how adding a worker’s video to eachrobot would support the situation in which a worker and arobot pass by each other, we experimented with a passing-by
task (approved by the Ethics Review Committee of the Faculty of Library, Information and Media Science, University ofTsukuba (Notiﬁcation number: 20-15)).
In the passing-by task, workers A and B at two remote locations passed by robots B and A, respectively (as shown in
Fig. 3), and a previous study [14]. However, in the previous study [14], the initial distance between the worker andthe robot was only 0.5 m, and the travel distance was 1.0 m,
which was too short for a passing-by experiment. Therefore,
in this study, we set the travel distance between the worker
and the robot to 3.0 m.
The workspaces were set up in an area of our laboratory.
The workspace size was approximately 2.0 m wide by 4.0 m
long. Two lines were drawn 3.0 m apart in each workspace;
4 Unity Real-Time Development Platform | 3D, 2D VR & AR Engine,
https://unity.com/ (Visited on Dec 10, 2022)

International Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101

Room Q

Room P
WGL

RSP

WSP
3.0 m

z

93

(a)
Room Q
Worker B

Room P
Robot B

RGL

3.0 m
z

3.0 m

View ofworker B

RGL

WSP

RSP

3.0 m

View ofworker A

WGL

Figure 6: Passing-by task
one indicated the worker’s starting point (WSP ), and the otherindicated the robot’s starting point (RSP ). We ensured thateach line’s z coordinates were the same (see Fig. 6). Whenthe experimenter signaled the start, the workers at each starting point started walking toward their goal lines. The robots
also moved forward in each workspace because each robot
moved in synchronization with a worker’s position. The
workers and the robots moved forward, slightly shifting directions to avoid collisions. We deﬁned task completion as
the time that both workers’ bodies crossed their respective
worker goal lines (WGL ) and both robots’ bodies crossed theirrespective robot goal lines (RGL ) in both workspaces. We observed the workers’ movements from when they started walking to task completion.

4.2 Experiment Conditions
In face-to-face environments, people are known to avoid
collisions and coordinate their movements by seeing the otherperson’s actions. Thus the appearance of the person is essential for predicting the other person’s movements [9]. For
robots and humans to coexist in the same space, methods havealso been studied in which the robot captures the human’s image with a camera and acts to avoid collisions [39]. However,as we focus on in this study, in an environment where a human and a surrogate robot operated by a human coexist, howa person moves when the other’s image, i.e., the robot’s operator, has not been studied before, excepting Inoue and Yuan’s
study [14]. So, we need to reveal the effect of showing thewhole body to a person in an environment with a telepresence
robot.
In this study, we conducted an experiment to investigate theeffect of projecting the operator’s entire body image onto arobot through an HMD when the human and the telepresence
surrogate robot pass by each other in an environment where
they coexist.
We compared two conditions in the experiment basedon what workers could see through the HMDs during thepassing-by task. One is the human condition; in this condition, as described thus far, workers were wearing HMDs
at both remote locations, and each HMD displayed the videofrom the camera mounted on the robot (as shown in Fig. 7).
The other is the robot condition; in this condition, worker
A’s HMD displayed the see-through video in front of them (asshown in Fig. 8). Unlike the human condition, worker A had
to move while looking at robot B, although worker B moved

Robot A

Worker A(b)
Room P

Robot B

Worker A

View of worker A

Room Q

View of worker B

Worker B

Robot A

Figure 7: The human condition: (a) an overview; (b) theview of each worker.
while looking at worker A to operate robot B during the task.
So, situations were not symmetrical between the participantsin a pair. Therefore, only worker A, who performed the task
while looking at the robot, was subject to the analysis, andworker B was not. Robot A, which passed by worker B andwas operated by worker A, was not used in the robot condition.
In both conditions, workers were not allowed to talk to eachother to stimulate visual information transfer between workers’ movements. Therefore, during the experiment, workers
moved based only on visual information. In comparing thetwo conditions, we analyzed the effect of displaying the remote worker’s full-body image to the local worker during thepassing-by task.

4.3

Participants

Six pairs of 12 graduate students (all male) participated in
the experiment. Of the 12, ﬁve had never used an HMD before
this experiment. Only one of the six pairs had never met eachother before.

4.4

Procedure

The experiment consisted of a within-participant design;
Table 1 shows the experiment’s procedure. First, each pair
was assigned to the human or robot condition, which werecounterbalanced. In each condition, participants were assigned to be workers A or B and then moved to the remote workspaces rooms P or Q. After the experimenter explained the system, the participants practiced the passing-by

94

K. Sasaki et al. / Utility of Remote Workers' Video of a Surrogate Telepresence Robot for Predicting Its Motion(a)also about the presence of the partner and the sense of being
in the same room with the robot. Participants responded to theevaluation items using a 7-point scale ranging from “strongly
disagree” to “strongly agree.” The questionnaire items areshown in Table 2.
In both conditions, we also conducted open-ended interviews after the task was completed. Participants were asked
to respond freely to questions about their impressions of thetask and the system.

Room Q
Worker B

Room P
Robot B
View ofWorker B

3.0 m

3.0 m

View ofWorker A

Worker A

4.5(b)Room P

Robot B

Worker A

View of worker A

Room Q

View of worker B

Worker B(as a controller)

Figure 8: The robot condition: (a) an overview; (b) the viewof each worker.
task twice to familiarize themselves with the task and use of
the system. The experimenter delineated the placement of
the goal line and how far the participants could move in theworkspace, explaining until both participants understood thetask. The experimenter also asked participants to walk without bumping into the robot and did not mention how fast or
long they should walk.
After the practice rounds, the participants performed thetask once. After the task, the sequence was repeated under another condition. The robot condition differed from thehuman condition in that worker A passed by robot B, butworker B walked alone-that is, the robot condition was asymetrical. Therefore, after completing the task, workers A andB switched roles and performed the task (including practicerounds) again. As a result, the task was performed a total of
six times (one time for each pair) for all pairs in the humancondition and 12 times (two times for each pair) in the robot
condition (excluding the practices).
One experimenter was assigned to room P, and another was
assigned to room Q, to prevent workers from colliding withthe robot and from catching their feet on the cords connecting
the robot and the PC during the experiment.
A questionnaire survey was conducted after the task in eachcondition. According to previous studies investigating remotecooperative work through robot assistance, whether workers
are satisﬁed with remote cooperative work involving robots
depends on the presence of the partners and the feeling thatthey are collaborating with the partners [44], [45]. Therefore,
in this study, ten questions involving subjective evaluations
were posed, asking not only about the robot’s usability but

Data Collection

The coordinates of the workers and the robots were acquired using HMDs worn by workers and controllers mounted
on mobile robots. In the human condition, coordinates wereacquired for six workers and six robots in six pairs and two
locations; in the robot condition, coordinates were acquiredfor 12 workers and 12 robots in six pairs and one location(Room P). The frequency of coordinate capturing was ﬁvetimes per second (every 0.2 seconds), the same as the frequency at which coordinates were exchanged between the
HMD and the PC.
To examine how the workers passed by each other in response to the visual information presented, we obtained thevideo that the HMD showed the workers during the task.
HMD videos were not gathered during practice rounds, so
there were 12 videos for six pairs in two rooms in the humancondition and 12 videos for six pairs in one room (Room P)
performing the task two times in the robot condition. Thus, a
total of 24 videos were obtained across both conditions.
The questionnaire survey results and the interview transcripts were also used for subjective evaluation.

5

RESULT

In the experiments conducted in this study, there was thepossibility of collisions between the robot and the worker.
However, no cases of collisions occurred between the robot
and the worker. There were two cases in which a worker’s
leg was touched lightly by the robot in the human condition,
but since none of these cases resulted in worker injury, theexperiment was not interrupted.
In this chapter, we present the results of our analysis of
the quantitative data (the users’ movements and the directionof the users’ ﬁeld of view) and of the qualitative data (thequestionnaire evaluation) [1].

5.1

The Workers’ Movement

Table 3 shows the mean values of the workers’ walking
time, workers’ walking distance, workers’ walking speed,
robots’ moving distance, robots’ moving speed during thetask, and time from commencement of walking to passing therobot (N=12). A one-way MANOVA (two levels of condition: human condition and robot condition) with the dependent variables of worker walking time, worker walking distance, worker walking speed, robot moving distance, robot
moving speed, and time from commencement of walking topassing the robot was conducted to test differences in theInternational Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101

95

Table 1: The experiment procedure
Procedure
1.

3.
4.
5.

Human condition
Workers A and B stood on their respective startpoints (see 7(a)).
Workers were informed of the system and practiced
the task twice.
Workers performed the task once.
Workers ﬁlled out the questionnaire.
-

6.

-

7.
8.
9.

-

2.

Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10

Robot condition
Workers A and B stood on their respective startpoints (see Fig. 8(a)).
Workers were informed of the system and practiced
the task twice.
Workers performed the task once.
Worker A ﬁlled out the questionnaire.
The role of workers A and B were switched, and theystood at their respective start points.
Workers were informed of the system and practiced
the task twice.
Workers performed the task once.
Worker A ﬁlled out the questionnaire.
Workers were interviewed after both conditions had been conducted to completion.

Table 2: Questionnaire items
Items
After using the system, I felt uncomfortable in away similar to motion sickness.
The video was clear and pretty.
I felt like the remote person was in the same roomwith me.
I could clearly perceive the motion of my partner.
I felt like I passed the other person in the sameroom.
The ﬁeld of view changed naturally in line withmy movements.
I perceived the movements and directions of mypartner.
I could predict where my partner was moving to.
My partner’s movement looked realistic.
The experience in the virtual environment wasconsistent with experiences in real life.

means between the conditions, but no signiﬁcant differenceswere found (F (1, 6) = 0.37, p = .89).

5.2 Workers’ Fields of View During the Task
Figure 9 shows an example of the video displayed on the
HMD during the experiment, which the worker was viewing.
This image presents the worker’s ﬁeld of view (FOV), whichis categorized into three patterns based on where the center of
the FOV (red dot in Fig. 9) was facing: the upper body, thelower body, or the ﬂoor. The deﬁnitions of the direction of
the worker’s FOV are shown in Table 4. For example, when
the center of the worker’s FOV was above the waist in thehuman condition, the worker was considered to be looking at
the upper body; in the robot condition, when the center of theworker’s FOV was facing the drive part, the worker was considered to be looking at the lower body. The measurements
were taken from the start of walking to the point of passing
each other. The worker and the robot faced either the upper
body, the lower body, or the ﬂoor, and they never faced another direction until they passed each other. The average time

Table 3: Averages of movements of workers and robots

Walking time [s]Walking distance of the worker[m]Walking speed of the worker[cm/s]
Moving distance of the robot[m]Moving speed of the robot[cm/s]
Time from starting walking
to passing by the robot [s]Humancondition
12.28(SD: 3.04)
3.64(SD: 0.29)
31.2(SD: 6.96)
2.94(SD: 0.58)
24.3(SD: 2.72)
7.27(SD: 1.48)

Robotcondition
11.67(SD: 3.61)
3.67(SD: 0.38)
33.7(SD: 8.19)
2.73(SD: 0.78)
24.4(SD: 3.50)
7.30(SD: 2.91)

spent looking at each part is shown in Table 4, and the average
ratio for all pairs is shown in Fig. 10.
The one-way MANOVA (two levels of condition: humancondition and robot condition) conducted with the dependent variable of the ratio of time spent looking at each viewing body part showed signiﬁcant differences (F (1, 22) =11.30, p = .0002). We also conducted corresponding Bonferroni t-tests, which revealed signiﬁcant differences in thefollowing six items (as shown in Fig. 10):
• Time spent looking at the upper body in the human condition vs. time spent looking at the lower body in thehuman condition (t(11) = 7.16, p = .0003)• Time spent looking at the upper body in the human condition vs. time spent looking at the ﬂoor in the humancondition (t(11) = 11.38, p < .01)
• Time spent looking at the upper body in the human condition vs. time spent looking at the upper body in the
robot condition (t(11) = 5.73, p = .002)• Time spent looking at the upper body in the human condition vs. time spent looking at the lower body in the
robot condition (t(11) = 4.37, p = .02)
• Time spent looking at the upper body in the human condition vs. time spent looking at the ﬂoor in the robot

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K. Sasaki et al. / Utility of Remote Workers' Video of a Surrogate Telepresence Robot for Predicting Its Motion

Table 4: Time viewing body partsDeﬁnition

Human condition
Direction of the center
Mean/SD [s]of the FoV

Upper
body

Above the waist

6.88 / 2.01

Lower
body

Below the waist
and above the ﬂoor

0.38 / 1.27

Floor

Floor

0.00 / 0.00

Robot condition
Direction of the center of the FoV

Mean/SD [s]Above the center of the robot(including the prop and 360-degree camera)
Below the centerand above the ﬂoor of the robot(including the prop)
Floor

1.40 / 2.24
3.25 / 2.40
2.65 / 2.94** p < .01 * p < .05

**
**
*
**
**

*

Figure 9: Screenshots displayed to workers. The upper left
shows the worker looking at the other worker’s upper body;the upper right shows the worker looking at the otherworker’s lower body; the lower left shows the worker
looking at the ﬂoor.
condition (t(11) = 5.28, p = .004)• Time spent looking at the ﬂoor in the human condition
vs. time spent looking at the lower body in the robot
condition (t(11) = −4.50, p = .01)
In particular, the ﬁrst ﬁve items indicated that more time isspent looking at the upper body in the human condition thanin either of the two conditions, implying that the worker islooking at the upper body longer because they can see thehuman image.

5.3 Questionnaire
Ratings were treated as a score from 1 to 7, and a Wilcoxon
signed-rank test was conducted for each question item; theresults are shown in Table 5. In particular, the scores of fourquestions were signiﬁcantly higher in the human condition
than in the robot condition: Q4 “I could clearly perceive themotion of my partner” (p < .01); Q7 “I perceived the movements and directions of my partner” (p < .01); Q8 “I could
predict where my partner was moving to” (p < .01); and Q9“My partner’s movement looked realistic” (p < .05).

6 DISCUSSION
In the human and the robot conditions, a signiﬁcant difference was found in the direction of the worker’s face during thetask: workers in the human condition spent relatively moretime looking at the upper body of the remote worker, while

Figure 10: Time viewing body parts. Asterisk(s) show
signiﬁcant differences, and error bars show standard
deviations.
workers in the robot condition spent more time looking at thelower body and the ﬂoor. First, we discuss the results of thequestionnaire survey and the interviews.
In the questionnaire survey, as mentioned in section 5.3,
there were signiﬁcant differences in the following items: Q4“I could clearly perceive the motion of the partner”, Q7 “Iperceived the movements and directions of my partner”, andQ8 “I could predict where my partner was moving to.” These
differences are considered to have occurred because the human whole-body image was presented exactly as the remoterobot’s camera captured it in the human condition.
In addition, in the interviews, participants in the humancondition provided such comments as “I could predict the
robot’s path when looking at a human (P3)” and “I could
clearly see its [the robot’s] direction of movement when looking at a human (P5).” In contrast, participants in the robot
condition provided these comments: “I could not see therobot’s direction of movement (P2, P7)” and “I was confused
about the robot’s direction of movement (P8).” Based on thecomments’ differences, the local workers seemed to predictthe robot’s movement in the human condition, in which thehuman image was seen in the HMD. In contrast, the robot’s
direction of movement was unpredictable in the robot condition, in which the robot’s image was seen in the HMD.
The result of the interviews suggests that the worker in theInternational Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101

97

Table 5: The study’s questionnaire; p-values are the results of Wilcoxon signed-rank test.
(∗∗ p < .01, ∗ p < .05, † p < .10)
Items
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10

After using the system, I felt uncomfortable in a way similar to motion sickness.
The video was clear and pretty.
I felt like the remote person was in thesame room with me.
I could clearly perceive the motion of mypartner.
I felt like I passed the other person in thesame room.
The ﬁeld of view changed naturally in linewith my movements.
I perceived the movements and directionsof my partner.
I could predict where my partner was moving to.
My partner’s movement looked realistic.
The experience in the virtual environment
was consistent with experiences in real life.

Mean / Median / Mode in
the human condition
4.0 / 5 / 2, 5, 6

Mean / Median / Mode in
the robot condition
4.3 / 5 / 5

5.1 / 5 / 5
6.3 / 6.5 / 7

4.3 / 4.5 / 3, 5
5.5 / 6 / 6

.08†
.13

6.3 / 6 / 6

5.3 / 5 / 5.008∗∗

5.3 / 5.5 / 5, 6, 7

5.6 / 5.5 / 5.69

5.5 / 5.5 / 5

5.4 / 5.5 / 5

1.00

6.1 / 6 / 6

4.6 / 4.5 / 4.003∗∗

6.2 / 6 / 6

4.2 / 4 / 4.001∗∗

5.8 / 6 / 6
5.6 / 5.5 / 5

4.2 / 4.5 / 5
4.8 / 5 / 4, 5

.02∗
.16

human condition predicted the robot’s next move by looking
at the upper body but that the worker in the robot condition
could not predict the robot’s movement by looking at the upper body. This may be why the worker looked at the lower
body, which is the robot’s moving part, longer in the robot
condition to try to predict where the robot would go. This indicates that presenting an image of the worker’s upper bodyis important to predict the remote worker’s action when cooperative work involving movement is performed across remotelocations.
Previous studies have also proven the importance of having the human image in collaborative work. A study of acollaborative bicycle repair task in face-to-face and remotecontexts found that the visual cues in the human image could
provide the grounding for their conversation and the task [10].
Another study involving a remote collaborative task of assembling a toy was conducted in a specially designed space
where the local worker and a table were surrounded by eightﬁxed displays showing a remote worker; it found that the local
worker could predict the remote worker’s movement by seeing the upper body of their remote worker and could thus prepare for the following action [11]. Unlike these studies of predeﬁned collaborative work in a ﬁxed place, our study demonstrates the usefulness of showing the human video when passing a robot, as this helps predict the robot’s moving direction.
So far, we have mentioned that by projecting human images onto a robot, a person can predict the robot’s direction of
travel. We will further discuss other effects of human images
on robots.
Considering the worker’s FOV, a robot with an image of aperson can not only predict the actions of the remote worker
but also prevent the worker from being unable to see its surroundings by looking down. This study considered a situation

p-value.79

in which workers and surrogate robots worked together in thesame workspace. Walking while looking down can lead to acollision with a robot, another person, or an object, not only ina narrow spaces but also in other spaces in an ofﬁce. Presenting a human video can prevent collision with others during
remote cooperative work.
It is important to see the other person passing by when
passing each other. However, the robot with no person’s image lost various sources of human information, such as body
movements and gaze, which may cause a certain sense of anxiety that the human may collide with the robot. Conversely,
giving a human image to a robot may help remove this insecurity and give a sense of safety, such as “I can avoid collision
with the robot.”
It should be noted that this study has limitations.
In this experiment, for safety reasons, robots with a low
center of gravity and concentrated major parts (including thedrive unit on the bottom surface) were used, which is the samemodel as the cleaning robots that are currently available inrelatively large numbers. Since this mobile robot has a geometrically symmetrical shape, it is difﬁcult to determine thedirection in which it is moving, especially when looking at
the ﬁgure of the robot, as in the robot condition. If this werea humanoid robot in which the direction of movement wasmore perceptible than a symmetrical-shaped robot, the experimental results of this study might be different. In any case,the ﬁndings of this study may be helpful in building a telepresence robot system using current major mobile robots, likethe one used in this study.
In the current experiment, the workers’ walking speed wasapproximately 30 cm/s, which is slower than the speed atwhich humans generally walk. Various factors should be considered:98

K. Sasaki et al. / Utility of Remote Workers' Video of a Surrogate Telepresence Robot for Predicting Its Motion• The workspace was small.
• The worker’s foot could have caught on the cord connecting the robot and the PC.
• Two practice rounds were insufﬁcient for conﬁdent
walking with the HMD.
We hope to study and analyze the results of higher walking
speeds in the future.
Finally, we discuss applying our ﬁndings to the real world.
This study assumes an in-company environment where
people and robots come and go freely. Therefore, besides acorridor, people and robots move in various places and situations, and it is hard to establish rules such as “any persons must keep to the left, and any robots must keep to theright.” In previous studies in which robots moved in ofﬁce environments or other environments with humans (e.g., [46] and[47]), the experiments were conducted in environments thatallowed movement within the same space without restrictions
on movement for both the persons and the robot. As in thesestudies, in studying ways for humans and robots to coexist,
it is also necessary to consider methods that do not depend
on rules. Thus, our ﬁndings on the effectiveness of showing
the person’s whole body may be helpful in situations where
humans and robots coexist.
However, preparing the equipment is more burdensome
than in a conventional environment. In our environment, therobots need to acquire the position of a remote person andshow the remote person to each other. We used sensors torealize the former and HMDs to realize the latter. In the future, it will be possible to reduce the burden of system construction and the user by using a camera attached to the HMD
to acquire location information through image recognition or
by using AR glasses, which are lighter and less burdensome
for the user. Additionally, to use our ﬁndings, a space forthe robot operator to walk around is necessary. This research
scenario is feasible if, for example, a satellite ofﬁce or coworking space is used as a remote ofﬁce. In addition, thesedevices that can freely walk in place in 360 degrees (e.g., thesystems of [48], [49], or the Virtuix™ Omni and Cyberith Virtualizer used in [50]) can be used to realize this environment
in a smaller physical space.

7 CONCLUSION
The purpose of this study is to support human-robot coexistence. Assuming an ofﬁce where humans and telepresence
surrogate robots work in the same place, we focused on a situation in which a human and a robot pass by each other. This
study uses a VR system that presents a remote user’s wholebody image to the robot [14], which was initially proposed tosupport real-world cooperative work between a human and arobot. We veriﬁed how the system could support the passingby of a human and a robot. We conducted an experiment andanalyzed subjective and objective data from the passing-by
task. The results show that workers using the system looked at
the human upper body to predict the robot’s movement. This
indicates that in an environment where humans and robots co-exist, projecting a full-body image of a person onto a robot is
helpful for smoothly moving in the same physical space.

ACKNOWLEDGEMENT
The authors would like to thank Dr. Yasuhito Noguchi for
his cooperation in conducting the experiment.

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International Journal of Informatics Society, VOL.15, NO.2 (2023) 89-101(Received: December 13, 2022)(Accepted: August 30, 2023)
Kosuke Sasaki received his M.Sc. in Informatics from the University of Tsukuba in 2016. He iscurrently studying in the University of Tsukuba,
Japan. He is also a Specially Appointed AssistantProfessor of the Faculty of Global Management,
Chuo University, Japan. His research interests include Computer-Supported Cooperative Work andEducational Technology.

Zijie Yuan received his M.Sc. in Informatics fromthe University of Tsukuba in 2020. He currently
works for Rakuten Mobile, Japan. His research interests include Computer-Supported Cooperative
Work and mobile computing application.

Tomoo Inoue is Professor of the Institute of Library, Information and Media Science of University of Tsukuba, Japan. His research interests include Computer-Supported Cooperative
Work, Human-Computer Interaction and Educational Technology. He received his Ph.D. in Engineering from Keio University in 1998. He isa recipient of awards including Outstanding Paper Award, Activity Contribution Award from Information Processing Society of Japan (IPSJ), andBest Faculty Member of 2021 from his university.
He has served a number of academic committees, including IPSJ JournalGroup Editor-in-Chief, IPSJ Transactions on Digital Content Editor-in-Chief,
IEICE Technical Committee on Human Communication Science Chair, IPSJSIG Groupware and Network Services Chair, ACM CSCW Papers Associate
Chair, CollabTech Steering Committee, and IEEE TC CSCWD.

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