# Unwinding Rotations Improves User Comfort
with Immersive Telepresence Robots

Markku Suomalainen, Basak Sakcak, Adhi Widagdo, Juho Kalliokoski, Katherine J.
Mimnaugh,
Alexis P. Chambers, Timo Ojala and Steven M. LaValle
Center of Ubiquitous Computing, Faculty of Information Technology and
Electrical Engineering,
University of Oulu, Oulu, Finland
<EMAIL_ADDRESS>This work was supported by Business Finland (project
HUMOR 3656/31/2019); Academy of Finland (projects PERCEPT 322637, CHiMP
342556); and the European Research Council (project ILLUSIVE 101020977)

###### Abstract

We propose unwinding the rotations experienced by the user of an immersive
telepresence robot to improve comfort and reduce VR sickness of the user. By
immersive telepresence we refer to a situation where a 360° camera on top of a
mobile robot is streaming video and audio into a head-mounted display worn by
a remote user possibly far away. Thus, it enables the user to be present at
the robot’s location, look around by turning the head and communicate with
people near the robot. By unwinding the rotations of the camera frame, the
user’s viewpoint is not changed when the robot rotates. The user can change
her viewpoint only by physically rotating in her local setting; as visual
rotation without the corresponding vestibular stimulation is a major source of
VR sickness, physical rotation by the user is expected to reduce VR sickness.
We implemented unwinding the rotations for a simulated robot traversing a
virtual environment and ran a user study (N=34) comparing unwinding rotations
to user’s viewpoint turning when the robot turns. Our results show that the
users found unwound rotations more preferable and comfortable and that it
reduced their level of VR sickness. We also present further results about the
users’ path integration capabilities, viewing directions, and subjective
observations of the robot’s speed and distances to simulated people and
objects.

###### Index Terms:

telepresence; virtual reality; head-mounted display; VR sickness

##  I Introduction

Immersive robotic telepresence refers to embodying a mobile robot in a far
away location through a Head-Mounted Display (HMD). The robot is equipped with
either a 360° camera or a regular camera with a fast pan-tilt-unit to follow
the user’s view, streaming video to the HMD such that he can look around in
the robot’s environment by turning his head (Fig. 1). This technology can
greatly improve the experience for remote participants of hybrid events where
a group meets locally, but others who would like to join cannot be there
physically. Use of an HMD instead of a regular camera streaming into a regular
screen as found in commercial telepresence robots (for example, GoBe and
Double 3) has great potential to increase the presence of the remote user, and
thus making them feel as if they really were there at the robot’s location.

However, the use of an HMD does not come without deficiencies, a major one
being Virtual Reality (VR) sickness [1, 2] causing, for example, headache,
blurred vision, and even vomiting. Different theories regarding the origin of
VR sickness are all related to sensory mismatch [3]; in immersive
telepresence, a user’s eyes observe motion but the vestibular organ does not
since the user does not move. Moreover, the increased immersion leading to an
increased feeling of presence can cause users to feel uncomfortable about
other aspects besides sickness, such as distance to walls or surprising turns
[4, 5]. Thus, to make the benefits of immersive telepresence available for
more people, care must be taken that the experience is comfortable and not
sickening to users.

Figure 1: Immersive robotic telepresence: a user wearing an HMD can embody a
robot far away and join events and facility tours even if physically joining
is impossible.

In this paper, we propose unwinding the rotations for the user immersed in a
telepresence robot. Essentially, this means that when the robot rotates, the
user’s viewpoint does not rotate along, such that the user always looks
towards the same direction in the robot’s environment unless they rotate
themselves (assuming that the users are either standing or sitting in a
swivelling chair). With the known result of rotational motion causing more
sickness than translational motion [6, 7], we target the rotational motions in
an attempt to reduce VR sickness. We predict that, according to the sensory
mismatch hypothesis [3], users will not get as sick when they have to perform
the physical rotation themselves.

The main contributions of this paper are 1) the method for unwinding
rotations, compensating for the rotational motion that the camera undergoes
due to the robot motions, and 2) a user study showing that unwinding rotations
reduces VR sickness, increases user comfort and is preferred. We performed the
user study in simulation to eliminate possible confounding factors from a real
robot causing additional sickness, such as vibrations or camera rotations
around other axes. We further show that users’ path integration, the ability
to mentally calculate updated position based on self-motions, does not
deteriorate by unwinding the rotations. Additionally, we show that users
rotate themselves in the chair to face the direction of motion, even though we
did not prompt them to do it, and that they find it intuitive. Finally, we
provide results from open-ended questions detailing reasons for preference of
one method or the other, and update general recommendations regarding
distances and speed of the robot in immersive telepresence. We believe that
this paper makes an important contribution towards making immersive, HMD-based
telepresence a more widely acceptable technology.

##  II Related work

Telepresence, a term originally coined by Minsky [8], means being present in a
remote location through technology.To increase the mobility of the user, it is
natural to mount a camera and a screen on a mobile robot, often referred to as
a telepresence robot. There is evidence that even this kind of simple
telepresence robots increase the feeling of presence, the degree of “being
there” often measured in VR research [9], of the remote user over a stationary
video call [10]. Research has shown multiple uses for robotic telepresence,
such as students who otherwise cannot attend lectures [11, 12], social
interaction [13], and telemonitoring on the basis of medical consultation
[14].

Despite showing promise, the increase in presence from having wheels may still
not be enough to enable communication comparable to physical presence: Stoll
et al. [15] showed that users communicating through a robot spoke
significantly less and perceived tasks and communication more difficult than
local, physical participants. This was very likely related to the lack of
presence, since the feeling of presence is shown to make people act more
naturally [16]; despite the increase due to the wheels, the immersion level of
a flat screen was not enough to create sufficient presence111We follow Mel
Slater’s framework for terminology, in which immersion is the physical
system’s capability of providing immersive experiences, and presence refers to
the subjective feeling of being in a virtual location [17] . Combining the
immersion of an HMD with wheels, i.e., using an immersive telepresencce robot,
has the potential to remedy this issue. Motivated by this potential, there has
recently been an increasing amount of work about HMD-based telepresence. The
immersive capacity of HMDs in telepresence has been shown beneficial in, for
example, education [12], general communication [18] and even a real earthquake
scenario [19], and there are recent advances on controlling such a robot [20].
However, an important aspect still remains: how to make the robot’s motions
comfortable and non-sickening for the users.

VR sickness is one of the major risks preventing widespread use of HMDs.
Whereas the umbrella term includes many issues causing users to feel sick,
such as field of view and latency [21], in this paper we use the term to refer
to Visually Induced Motion Sickness (VIMS) in VR context. It is estimated that
only 10% of people have never experienced VIMS symptoms in a vehicle [22]; as
previous history in motion sickness is a good predictor for susceptibility to
VR sickness [23], it is easy to grasp the gravity of the problem. In
mainstream VR research, the problem is often alleviated by using teleportation
or another locomotion model infeasible for a real robot. Beyond teleportation,
other approaches to reduce VR sickness include slowing everything down (as
optical flow correlates with experienced VR sickness [1]), using a rest frame,
i.e., always keeping something constant in the user’s view [24], or narrowing
the field of view [25]. However, all of these methods also deteriorate overall
experience; slow motions are simply frustrating and a rest frame or narrower
field of view reduce what the user sees and thus the feeling of presence [26].
Methods reducing VR sickness but not restricting the user’s field of view are
needed to make immersive telepresence more available for everyone.

Besides VR sickness and user comfort, another metric of interest in this study
is the path integration of the user, which should not deteriorate when
unwinding rotations, since disorientation can cause anxiety and discontent
[27]. Studies related to ours show that physical rotation increased spatial
awareness (which correlates with path integration) over moving with a joystick
[28] and a sviwel chair increases spatial awareness over a fixed chair [29].
In this study we do not explicitly tell the users to rotate themselves along
with the robot while watching the videos, but they do sit in a swivel chair
making it easily possible. We hypothesized there to be no difference in path
integration between unwinding rotations and automatic, coupled rotations, a
sufficient result if confirmed since reducing VR sickness is the main goal of
unwinding rotations. We believe, however, that our study will be an
interesting addition to the research in path integration as well.

##  III Unwinding Rotations

Unwinding rotations refers to rotating the camera frame to decouple the user’s
viewpoint from the rotations of the robot. This is illustrated in Fig. 2: the
robot is moving, either autonomously or controlled by a user directly, and
will take a right at the corner. Suppose the user’s viewpoint before the turn
is towards the wall at the end of the first corridor (view in Fig. 2a). If we
are not unwinding the rotation that the camera frame undergoes (Fig. 2f) due
to the robot motion, user’s viewpoint will change towards the robot heading as
the robot rotates, even if she has not moved her head (Fig.2c). The proposed
unwinding rotations method ensures that, by rotating the camera frame (Fig.
2e), after the robot has made the turn, user’s viewpoint will no longer be
towards the robot’s direction of motion but it will be towards the wall unless
she physically moves her head (Fig.2b).

Figure 2: The concept of unwinding rotations, assuming that user did not move
her head. (a) Initial view of the immersed user. (b) View after the rotation
is unwound. (c) View after the rotation is not unwound. (d) Initial robot
configuration with robot and camera (in yellow sphere) coordinate frames.
Robot and camera coordinate frames corresponding to (e) unwound and (f) not
unwound rotation.

The main requirement for rotations to be unwound is that the camera mounted on
the robot is capable of adjusting the user’s viewpoint independently of the
robot, through hardware (for example, a pan-tilt-unit) or software as in a
360° camera. Let $\mathcal{R}_{R}$ be the set of all the rotations that can be
applied to the robot to change its rotation, accordingly, let
$\mathcal{R}^{-1}_{R}$ denote the set corresponding to the inverses of all the
elements in $\mathcal{R}_{R}$. Note that, by the definition of the rotation
matrix, the inverse always exists. Then, for unwinding rotations to be
possible, the set of rotational transformations that can be applied to the
camera frame $\mathcal{R}_{C}$ should contain $\mathcal{R}^{-1}_{R}$, i.e.,
$\mathcal{R}^{-1}_{R}\subseteq\mathcal{R}_{C}$. Essentially, this means that
the camera frame should be capable of rotating to compensate for the robot
rotation. For example, a 360° camera can unwind any rotation through software,
but a pan-tilt unit that cannot roll cannot be used to unwind rotations on a
drone rotating about all three axes; however, such a pan-tilt-unit can be used
to unwind all the rotations of a telepresence robot traversing a flat surface.
Note that very small rotations can also be compensated using, for example, the
post-rendering image warp [30].

Consider a robot carrying a camera and moving in 3D space (for example, a
drone or the end effector of a manipulator) such that $\mathcal{R}_{R}\in
SO(3)$. Let $R_{R,k}\in\mathcal{R}_{R}$ be the rotation matrix corresponding
to the robot orientation at time step $k\in\mathbb{Z}_{\geq 0}$. We assume
that the robot orientation, hence $R_{R,k}$, is known or can be estimated
accurately at $k$, guaranteed for robotic systems which have sufficient
sensing and filtering capabilities. Then, we can define unwinding rotations as
rotating the camera frame such that any point $p_{c}\in\mathbb{R}^{3}$
represented in the camera frame is related to the point $p^{\prime}_{c}$,
whose coordinates are expressed in the rotated camera frame, through

$p^{\prime}_{c}=R_{C,k}p_{c}$ (1)

in which $R_{C,k}=R_{R,k}^{-1}\in\mathcal{R}_{C}$. This operation negates the
rotation of the viewpoint caused by the robot rotation. Thus, the users need
to rotate themselves in order to face the direction that the robot is or face
the same direction in the virtual environment for the whole motion.

The study presented in this paper considers a mobile robot moving in a two-
dimensional plane, meaning that $R_{R,k}$ corresponding to the robot
orientation $\theta_{k}$ at time step $k$ describes a rotation about the axis
orthogonal to the plane that the robot is moving in. Therefore, for this
special case, $\mathcal{R}_{R}$ is a subspace of $SO(3)$. In particular, if we
consider that the axis about which the robot rotates is the $z$-axis (pointing
up), then

$R_{R,k}=\begin{bmatrix}R(\theta_{k})&0_{2\times 1}\\\ 0_{1\times
2}&1\end{bmatrix}$ (2)

in which $R(\theta_{k})\in SO(2)$ is the rotation in the $x\text{-}y$ plane.
Then, we can use (1) to unwind the rotations applied to the camera frame due
the changes in the robot orientation.

##  IV Hypotheses

We pre-registered the following four hypotheses, together with the procedure
and analyses to be used in the study, in Open Science Foundation (OSF)
https://osf.io/eks6t. From now on, we will refer to the unwound rotations
condition as UR, for which the user’s viewpoint does not change unless she
physically rotates her head. Accordingly, we will refer to Coupled rotations
as CR, for which the user’s viewpoint always rotates when the robot rotates.

  1. H1:

Less VR sickness in UR condition as indicated by lower total weighted
Simulator Sickness Questionnaire (SSQ) score.

  2. H2:

The UR condition is more comfortable as indicated by asking directly which was
more comfortable (forced-choice).

  3. H3:

The UR condition is preferred as indicated by asking directly which the user
preferred (forced choice).

  4. H4:

No difference in path integration across conditions when measured by error in
angle when asked to point towards the point of origin after the robot has
finished the path.

H1 is based on the literature; as explained in Section II, the sensory
conflict theory postulates that a large part of VR sickness is caused by a
mismatch between vision and vestibular organs. Therefore, we predicted that if
the user experiences only the rotations that he performs physically, there
will no sensory conflict; as a result, sickness should not be induced. H2 and
H3 were then prompted by the assumption that people do not want to be sick,
and we did not foresee other major downsides that could make UR not preferred
or uncomfortable. H4 is derived from the literature as well; there seemed to
be conflicting results regarding path integration, as presented in Section II,
which prompted us to propose no difference. The reason we test this is to show
that UR does not deteriorate this important aspect of the experience.

##  V Experiments

###  V-A Study setup

The image on the left in Fig. 1 shows the setup used in this study. During the
experiment, the participants sat on a swivel chair without wheels, to be able
to easily rotate themselves as much as they wanted without translations
possibly caused by wheels. We chose sitting over standing for a few reasons;
first, many people seem to prefer sitting over standing in VR [31]; second, it
is more inclusive since standing is physically more difficult for many people,
and third; this way we can be sure that the people do not translate (take
steps around), which could confound the study. To avoid the participants
getting entangled with the cable, we installed a pulley system that feeds the
cable from the ceiling.

The virtual environment was designed using Blender and Unity. It is a
combination of a real room at the University of Oulu and custom designed,
imaginary areas (see Fig. 3 for screenshots from the environment); thus, even
the people who are familiar with the university have no advantage in the path
integration task. The virtual robot has been modelled after a real robot owned
by the research group, a balanced differential drive robot with the 2 driving
wheels in the middle and 4 caster wheels in corners to keep it steady (see
Fig. 2). We ensured that the dynamics governing the robot motion in simulation
is sufficiently realistic. A virtual 360° camera is attached 1.5 meters above
the base, from which the user sees the virtual world; this is a height
suggested by [32] for 360° videos. To increase the ecological validity of the
environment and the path, there are simulated people walking in the
environment (Fig. 3(a)) that the robot occasionally evades.

We used Nav2 project of Robot Operating System (ROS) 2 Galactic for the
navigation of the robot, and Unity as the physics-based simulator. The robot
route is described as a sequence of waypoints, consequently, the path is
calculated and tracked autonomously. In particular, we chose Theta* [33] as
the path planning algorithm and DWB (a controller based on Dynamic Window
Approach [34]) as the controller to track the computed path. Since navigation
is probabilistic in nature, due mainly to the localization and control methods
used, one instance of the robot motion, together with the motion of the
simulated humans, was recorded and then played back to the users in a Unity
executable to avoid small differences in emerging motions. In the recorded
motion, the robot moves at a maximum speed of $1\ m/s$ and rotates with
velocities within the range $[0,1]\ rad/s$ with maximum rotational
acceleration $3.2\ rad/s^{2}$ as suggested in [4, 5]. The path is $129.5$
meters long, the video lasts for 2 minutes 18 seconds, and the total amount of
rotation that the robot executes during the video is $438^{\circ}$. The lowest
distance to a wall or another inanimate object was $0.9m$ and the closest
distance to people passing by $1\ m$; these values correspond to the distance
accepted by users in [4]. The application was viewed through Oculus Quest 2
via a link cable, with refresh rate 80Hz and resolution 5152x2608.

(a)

(b)

(c)

(d)

Figure 3: Screenshots from the environment from the user’s view; (a) a person
passing by in a corridor; (b) general view; (c) raycasting with controllers at
the end to point towards origin; (d) a birds-eye view of the environment with
the taken path illustrated.

###  V-B Procedure

The two videos of the UR and CR conditions were presented to the participants
in a counterbalanced order such that both videos were seen first and second
equal number of times by both men and women. Upon arrival, participants were
greeted by a researcher and asked for consent, after which the experimenter
asked the participants if they were feeling nauseous or had a headache in an
effort to pre-screen people feeling sick already before the experiment (we
would have re-scheduled a sick-feeling participant). Then, the users were
asked to move to the swivel chair and adjust the chair height such that they
could easily rotate around. Next, the experimenter read out the instructions,
told the participant how to put on the HMD and asked them to rotate around
once more while wearing the HMD; finally, the experimenter asked them to
orient themselves towards “the corridor with the fire hose”, which was the
direction the robot would start moving. Once this was confirmed and done (the
experimenter could always see the participants’ view on a mirrored screen) the
experimenter started the video. During the video, the head orientation of the
participant was recorded.

When the video finished, the participants were asked not to take off the HMD
but instead grab the controllers from a desk in front of them. Then, the users
were asked to point towards the beginning of the robot’s path, which was
clarified as the place with the fire hose to make sure participants understood
this correctly. Then, the participants were asked to remove the HMD and fill
out a questionnaire regarding their experience, after which the same procedure
was repeated for the second video. At the end, the participants were rewarded
with 20€ Amazon vouchers. Finally, we took the recommended precautions
regarding Covid-19 when running the study. The experimenter was always wearing
a masks and kept distance from the participant except if help was needed with
the HMD. With the HMD disposable face covers were used.

###  V-C Measures

The path integration error was measured at the robot’s final position as the
absolute angle between the direction indicated by the participant and the
direction of the origin (robot start position). Thus, the error lies within
the interval $[0^{\circ},180^{\circ}]$. We computed the direction using the
projection of the line segment protruding from the controller (see Fig. 3(c))
onto the plane that the robot moves in.

Head motion data was used to compute the average deviation from the robot
heading to see whether participants aligned themselves with the direction of
motion as they would if the rotations were not unwound. Deviation was
calculated as the absolute angle difference between the robot orientation and
the head orientation in the plane that the robot moves in; absolute angle was
used to avoid bias due to the robot’s turns not being equally divided between
left and right. Furthermore, head orientation is plotted along the robot path
to qualitatively analyze the emergent view patterns across conditions.

In the questionnaires, we measured sickness with the SSQ [35], an established
questionnaire for measuring sickness in VR by presenting 16 possible sickness
symptoms, which the participants gauge on a scale none (0) to severe (3). The
answers are weighted for a maximum score of 236 [36]. Additionally, we used
7-point Likert-scale questions, forced-choice questions comparing the two
videos, and open-ended questions about reasons for some choices and
demographic questions. The first question after each video was about the
user’s confidence in the path integration, after which SSQ was administered
and we asked how comfortable the experience was on Likert-scale. After the
second video, these questions were followed by forced-choice questions of
choosing the preferred, more comfortable and more intuitive condition for the
participant. Then, we asked Likert-scale questions about how the participants
felt regarding distance to walls, distance to humans, and the linear speed of
the robot; these questions were asked to make further suggestions for
researchers working on autonomous motion planners for telepresence robots.
Finally, we asked about VR and gaming experience and demographics. The whole
questionnaire can be found in Appendix 1.

###  V-D Participants

Based on the effect size from a power analysis of a previous study comparing
two immersive telepresence experiences [37], the necessary sample size was
determined as 32 participants. We required an exact split between male and
female participants due to earlier, contradicting evidence that gender may
have an effect on the susceptibility of VR sickness, either women suffering
more [38] or less [39] from VR sickness. When two participants from the first
32 recruited preferred not to report their gender, we recruited two more
participants to be certain of the gender balance, ending up with 34
participants recruited from the university of Oulu campus and the
surroundings. We used only the gender-specified 32 participants for testing
for VR sickness differences between genders, and all 34 participants for all
other analyses. Participants’ age ranged from 18 to 48 with the mean at 26.21
($sd=7.1$). All participants reported having normal or corrected-to-normal
vision and none of the participants were colorblind. The responses of the
participants to how often they use VR systems were: $41.2\%$ never used any
before, $35.3\%$ just a couple of times and at least once, $11.8\%$ once or
twice a year, $3\%$ once or twice a month, and $8.8\%$ several times a week.
Their responses to how often they play computer games were: $11.8\%$ never
played before, $8.8\%$ once or just a couple of times ever, $20.6\%$ once or
twice a year, $11.8\%$ once or twice a month, $17.6\%$ once or twice a week,
$14.7\%$ several times a week, and $14.7\%$ every day.

##  VI Results

Four confirmatory hypothesis tests were preregistered, as well as several
topics for exploratory analysis. All tests were run in SPSS with (two-tailed)
significance levels set to $0.05$ and with a $95\%$ confidence interval.

###  VI-A Confirmatory Results

A Wilcoxon Signed-Ranks test (two sided) is performed to compare the
differences between the total weighted SSQ scores for UR (${Mdn}=9.35$) and CR
(${Mdn}=16.83$) conditions (see Fig. 4(a) for the score distributions). The
test indicated that Unwound Rotations (UR) elicited significantly lower SSQ
scores compared to Coupled Rotations (CR), $Z=-3.46$, $p=.001$, $r=0.59$.

(a)

(b)

Figure 4: Comparison of (a) total weighted SSQ scores, and (b) path
integration errors.

Fig. 5 shows the distributions of the responses given by the participants to
the forced-choice questions regarding preference and comfort. When asked
“Which of the two videos do you prefer?” 28 out of 34 participants $(82.4\%)$
selected the UR condition. An exact binomial test with exact Clopper-Pearson
$95\%$ CI indicated that this tendency in preference is statistically
significant, $p=.000$ (two-sided, one-sided $p=.000$) and had a $95\%$ CI of
$65.5\%$ to $93.2\%$. Consequently, 27 participants $(79.4\%)$ selected the UR
condition in response to the question “Which of the two videos is more
comfortable?”. An exact binomial test with exact Clopper-Pearson $95\%$ CI was
performed, showing that the condition UR was found significantly more
comfortable, $p=.001$ (two-sided, one-sided $p=.0005$) and had a $95\%$ CI of
$62.1\%$ to $91.3\%$.

Figure 5: The distributions of responses to the questions regarding
preference, comfort, and intuitiveness.

The medians of the path integration error distributions for UR and CR
conditions were $20.57^{\circ}$, and $23.9^{\circ}$, respectively (see Fig.
4(b) for the error distributions). A Wilcoxon Signed-Ranks test (two sided)
indicated that UR did not elicit any significant change in the path
integration errors, $Z=-0.71$, $p=.478$, $r=0.12$.

###  VI-B Exploratory Results

In addition to the confirmatory analyses we performed exploratory analyses to
interpret the results better.

####  VI-B1 Quantitative Data

We checked if there were any carryover effects on sickness with 5-10 minute
breaks and did not observe an order effect in the weighted total SSQ scores
across conditions UR and CR, as indicated by a Wilcoxon Signed-Ranks test
(two-sided), $Z=1.1$, $p=.272$, $r=0.19$. We also looked for the potential
effect of gender on sickness. For this analysis, we used only the gender-
specified participants $(n=32)$ , balanced equally between men and women. For
CR condition, we did not observe any significant difference between the total
weighted SSQ scores of women $(Mdn=33.66)$ compared to men $(Mdn=14.96)$, as
indicated by a Mann-Whitney U test (two-sided), $U=76.5$, $Z=-1.96$, $p=.051$,
$r=0.35$. Similarly, there was not a statistically significant difference
between the SSQ scores of men $(Mdn=3.74)$ and women $(Mdn=13.09)$ in UR
condition either, as indicated by a Mann-Whitney U test (two-sided), $U=98.5$,
$Z=-1.14$, $p=.26$, $r=0.2$.

In addition to the forced-choice question, Likert-scale is used to measure the
user comfort. Comparing the comfort ratings in UR condition $(Mean=5.94)$ with
the ones in CR condition $(Mean=5.54)$, we found that UR elicited a
statistically significant increase in the comfort rankings, as indicated by a
Wilcoxon Signed-Ranks test (two sided), $Z=2.16$, $p=.032$, $r=0.37$.

We observed an order effect in the path integration errors. Watching the same
environment twice resulted in a statistically significant decrease in the
errors after the second video compared to the first one, as indicated by a
Wilcoxon Signed-Ranks test (two-sided), $Z=-2.11$, $p=.035$, $r=0.36$.
However, we remind the reader that the experiment was counterbalanced such
that half of the participants had UR as the starting condition and the
remaining half had CR to keep potential order effects equal in both
conditions.

The head motion data is analyzed to see whether people aligned themselves with
the robot heading in UR condition. The distributions of average deviations
from the robot heading for both conditions, UR and CR, followed a normal
distribution as indicated by a Shapiro-Wilk test, $W(34)=0.94,p=.055$,
$W(34)=0.97,p=.45$, respectively. Therefore, a paired t-test was run to
determine whether there was a statistically significant mean difference
between the average deviations in two conditions. The mean average deviation
was higher in UR condition $(32.14^{\circ}\pm 13.16^{\circ})$ as opposed to in
CR condition $(27.53^{\circ}\pm 12.46^{\circ})$; a statistically significant
increase of $4.6^{\circ}$ ($95\%$ CI, $0.76^{\circ}$ to $8.43^{\circ}$),
$t(33)=2.44$, $p=.02$, $d=0.42$.

We tested whether one condition felt more intuitive for the participants by
explicitly asking: Which of the two experiences feels more intuitive for you?
24 participants $(71\%)$ felt that the condition UR felt more intuitive (Fig.
5). An exact binomial test with exact Clopper-Pearson $95\%$ CI indicated that
this tendency to pick UR condition was significant and had a $95\%$ CI of
$52.5\%$ to $84.9\%$, $p=.024$ (two-sided).

We also asked Likert-scale questions about how the users perceived the speed,
distance to walls and distance to people while moving with the robot. The
statistics can be seen in Table I; as mentioned in Section V-A, the values for
distances and speed were mainly based on [4]. The distance to walls and speed
were considered, on average, suitable (median 4 on 7-point Likert scale),
whereas the distance to people was on average considered slightly too short
(median 3).

| Value | Median | Variance |
---|---|---|---|---
Distance to walls | min. 0,9 m | 4 | 0.78 |
Distance to people | min. 1 m | 3 | 0.86 |
Speed | max. 1 m/s | 4 | 0.94 |
TABLE I: The values of certain attributes of the path, and the median and
variance of 7-point Likert scale subjective opinions (1 meaning too small/too
close). Figure 6: Frequently found codes for question “Please explain why you
prefer that video.”. Figure 7: The frequently found codes from the question
“Please explain why that video is more comfortable.” which was asked after the
participant watched both videos.

####  VI-B2 Qualitative data

The open-ended data was analyzed using the thematic analysis method with
inductive approach [40]. In the first stage of analysis, two researchers
independently identified codes from the response data and mutually agreed on
the codings in the second phase. It was not mandatory for participants to
answer the open-ended questions, and these fields were often left blank.

The frequent codes used in open-ended question asking why participants
preferred the video can be seen in Fig. 6, divided by which video was
preferred. The greatest number of comments (6 comments given by $21\%$ of the
28 participants who preferred UR) said that UR was less sickening (for
example,“Because I have to turn around by myself, so it will reduce the
vertigo feeling.”). The next most frequently mentioned keywords were control
over viewpoint (4 ($14\%)$,“My head orientation was not forced”), comfort (3
($11\%$),”I think second video is more comfortable for eyes when the scene
turning”) and smoothness of the motion (3 ($11\%$), “If I try to remember,
there might have been smoother movement in the second video.”). In contrast,
one person preferred CR because the physical effort of rotating in the chair
felt pointless.

The responses to open-ended questions regarding choosing one video as more
comfortable are seen in Fig. 7. Here, less sickening was even more evidently
the biggest factor (8 ($30\%$)), with control over viewpoint being the second
(5 ($19\%$)) with similar arguments as in preference. Codewords frequently not
found in the question for preference but found here were stable video (5
($19\%$),“The second one was not stable, the view kept moving, was terrible.”)
and easiness to look around (3 ($11\%$),“It was easier and more natural to
look around and I experienced no motion sickness.”).

##  VII Discussion

###  VII-A Original hypotheses

The results confirmed all hypotheses H1-H4. We were confident that H1 would
hold due to evidence from literature. However, whether less sickness would be
enough to confirm H2 and H3 was an open question, since the immersion of an
HMD and the need to physically rotate to look towards the direction of motion
could have caused unexpected adversarial effects. Eventually, there was only
one comment in the open-ended questions about not enjoying turning in the
chair (“Since I have no control on the direction, I don’t want to have to turn
the chair towards the direction of the video.”), but, as seen in Figs. 6 and
7, more people enjoyed being in control of the viewpoint and did not mind
having to rotate the chair (“The first one felt much smoother and more
comfortable. I did not mind having to turn around in my chair to look to the
direction I was going.”,“I preferred the lack of forced turns.”).

(a) P1 UR

(b) P1 CR

(c) P2 UR

(d) P2 CR

Figure 8: Viewpoints of participants P1 and P2 along the robot’s path.

We predicted, as stated in H4, that UR does not deteriorate the spatial
awareness of the users, and the results confirmed it. Even though there is
some evidence in literature that physical rotations increase spatial awareness
[28], we did not explicitly tell the participants to always turn to face the
direction of motion of the robot. Thus, if the participants decided not to
follow up with the robot’s motion, it could have made path integration more
challenging. However, the increase in the mean of average deviation from the
robot heading in UR as compared to CR was less than $10^{\circ}$. This is
negligible considering the total amount of rotation that the robot undergoes
(438°) and the corresponding compensatory motions the user needs to do. Thus,
we conclude that people aligned themselves with the robot as they would if the
rotations were not unwound. This is supported by the qualitative analysis of
the plots that show viewpoints along the robot path (see Fig. 8). The users
were looking forwards approximately equally in both conditions regardless of
how much they looked around, prompting us to deduce that the users rotated to
face the robot’s direction of motion. Moreover, both the score on
intuitiveness being in favor of UR, as well as some open-ended answers
regarding intuitiveness ( “In the first video from what I can recall, I barely
moved the chair. Nonetheless, on the second video I did move it every time the
video changed directions, even though the instructors did not prompt me to do
so.) strengthened our belief that users do find it intuitive and natural to
rotate along with the robot to help confirming H4.

###  VII-B Exploratory data

We also wanted to contribute to the literature in terms of the recommended
values for the clearance and speed of an immersive telepresence robot, since
there is little research on the topic. The closest distance to walls in this
study ($0.9m$) was significantly larger than the one suggested in [4] ($>0.4$
m) for an immersive telepresence situation. Even though there were some
comments like “On some moments it felt too close. For instance, in the first
turn, it felt as If I was going to crash.”, the median rating was $4$ (not too
close or not too far). This suggests that $0.9$ m was an appropriate distance
to keep from inanimate objects and walls. Interestingly, despite the slightly
larger distance from the (simulated) people ($1\ m$), participants considered
that the robot passed slightly too close $(Mdn=3)$. This is reflected also in
their responses to the open-ended questions (for example, “For some of the
people crossing, it felt as If I should step a bit away, as I perceived them a
bit too close as well. But overall it was okay.” and ”felt like I was going to
run into the woman in the hallway”). Based on the results in this study we
recommend that a minimum of $1\ m$ distance should be kept from people, but
further research is needed to determine an appropriate value. Whereas there is
research on human-robot proxemics in VR [41], it lacks the telepresence
perspective. Finally, most of the participants rated the robot speed of $1\
m/s$ as suitable $(Mdn=4)$ with several answers to the open-ended question
similar to “It felt like a walking speed of a normal person.”. On the other
hand, some people perceived it to be slightly too slow (“It was slightly too
slow, but not terrible.”) or too fast (“It could be a bit slower so we could
see the environment with more details”). Enabling the user to adjust the
speed, or having an adaptive speed controller [42] can remedy the discrepancy
in the perceived speed among users (as supported by higher variance).

We did not observe carryover effects in the SSQ scores. In literature, a large
variation in recovery times has been observed, with recovery times shorter
than 10 minutes risking carryover effects [43]; in this study the breaks
between videos were approximately 5-10min. One possible reason for us not
observing carryover effects, regardless of the short and uncontrolled recovery
time, is that the sessions were short with the 2min 18s duration; in many
publications, the sessions are longer, and there are suggestions that even
55-70 minute sessions times would be acceptable to avoid excessive VR sickness
[44]. Even if there were carryover effects, the counterbalancing would counter
for them.

Until recently, women were considered to suffer more from VR sickness compared
to men [38]. However, a recent study proposes that the design of commercial
HMDs, which are based on the interpupillary distance of men, can cause women
to suffer more [39]. Despite observing no significant differences in total
weighted SSQ scores across genders, we found that the median of scores
corresponding to women were higher as compared to men in both conditions. In
particular, this increase was close to being borderline significant for the CR
condition. This result was not surprising and can hopefully help designing
future studies and better HMDs.

In the open-ended answers for both comfort and preference there were six
comments (the same person for both questions) justifying their choice by the
order of the videos, such as ”I was more familiar with the setting since I had
seen it before.” and ”It was more exciting because it was the first time for
me to wear vr-glasses and the second one was more like repeating that
experience”. The choices for both questions were four people choosing UR and
two people choosing CR; this shows that the counterbalancing worked and the
order effect did not corrupt the results. Evidently, using the same
environment and the same robot path to avoid confounding factors lead to
directing participants’ attention to aspects that were tangential to the
study. Even though counterbalancing ensured that this occurred approximately
evenly for both conditions, injecting small changes to the environment in
between conditions can be considered for future studies.

###  VII-C Limitations and Future Work

The observed order effect in the path integration could have been avoided by
telling participants before the first video exactly what would be asked from
them afterwards. The participants were not told their error after the first
video, but simply focusing more on the second video caused the observed order
effect. Nonetheless, due to counterbalancing there is no reason to doubt the
overall result of not having a significant difference in the errors. Thus, it
seems that path integration mainly depends on how much the participants focus
on it, and not as much on physical or automatic, coupled rotations.

An immediate future work is to test the proposed method in real world
experiments using a system comprising a wheeled robot equipped with a 360°
camera, and an Inertial Measurement Unit (IMU) to estimate the rotations that
are applied to the camera frame. Although there is some evidence that 360°
videos induce more sickness than virtual environments [45], the physiological
causes of VR sickness remain the same. Thus, we expect unwinding rotations to
improve the experience, despite the likelihood of having more vibrations even
with filtering. Further future work involves applying the method proposed in
this paper to robots that are capable of rotating about more axes than just
one, such as a drone, and researching whether the method can be applied to
manually controlled vehicles as well.

We decided not to administer the participants the Slater-Usoh-Steted (SUS)
[46, 47] presence questionnaire, since we did not expect a significant
difference in presence between conditions. However, there are some hints in
the open-ended questions pointing towards increased presence with UR, such as
”I felt more immersed and I didn‘t feel the changes of direction as I felt in
the second video.” and ”It feels like I was more in control of the experience
than in the second video. It felt like I was more present and could effect the
experience better.” (we note that ”immersion” is often understood as what we
defined as ”presence”). One possible reason why this could affect presence is
that when users rotate more to match with the robot’s direction of motion, the
sensorimotor contingencies, meaning that the user’s physical actions are
matched visually in the virtual world, become more utilized; for example, one
user mentioned that ”Because I could turn around in my chair physically, I
could adjust easily where I was looking. Of course I could turn around in my
chair during the second video as well, but it felt futile to do so.”.
Sensorimotor contingencies have been shown to be an important building block
of presence [48]. An interesting follow-up study would be to compare the
presented approach to an automatically turning chair [49]; in theory the
suffered VR sickness should be the same, but we may observe a difference in
presence.

The general concept of immersive telepresence requires further research on
many fronts to become competitive against current commercial flat-screen
alternatives, especially for tasks that require bidirectional communication.
It is simple to show the remote user’s face on a screen carried by the robot
using standard solutions. However, immersion brings two issues: first, if a
360° camera is used, should the people around the robot know where the remote
user is looking at? Even though we are not aware of a research confirming
this, the likely answer is yes. In this case, research should be done to
reveal, for example, whether a simple solution of using an array of lights
indicating the remote user’s view direction would suffice. Alternatively, a
rotating screen could be attached to the robot, showing the remote user’s face
in the direction that he is looking at. However, this brings up the second
problem; when the user is wearing an HMD, the user’s face cannot be shown as
simply. Fortunately, there is research towards rendering the face of an HMD-
user on a screen as if she is not wearing an HMD [50]. As these technologies
would likely ease two-way communicatiton with immersive telepresence robots,
we believe they will gain in popularity in the near future.

##  VIII Conclusion

We show that unwinding the rotations decreases VR sickness, increases user
comfort, and is preferred by the users when immersed in an autonomously moving
telepresence robot. Further, users physically rotate themselves to face the
direction of motion even without explicit instructions to do so and find it an
intuitive way to embody an autonomously moving robot. We also observed no
differences in path integration abilities between unwinding rotations and
letting the user’s viewpoint rotate. Thus, unwinding rotations shows great
potential to make immersive telepresence more popular.

## References

  * [1] J. J. LaViola Jr, “A discussion of cybersickness in virtual environments,” _ACM Sigchi Bulletin_ , vol. 32, no. 1, pp. 47–56, 2000.
  * [2] S. M. LaValle, _Virtual reality_. Cambridge University Press, 2021.
  * [3] J. T. Reason and J. J. Brand, _Motion sickness._ Academic press, 1975.
  * [4] K. J. Mimnaugh, M. Suomalainen, I. Becerra, E. Lozano, R. Murrieta-Cid, and S. M. LaValle, “Analysis of user preferences for robot motions in immersive telepresence,” in _2021 IEEE/RSJ International Conference on Intelligent Robots and Systems_ , 2021.
  * [5] M. Suomalainen, K. J. Mimnaugh, I. Becerra, E. Lozano, R. Murrieta-Cid, and S. M. LaValle, “Comfort and sickness while virtually aboard an autonomous telepresence robot,” in _EuroXR 2021: Virtual Reality and Mixed Reality_. Cham: Springer International Publishing, 2021, pp. 3–24.
  * [6] A. Kemeny, P. George, F. Mérienne, and F. Colombet, “New VR navigation techniques to reduce cybersickness,” _Electronic Imaging_ , vol. 2017, no. 3, pp. 48–53, 2017.
  * [7] S. Hu, K. A. McChesney, K. A. Player, A. M. Bahl, J. B. Buchanan, and J. E. Scozzafava, “Systematic investigation of physiological correlates of motion sickness induced by viewing an optokinetic rotating drum.” _Aviation, space, and environmental medicine_ , 1999.
  * [8] M. Minsky, “Telepresence,” 1980.
  * [9] R. Skarbez, F. P. Brooks, Jr, and M. C. Whitton, “A survey of presence and related concepts,” _ACM Computing Surveys (CSUR)_ , vol. 50, no. 6, pp. 1–39, 2017.
  * [10] I. Rae, B. Mutlu, and L. Takayama, “Bodies in motion: mobility, presence, and task awareness in telepresence,” in _Proceedings of the 32nd annual ACM conference on Human factors in computing systems_. ACM, 2014, pp. 2153–2162.
  * [11] M. Rueben, M. Syed, E. London, M. Camarena, E. Shin, Y. Zhang, T. S. Wang, T. R. Groechel, R. Lee, and M. J. Matarić, “Long-term, in-the-wild study of feedback about speech intelligibility for k-12 students attending class via a telepresence robot,” _arXiv preprint arXiv:2108.10456_ , 2021.
  * [12] J. Botev and F. J. Rodríguez Lera, “Immersive robotic telepresence for remote educational scenarios,” _Sustainability_ , vol. 13, no. 9, p. 4717, 2021.
  * [13] A. Kristoffersson, S. Coradeschi, and A. Loutfi, “A review of mobile robotic telepresence,” _Advances in Human-Computer Interaction_ , vol. 2013, 2013\.
  * [14] K. A. R. Carranza, N. J. B. Day, L. M. S. Lin, A. R. Ponce, W. R. O. Reyes, A. C. Abad, and R. G. Baldovino, “Akibot: a telepresence robot for medical teleconsultation,” in _2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)_. IEEE, 2018, pp. 1–4.
  * [15] B. Stoll, S. Reig, L. He, I. Kaplan, M. F. Jung, and S. R. Fussell, “Wait, can you move the robot? examining telepresence robot use in collaborative teams,” in _Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction_ , 2018, pp. 14–22.
  * [16] M. V. Sanchez-Vives and M. Slater, “From presence to consciousness through virtual reality,” _Nature Reviews Neuroscience_ , vol. 6, no. 4, pp. 332–339, 2005.
  * [17] M. Slater and S. Wilbur, “A framework for immersive virtual environments (five): Speculations on the role of presence in virtual environments,” _Presence: Teleoperators & Virtual Environments_, vol. 6, no. 6, pp. 603–616, 1997.
  * [18] J. Du, H. M. Do, and W. Sheng, “Human–robot collaborative control in a virtual-reality-based telepresence system,” _International Journal of Social Robotics_ , pp. 1–12, 2020.
  * [19] F. Negrello, A. Settimi, D. Caporale, G. Lentini, M. Poggiani, D. Kanoulas, L. Muratore, E. Luberto, G. Santaera, L. Ciarleglio _et al._ , “Humanoids at work: The walk-man robot in a postearthquake scenario,” _IEEE Robotics & Automation Magazine_, vol. 25, no. 3, pp. 8–22, 2018.
  * [20] G. Baker, T. Bridgwater, P. Bremner, and M. Giuliani, “Towards an immersive user interface for waypoint navigation of a mobile robot,” in _The Second International Workshop on Virtual, Augmented and Mixed Reality for Human-Robot Interaction_ , 2020.
  * [21] E. Chang, H. T. Kim, and B. Yoo, “Virtual reality sickness: A review of causes and measurements,” _International Journal of Human-Computer Interaction_ , vol. 36, no. 17, p. 1–25, 2020.
  * [22] B. D. Lawson _et al._ , “Motion sickness symptomatology and origins.” 2014\.
  * [23] J. F. Golding, “Motion sickness susceptibility questionnaire revised and its relationship to other forms of sickness,” _Brain research bulletin_ , vol. 47, no. 5, pp. 507–516, 1998.
  * [24] Z. Cao, J. Jerald, and R. Kopper, “Visually-induced motion sickness reduction via static and dynamic rest frames,” in _2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)_ , 2018, pp. 105–112.
  * [25] J. Teixeira and S. Palmisano, “Effects of dynamic field-of-view restriction on cybersickness and presence in hmd-based virtual reality,” _Virtual Reality_ , pp. 1–13, 2020.
  * [26] S. Weech, S. Kenny, and M. Barnett-Cowan, “Presence and cybersickness in virtual reality are negatively related: a review,” _Frontiers in psychology_ , vol. 10, p. 158, 2019.
  * [27] R. P. Darken and B. Peterson, “Spatial orientation, wayfinding, and representation,” in _Handbook of virtual environments_. CRC Press, 2002, pp. 533–558.
  * [28] B. E. Riecke, B. Bodenheimer, T. P. McNamara, B. Williams, P. Peng, and D. Feuereissen, “Do we need to walk for effective virtual reality navigation? physical rotations alone may suffice,” in _International Conference on Spatial Cognition_. Springer, 2010, pp. 234–247.
  * [29] Y. Hong, A. MacQuarrie, and A. Steed, “The effect of chair type on users’ viewing experience for 360-degree video,” in _Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology_ , 2018, pp. 1–11.
  * [30] W. R. Mark, L. McMillan, and G. Bishop, “Post-rendering 3d warping,” in _Proceedings of the 1997 symposium on Interactive 3D graphics_ , 1997, pp. 7–ff.
  * [31] D. Zielasko and B. E. Riecke, “To sit or not to sit in vr: Analyzing influences and (dis) advantages of posture and embodied interaction,” _Computers_ , vol. 10, no. 6, p. 73, 2021.
  * [32] T. Keskinen, V. Mäkelä, P. Kallioniemi, J. Hakulinen, J. Karhu, K. Ronkainen, J. Mäkelä, and M. Turunen, “The effect of camera height, actor behavior, and viewer position on the user experience of 360 videos,” in _2019 IEEE conference on virtual reality and 3D user interfaces (VR)_. IEEE, 2019, pp. 423–430.
  * [33] A. Nash, S. Koenig, and C. Tovey, “Lazy theta*: Any-angle path planning and path length analysis in 3d,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 24, no. 1, 2010.
  * [34] D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” _IEEE Robotics & Automation Magazine_, vol. 4, no. 1, pp. 23–33, 1997.
  * [35] R. S. Kennedy, N. E. Lane, K. S. Berbaum, and M. G. Lilienthal, “Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness,” _The international journal of aviation psychology_ , vol. 3, no. 3, pp. 203–220, 1993.
  * [36] P. Bimberg, T. Weissker, and A. Kulik, “On the usage of the simulator sickness questionnaire for virtual reality research,” in _2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)_. IEEE, 2020, pp. 464–467.
  * [37] I. Becerra, M. Suomalainen, E. Lozano, K. J. Mimnaugh, R. Murrieta-Cid, and S. M. LaValle, “Human perception-optimized planning for comfortable vr-based telepresence,” _IEEE Robotics and Automation Letters_ , vol. 5, no. 4, pp. 6489–6496, 2020.
  * [38] L. Rebenitsch and C. Owen, “Individual variation in susceptibility to cybersickness,” in _Proceedings of the 27th annual ACM symposium on User interface software and technology_ , 2014, pp. 309–317.
  * [39] T. C. Peck, L. E. Sockol, and S. M. Hancock, “Mind the gap: The underrepresentation of female participants and authors in virtual reality research,” _IEEE Transactions on Visualization and Computer Graphics_ , vol. 26, no. 5, p. 1945–1954, 2020.
  * [40] M. Q. Patton, “Qualitative research,” _Encyclopedia of statistics in behavioral science_ , 2005.
  * [41] R. Li, M. van Almkerk, S. van Waveren, E. Carter, and I. Leite, “Comparing human-robot proxemics between virtual reality and the real world,” in _2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_. IEEE, 2019, pp. 431–439.
  * [42] A. U. Batmaz, J. Maiero, E. Kruijff, B. E. Riecke, C. Neustaedter, and W. Stuerzlinger, “How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments,” _Plos one_ , vol. 15, no. 11, p. e0242078, 2020.
  * [43] N. Dużmańska, P. Strojny, and A. Strojny, “Can simulator sickness be avoided? a review on temporal aspects of simulator sickness,” _Frontiers in psychology_ , vol. 9, p. 2132, 2018.
  * [44] P. Kourtesis, S. Collina, L. A. A. Doumas, and S. E. MacPherson, “Validation of the virtual reality neuroscience questionnaire: maximum duration of immersive virtual reality sessions without the presence of pertinent adverse symptomatology,” _Frontiers in human neuroscience_ , vol. 13, p. 417, 2019\.
  * [45] D. Saredakis, A. Szpak, B. Birckhead, H. A. Keage, A. Rizzo, and T. Loetscher, “Factors associated with virtual reality sickness in head-mounted displays: a systematic review and meta-analysis,” _Frontiers in Human Neuroscience_ , vol. 14, 2020.
  * [46] M. Slater, M. Usoh, and A. Steed, “Depth of presence in virtual environments,” _Presence: Teleoperators & Virtual Environments_, vol. 3, no. 2, pp. 130–144, 1994.
  * [47] M. Usoh, E. Catena, S. Arman, and M. Slater, “Using Presence Questionnaires in Reality,” _Presence: Teleoperators and Virtual Environments_ , vol. 9, no. 5, pp. 497–503, 10 2000. [Online]. Available: https://doi.org/10.1162/105474600566989
  * [48] M. Slater, “Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments,” _Philosophical Transactions of the Royal Society B: Biological Sciences_ , vol. 364, no. 1535, pp. 3549–3557, Dec. 2009. [Online]. Available: https://royalsocietypublishing.org/doi/10.1098/rstb.2009.0138
  * [49] J. Gugenheimer, D. Wolf, G. Haas, S. Krebs, and E. Rukzio, “Swivrchair: A motorized swivel chair to nudge users’ orientation for 360 degree storytelling in virtual reality,” in _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ , 2016, pp. 1996–2000.
  * [50] N. Matsuda, B. Wheelwright, J. Hegland, and D. Lanman, “Reverse pass-through vr,” in _Special Interest Group on Computer Graphics and Interactive Techniques Conference Emerging Technologies_ , 2021, pp. 1–4.

  *[HMD]: Head-Mounted Display
  *[VR]: Virtual Reality
  *[HMDs]: Head-Mounted Display
  *[VIMS]: Visually Induced Motion Sickness
  *[OSF]: Open Science Foundation
  *[SSQ]: Simulator Sickness Questionnaire
  *[ROS]: Robot Operating System
  *[UR]: Unwound Rotations
  *[CR]: Coupled Rotations
  *[IMU]: Inertial Measurement Unit