# Using Design Metaphors to Understand User Expectations
of Socially Interactive Robot Embodiments

Nathaniel Dennler<EMAIL_ADDRESS>,  Changxiao Ruan<EMAIL_ADDRESS>,
Jessica Hadiwijoyo<EMAIL_ADDRESS>,  Brenna Chen<EMAIL_ADDRESS>,
Stefanos Nikolaidis<EMAIL_ADDRESS>and  Maja Matarić<EMAIL_ADDRESS>University of Southern California941 Bloom Walk, Los Angeles, CA 90089Los
AngelesCaliforniaUSA90089

(2021)

###### Abstract.

The physical design of a robot suggests expectations of that robot’s
functionality for human users and collaborators. When those expectations align
with the true capabilities of the robot, interaction with the robot is
enhanced. However, misalignment of those expectations can result in an
unsatisfying interaction. This paper uses Mechanical Turk to evaluate user
expectation through the use of design metaphors as applied to a wide range of
robot embodiments. The first study (N=382) associates crowd-sourced design
metaphors to different robot embodiments. The second study (N=803) assesses
initial social expectations of robot embodiments. The final study (N=805)
addresses the degree of abstraction of the design metaphors and the functional
expectations projected on robot embodiments. Together, these results can guide
robot designers toward aligning user expectations with true robot
capabilities, facilitating positive human-robot interaction.

Socially Interactive Robots, Robot Morphology, Social Perceptions

††copyright: acmcopyright††journalyear: 2021††doi:
10.1145/1122445.1122456††journal: THRI††journalvolume: 37††journalnumber:
4††article: 111††publicationmonth: 8††ccs: Human-centered computing HCI
theory, concepts and models††ccs: Human-centered computing Empirical studies
in HCI††ccs: Computer systems organization Robotics

Figure 1. Examples of robots’ physical designs measured by abstraction level
along three different design metaphors.

The image shows three axes, each labelled from increasingly abstract on the
left to increasingly literal on the right. The first axis is labeled
”Metaphor: a person” and shows nine robots ranging from robots that use wheels
to move on the left to robots that have arms and legs on the right. The second
axis is labeled ”Metaphor: a dog” and shows six robots ranging from robots
that sit upright on a table on the left to robots that have four legs and dog-
like ears on the right. The third axis is labeled ”Metaphor: a kiosk” and
shows robots that range from robots that have human-like qualities but are box
shaped on the left to robots that look like ATM machines (kiosks) on the
right.

##  1\. Introduction

Broad acceptance of socially interactive robots by users is a barrier to
increasing their prevalence in daily life. One of the key factors that drives
acceptance of new technologies is user expectations (Cha et al., 2015; Davis,
1989). In addition to perceived usefulness, extensive past research has shown
that users consider some computer and robot systems as social actors (Nass et
al., 1994; Carpinella et al., 2017; Shadbolt et al., 2013; Mathur and
Reichling, 2016) and thus assign social judgements to these systems in
addition to judgements of purely functional affordances. The formation of
these social and functional expectations is crucial to understanding how to
design effective human-robot interactions (HRI) for various real-world
contexts.

Physical embodiment is fundamental to robotics and HRI. Embodiment has been
studied in several fields, including social psychology (Meier et al., 2012),
cognitive sciences (Varela et al., 2016), and philosophy (Anderson, 2003).
Embodiment broadly refers to the agents’ structural coupling to their physical
environment (Ziemke, 2001). In this work, embodiment is the robot’s connection
with the physical world, through sensors and actuators as the means of acting
on the world, similar to the definition used by Deng et al. (2019). The
physical robot embodiment conveys a sense of robot identity and allows the
robot to become situated in social contexts. Socially interactive robots (Fong
et al., 2003) are designed to work and interact with people in social
contexts, and adapt to the social preferences of different users.

Adapting technology to the user has multiple benefits, including increasing
trust (Nikolaidis et al., 2017), increasing intention to use (Tay et al.,
2014), and improving behavioral quantities of interest such as time spent in
the interaction (Goetz et al., 2003; Tapus et al., 2008; Cruz-Maya and Tapus,
2016). Some aspects of a robot’s perceived identity are readily changed over
time with few noticeable inconsistencies, such as expressing personality
through gaze patterns (Andrist et al., 2015), prosodic features (Fischer et
al., 2019), and gesture (Rifinski et al., 2020). Other aspects of a robot’s
perceived identity, however, can be disruptive if changed, such as voice pitch
(Tapus et al., 2008). Physical design is difficult or impossible to change
over the course of an interaction without significant expense and disruption.
Thus, robot design is a complex process of optimizing over both fixed physical
design and changing social presence (Deng et al., 2018). An effective design
team must jointly explore the coupled spaces of embodiment and personality. If
those design spaces are large, finding designs to satisfy goals becomes costly
while if they are small, potentially effective solutions may be excluded.

This work contributes an open-source database of 165 robot embodiments with
associated descriptors and results of three crowd-sourced studies that provide
insights toward the effect of robot design on user expectations of robot
capabilities. Section 3 describes the crowd-sourced data collection process
through Mechanical Turk. The first study (N=382) associates crowd-sourced
design metaphors to different robot embodiments as illustrated in Figure 1.
The second study (N=803) assesses initial social expectations of robot
embodiments. The final study (N=805) addresses the degree of abstraction of
the design metaphors and the functional expectations projected on robot
embodiments. Section 4 examines trends in the design of socially interactive
robots as a whole. To evaluate the usefulness of the database, we show how
viewing robots through design metaphors can contextualize and extend prior
work in HRI, focusing on social expectations in Section 6 and functional
expectations in Section 7. We discuss the implications of using metaphors as a
tool for both designers of interactions and designers of robots in Section 8.
To support replicable research, the collected dataset and interactive
explorations of the dataset are available at  interaction-lab.github.io/robot-
metaphors/.

##  2\. Background and Related Work

This section provides background about research through design and about
design metaphors for setting user expectations. Our work extends those methods
toward studying the formation of user expectation about a robot’s embodiment.
We additionally present a review of work that aims to achieve similar goals
regrading robot design and identify how our contributions can contextualize
findings from past works.

###  2.1. Research Through Design

The paradigm of research through design (RtD) (Zimmerman et al., 2007)
provides a method for tackling the complexity of designing for real-world
interactions. The paradigm highlights the importance of the exploratory
implementation of systems to solve real-world scenarios. These specific
implementations are called design artifacts and their production creates
knowledge of different design patterns, design processes, and other forms of
design knowledge that may be useful in diverse settings.

The RtD paradigm has been explored in robot design. Examples include
prototyping practices in virtual, physical, and video-based contexts
(Zamfirescu-Pereira et al., 2021). Another avenue of RtD is through the
documentation of participatory design approaches with specific end-user
populations, such as designing creative robots with children (Alves-Oliveira
et al., 2021) and designing augmented communication robots with users that
have cerebral palsy (Valencia et al., 2021). To properly evaluate the produced
design artifacts, an inspection of several disparate artifacts is required, in
order to identify trends and patterns across many different contexts. This
meta-knowledge across design artifacts is termed intermediate-level design
knowledge (Höök and Löwgren, 2012). Due to their structural coupling to the
real world, robots in particular have unique characteristics and limitations
that allow for the formulation of different forms of design knowledge (Lupetti
et al., 2021). Recently, emphasis has been placed on using design techniques
from data visualization to properly interpret robot intentions as they
interact with humans (Szafir and Szafir, 2021). The use of the RtD paradigm
provides an expanded understanding of what users expect when interacting with
various robots in different contexts. The dataset and visualization techniques
used in this work serve as a form of intermediate-level design representation
of the space of embodiments, similar to other commonly used representations,
such as annotated design portfolios (Gaver and Bowers, 2012).

###  2.2. Understanding Design Through Metaphors

Design metaphors concisely describe complex ideas by associating unfamiliar
objects with other objects that have similar characteristics the user has
already experienced. Design metaphors are extensively studied in human-
computer interaction (HCI) as a way to help users develop mental models of the
system they are interacting with (Voida et al., 2008; Jung et al., 2017;
Khadpe et al., 2020; Kim and Maher, 2020). For example, HCI research shows
that metaphors shape user perceptions of fundamentally identical chat-bot
systems with different metaphors, resulting in varying levels of perceived
warmth and competence affecting both the users’ pre-interaction intention to
use the system and their intention to adopt the system post-interaction
(Khadpe et al., 2020).

The notion of design metaphors has also been recently applied to formalizing
general design processes for socially interactive robots (Deng et al., 2018).
Deng et al. (2019) provides a comprehensive review of HRI studies through the
lens of design metaphors of the robot embodiments used and provides a design
metaphor-based analysis of the relationships between different studies and
their outcomes (Deng et al., 2019). We apply this framework to explore how
design metaphors shape the formulation of social and functional expectations
of robots.

###  2.3. The Social Perception of Robot Embodiment:

Past work in perception of robot embodiments is extensive, but is typically
limited to few embodiments in a few contexts (Walters et al., 2007; Ventre-
Dominey et al., 2019). One study found that a human-like embodiment is
perceived as more competent than a speaker embodiment even when both
embodiments fail in a cooking instruction task (Kontogiorgos et al., 2020).
Another study reported on relationships between the effects of an
anthropomorphic, a zoomorphic, and a functional embodiment across four
different tasks on social measures including trust, likeability, and
engagement (Li et al., 2010).

The effect of robot embodiments on their perceived gender has been studied in
depth (Eyssel and Hegel, 2012). One study found that certain physical robot
characteristics, such as a particular waist-to-hip ratio, have a significant
impact on the perceived gender (Trovato et al., 2018). Other studies showed
that the combination of robot gender and occupation affect the perceived
competence of the robot and the user’s trust in the robot to accomplish
occupational tasks (Bryant et al., 2020).

The uncanny valley and robot likeability more generally have also been
extensively explored (Mathur and Reichling, 2016; Strait et al., 2017a; Woods
et al., 2004). One study aimed to link aversion related to the uncanny valley
to the ambiguity and atypicality of a robot’s design (Strait et al., 2017b).
Robots that have more ambiguous and atypical embodiments were shown to be
perceived as more eerie, which can have implications on design metaphors in
general. Another study showed similar effects with zoomorphic types of
embodiments and offered empirical evidence that something similar to the
uncanny valley effect also happens with animals (Löffler et al., 2020). While
many studies group social expectations of large groups of robots along a
single axis (e.g., human-likeness or animal-likeness), our work aims to
explain social expectation of many distinct robots through the use of design
metaphors.

###  2.4. Social Perception of Robot Faces

The design of robot faces specifically has been an area of research focus, as
the face is often one of the most salient aspects of a socially interactive
robot. McCloud’s triangular design space for robot face design is used to
contextualize design decisions (Blow et al., 2006). A comprehensive survey by
Kalegina et al. (2018) of screen-rendered robot faces reports on commonly-used
design patterns, and the features that correspond with relevant social
characteristics of those faces. Our work aims to show common patterns in the
design space of robot embodiments and to clarify how they impact social and
functional expectations of robots.

##  3\. Collecting Crowd-Sourced Data on User Perceptions

The dataset we collected aims to describe robots holistically in terms of
design metaphors as well as users’ expectation of the robots’ social and
functional capabilities. To minimize participant fatigue due to the large
number of measures we collected, we split the data collection over three
separate studies. All studies were conducted on Amazon Mechanical Turk
(MTurk).

###  3.1. Data Quality

To maximize the quality of collected responses, we used the following
inclusion criteria for all studies: user approval ratings of $\geq 99\%$,
$\geq 1000$ completed tasks, and normal or corrected to normal vision.
Furthermore, we limited the participants to residents of the United States in
order to control for cultural factors of design metaphors. The full set of
survey questions is presented in Appendices A.2-A.4.

In addition to the inclusion criteria, several measures were taken throughout
the survey to protect against non-human MTurk participants. We employed a
”honeypot” question that was invisible on the survey, but visible through the
HTML files, thereby identifying electronic participants when answered.
Participants who responds to this question were excluded from the study.
Additionally, we employed random ”attention checks” that instructed users to
answer a question by selecting a specific option to continue the survey,
requiring careful reading of the question. For quantitative questions, if the
user responded with all neutral responses, their survey ended early and the
data were discarded. For qualitative questions, if the user responded with
identical strings to ones that they had used previously (indicating copy-
pasting), their survey ended early and the data were discarded. This study was
approved by the IRB (UP-18-00510); per IRB regulations and ethical
considerations, participants were paid for the portion of the survey they
completed.

###  3.2. Data Collection Methodology

(a) Stimulus for Aeolus.

(b) Stimulus for TJBot.

Figure 2. Example stimuli from the database.

Due to the immense cardinality of the design space of robots in widely varied
contexts (i.e., drones, autonomous vehicles, industrial robots, etc.), we
limited the scope of our dataset to robots that fit the definition of a
socially interactive robot as proposed by Fong et al. (2003), with the
exception of the requirement for high-level dialogue, in order to include more
non-humanoid embodiments. Using those guidelines, we assembled a collection of
165 robots from the IEEE ”ROBOTS: Your guide to the world of robots” site
(Spectrum, 2018) and Google searches of ”Social Robot”, ”Socially Interactive
Robot”, ”Socially Assistive Robot”, ”Robot Pet”, and ”Social Robot Animal”.
Google searching was performed under several user profiles as well as in
incognito modes to mitigate the effects of prior search histories and stored
user information. The data collection took place in June of 2020.

Each robot was presented to the study participants as two high-quality images,
one of a front view and one of a side view, to convey the 3D structure of the
robots’ design. The sense of scale was provided by showing a common reference
photo: a gender-neutral silhouette for robots at/over 80 centimeters in height
or a silhouette of an averaged-sized human hand for robots under 80
centimeters in height. The image backgrounds were solid white, to control for
contextual factors, cues, and influence. In addition, any objects that a robot
was holding in the original image were edited out; we prioritized the use of
images of robots in neutral poses with neutral facial expressions. All images
were created with identical aspect ratios and similar sizes for each view of
each robot, with minimal necessary variations due to the size and structure of
different robot embodiments. Two example stimuli from our database are shown
in Figure 2.

###  3.3. Study 1: Crowd-Sourcing Design Metaphors

The goal of the first study was to crowd-source the design metaphors to be
used as the basis for subsequent analyses. The interface for the study is
provided in Appendix A.2.

####  3.3.1. Study Design

A total of 382 participants (full demographic information shown in Appendix
A.1) took part in the study and were paid $1.00 per robot for which they
provided 2-5 design metaphors. Participants viewed up to five robots that were
presented in a randomized and counter-balanced manner. Each valid response
took a median value of 3 minutes, and the whole survey took around 15 minutes.

####  3.3.2. Qualitative Measures

Description of Robot: We provided an open-form response box with the prompt to
describe the robot to a friend using two to three sentences.

Related Design Metaphors: We provided an open-form response box to input at
least two and up to five specific persons, animals, plants, characters, or
objects that the robot looks like.

Reasoning for Related Design Metaphors: We provided an open-form response box
to describe why the above design metaphors were chosen. This box was
immediately to the right of the previous response box.

####  3.3.3. Overview of Collected Data

We collected 1716 responses from the participants for the 165 robots we
collected. Answers that did not provide specific persons, animals, plants,
characters, or objects (i.e., ’good’, ’nice’, or paragraphs of copy-pasted
text) were excluded from analysis. The removal of these responses did not
result in a difference from the original distribution of responses, as
evidenced by a chi-square test $\chi^{2}$ (1, N = 165) = 12.62, $p>.999$. This
indicates that the excluded answers followed a uniform distribution and that
the assignment of robots did not cause the participants to provide non-
specific answers.

###  3.4. Study 2: Social Perception

The goal of the second study was to measure the social attributes of the
robots that form the user’s expectation of how a robot should behave. The
interface for the study is provided in Appendix A.3.

####  3.4.1. Study Design

The study was first pilot-tested with 10 naïve users to confirm the
reliability of the constructed scale and get a sense of the time to complete
it in order to inform pricing, following recommendations from (Rueben et al.,
2020). These users were excluded from the full study.

A total of 803 participants (full demographic information shown in Appendix
A.1) took part in the study. The study followed a mixed design where each
participant provided ratings for up to five robots. Assignment of robots was
randomized and counter-balanced. Each rating was paid $0.20, and took a median
of 1.5 minutes per robot for an expected maximum length of 7.5 minutes for the
whole survey.

####  3.4.2. Quantitative Measures

RoSAS Scale: We used a modified version of the validated RoSAS scale
(Carpinella et al., 2017) to assess the constructs originally defined in
RoSAS, which we deemed to be reliable from the pilot study. All items followed
the prompt ”Indicate how closely the following words are associated with the
robot” and were rated on a 7-point Likert scale of ”strongly disagree” to
”strongly agree”. The scale measures the following constructs:

  * •

Warmth is related to the perception that another agent may want to help or
harm us.

  * •

Competence is related to the perception that another agent has the ability to
help or harm us.

  * •

Discomfort is related to the awkwardness of a robot.

Robot Gender Expression:  While gender is a complex social phenomenon, we
measured perceived gender expression as proposed by the Bem Sex-Role Inventory
Scale (Bem, 1981), using two axes– masculinity and femininity–as 7-point
Likert scales. This approach allowed for perceptions of androgyny and agender
in terms of the two axes.

Social Role: The social role is a measure of the interaction dynamics between
the person and robot in an interaction (Rae et al., 2013; Deng et al., 2019).
The scale uses a 9-point differential scale, where 1 labels the robot as ”a
subordinate”, 5 labels the robot as ”a peer”, and 9 labels the robot as ”a
superior”.

Identity Closeness: Identity closeness measures the degree of in-group
identification of the person with the robot (Tajfel, 1974). The scale uses a
9-point differential scale where 1 corresponds to the rater viewing the robot
as ”not at all like me”, and 9 corresponds to the rater identifying the robot
as ”exactly like me”. This scale has been shown to achieve high validity and
reliability in related contexts (Reysen et al., 2013).

Likeability: Likeability measures the general attitude toward a robot, and has
been used in other robot assessment studies (Kalegina et al., 2018; Mathur and
Reichling, 2016). It is assessed using a 9-point differential scale, where 1
indicates the rater ”strongly dislikes” the robot, and 9 indicates that the
rater ”strongly likes” the robot, from the Godspeed Scale (Bartneck et al.,
2009).

####  3.4.3. Qualitative Measures

Reasoning for Likeability Rating: In addition to the likeability rating, we
collected an optional open-ended response about the reasons for liking or
disliking the robot.

####  3.4.4. Overview of Collected Data

We collected 3481 ratings from the participants for the 165 robots in the
dataset. Participants who failed random attention checks ended the survey
early. Entries that provided nonsensical answers to the qualitative questions
or behaved randomly on the questionnaire were excluded. A total of 3155
responses were ultimately included in the analysis. A chi-square test showed
that the exclusion of these 326 responses did not significantly affect the
uniform distribution of assignment, $\chi^{2}$ (1, N = 165) = 72.83, $p>.999$,
indicating that it is unlikely that certain robots are more associated with
excluded answers than other robots. The modified version of the RoSAS scale
showed high reliability with Cronbach’s alphas of $\alpha=0.84$ for Warmth,
$\alpha=0.87$ for Competence, and $\alpha=0.81$ for Discomfort. The values of
the four questions that measured each construct are averaged for analysis.

###  3.5. Study 3: Functional Perception

The goal of the third study was to measure the expected functional affordances
of the robots. The interface for the study is provided in Appendix A.4.

####  3.5.1. Study Design

A total of 805 participants (full demographic information shown in Appendix
A.1) took part in the study. The study followed a mixed design where each
participant provided ratings for up to five robots. Each rating that a
participant gave was paid $0.50. Each rating took approximately 2 minutes to
provide, and the whole survey had an expected length of 10 minutes. The robots
each participant saw were randomized and counter-balanced to mitigate ordering
effects.

####  3.5.2. Quantitative Measures

EmCorp Measures: We used a modified version of the 7-point Likert EmCorp-Scale
(Hoffmann et al., 2018) that has been validated in online survey contexts. We
are interested in the constructs of Shared Perception and Interpretation,
Tactile Interaction and Mobility, and Nonverbal Expressiveness. We did not
consider the Corporeality construct as the robots used in the study are all
images and corporeality represents how co-present the robot is in the room
with an observer. All items were rated on a scale from ”strongly disagree” to
”strongly agree”. The scale measured the following constructs.

  * •

Shared Perception and Interpretation is a measure of the perceived perceptual
capabilities (such as vision and hearing) of the robot.

  * •

Tactile Interaction and Mobility is a measure of the perceived ability of the
robot to move around and manipulate objects in space.

  * •

Non-verbal Expressiveness is a measure of the robots ability to use natural
cues such as gestures and facial expressions.

Design Ambiguity and Design Atypicality Measures: Design ambiguity and
atypicality have been linked to aversion toward different robot designs in
previous works (Strait et al., 2017b). In this work, we define ambiguity as
the difficulty of placing a robot in a single category, and atypicality as a
robot having embodiment features not usually associated with the category they
represent. We quantify these measures with differential scales valued from 1
to 9.

Metaphor Abstraction Measures: The abstraction level of a metaphor provides a
way to quantify how abstractly or literally the robot embodiment follows the
metaphor. We quantified these values as a 9-point differential scale where 1
represented ”highly abstract” interpretations of the design metaphor, and 9
represented ”highly literal” interpretations of the design metaphor.

####  3.5.3. Qualitative Measures

Task Descriptions: We required participants to report two to five kinds of
tasks each robot would be appropriate for, using open-ended responses.

####  3.5.4. Overview of Collected Data

We collected 3435 ratings for the 165 robots in the dataset. Participants who
failed random attention checks ended the survey early. Some responses were
excluded for exhibiting disengaged or automated behaviors, as outlined in
Section 3.1. A total of 3092 responses were ultimately included in the
analysis. A chi-square test confirmed that the exclusion of the 343 responses
did not significantly affect the uniform distribution of assignment,
$\chi^{2}$ (1, N = 165) = 25.39, $p>.999$, indicating that it is unlikely that
certain robots are more associated with excluded answers than others. The
modified version of the EmCorp-Scale showed high reliability with Cronbach’s
alphas of $\alpha=0.91$ for Shared Perception and Interpretation,
$\alpha=0.84$ for Tactile Interaction and Mobility, and $\alpha=0.87$ for
Nonverbal Expressiveness. The values of the four questions that measured each
construct were averaged.

##  4\. Overall Findings in the Design Space

###  4.1. Describing Embodiments Through Features

Similar to Kalegina et al. (2018), we described the robot embodiments with a
series of manually labeled features derived from observed design patterns of
the robots in the dataset in conjunction with qualitative analyses of
descriptions participants gave when describing the robot. In total, we created
43 binary or categorical variables related to present/absent features, 4
ordinal variables related to feature counts, and 5 continuous variables. The
full set of coded features and their descriptions are shown in Appendix B.
Because these features were developed specifically for the robots in this
database, they are specific to design patterns in socially interactive robots.
Through the inspection of this set of features, designers from other areas of
design can identify holes in the design space that have been addressed in
other areas of design.

We noted some interesting trends in the design space from this coding process.
For example, the heights of the robots in this database appears strongly
bimodal, with one peak at robots near 25cm in height and the other peak at
robots 150cm in height. The most common color of robot in out database was
overwhelmingly white with 101 robots, followed by blue with 23 robots, and
black with 13 robots. By manually describing embodiments in terms of design
choices encompassed in their physical appearance, designers can evaluate how
the combination of several design choices may affect different expectations.
This coding process allows designers to develop new ways of describing
embodiments as design patterns change over time as societal tastes change.

###  4.2. Visualizing the Design Space

To visualize the design space, we used the hand-crafted features developed in
Section 4.1 as descriptions of the physical attributes of the robots in our
dataset. To learn a mapping without supervision from that high-dimensional
feature space to 2D, we used t-Stochastic Neighbors Embedding (Maaten and
Hinton, 2008) that preserves distances between points from high-D to 2D space.
Figure 3(b) demonstrates that robots mapped near each other share similar
characteristics. We show evaluations of different robots with different color
values in 2D space. Higher values are concentrated in different parts of the
space, indicating differences in social and functional expectations of the
robot embodiments. This visualization technique can be used as a design tool
to rapidly explore different robot embodiments for a desired set of
expectations related to specific tasks.

(a) t-SNE plot of expected social role.

(b) t-SNE plot of tactile interaction and mobility.

Figure 3. A t-SNE visualization of the design space of robot embodiments. Each
point represents one robot in the dataset. Brown represents high values and
teal represents lower values of the measured ratings.

Two scatterplots of points are shown on a color-blind safe and perceptually-
linear diverging scale from teal to brown where teal represents low values and
brown represents high values for a given construct. For each scatter plot,
three robots are selected from nearby points with high or low values on the
scatterplot. The three robots shown for higher expected social role are called
Alfa KIKI, Pepper, and Nao. They have similar abstractly human looking
embodiments as well as minimal faces and white exteriors. The robots with
lower expected social roles are called Kiwi, Chili, and Tega. All three are
colorful robots that have fuzzy exteriors. The high tactile interaction and
mobility robots are called ARI, Chip CANdroid, and KK-X1. All three are
mounted on top of wheeled platforms, and have two arms and a screen chest. The
low tactile mobility robots are Mykie, Jelly,and Otto. All three look like
screens on a stand with minimalist rendered faces.

Figure 4. Correlations between attributes collected through surveys. All shown
correlations are significant with $p<0.001$. Pearson’s r value is shown in
each square. The first six items are from the survey of social perceptions,
and the next five are from the survey of functional perceptions.

A heat map shows the values for all pairwise correlations of constructs that
were measured in the surveys.

###  4.3. Correlations of Measures

We show the correlations between all measures in Figure 4. While many
correlations in the large collected dataset are significant, the coefficients
of correlation are relatively small. We consider values of Pearson’s $r>0.5$
to be of importance for reporting and discussion, representing a moderate
correlation in similar contexts (Akoglu, 2018). The key findings are described
in detail, with all reported correlations being significant with $p<.001$
after correcting for 110 pairwise comparisons of the 11 measures. All
statistical analyses were performed with the Pinguoin library for Python
(Vallat, 2018). The following are the main trends in the results.

####  4.3.1. Identity closeness is correlated with positive social
perceptions.

Identity closeness had a moderately strong correlation with warmth
(r(163)=.55) and competence (r(163)=.52). Interestingly, the correlation with
the discomfort construct is small. This is possibly related to the idea that
discomfort is a construct that uniquely applies to robots, rather than warmth
and competence, which apply to people as well as robots (Carpinella et al.,
2017). Furthermore, the identity closeness of the user with the robot is
strongly correlated with the likeability of the robot (r(163)=.63) and the
expected social role of the robot (r(163)=.53). For the socially interactive
robots we tested, we found that the closer the raters identified with the
robot, the more positively they viewed the robot and the more likely the were
to view the robot as a peer or superior.

####  4.3.2. Likeability is not significantly correlated with perceived
functionality.

Likeability responses are moderately correlated with warmth (r(163)=.37) and
competence (r(163)=.46), strongly anticorrelated with discomfort
(r(163)=-.74), and strongly correlated with identity closeness (r(163)=.63).
However, the reported likeability is not strongly correlated with any measures
of perceived functional ability. In general, as raters felt more socially
close to the robot and the design of the robot was less discomforting, users
reported liking the robot more. Changes in perceived physical capabilities of
the robot, however, did not correspond with a discernible change in how much
users liked the robot.

####  4.3.3. A robot’s role is correlated with its functionality.

The expected social role of the robot is strongly correlated with perception
and interpretation (r(163)=.59), non-verbal communication (r(163)=.64), and
tactile interaction and mobility (r(163)=.60). The social role is also
correlated with the competence (r(163)=.51) and social identity (r(163)=.63).
As functional abilities increased for robots in our dataset, raters were more
likely to view them as peers or superiors.

####  4.3.4. The perceived functionalities of robots are entangled.

For all pairwise comparisons of the functional constructs from the modified
EmCorp-Scale, we observed correlation values larger than r(163)=.63. Thus,
increases of one construct from this scale were associated with increases in
the other two constructs of the scale for the set of socially interactive
robots we tested. This implies that robots that appear more capable of moving
through space additionally elicit higher expectations of perceptual and
interpretive abilities, as well as higher expectations of non-verbal
expressivity than robots that do not appear as capable of moving through
space. These three functional constructs can be interpreted together as a
generic measure of the robot’s holistic capability to interact with other
agents and the world.

###  4.4. Predictive Features for Each Construct

To relate the physical aspects of a robot’s design to its expected social and
functional affordances, we performed feature selection on the manually coded
features for each of the measured quantitative perceptual constructs. We used
the Boruta algorithm (Kursa et al., 2010) because it aims to find all-relevant
features (i.e., all features that carry information on the modeled construct)
as opposed to minimal-optimal features (i.e., the minimum set of features that
maximize predictive accuracy for some specific model). The Boruta algorithm
selects important attributes and is stable and unbiased when feature
importance is measured with random forests of unbiased weak classifiers (Kursa
et al., 2010).

We performed feature selection by creating ”shadow features” of the true
features by randomly permuting the true values. Both the true and shadow
features are used to predict the value of a construct. If a true feature is
given importance that is higher than its shadow feature, it is considered
useful in classification. This process was run 500 times for statistical
validity. Due to the exploratory nature of this work, we selected features
that were more relevant than their shadow features with a probability of
$0.5$. By selecting features that were relevant to specific constructs, we
presented possible directions for investigating the relationship between
robots’ embodiments and their perceived expectations. Table 1 shows the
selected features as they relate to the measured constructs.

Construct |  Relevant Selected Features (and Relationship)
---|---
Warmth |  Mouth? (+)
Competence |  Height (+), Humanoid Embodiment? (+)
Discomfort |  Height (+), Year (-), Mechanical Face? (+), Industry? (-)
Femininity |  Height (-), Weight (-), Most Prominent Color = Beige (+),
|  Blush? (+), High Waist-Hip Ratio? (+), Curved Embodiment? (+)
Identity Closeness |  Height (+), Humanoid Embodiment? (+)
Likeability |  Height (-), Industry? (+)
Masculinity |  Height (+), Weight (+), Year (-), Curved Embodiment (-),
|  Jointed Limbs? (+)
Social Role |  Height (+), Year (-), Humanoid Embodiment? (+),
|  Number of Arms (+)
Perception and |  Height (+), Humanoid Embodiment? (+),
Interpretation |  Dominant Classification=Anthropomorphic (+)
Tactile Interaction |  Height (+), Mobile? (+), Number of Wheels (+),
and Mobility |  Number of Arms (+), Jointed Limbs? (+)
Nonverbal |  Height (+), Year (-), Humanoid Embodiment? (+),
Communication |  Number of Wheels (-), Number of Legs (+), Number of Arms (+), Dominant Classification = Anthropomorphic (+),
|  Jointed Limbs? (+)
Design Ambiguity |  Height (-), Weight (-), Number of Legs (-),
|  Dominant Classification = Anthropomorphic (-)
Design Atypicality |  Weight (-), Number of Legs (-),
|  Dominant Classification = Anthropomorphic (-)
Table 1. The important features as selected by the Boruta algorithm from our
manually coded feature set that corresponded to the constructs measured in the
survey.

These selected features can be separated into two categories: unobservable and
observed. From the unobservable features we can analyze trends in the design
space that are reflected in the overall design of robot embodiments as opposed
to specific aspects of embodiments. The main unobservable features of
importance were: Year, the year of release of the embodiment and Industry?
whether or not the robot was at one point commercially available. The year
descriptor allowed us to capture the non-stationary nature of design practices
over time. Most notably, newer robots in our database were in general less
discomforting, less stereotypically masculine, had a lower expected social
role, and had fewer non-verbal communicative capabilities. The commercially
available attribute is related to the effect that larger teams of designers
have on the development of robotic systems. Robots that were commercially
available were more likely to be more likeable and less discomforting. This
suggests that robots used in settings that require the robot to be a
comforting partner in interaction may benefit from using robots that are
commercially available rather than robots developed for research purposes.

Of the observed features of importance, height was the most frequently
selected feature across all of the measured constructs. Height has previously
been related to increased expected social role (Rae et al., 2013) in
controlled settings, height as an important characteristic in this in-the-wild
setting suggests that this finding generalizes beyond the laboratory setting.
However, height’s relationship with other constructs has not been studied
closely, opening up interesting research questions for future work.

Another trend we noted is the importance of anthropomorphism in functional
constructs. In general, robots that are seen as human-like are expected to
have higher degrees of functional capability, as well as to take on more
superior social roles. This elevated expectation, however, requires that the
robot actually meet these expectations. Thus, when using anthropomorphic
embodiments, care should be taken to ensure that the robots operate to their
expectation.

Our results replicated findings that related body shape to the expression of
femininity in robots. Previous work has similarly linked the relationship
between robots’ waist-to-hip ratio to their perceived gender expression
(Bernotat et al., 2017; Trovato et al., 2018). Similarly, Kalegina et al.
(2018) found a relationship between perceived gender and the presence of
blush. These features, however, are not important in predicting the
masculinity ratings of the robots. This suggests that the axes of femininity
and masculinity in robots are not diametrically opposed.

##  5\. Utility of Metaphors

###  5.1. Metaphor Summary

The participants’ metaphors were unconstrained in the data collection, and the
frequency of metaphors approximated an exponential distribution, as shown in
Figure 5, with several metaphors appearing repeatedly throughout the dataset.

Figure 5. Histogram of metaphor counts for metaphors that were associated with
more than one robot in our dataset. An additional 129 singleton design
metaphors are not shown. The specific sources of the referential metaphors
(e.g., movies, shows) are removed for visualization, but were provided to
participants during data collection.

A histogram of design metaphors, approximating an exponential distribution.
The metaphors in decreasing order are ”a person”, ”a toy”,”a vacuum cleaner”,
”a child”, ”a woman”, etc.

In total, 199 metaphors were used by the participants to describe all the
robots in the database. We classified these metaphors as: anthropomorphic,
zoomorphic, or mechanical. Metaphors were sorted by their literal
interpretations; all nonliving metaphors were considered mechanical, living
metaphors that represent animals were considered zoomorphic, and living
metaphors representing humanoids were considered anthropomorphic. Of the 199
metaphors, 46 were classified as anthropomorphic, 46 were zoomorphic, and 107
were mechanical. We additionally observed that 38 of the metaphors were
references to robots from popular media, such as Disney’s WALL-E and The
Jetsons’ Rosey the Robot.

###  5.2. Robot Metaphor Category Ascription and Manipulation Verification

To quantitatively evaluate the differences in robot perception between
metaphors, we assigned each design metaphor to one of the following groups:
anthropomorphic, zoomorphic, or mechanical, as in previous studies (Kalegina
et al., 2018; Li et al., 2010). Embodiments were assigned to the three
categories based on the majority of the assignments of the top three
metaphors. For robots with one of each kind of metaphor, we chose the metaphor
with the highest number of responses. Based on these criteria, the database
consistent of 46 anthropomorphic, 28 zoomorphic, and 91 mechanical robots.

To verify that these groups are meaningful, we performed a manipulation check
with the open-source ABOT database (Phillips et al., 2018), a collection of
similar robots that are rated on their human-likeness. We selected the robots
that occurred in both databases and compared their human-likeness to verify
that the assignment of those categories are meaningful. The intersection of
the two databases contained 44 mechanical, 36 anthropomorphic, and 6
zoomorphic robots. Using a Welch’s ANOVA test for unequal group sizes, we
found that robots with both mechanical metaphors ($M_{human-likeness}=23.58$)
and zoomorphic metaphors ($M_{human-likeness}=30.61$) were significantly less
human-like than robots with anthropomorphic metaphors ($M_{human-
likeness}=47.87$), with Welch’s F(2,14.60) = 17.92, $p<.001$, $\eta^{2}=.33$.
This affirms that our assignment of robots to metaphor types is not arbitrary.

###  5.3. Differences Between Metaphor Categories

We found differences in mean construct ratings for each classification of
metaphor via a Welch’s ANOVA tests, due to the unequal group sizes and
heteroscedasticity of variances between groups. For post hoc analysis, we used
the Games-Howell post hoc test to test pairwise comparisons between groups of
unequal size and different variances, with results shown in Figure 6.

Figure 6. Differences in means for anthropomorphic, zoomorphic, and mechanical
embodiments for social and functional constructs of embodiment. All
differences are significant with $p<0.001$ unless marked otherwise. Error bars
represent 95% CI of means.

The six graphs show the means of the different forms of embodiment
(anthropomorphic, zoomorphic, and mechanical) for the six main constructs that
were measured (Perceived Warmth, Perceived Competence, Perceived Discomfort,
Perception and Interpretation, Tactile Interaction, and Mobility).

####  5.3.1. Metaphor Type and Social Expectation

For the socially interactive robots we tested, the different categorizations
of robot types had significant effects on participants’ social expectations.
For warmth, the main effect of group type was significant, Welch’s F(2,
1382.94) = 9.28, $p<.001$, $\eta_{p}^{2}=.005$. Post hoc analysis revealed
that the mean warmth of anthropomorphic embodiments (M=-0.58) was
significantly higher than the mean warmth of mechanical embodiments (M=-.83),
$p=.001$, $\eta^{2}=.007$, and the mean warmth of zoomorphic embodiments
(M=-.65) was significantly higher than that of mechanical embodiments,
$p=.003$, $\eta^{2}=.004$.

The main effect of group type for competence was also significant with Welch’s
F(2, 1353.95) = 24.09, $p<.001$, $\eta_{p}^{2}=.016$. The difference between
perceived competence in anthropomorphic embodiments (M=.86) was significantly
higher than the mean competence of zoomorphic embodiments (M=.50), $p=.001$,
$\eta^{2}=.022$, and the mean competence of mechanical embodiments (M=.90) was
significantly higher than the mean competence of zoomorphic embodiments,
$p=.001$, $\eta^{2}=.026$.

Significant differences in discomfort were also observed across robot types,
with Welch’s F(2, 1399.77) = 16.13, $p<.001$, $\eta_{p}^{2}=.010$. Zoomorphic
embodiments (M=-.73) were rated significantly lower in discomfort than
mechanical embodiments (M=-.57), $p=.04$, $\eta^{2}=.003$, followed by
anthropomorphic embodiments (M=-.31), $p=.001$, $\eta^{2}=.007$, and
zoomorphic embodiments were rated significantly lower in discomfort than
anthropomorphic embodiments, $p=.001$, $\eta^{2}=.042$.

####  5.3.2. Metaphor Type and Functional Expectation

The different categorizations of metaphor type additionally had effects on how
participants expected a robot to perceive and interpret the world, with
Welch’s F(2, 1297.52) = 94.36, $p<.001$, $\eta_{p}^{2}=.053$. Post hoc
analysis revealed significance between all pairwise comparisons with $p<.001$.
Zoomorphic embodiments were perceived as having the lowest perceptual
capabilities (M=-.14), followed by mechanical embodiments (M=.26),
$\eta^{2}=.018$, and then followed by anthropomorphic embodiments (M=.95),
$\eta^{2}=.046$. Therefore, anthropomorphic embodiments had a much larger
difference in perceived perceptual abilities than zoomorphic embodiments,
$\eta^{2}=.118$.

Tactile interaction and mobility also showed differences in metaphor types
with Welch’s F(2, 1209.25) = 27.81, $p<.001$, $\eta_{p}^{2}=.019$. All
pairwise comparisons were significant in the post hoc analysis with $p<.001$.
Zoomorphic embodiments were perceived as having the lowest ability to
manipulate objects in the world (M=.84), followed by mechanical embodiments
(M=-.58), $\eta^{2}=.007$, and then followed by anthropomorphic embodiments
(M=-.22), $\eta^{2}=.013$. Zoomorphic embodiments therefore had much lower
perceived tactile abilities than anthropomorphic embodiments, $\eta^{2}=.037$.

The different forms of embodiment showed different expectations to communicate
non-verbally with Welch’s F(2, 1239.08) = 49.65, $p<.001$,
$\eta_{p}^{2}=.032$. Zoomorphic embodiments (M=-1.06) were perceived as less
capable of communicating non-verbally than anthropomorphic embodiments
(M=-.33), $p=.001$, $\eta^{2}=.055$. Mechanical embodiments (M=-.91) were also
viewed as having lower non-verbal communicative abilities than zoomorphic
embodiments, $p=.001$, $\eta^{2}=.033$.

Figure 7. Perceived competence and perceived perceptual ability by metaphors
for different maturity levels.

Bar graphs showing the mean of rating for perceived competence and perception
and interpretation as a metaphor progresses by maturity. The progression of
metaphors is ”baby”, ”toddler” and ”person”. Perceived competence increases
along this progression, as does Perception and Interpretation Abilities.

###  5.4. Design Metaphor Semantics

We were also interested in exploring whether design metaphors are semantically
meaningful in terms of user perceptions. While the semantic space of metaphors
is difficult to describe, there are some locally ordered areas. To examine the
effects of social and functional perceptions, we selected three metaphors: ”a
baby”, ”a toddler”, and ”a person”. Because age is associated with competence
(Khadpe et al., 2020) and interpretation of the world, we expected that robots
described with more mature metaphors would have higher competence and
perceptual capabilities.

As expected, we found that a main effect was present on metaphor name and
competence with Welch’s F(2, 127.81) = 16.55, $p<.001$, $\eta_{p}^{2}=.057$.
The perceived competence is lower for robots labeled with the metaphor ”a
baby” (M=.08), followed by robots described with the metaphor ”a toddler”
(M=.78), $p=.001$, $\eta^{2}=.09$, and then followed by robots described with
the metaphor ”a person” (M=.96), $p=.001$, $\eta_{p}^{2}=.110$.

There was an additional effect on the perceived perceptual abilities of robots
with Welch’s F(2, 96.60) = 30.81, $p=.001$, $\eta_{p}^{2}=.120$. Robots
described as babies were assumed to have lower expected perceptual
capabilities (M=-.45) than robots described as toddlers (M=.67), $p=.001$,
$\eta_{p}^{2}=.118$. Robots associated with the toddler metaphor were, in
turn, perceived as having lower perceptual abilities than robots described as
persons (M=1.23), $p=.001$, $\eta_{p}^{2}=.110$. Additionally, robots
associated with the baby metaphor had significantly lower perceived perceptual
abilities than robots associated with a person metaphor, $p=.001$,
$\eta_{p}^{2}=.212$.

##  6\. Robots as Social Actors

In addition to the overall trends, we aimed to investigate two other major
areas of social expectations that are influenced by robot embodiment: (1) the
expression of robot gender (Eyssel and Hegel, 2012; Trovato et al., 2018;
Winkle et al., 2021) and (2) the formation of social group memberships with
robots (Eyssel and Kuchenbrandt, 2012; Fraune et al., 2017, 2020). We sought
to understand how metaphor attribution may affect these two social constructs.

###  6.1. The Space of Robot Gender Expression

To examine the space of gender expression (i.e., how masculinity and
femininity are embodied (Anderson, 2020); see Section 3.4.2 regarding the
selection of these axes), we constructed the space according to the results of
a two-tailed Wilcoxon signed-rank test. For each robot, we independently
determined if the robot’s average ratings for femininity and masculinity were
significantly above zero, significantly below zero, or the null hypothesis
that the value is zero could not be rejected. A value of zero corresponded to
masculinity or femininity being neither associated with the robot nor not
associated with the robot. Approximately, the cutoffs for the robots were
around average values of $\pm 1$, corresponding to ”slightly agree” and
”slightly disagree”. The results of this investigation are shown in Figure 8.

(a) Anthropomorphic embodiments.

(b) Zoomorphic embodiments.

(c) Mechanical embodiments.

Figure 8. A visualization of the space of gender expression by robot metaphor
type.

By separating across design metaphor classification, we observed patterns in
the way that the gender expressions were perceived. Anthropomorphic
embodiments were more likely to be perceived as having a significant
association with either femininity or masculinity. Zoomorphic robots were
unlikely to be associated with a particular axis of gender expression.
Mechanical embodiments were more likely to have no gender expression
association, but in some cases they were associated with either masculinity or
femininity.

These findings highlight areas of the design space that merit further
exploration. There were relatively few robots in the database that portrayed
both masculine and feminine characteristics of gender expression. One
anthropomorphic robot did exhibit masculine characteristics and some degree of
feminine characteristics. Interestingly, we also observed that anthropomorphic
embodiments and mechanical embodiments skewed toward not being associated with
femininity, while zoomorphic embodiments skewed slightly toward not being
associated with masculinity. This suggests future directions of design
research that could explore how to balance these trends in the current design
space.

###  6.2. Formation of Ingroups and Outgroups

To evaluate the formation of social group membership, we used the measure of
identity closeness. We observed that many of the robots in the database
experienced bimodal distributions, indicating that the formation of ingroups
and outgroups may occur based on the robots’ embodiments. Responses to Likert
scale questions have been shown to follow binomial distributions in past work
(Allik, 2014). To evaluate this possibility in our data, we modeled the
responses as coming from two possible models: a unimodal binomial model and a
bimodal binomial model, with priors as described below. All models were
developed and fit to the observed data using the pymc3 framework (Salvatier et
al., 2016). The following describes the unimodal model:

$\displaystyle p\sim Beta(1,1)$ $\displaystyle Y\sim Binomial(p,N)$

where $p$ represents the probability of success of a binomial trial with N
repetitions. We used a 9-point Likert scale, thus N was set to 8. The prior
for $p$ was characterized as an uninformative Beta distribution. Y represented
the values that users responded with. The following describes the bimodal
model:

$\displaystyle p_{ingroup}\sim Beta(2,1)$ $\displaystyle p_{outgroup}\sim
Beta(1,2)$ $\displaystyle w\sim Dirichlet(1,1)$ $\displaystyle
Y\sim\sum_{i\in\\{ingroup,outgroup\\}}w_{i}\cdot Binomial(p_{i},N)$

The bimodal model is a weighted sum of two binomial distributions
characterized with two different probabilities of binomial trial success,
$p_{ingroup}$ and $p_{outgroup}$, which have weights corresponding to $w$. The
ingroup distribution and outgroup distribution had equal but opposite
uninformative priors to ensure stability across different threads of MCMC
sampling, since prior research has shown that ingroup membership is related to
being closer than outgroup membership (Tajfel, 1974; Reysen et al., 2013). All
models were evaluated with two independent sampling chains with 20,000
iterations to guarantee convergence to the observed posterior distribution.

The binomial model was selected over the unimodal model if it was more than 10
times more likely based on the Watanabe Aikiake Information Criterion (WAIC)
for the observed data. We found that of the 166 total robots, 60 were best
described with the unimodel model of group membership, and 106 were best
described with the bimodal model of group membership. A Chi Square test
revealed that the distributions of metaphor types within these groups were
significantly different from each other, $\chi^{2}(2,N=166)=59.33,p<.001$,
with anthropomorphic and mechanical metaphors being more represented by
unimodal model and zoomorphic robots being more often represented by the
bimodal model. This suggests that robots were likely to form ingroups and
outgroups based on their design.

Our results extend work that has identified the effect of group membership by
developing robot identity through personality (Eyssel and Kuchenbrandt, 2012)
to include robot identity defined through its embodiment. They also extend
work that focused on group formation based on robot color (Kuchenbrandt et
al., 2013) to encompass holistic morphologies.

##  7\. Robots as Functional Agents

In addition to social expectations discussed so far, functional expectations
are also set by the robot’s embodiment. To understand how, we investigated the
tasks that study participants assigned to each robot using a grounded theory
approach. We additionally examined specific metaphors in detail to understand
the effects of functional expectations based on how strongly the metaphor is
evoked in the robot.

###  7.1. Embodiment and Task

Assigned Task | Specific Population
---|---
Companion | Home Assistant | Children
Customer Service | Informant | Elderly
Educator | Manufacturer | Persons with Disabilities
Entertainer | Surveillant |
Table 2. A table of codes developed through qualitative analysis of the user-
reported tasks for socially interactive robot embodiments.

To evaluate the task expectations of the different robot embodiments in the
database, we developed a coding scheme from the participants’ free-response
answers to the question regarding what task the robot appeared to be useful
for. We observed both task-related and intended population remarks from the
participants. Eight main task-related codes were developed and three specific
population labels were identified as trends in the design space of socially
interactive robots, as summarized in Table 2. Interestingly, these codes have
considerable alignment with the task categorization used by Kalegina et al.
(2018), despite being collected in an open format. The key differences we
found are that we did not observe high numbers of responses for performing
research, nor for health-related tasks. In addition, we observed two
additional categories: being used as a companion and being used to collect or
provide information.

Figure 9. A map of the distribution of the top two tasks for robots in our
dataset separated by their metaphor type.

The companion context is characterized by tasks involving the robot acting
socially to improve mood or mental health over long periods. Examples of
common tasks for this context were robots that ”provide warmth and comfort”,
”are an interactive friend for my child”, and ”being a conversation partner”.
Most commonly, zoomorphic robots were described as being appropriate for this
task. This aligns with the zoomorphic robots’ tendency to be perceived as
comforting and warm, a key component of these tasks where functional
expectation are not as important.

Robots ascribed for customer service contexts were expected to directly
interact with people in public places such as stores, restaurants, or hotels.
Example tasks were robots that function as a ”greeter or a receptionist”, ”a
waiter” and ”a museum guide”. Both anthropomorphic and mechanical embodiments
were described as being useful for customer service-type tasks. This aligns
with the high expected functionalities of these embodiments to perform the
services those tasks require.

Educator tasks involved knowledge transfer from or through the robot to a
person interacting with the robot. Tasks fitting this category involved robots
that could be used ”in language education”, ”to interact with students in
class”, and to provide ”light educational lessons like spelling or math”.
Interestingly, the embodiment of the robot was often related to the topic that
the robot was meant to teach. For example, the baby-like robot Babyloid was
described as a ”a training baby for expecting mothers”, and the cat-like robot
MarsCat could be used ”to help educate about cats”. Most commonly
anthropomorphic embodiments were assigned to tasks relating to that topic.
This is consistent with the high perceived competence and functionality of
anthropomorphic embodiments.

For robots that played the role of entertainers, expected tasks aligned with
short-term entertainment purposes. For example, robots in this category were
expected to ”play music”, ”be used like a toy”, and ”tell jokes”. This
category was common across all types of metaphors, however each metaphor was
described as entertaining in a specific way. Anthropomorphic metaphors were
described as being used as ”a game-playing partner”, zoomorphic metaphors were
most often seen as functioning like ”a pet that doesn’t require attention when
not in use”, and mechanical metaphors fulfilled roles that are common in other
forms of technology such as ”playing music”.

Home assistant robots were expected to work within the household, performing
chores and other daily tasks, including ”cleaning up after kids”, ”making
coffee”, and ”carrying groceries”. These tasks are similar to the customer
service task, but are distinct in that they occur in the home and consist of
repeated interaction with a few people. Similar to customer service tasks,
both mechanical and anthropomorphic metaphors were well-suited for the home
assistant task.

Robots that act as informants were described with tasks that answer questions
or otherwise provide information. Common tasks in this category were robots
that ”verbally answer questions”, ”tell time”, or ”report daily events like
news or weather”. Mechanical robots were most frequently described as being
useful for these impersonal and intellectual tasks, consistent with their
perceived high competence.

Manufacturer robots were used in contexts where they build or move objects,
typically without constant direct human interaction. These robots were
expected to ”carry heavy objects”, ”be a factory worker”, and ”pack in a
warehouse”. Mechanical embodiments and, to some extent, anthropomorphic
embodiments, were selected for tasks like these, primarily for their
functional capabilities, as these tasks were parceived to not require social
interaction.

Robots that were perceived as surveillants were those that monitor behavior,
and were typically expected to provide security in some way. These robots were
expected to be similar to ”security alarms”, ”spy cameras”, or ”a sentry”.
Mechanical embodiments were most frequently attributed to this task. Similar
to informants, these types of tasks are impersonal but require high levels of
competence and perceptual capabilities, qualities attributed to mechanical
embodiments.

###  7.2. Abstraction and Functionality

To investigate the level of abstraction of a metaphor, we selected the top two
most frequent metaphors from each category. For anthropomorphic metaphors, the
two were ”a person” and ”a child”; for zoomorphic metaphors they were ”a dog”
and ”a cat”; and for mechanical metaphors, they were ”a toy” and ”a vacuum”.
For all metaphors, the rating of functional expectations were regressed onto
the level of abstraction of the given metaphor. Significant regressions are
shown in Figure 10. The equation of the regression line is given along with
the corresponding $r^{2}$ value.

Figure 10. Plots showing the effect of metaphor abstraction on perceived
functional characteristics of robots. Regression lines are shown for
significant effects, after Bonferroni correction for 18 hypotheses.

For all anthropomorphic metaphors, we observed a significant increase in all
perceived functional constructs as the robots were seen as more literal
humans. This reflects similar findings from Section 5.3, where anthropomorphic
metaphors were consistently rated as having the highest functional
expectation. This trend of increasing functional expectation as embodiments
were perceived more literally across all constructs was shown in most of the
anthropomorphic metaphors that we measured. This finding aligns with the idea
of anthropomorphization of robots as assigning more human-like abilities to
these embodiments, not only socially but functionally as well (Duffy, 2003).

For zoomorphic metaphors, we observed different trends across metaphors. For
dog-like robots, the perception and interpretation and the non-verbal
expressiveness constructs significantly increased as the robots appeared more
like real dogs. For cat-like robots, however, only tactile interaction and
mobility construct increased with increasingly literally perceived
implementations. This difference may be a result of commonly held views about
these animals in the United States; dogs are typically seen as more attentive
to their owners and are non-verbally expressive through bodily modalities such
as tail-wagging. Cats, on the other hand, are seen as more passive in
interaction but also as more dexterous.

Mechanical metaphors also exhibited different trends across metaphors in terms
of their level of abstraction. Robots described as ”a toy” were significantly
less perceptive and interpretive as they looked more like literal toys. A
similar phenomenon was observed by Hegel et al. (2008), where users reported a
Lego robot as looking like a ”toy” and perceived it as simply pushing buttons,
whereas they described computers as performing calculations. Vacuum-like
robots instead showed an increase in perceived tactile interaction and
mobility as well as non-verbal communication capabilities as they appeared
more like vacuums. This is likely because vacuums typically move around rooms
as part of the cleaning process, and robot vacuums prevalent in popular
culture (e.g., the iRobot Roomba) exhibit mechanistic non-verbal communicative
abilities.

##  8\. Discussion

The analysis of the large dataset resulting from the three studies conducted
in this work demonstrates many nuances of the design space of socially
interactive robot embodiments. The results include multiple insights that can
be used to inform design processes for various robotics contexts.
Specifically, this work suggests how measuring social and functional
attributes of embodiment via design metaphors can be used to estimate and
evaluate designs of robot embodiments, and provides a methodology to situate
novel embodiments in the space of extant embodiments. These tools inform the
development of future socially interactive robots for effective human-robot
interaction.

###  8.1. Implications for Study Designers

From a research perspective, design metaphors can be used to align the
specific robot being used in an interaction with the affordances the
interaction dictates. By using the database we developed, a set of candidate
robots can be selected for a given interaction based on the similarity to the
robot’s expected task contexts. By considering several options, a practitioner
can be informed about the embodiment choice for a specific context. Similarly,
depending on the robots that a study designer has access to, minor
modifications can be made to make the selected robot fit a given task.

In general, we find that the high-level metaphor groups are particularly
important to consider when designing an interaction. Anthropomorphic robots
are best suited for highly-functional tasks, however care must be taken to
ensure that these robots perform to their expectation. Zoomorphic robots are
generally perceived as the least functional, but are rated much higher in
warmth and comfort. Thus, designs that are described like animals are most
appropriate for tasks that focus on providing support rather than manipulating
objects or moving through space. Mechanical designs are the least warm,
despite high perceptual capabilities and competence. Mechanical metaphors may
best be used in more impersonal contexts where the robot is viewed a tool to
accomplish objective non-social tasks.

###  8.2. Implications for Robot Designers

For research in robot design, crowd-sourcing of design metaphors through the
questionnaires used in this work provides a process to evaluate novel designs.
This quantification of the design space presents the trends and voids in the
design space. This understanding can help to direct the flow of design
research toward new or under-explored areas by partitioning the space of
socially interactive robots in appropriate ways: by metaphor, by gender
expression, or by task expectation. By such partitioning of the design space,
the contributions of specific designs can be explained in terms of their
contributions to the space of socially interactive robot designs. By
summarizing design findings in this form of intermediate-level design
representation, specific design artifacts can be readily found and used to
inform the design of socially interactive robots.

###  8.3. Limitations and Future Work

A key limitation of this work is the use of images to convey representations
of inherently 3D real-world objects. Multiple views of the robots were shown
in an attempt to mitigate this, but a 2D screen cannot fully reconstruct the
impression of the 3D real-world embodiments. Additionally, social and physical
contexts are not considered in this work. Contextual information can have
great impact on how a user expects a robot to behave (e.g., (Banerjee et al.,
2018; Nigam and Riek, 2015)). Since this was a Mechanical Turk study, users
did not interact with the robots. We cannot, therefore, infer how user
expectation may be altered through real-world interactions and over time.
These findings are best viewed as priors on robot expectation before
interaction occurs.

We restricted our participants to the United States. Because many metaphors
and perceptions may be culturally situated, it is not certain how these
results may generalize to other cultures. This issue could be addressed by
following a similar design in future examinations of design metaphors in
different cultural contexts.

This work utilizes the ontology of anthropomorphic, zoomorphic, and mechanical
metaphors as a means of analysis. While that was a useful classification for
our analysis, as the space of robot design expands and other metaphors are
used in design processes, that ontology should be reconsidered to overcome its
limitations (for example to capture robots that look like plants).
Importantly, the utility of design metaphors as a tool for understanding user
expectations does not directly depend on this ontology, allowing for the
evolution of classification systems.

##  9\. Conclusion

This work aims to provide a framework for understanding and informing robot
design to set realistic expectations through the use of design metaphors. We
contributed a methodology for determining the design metaphors of a given
robot embodiment, set up a database of 165 socially interactive robot
embodiments, and collected a rich dataset of participant responses about
social, functional, and other relevant expectations for of those embodiments.
The proposed set of features can inform novel robot designs and compare them
with extant designs through visualization techniques. The analysis results
offer general guidelines for designing socially interactive robots for
different contexts and ways in which user expectation of functional and social
capabilities are impacted by robot embodiments.

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##  Appendix A Survey Information

###  A.1. Demographic Information

Figure 11. The intersection of participant identities from the design metaphor
survey (Study 1). Figure 12. The intersection of participant identities from
the social expectation survey (Study 2). Figure 13. The intersection of
participant identities from the functional expectation survey (Study 3).

###  A.2. Design Metaphor Survey Questions and Interface

Figure 14. The interface and questions participants in the design metaphor
survey saw (Study 1).

###  A.3. Social Expectation Survey Questions and Interface

Figure 15. The interface and questions participants in the social expectation survey saw (Study 2). The order of the questions in the Likert section were randomized both during pilot studies and during the full deployment. Table 3. This table describes the assignment of the Likert items to the specific social constructs they measured. Construct | Items
---|---
Warmth |  ”Social”, ”Organic”, ”Compassionate”, and ”Emotional”
Competence |  ”Capable”, ”Responsive”, ”Interactive”, and ”Competent”
Discomfort |  ”Scary”, ”Strange”, ”Awkward”, and ”Aggressive”
Femininity |  ”Feminine”
Masculinity |  ”Masculine”

###  A.4. Functional Expectation Survey Questions and Interface

Figure 16. The interface and questions participants in the functional expectation survey saw (Study 3). Table 4. This table describes the assignment of the Likert items to the specific functional constructs they measured. The order of the questions in the Likert section were randomized both during pilot studies and during the full deployment. Construct | Items
---|---
Perception and Interpretation |  ”…is able to react to the same environmental stimuli as I do”, ”…is able to recognize emotions”, ”…is able to perceive what I perceive”, and ”…is able to understand my behavior”
Tactile Mobility and Interaction |  ”…is able to autonomously navigate in space”, ”…is able to move toward me”, ”…is able to touch objects”, and ”…is able to carry objects”
Non-verbal Communication |  ”…is unrestricted in its actions”, ”…is unrestricted in its movements”, ”…is unrestricted in its facial expression”, and ”…is unrestricted in its gestures”

##  Appendix B Robot Descriptors

Table 5. A table of the binary and ordinal robot descriptors that were developed through inspection of the robot designs, and user descriptions. For each feature, we provide a description of what the feature means, and the measure of Cronbach’s alpha that we obtained between two raters of the robotic systems. Robot Feature | Description | Cronbach’s $\alpha$
---|---|---
Anthropomorphic Embodiment? |  Presence of human-like features (e.g., is bipedal, has two arms, two legs, or hair on the head). |  .87
Zoomorphic Embodiment? |  Presence of animal-like features (e.g., a tail, wings, animal-like ears) |  1.00
Mechanical Embodiment? |  Presence of visible mechanical parts (e.g., exposed wires, wheels, or visible motors). |  .89
Dominant Classifaction |  One of {Anthropomorphic, Zoomorphic, Mechanical}, which describes the overall form of embodiment. |  .83
Number of Wheels |  The assumed number of wheels that the embodiment uses to move. |  .70
Number of Legs |  The number of appendages that can be used for locomotion. |  .88
Number of Arms |  The number of assumed appendages that could be used for gesturing and grasping. |  .95
Number of Eyes |  The number of round components that can be perceived as eyes. |  1.00
Mobile? |  Can physically move between points in space. |  .89
Does it ride on something? |  Presence of a platform that the robot appears to rest on top of. |  .86
Drivetrain Skirt? |  Indicates that the wheels and motors were contained within a skirt-like shape that smoothly connects with the rest of the embodiment. |  .79
Treads? |  Presence of treads as a means of locomotion. |  1.00
Spherical Head? |  Presence of a head that appears to be a near-perfect sphere. |  .92
Box Head? |  Indicates that the head is approximately box-shaped (but not just a standalone screen). |  .87
Tablet Head? |  Indicates that the head consists of a single screen (e.g., a phone, tablet, etc.) |  1.00
Human Head? |  Indicates that the head is human-like in appearance and has a skin-like quality. |  1.00
Wearing a Helmet? |  Indicates that the robot appears to be wearing a helmet or face shield. |  .61
Antennae? |  Presence of one or more antenna-like structures on the head |  1.00
Hair Follicles? |  Presence of many separate hair-like protrusions from the head in a distinct region that represents hair. |  .87
Mechanical Hair? |  Presence of mechanical structure on the head that can be interpreted as a hair style. |  1.00
Ears? |  Presence of shapes or mechanisms that resemble ears. |  .81
Screen Face? |  Presence of a screen near the top of the robot that displays at least one facial feature. |  .94
Static Face? |  Presence of physical facial features that are not physically actuated. |  .78
Mechanical Face? |  Presence of a physical facial features that contains components that are physically actuated. |  .77
Mouth? |  Presence of a shape or mechanism that resembles a mouth. |  .89
Nose? |  Presence of a shape or mechanism that resembles a nose. |  .83
Eyebrows? |  Presence of shapes or mechanisms that resemble eyebrows. |  1.00
Blush? |  Presence of a shape, mechanism, or coloring that resembles rosy cheeks. |  .72
Eyelids? |  Presence of a shape or mechanism that resembles eyelids |  .72
Pupils? |  Presence of a shape within a round shape perceived as eyes that represents a pupil. |  .92
Irises? |  Presence of a (colorful) shape within a round shape perceived as eyes that represents an iris, which contains a pupil. |  .78
Eyelashes? |  Presence of hair-like protrusions from the eye that represent eyelashes. |  .89
Lips? |  Presence of shapes or mechanisms that resemble lips. |  .82
Mechanical Lips? |  Presence of physical tube-like structures that represent lips. |  1.00
Low Waist-to-Hip Ratio? |  Indicates that the perceived waist width of the robot is much smaller than ($<0.8$ times) the perceived hip width. |  .80
High Shoulder-to-Waist Ratio? |  Indicates that the perceived shoulder width is much larger than ($>1.25$ times) the perceived waist width. |  .93
High Shoulder-Hip Ratio? |  Indicates that the perceived shoulder width is much larger than ($>1.25$ times) the perceived hip width. |  .62
Screen On Chest? |  Presence of a display interface at a medium height on the embodiment. |  1.00
Furry? |  Indicates that the robot’s embodiment is covered in multiple hair-like protrusions. |  1.00
Matte Body? |  Indicates that the external sheen of the embodiment is not highly reflective. |  .94
Hard Exterior? |  Indicates that the robot’s exterior is constructed from hard materials (e.g., plastic, metal, etc.). |  1.00
Skin-like Material? |  Indicates the presence of a skin-like, flexible, and non-furry material covering any part of the embodiment. |  1.00
Exposed Wires? |  Presence of visible string-like structures that are needed for power requirements of the embodiment. |  .80
Jointed Limbs? |  Indicates that the limbs of the robot contain visible joints (i.e., not hidden under fabrics or outer casings). |  .79
Industry? |  Indicates that the robot was released for purchase by end-users. |  .95
Curvy Embodiment? |  Indicates that the embodiment is designed with organic-looking curves and the embodiment is not obviously partitioned into simple shapes (e.g., rectangular prisms or cylinders). |  .73
Symmetric Embodiment? |  Indicates that the embodiment exhibits reflective symmetry across its sagittal plane. |  .79
Table 6. A table of the continuous feature descriptors taken from the robots’ websites. Robot Feature | Description
---|---
Height |  The total height of the robot in centimeters.
Weight |  The total mass of the robot in kilograms, or ”UNK” if this information was not available.
Year |  The year in which the robot was created or first written about publicly.
Country of Origin |  The country in which the robot was developed
Most Prominent Color |  The color that is used in most of the embodiment.