# Affective Robotics For Wellbeing: A Scoping Review

Micol Spitale  Department of Computer Science and Technology
University of Cambridge
Cambridge, UK
<EMAIL_ADDRESS>Hatice Gunes  Department of Computer Science and Technology
University of Cambridge
Cambridge, UK
<EMAIL_ADDRESS>
###### Abstract

Affective robotics research aims to better understand human social and
emotional signals to improve human-robot interaction (HRI), and has been
widely used during the last decade in multiple application fields. Past works
have demonstrated, indeed, the potential of using affective robots (i.e., that
can recognize, or interpret, or process, or simulate human affects) for
healthcare applications, especially wellbeing. This paper systematically
review the last decade (January 2013 - May 2022) of HRI literature to identify
the main features of affective robotics for wellbeing. Specifically, we
focused on the types of wellbeing goals affective robots addressed, their
platforms, their shapes, their affective capabilities, and their autonomy in
the surveyed studies. Based on this analysis, we list a set of recommendations
that emerged, and we also present a research agenda to provide future
directions to researchers in the field of affective robotics for wellbeing.

###### Index Terms:

survey, affective computing, robots, affective robotics, wellbeing

##  I Introduction

The number of people with wellbeing related concerns has increased in the last
decade. In addition, the current COVID-19 pandemic has exacerbated this growth
leading to societal changes (such as social isolation and work-from-home
arrangements) that have severely impacted mental and physical wellbeing. This
has resulted in a more urgent need to support people’s wellbeing.

Affective robotics is a promising venue to support people and help improve
their wellbeing. Affective robots can recognize human emotions and show
affective behaviors [1], key factors for a successful interaction to promote
human wellbeing. Also, past works have largely used affective robots to
improve and maintain both mental (e.g., to aid the evaluation of children’s
wellbeing related concerns [2], cognitive therapy for people with dementia
[3]), and physical human wellbeing (e.g., to promote exercise activities for
the elderly [4]).

However, making an affective robot that is able to recognize, interpret,
process and simulate human affect is still an open challenge, because of the
several technical challenges (e.g., adaptation to human behavior,
personalization of the interaction), and the social and ethical implications
of developing and deploying such robots.

This paper aims at investigating the current state of the art of affective
robots for wellbeing. Specifically, our main research questions is: “How have
affective robots been used to promote people’s wellbeing, and to what extent
are their affective capabilities suitable to promote wellbeing?”. We focused
on the following sub-questions:

  * •

RQ1. What are the wellbeing goals of affective robots?

  * •

RQ2. What are the affective robots’s platforms (e.g., Nao, Pepper) that have
been used for wellbeing?

  * •

RQ3. What are the shapes (humanoid, non-humanoid, animal-like) of the
affective robots for wellbeing?

  * •

RQ4. What are the affective capabilities (e.g., emotion recognition) that the
affective robots for wellbeing are endowed with?

  * •

RQ5. What are the levels of autonomy (non-autonomous, semi-autonomous,
autonomous) of the affective robots for wellbeing?

To answer our research questions, we run a scoping literature review following
the PRISMA schema [5] to avoid any bias in the identification, screening,
eligibility or inclusion phases. We reviewed the last decade (from January
2013 to May 2022) of HRI literature to provide a detailed picture of affective
robotics for wellbeing field.

This paper contributes the following:

  1. 1.

we provide the community with a scoping review of the last decade of HRI works
in the affective robotics for wellbeing field;

  2. 2.

from the data synthesized, we formulate a list of recommendations for future
research in affective robotics;

  3. 3.

we identify a research agenda for the affective robotics research field to
promote wellbeing.

The rest of the paper is structured as follows.First, Section II defines
affective robotics. Next, Section III, IV, and V are dedicated to the scoping
review; describing the methodologies, presenting the main findings, and
discussing those findings respectively. We then present our limitations and
future works in Section VI and conclude the paper in Section VII.

##  II Definition of Affective Robotics And Challenges

With the term affective robotics, previous work has referred to the use of
affective computing in human-robot interaction. In fact, authors in [1]
defined affective robots as “robots that can recognize human emotions and show
affective behaviors”. Also, in [6], they claimed that affective robotics focus
on “understanding human socio-emotional signals to enhance HRI”. Paiva et al.
[7] defined the affective loop of emotional robots as composed of emotion
adaptation, emotion expression, and emotion synthesis. However, we acknowledge
that having a robot that is able to adapt, express, and synthetise emotions is
still difficult, due to several open challenges. First, affective robotics
research has to understand the fundamental mechanisms of human behaviour in
real-life circumstances - including nonverbal behavioural cues - and model
these for human-inspired behaviours in robots. Second, robots are expected to
dynamically adapt to human behaviour, meeting the needs of each individual and
personalising their behavior accordingly. Third, although affective robots
take advantage of the advances in affective computing, the generalisation of
those results into real-world context is not straightforward because of the
controlled settings usually adopted for creating datasets that inform those
affective models. All those challenges make it even more difficult to design
affective and intelligent social robots that can support people in promoting
their wellbeing.

Alongside these technical challenges, researchers must also consider the
social and ethical implications of developing and deploying such robots, at
both the individual and societal levels.

In this survey, we refer to the term affective robots as robots that can
recognize, interpret, process, or simulate human affect.

##  III Method

This survey aims to understand how affective robots have been used to promote
people’s wellbeing. To address this research question, we followed the PRISMA
schema [5] to identify, screen, select, and include the surveyed papers. The
steps of this process are shown in Fig. 1.

Figure 1: PRISMA schema for this survey.

###  III-A Search Query

We identified the surveyed papers searching in the ACM Digital Library, IEEE
Explore, and Scopus databases. To identify the search query, we exploited the
SPIDER [8] framework. We used similar queries for different databases (i.e.,
they differ only for the database’s search requirements). For the sake of
clarity, we present an example of the Scopus search query as follows:

    TITLE-ABS-KEY ( ( "affective robotic*"  OR
    "social robot*"  OR  "emotional robot*" OR
    "socially assistive robot*" )  AND
    ( "wellbeing"  OR  "well-being"  OR
    "mental health"  OR  "health" ) )
    AND  PUBYEAR  >  2012

We collected the papers to review by searching via queries in the databases
selected, then we removed the duplicates and stored all the resulted
references into a CSV file.

###  III-B Eligibility Criteria

We followed the guidelines of [9] for selecting papers in engineering to
identify the set of inclusion and exclusion criteria for this survey.

We included the papers that:

  * •

address wellbeing or health (both physical - defined as “mantaining healthy
quality of life”, such as eating well, exercising, getting enough sleep,
staying hydrated - and mental);

  * •

employ a physical robot with affective or emotional capabilities (i.e.,
affective robot);

  * •

model or analyse affective capabilities of a robot;

  * •

have their title, abstract, and keywords containing at least one keyword
describing such technology and one keyword from the Search Query Keywords.

We excluded the papers that:

  * •

were published before January 2013 and after the day of actual running of the
research, i.e., May 16, 2022;

  * •

are not in English;

  * •

employ a virtual robot (e.g., in VR, or on mobile-based applications);

  * •

do not involve human-robot interaction in any form (e.g., running study,
analysis data of HRI);

  * •

are not in peer-reviewed journals and conference proceedings;

  * •

are surveying another topic or theoretical papers;

  * •

are inaccessible to the authors;

  * •

do not report the details necessary to evaluate their eligibility.

###  III-C Selection Process

We first screened the papers collected at a high level, and then we selected
the ones to include in this survey after in-depth analysis. One reviewer
screened the titles and abstracts based on the eligibility criteria listed in
Section III-B. The full text of the remaining papers was analyzed and assessed
by two reviewers.

###  III-D Data Extraction and Analysis

TABLE I: Correspondence between the research questions and variables, types of variable for the extracted data.  Research | Variables | Type
---|---|---
Questions |  |
RQ1 | Robot’s Goal (e.g., assess/train mental | Qualitative
| wellbeing) |
RQ2 | Robotic Platform (e.g., NAO, Pepper) | Categorical
RQ3 | Robot’s Shape (e.g., humanoid) | Categorical
RQ4 | Robot’s Affective Capabilities | Qualitative
RQ5 | Robot’s Autonomy (e.g., non- or semi- | Categorical
| or fully- autonomous ) |

To extract data from the surveyed papers, we assigned a variable to each of
the five research questions. The types of the variables were either
categorical (e.g., robot’s autonomy) or qualitative (e.g., robot’s goal). Tab.
I collects the variables assigned to each research question. For the
categorical data, we defined a priori the classes based on previous
literature, e.g., for the robot’s autonomy variable we identified three
classes - non-autonomous, semi-autonomous, and fully-autonomous - based on
[10]. While for the qualitative data we exploited pattern-based method to
extract main themes of the surveyed papers.

##  IV Results

Following the PRISMA schema, 25 papers were included in this review. The
following sections collect the data synthesized and the corresponding research
questions addressed. Tab. II collects the survey results.

TABLE II: Results from the 25 surveyed papers collected by research questions. PW: physical wellbeing, MW: mental wellbeing Ref | RQ1 | RQ2 | RQ3 | RQ4 | RQ5
---|---|---|---|---|---
[11] | Emotional Support (MW) | Hoffman robot | Non-humanoid | Movement | Autonomous
[12] | Cognitive Stimulation (MW) | Nadine | Humanoid | Emotion Recognition | Autonomous
[13] | Emotional Support (MW) | Sato | Humanoid | Semantic Understanding | Autonomous
[14] | Cognitive Stimulation (MW) | Stevie | Humanoid | Facial Expressions | Semi-autonomous
[15] | Mindfulness (MW) | Pepper | Humanoid | Facial Expressions and Movement | Non-autonomous
[16] | Self-disclosure (MW) | Darwin mini | Humanoid | Semantic Understanding | Autonomous
[17] | Physical Stimulation (PW) | Nao and Poppy | Humanoid | Facial Expressions and Movement | Semi-autonomous
[18] | Assessment Via | 3D printed robot | Humanoid | Emotion Recognition | Autonomous
| Clinical Interviews (MW) |  |  |  |
[19] | Emotional Support (MW) | Nao | Humanoid | Facial Expressions and Movement | Non-autonomous
[20] | Physical Stimulation (PW) | Cozmo | Humanoid | Facial Expressions and Movement | Autonomous
[21] | Emotional Support (MW) | Jibo | Humanoid | Movement | Autonomous
[22] | Emotional Support (MW) | Nao | Humanoid | Facial Expressions and Movement | Semi-autonomous
[16] | Emotional Support (MW) | Emarv4 and Blossom | Non-humanoid and Animal-like | Facial Expressions and Movement | Non-autonomous
[23] | Fall Detection (PW) | Side-bot | Humanoid | Facial Expressions | Autonomous
[24] | Physical Stimulation (PW) | Reeti | Non-humanoid | Facial Expressions and Movement | Non-autonomous
[25] | Food Promotion (PW) | Cozmo | Non-humanoid | Facial Expressions and Movement | Semi-autonomous
[26] | Physical Stimulation (PW) | Pepper | Humanoid | Emotion Recognition | Autonomous
[1] | Emotional Support (MW) | Pepper | Humanoid | Facial Expressions and Movement | Semi-autonomous
[27] | Emotional Support (MW) | IRS | Animal-like | Facial Expressions and Movement | Semi-autonomous
[28] | Emotional Support (MW) | PR2 | Not-humanoid | Movement | Semi-autonomous
[29] | Food Promotion (PW) | Reeti | Non-humanoid | Facial Expressions | Autonomous
[30] | Emotional Support (MW) | Alice | Humanoid | Facial Expressions and Movement | Semi-autonomous
[31] | Emotional Support (MW) | Huggable | Animal-like | Facial Expressions and Movement | Non-autonomous
[32] | Entertainment (MW) | Nao | Humanoid | Facial Expressions and Movement | Semi-autonomous
[33] | Physical Stimulation (PW) | Nao | Humanoid | Facial Expressions and Movement | Non-autonomous

###  IV-A Affective Robot’s Goal (RQ1)

Figure 2: Affective robot’s goal (RQ1) in the surveyed papers among January
2013 to May 2022

Overall, 17 studies used affective robots to promote mental wellbeing, in 8
studies the robot was used for physical wellbeing. Fig. 2 details the robot’s
goal, and specifically, we identified 9 different goals for affective robots
that promote wellbeing among the surveyed papers. Eight studies focused on
physical wellbeing. Five of the studies (20%) focused on physical stimulation
to promote physical wellbeing. Two studies (8%) explored the use of affective
robots to promote healthy food (or drink) for physical wellbeing. One study
(4%) used the affective robot to detect fall. Then, the remaining 17 studies
focused on promoting mental wellbeing. 11 studies (44%) aimed to provide an
emotional support - including reducing stress or anxiety - to promote mental
wellbeing of the participants. 2 (8%) investigated affective robots to
facilitate cognitive stimulation promoting mental wellbeing. 1 of the studies
(4%) aimed at promoting self-disclosure by using an affective robot. One study
(4%) adopted affective robots to assess participants via clinical interviews.
One study (4%) used the robots to entertain participants. Finally, one study
(4%) exploited the affective robot to provide a mindfulness session.

To sum up, to date the HRI community focused more on the application of
affective robotics to promote mental wellbeing. Some of them exploited
affective robots to facilitate physical wellbeing as well.

###  IV-B Affective Robot’s Platform (RQ2)

Figure 3: Affective robot’s platform (RQ2) in the surveyed papers among
January 2013 to May 2022

From the surveyed studies, we identified 17 different robotic platform, as
depicted in Fig. 3. Specifically, 5 of the studies (19.5%) used a Nao robot, 3
studies (11%) Pepper, 2 studies (7.5%) Cozmo, 2 studies (7.5%) Reeti, one
study (3.7%) Alice, one study (3.7%) PR2, one study (3.7%) a non-humanoid
robot by Hoffman [34], one study (3.7%) Stevie, one study (3.7%) Darwin-mini,
one study (3.7%) Blossom, one study (3.7%) Jibo, one study (3.7%) Side-bot,
one study (3.7%) Emarv-4, one study (3.7%) IRS, one study (3.7%) Huggable, and
one study (3.7%) a 3D printed social robot.

Those results show that the HRI community is gradually exploring different
robotics platforms that are now more available in the market. Still, the
robotics platforms from SoftBank (Nao and Pepper) are the most commonly used
within the HRI community.

###  IV-C Affective Robot’s Shape (RQ3)

Figure 4: Affective robot’s shape (RQ3) in the surveyed papers among January
2013 to May 2022

To cluster the affective robot’s shape of the surveyed papers, we used the
definition by [35] and [36], who defined the robot agent shape as bio-inspired
(e.g., animal-like, humanoid), and non bio-inspired (artificial, i.e., showing
artificial characteristics, and functional, i.e., performing specific
functional tasks).

Among the survey papers, 16 out of 26 (65%) used a bio-inspired robot (note
that one study [16] adopted two robotics platforms). Particularly, 14 studies
(54%) adopted humanoid affective robots, while 3 studies (11%) employed
animal-like affective robots. The remaining studies (9 out of 26, 35%) adopted
affective robots with non-humanoid shape. Fig. 4 depicts the results of the
affective robot’s shape for the surveyed papers.

In summary, those findings show that HRI researchers opted for affective
robots with bio-inspired shape (specifically humanoids), just a few of them
investigated the non bio-inspired shapes.

###  IV-D Affective Capabilities (RQ4)

Figure 5: Affective robot’s capabilities (RQ4) in the surveyed papers among
January 2013 to May 2022

We clustered the affective capabilities of the robots included in the surveyed
papers into four main classes (see Fig. 5): emotion recognition, semantic
emotional understanding, facial expressions, and emotional movements.

11 out of the surveyed studies endowed the robot with both facial expression
and emotional movement capabilities. Particularly, 14 studies (39%) equipped
the robot with the capability of expressing emotional movements, while 15 out
of 25 (42%) endowed the robots with the capability of expressing emotion
through facial expressions. 4 studies (11%) adopted robots that were able to
automatically recognize emotions in participants. Finally 3 studies (8%) used
robots that were able to semantically understand the participants’ emotions.

To sum up, our findings show that the HRI community has mainly focused on
endowing their robots for wellbeing with the capability of expressing emotions
(via facial expressions or movements). More recently, the HRI researchers
started to equip their robots with automatic user affect detection
capabilities to promote wellbeing.

###  IV-E Affective Robot’s Autonomy (RQ5)

Figure 6: Affective robot’s autonomy (RQ5) in the surveyed papers among
January 2013 to May 2022

To identify the level of autonomy of the robots, we followed the definition
provided by [10], in case the authors did not provide any detailed
specifications.

Among the surveyed studies, 6 of them (25%) adopted a non-autonomous affective
robots where the researcher acted as “wizard” in the interaction (aka, Wizard-
of-Oz method). 9 of the studies (37.5%) exploited a semi-autonomous affective
robot. For example, in [14], the robot could automatically execute the task
interaction, but the research controlled its navigation in the care center.
The other 9 studies (37.5%) adopted a fully autonomous affective robot (see
Fig. 6).

Our findings showed that the HRI community is moving towards more autonomous
robots, despite some researchers preferring to exploit tele-operated robots
(i.e., non-autonomous) to better control the design variables in their
studies.

##  V Discussion

The next sections will discuss the results gathered in this survey.
Specifically, we extrapolated a set of recommendations that we list in Section
V-A and a research agenda in Section V-B

###  V-A Recommendations

We list a set of observations and subsequent recommendations as follows.

First (from RQ1), we found that the surveyed papers exploited affective robots
to promote either mental (e.g., [13, 37, 12]) and physical wellbeing (e.g.,
[17, 20]). Specifically, we observed that affective robots that addressed
mental wellbeing adopted both expressive behaviors (e.g., facial expression,
emotional movements) and emotion recognition capabilities (e.g., semantic
understanding), while the affective robots for physical wellbeing mostly were
endowed with expressive capabilities only. Also, our results showed that the
authors who focused on affective robots for physical wellbeing opted mostly
for human-like shape. The humanoid shape is extremetely important when
delivering physical exercise, so that human participants can replicate the
robot movements, as in [17]. On the other hand, affective robots for mental
wellbeing used both humanoid and non-humanoid robots to deliver tasks that can
aid the emotional support [16] or the cognitive stimulation [14]. Finally,
most of the papers on physical wellbeing chose an automated or semi-automated
affective robot, while the one that focused on mental wellbeing exploited both
autonomous and non-autonomous robots with Wizard-of-Oz approach

Second (from RQ2 and RQ3), we found that most of the surveyed papers adopted a
bio-inspired robot form, especially humanoid (e.g., [1, 26]). Past works
demonstrated that the human-like appearance of the robot influences the
expectations of the users. For example, participants attributed human-like
behaviors to humanoid robots, because they associated their shape with their
functionalities [38]. On the other hand, the remaining papers in the review
adopted a non-humanoid shape. In fact, a previous work [38] showed that
participants who were asked to rank different robotic platforms (e.g., Jibo,
Pepper, Miro) provided contradictory responses. Some of the participants
preferred more humanoid robot shape, while other chose an abstract shape as
the most suitable to be a robotic coach delivering wellbeing interventions.

Then (from RQ4), our results showed that the affective capabilities of the
robots focused more on generating expressive behaviors (e.g., [24, 27]). Just
few of them (e.g., [22]) displayed capabilities of affect detection. In the
context of promoting wellbeing, having a robot that is endowed with the
capabilities of both affect synthesis and affect detection is a key factor for
a successful interaction. In fact, in [38], the authors reported that
participants, in a participatory design study, remarked the need to endow the
wellbeing robot coach with both the capabilities of emotion/empathy generation
and emotion detection to be able to adapt to users.

Finally (from RQ5), we found that most of the surveyed papers adopted
autonomous or semi-autonomous affective robots, that is likely because of the
advance in the fields of natural language processing [39], computer vision
[40], and speech recognition [41] in the last decade. Despite the increasing
interest in autonomous robots, many researchers exploited non-autonomous
robots that are directly controlled or tele-operated by a human operator. This
method allows researchers to overcome technical issues, that can compromise
the interaction and the user’s perception, and unpredictable events, typical
of user studies.

As a result of these observations we provide the following four
recommendations:

  * •

Recommendation 1 - We recommend to use autonomous humanoid robots to deliver
exercises that promote physical wellbeing to enable users to imitate the robot
movement, and endow the robots with detection capabilities to check the user’s
affective state during the activity. To promote mental wellbeing, we suggest
that the researchers design the affective robot as autonomous and endowed with
both affect expression and detection capabilities. Both humanoid and non-
humanoid shape can be exploited depending on the task or exercise that the
robot has to deliver.

  * •

Recommendation 2 - We recommend to choose the shape of the affective robot to
employ in the study according to its functionalities. For example, in physical
rehabilitation, it could be more useful to use a humanoid robot that can
display movement to perform physical exercises, while for reducing loneliness
in elderly, an animal-like shape could be more appropriate to resemble the
function of a pet-companion. Both humanoid and non-humanoid robots seem to be
appropriate for delivering mental wellbeing exercises.

  * •

Recommendation 3 - We then recommend to endow the affective robots with both
capabilities of generating expressions and detecting the affective state of
their user to be able to deliver wellbeing exercises in more effective and
adaptive ways.

  * •

Recommendation 4 - We recommend to lean toward the autonomous affective robots
to advance further the field of robotics and provide evidence of the efficacy
of real robots in the real world.

###  V-B Research Agenda

Future research should focus on the design features of affective robots to
promote wellbeing. Specifically, much work needs to be done to investigate
which robot form is more appropriate for which specific task (e.g., physical
exercise, cognitive stimulation) to guide researchers in the design choice of
the robotic platform. For instance, [42] demonstrated that humans have a form
function attribution bias which affects their perception of the robots. People
take a cognitive shortcut to attribute the functionality of the robot using
the visual information. Within the HRI literature, many efforts have been made
to better understand how the robot’s form - in terms of size, gender, and
appearance - affects the user’s perception of the robot [43, 42, 44]. However,
future research should specifically focus on better understanding how form
impacts user perception of the robots utilised for wellbeing related
applications, and how robot form can make a difference in the efficacy of the
delivered intervention.

Then, we believe that future research should focus on empirical studies that
adopt affective robots endowed with the full spectrum of capabilities, i.e.,
to recognize, understand, and generate expressions, to advance the affective
robotics field, providing also evidence on the current technology readiness
level for the real world. Deploying social robots in real human-robot
interaction settings is still an open challenge [45] mainly due to the need
for real-time processing capabilities and the lack of computational power of
the robotic platforms available in the market. The lack of cross-fertilization
between affective computing and social robotics fields also contributes to
this problem [46]. Future efforts should focus on how to overcome those
technological limitations, for example, using cloud computing, or using
external/enviromental sensors, as suggested in [46].

Finally, affective robotics should lean towards creating and/or using
autonomous robots. To this end, we acknowledge that future research should
address some of the technological limitations related to robotic deployment
[37]. In parallel to the advances in computing power, the field of affective
computing has seen a rapid progress, however, there is still a lack of real-
time studies with robots endowed with, for example, affect recognition
capability that can adapt and personalize to each user during the interaction
[6]. A survey on 10 years of HRI studies [47] showed that the most common
types of Wizard control employed were natural language processing and non-
verbal behaviors, including affective capabilities of the robot. Also, the
Wizard-of-Oz technique has been widely used in the HRI community [48], because
deploying autonomous robots is an open problem. In fact, the main challenges
are:

  1. 1.

programming autonomous social behavior for a robot is very difficult and time
consuming;

  2. 2.

researchers usually program human-robot interactions as a one off experience,
for a limited scope and very short interaction durations (usually no longer
than 20 minutes);

  3. 3.

the off-the-shelf robotic platforms usually fail in meeting the user
expectations in terms of robot capabilities

To overcome those limitations, the next generation of HRI works should focus
on how to make robots more autonomous using data-driven approaches to fully
understand the dynamics of human and autonomous robot interactions.

##  VI Limitations and Future Work

One of the main limitations of this survey is that the screening of the papers
have been conducted by a single reviewer. This could have introduced bias into
the paper inclusion. We plan to involve at least one additional researcher to
provide a more solid method to include the papers to review. Another issue is
that the set of research questions explored are limited. There are several
aspects that we have not covered in this paper that are known to impact HRI,
such as context [49]. With a deeper analysis, we can identify missing points
relevant for the HRI community that can further inform our research agenda.
The recommendations of this paper were also mentioned previously in different
articles in the literature but we haven’t reported all of them. Moreover, many
of these recommendations are not based on the findings of the review, but we
grounded them on literature. Finally, in the recommendations we haven’t
mentioned the difficulties involved in developing the affective robots that
could have impacted the works of the reviewed papers. In our future work, we
will extend this survey with a broader set of research questions (e.g., study
design, participant population etc.) addressing the above-mentioned
limitations.

##  VII Conclusions

This paper reviewed the last decade (2013-2022) of HRI literature on affective
robotics for wellbeing utilising the PRISMA schema. We aimed to understand how
affective robots have been used in previous studies to promote human wellbeing
and to what extent their affective capabilities were useful. Our findings
showed that the HRI community: i) focused mostly on affective robots to
promote mental wellbeing, ii) explored different robotic platforms, iii) opted
for human-like affective robots, iv) endowed robots mostly with the capability
of generating expressive behaviors, and v) adopted mostly autonomous and semi-
autonomous robots. The results from this review enabled us to list a set of
recommendation guidelines and a research agenda for future research in the
affective robotics field.

## Ethical Impact Statement

We acknowledge that our paper did not survey the ethical implications of using
affective robots. However, this is out of the scope of this paper. In our
future work, we will address also ethical concerns in the field of affective
robotics that aims to promote wellbeing.

## Acknowledgment

This work is supported by the EPSRC project ARoEQ under grant ref.
EP/R030782/1. For the purpose of open access, the authors have applied a
Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript
version arising.

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