# Toward Family-Robot Interactions: A Family-Centered Framework in HRI

Bengisu Cagiltay<EMAIL_ADDRESS>Computer Sciences Department, University
of Wisconsin-MadisonMadisonWIUSA and  Bilge Mutlu<EMAIL_ADDRESS>Computer
Sciences Department, University of Wisconsin-MadisonMadisonWIUSA

(2024)

###### Abstract.

As robotic products become more integrated into daily life, there is a greater
need to understand authentic and real-world human-robot interactions to inform
product design. Across many domestic, educational, and public settings, robots
interact with not only individuals and groups of users, but also families,
including children, parents, relatives, and even pets. However, products
developed to date and research in human-robot and child-robot interactions
have focused on the interaction with their primary users, neglecting the
complex and multifaceted interactions between family members and with the
robot. There is a significant gap in knowledge, methods, and theories for how
to design robots to support these interactions. To inform the design of robots
that can support and enhance family life, this paper provides (1) a narrative
review exemplifying the research gap and opportunities for family-robot
interactions and (2) an actionable family-centered framework for research and
practices in human-robot and child-robot interaction.

family-robot interactions, family-centered design, child-robot interaction,
theory and methods

††journalyear: 2024††copyright: acmlicensed††conference: Proceedings of the
2024 ACM/IEEE International Conference on Human-Robot Interaction; March
11–14, 2024; Boulder, CO, USA††booktitle: Proceedings of the 2024 ACM/IEEE
International Conference on Human-Robot Interaction (HRI ’24), March 11–14,
2024, Boulder, CO, USA††price: 15.00††doi: 10.1145/3610977.3634976††isbn:
979-8-4007-0322-5/24/03††ccs: Human-centered computing Interaction design
theory, concepts and paradigms††ccs: Human-centered computing HCI theory,
concepts and models

Figure 1. Toward Family-Robot Interactions: The proposed family-centered
framework bridges child-robot interaction practices with family systems
theories. The framework includes three systems: Family system (e.g., family
members and characteristics), robot system (e.g., its technical and
interactive attributes), and the broader contextual system (e.g., the physical
environment).

Collection of figures and text summarizing the three systems included in the
framework. The contextual system is the physical environments in which HRI
takes place, it is bounded by roles, goals, processes and time, and consists
of the family system and the robot system. The family system includes a
subtitle “Family Characteristics” and includes several images of family
members and arrows connecting between them. The robot includes a subtitle
“Robot Characteristics” and includes three robots, one which is waving, one
looks sad, and one says ’hello’. The family system and robot system are
connected with arrows, labeled ‘interactions’.

##  1\. Introduction

Families and their surrounding care-ecosystems (e.g., relatives, friends, or
their neighborhood and school) are important parts of children’s development,
embodying a rich space of shared experiences, values, and emotions. Research
has shown that children and families’ joint engagement with media and
technology can support learning (Holloway et al., 2019), entertainment
(Chambers, 2012), building routines (Sonne et al., 2016), and achieving common
daily tasks and goals (Beneteau et al., 2020). Introducing social robots into
the broader context of families can potentially lead to similar positive
outcomes. However, despite the field’s movement toward more realistic, in-the-
wild HRI, a family-centered approach remains largely understudied in both
theoretical and practical domains. So far, design paradigms for research in
child-robot interaction often sideline the multi-layered relationships and
differential expectations between children and family members (e.g., child-
parent, child-sibling, grandparent-grandchild), typically focusing on
children’s individual interactions with robots (e.g., for therapy
(Scassellati, 2007) or learning (Belpaeme et al., 2018)). Family members are
rarely active participants in interactions with robots, and typically serve as
providers of consent or feedback. In recognition of this potential, a small
but growing number of studies focus on robots designed with both the children
and parents in mind (e.g., parent-child co-learning (Ahtinen et al., 2023),
parent-child storytelling (Chen et al., 2022), or parent-child goal-oriented
play (McHugh et al., 2021)). Still, understanding human-robot interaction
within a broader family context is challenging, due to the limited
applications of current robot technologies to autonomously maintain multi-
party and long-term interactions in domestic settings. Overall, there is
limited representation for any theoretical application that captures the
broader domain of family needs, preferences, and interactions with robots.

Given this gap, we aim to highlight the importance of exploring the
theoretical foundations necessary to construct a robust family-centered design
framework. In this work, we first review the small but emerging body of work
in the HRI literature that considers children and families, we then bridge
knowledge from family theories, and provide a theoretically motivated
framework toward a family-centered approach in HRI. Sampling from domains
including healthcare, education, and emotional well-being, we synthesize the
literature and demonstrate how interactions with robots can positively impact
family life (e.g., fostering family connections, bonding, co-learning, and
dynamics) as well as how family members’ characteristics may inform robot
design (e.g., robot perceptions, long-term adoption, and acceptance). We
present a family-centered framework consisting of three components: the family
system (characteristics of and relationships between the family members
involved); the robot system (the robot’s technical and interactive
attributes); and the contextual system (e.g., the physical environment in
which interactions takes place). We illustrate the relationship between these
systems and discuss how they are bounded by dimensions including time (e.g.,
changes that happen over time) as well as the roles, goals, and processes
executed by each family member and robot.

By acknowledging the multi-layered relationships between family members, this
framework seeks to enable the design of social robots that not only serve as
functional aids for the individual but also integrate into the complex aspects
of family life. Although we take a lens of domestic robots for families with
children, we encourage researchers to extend this framework to other family
structures and contexts that may be significant for family-robot interactions.
Our contribution to this work is two-fold:

  * •

A narrative review exemplifying the limitations and gaps in the current state
of family-robot interactions,

  * •

An actionable theoretical framework for HRI researchers to consider when
following a family-centered approach.

##  2\. Background

The theoretical background of this work lies in the intersection of child-
robot interaction, family theories, and ecological theories.

###  2.1. Child-Robot Interaction

Research in child-robot interaction has demonstrated that robots engaging with
children at home can positively influence their social and cognitive
development. Robots can motivate children to undertake household chores, such
as tidying their rooms (Fink et al., 2014), encourage reading habits
(Michaelis and Mutlu, 2019), and aid in emotion regulation (Isbister et al.,
2022; Slovák et al., 2018). Interactive agents can facilitate children’s
engagement in social play and foster positive bonds with robots through games
and entertainment (Belpaeme et al., 2012). In such interactions, social robots
may assume diverse roles, including as home assistants (De Ruyter et al.,
2005; Dautenhahn et al., 2005), socially assistive agents for autism
interventions (Scassellati et al., 2018), and educational interventions
(Belpaeme et al., 2018). Children and adolescents attribute roles like
coaches, assistants, companions, and confidants to social robots (Michaelis
and Mutlu, 2018; Cagiltay et al., 2020; Alves-Oliveira et al., 2022). Social
robots can even encourage family playtime and participation (Kim et al.,
2022). However, children and parents may have conflicting needs and
expectations from in-home social robots (Cagiltay et al., 2020) as also seen
in family interactions with smart home devices (Beneteau et al., 2019, 2020;
Sun et al., 2021). Such conflicts may contribute to challenges that hinder
sustaining long-term engagement with social robots and user acceptance (de
Graaf et al., 2016; de Graaf, 2015; Cagiltay et al., 2022). Overall, child-
robot interaction research has shown that robots in the home can positively
impact children’s social and cognitive development, motivate them in various
tasks, support reading habits, help with emotional regulation, and serve as
companions, assistants, and educational aids. However, there is little
evidence on how robots can be used in the broader family context.

###  2.2. Family Theories

Early applications of family theories draw from family clinical practices,
e.g., Bowen’s Family Systems Theory (FST) (Bowen, 1966). FST argues that
families form intricate social systems, wherein members continually influence
each other’s actions. Similar to biological systems (Cox and Paley, 1997),
family systems are argued to be more than the sum of its parts, capturing
complex dynamics and capable of adaptive self-stabilization and self-
organization in response to changes in the broader system (Cox and Paley,
1997). Families consist of smaller subsystems which may capture interactions
between parents, spouses, siblings, etc., as well as part of larger systems
such as a neighborhood or community. From this perspective, a social robot
residing in a household arguably becomes a member of the family system. It can
form diverse connections and relationships with other members, exert influence
over their behaviors, and, conversely, be influenced by their actions. The
application of a family-systems approach in child-robot interaction research
allows us to explore how robots can facilitate connections between parents and
children, siblings, or across generations through shared social interactions
(Cox, 2010). A family-systems approach can also enable us to understand
parental expectations, concerns, and acceptance of social robots within the
home context (Lin et al., 2020). By integrating family-systems theories into
the study of child-robot interactions (e.g., (Cagiltay et al., 2023)), we can
better inform the design of social robots to support the well-being of
families.

###  2.3. Ecological Theories

Ecological theories provide holistic views for understanding how people
interact with technology. The “Product Service Ecology” model proposed by
Forlizzi (2013) adapts the “Social Ecological” model, providing designers with
a holistic perspective for understanding the contextual factors influencing
product design and usage. Forlizzi and DiSalvo (2006) applies an ecological
model to investigate how autonomous service robots, such as Roomba vacuum
cleaners, integrate into family homes through ethnographic observations.
Similarly, Sung et al. (2010) presents the “Domestic Robot Ecology” model to
foster a comprehensive understanding of complex in-home settings and the
acceptance of autonomous service robots for long-term use. Moreover, an
“Ecology of Aging” approach explores how robots can be designed to assist the
elderly at home (Forlizzi et al., 2004). These applications mainly focus on
domestic service robots, such as robotic cleaners, as opposed to social
companion robots. Bronfenbrenner’s bioecological theory (Bronfenbrenner, 1979;
Bronfenbrenner and Morris, 1998, 2007) describes four layers that shape an
individual’s development: individual’s immediate relationships (microsystem);
interactions between or within levels (mesosystem); relationships with
indirect environments (exosystem); and societal or belief-level influences
(macrosystem). These systems are influenced by processes (interactions with
objects or people), personal attributes (interests, appearances), contexts
(home, school, community), and time (historical changes and duration of
processes). Family Ecological Model (FEM) (Andrews et al., 1981) is an
adaptation of this bioecological theory, that similarly argues that family
interactions, such as parenting, are shaped by the context in which the
families are situated (Davison et al., 2013). Recognizing the significance of
ecological theories, we aim to extend the application of these models to the
design and development of a framework for family-robot interactions.

##  3\. Social Robots for Families: Review

We conducted a literature review to investigate the current state of family-
centered approaches within human-robot interaction. We specifically
prioritized applications of social robots, within real-world settings, for
families with children. We focus on these three areas because they pose a
particularly challenging context for robot design where actionable frameworks
can be useful. Unlike service robots, social robots are capable of fostering
emotional connections, rich interactions, and perceived as a member within a
family setting, making them more relevant for exploring the impact of
technology on family dynamics and relationships. Acknowledging that “family-
robot interactions” is not yet a widely established term within HRI, we
grounded our review within child-robot interactions as our primary keyword,
hence focusing on “social robots for families with children.” From this, we
expanded our search to explore the participation of broader family structures
and real-world contexts in this evolving field.

###  3.1. Research Questions

We asked the following research questions:

RQ1. Which family members are typically involved in CRI research? (e.g.,
Siblings, mothers, fathers, grandparents, relatives)

RQ2. What are the roles of families in CRI research?
(e.g., Active participant, observer, informant, translator, non-participant)

RQ3. What context are social robots used for children and families? (e.g.,
Education, healthcare, therapy, in-home, museum)

###  3.2. Search Method and Review Criteria

We conducted a semi-structured narrative literature review to identify
relevant studies and scholarly works in the intersection of family-centered
human-robot interaction. We searched from the ACM Digital Library and Scopus
databases. Since our review is contextualized around “families with children”
our initial querying approach focused on keywords surrounding social robots,
families, and children or teens: TITLE OR ABSTRACT OR KEYWORDS (social AND
robot*) AND (family OR families) AND (teen* OR child*). However, the search
results yielded limited entries (ACM DL: 23, Scopus: 97). We then broadened
the term “family” in our search to: TITLE OR ABSTRACT OR KEYWORDS (social AND
robot*) AND (family OR families OR parent* OR mother* OR father* OR sibling*)
AND (teen* OR child*). In total, this search returned 315 papers (ACM DL: 54,
Scopus: 261). After excluding duplicates, non-English papers, and non-papers
(e.g., proceeding reports) there were 248 remaining potential sources (See
Figure 2). This final query indexed all articles until April 16, 2023. Two
authors participated in the review process and screened the potential 248
papers in three-steps:

Figure 2. Search method and criteria for the review process.

A table describing the search method and criteria for the review process. The
table has three rows “Identification” “screening” and “included”, and two rows
describing the number of records in each column.

####  3.2.1. Initial Screening

The first author conducted the initial paper screening. We included papers
focusing on children’s robot use with an emphasis on family involvement.
Papers were excluded if they were not related to the HRI field and research
questions (e.g., surgical robots in pediatrics). 50 papers were excluded.

####  3.2.2. Secondary Screening

For the remaining 198 papers, the authors created a codebook and identified
six categories for secondary screening: the domain of the study (e.g.,
healthcare, learning, telepresence); type of study (e.g., review, user study,
position paper); contextual location of the study (e.g., lab, in-home, school,
hospital); the role of the robot in the study (e.g., social companion,
assistant, mediator); the type of robot used (e.g., Nao, Pepper, Cozmo etc.);
which family members were included (e.g., parents, siblings); and the role of
family members in the study (e.g., informer, observer, participant). The first
author coded the 198 entries based on the codebook and revised the coded
entries with the second author. Through iterative discussions, the entries
were organized into thematic categories, allowing for the identification of
common trends, patterns, and gaps in the literature.

####  3.2.3. Sampled Papers for Framework Development

After the secondary screening of 198 papers, the first author conducted a
relevance scoring to determine the extent to which each paper aligned with the
focus on family-robot interaction. Papers were scored high, mid, or low within
the following categories: (1) Family: Papers that directly involved family
members OR those that explored family-oriented contexts; (2) Robot: Papers
that addressed the robot’s ability to engage in social interactions within the
family OR the perceived influence of the robot on family dynamics; and (3)
Setting: Papers capturing naturalistic real-world settings OR long-term
interactions. Papers that showed evidence in these categories were labeled
high due to their relevance to understanding interactions in authentic family
environments. At this step, 65 out of 198 papers were ranked high or mid in
one or more of these three categories. Notably, in this work, we sampled a
smaller subset of 12 papers that include studies where children and family
members were involved as participants in interactions with a social robot in
real world settings. We further synthesize these papers that showcase
actionable and novel contributions which served as a feasible first step
towards structuring our proposed framework111 The full list of selected papers
can be found in this embedded repository link..

Table 1. Categories of the sampled papers used to construct an initial family-
centered framework

Robot Interactions That Impact Family Life |  Family Characteristics That Inform Robot Design
---|---
Family Connections (Chen et al., 2022; Abe et al., 2018); At Home Special Education and Family Bonding (Armstrong and Huh, 2021); Family Co-Learning (Ahtinen et al., 2023); Family Dynamics (Pelikan, 2019) |  Robot Perceptions (McHugh et al., 2021); Robot Adoption in Long-Term (Henry et al., 2020; Amirova et al., 2023; Levinson et al., 2022); Robot Acceptance (de Jong et al., 2021; Lin et al., 2021; Lee et al., 2022)

A two column table listing the synthesis of sampled papers. Left column lists
papers that were labeled “robot interactions that impact family life” and the
right column lists papers that were labeled “family characteristics that
inform robot design”

###  3.3. Descriptive Insights From Screened Papers

To answer our three research questions, we provide a descriptive overview of
the 198 papers within our search scope.

Over half of the screened papers (54%) included robot use in healthcare
settings for children. Applications included interventions for Autism Spectrum
Disorder (ASD), chronic illnesses, sensory impairments, ER visits, and
hospitalization. Notably, 68 of 105 papers in this category focused on the use
of robots to support children with disabilities and special needs. These
studies typically took place in hospitals and labs. Approximately 16% of the
papers explored robot use in social interactions and companionship, including
topics such as parenting support, collaborative play, connection-building,
diary keeping, storytelling, and sleep hygiene. These studies occur in
settings such as homes, labs, museums, and shopping malls. Nearly 13% of the
papers focused on robots use in educational roles, such as reading, language
learning, math, tutoring, and speech therapy. These studies took place in
settings like classrooms, labs, or science and technology clubs. Nine percent
of the papers examined topics within psychology, including developmental
robotics, joint attention, synchrony, attribution of intelligence, and
culture. These studies took place in controlled laboratory studies. Five
percent of the papers addressed robot use for emotional wellbeing within
activities for pain and anxiety management, music therapy, loneliness
reduction, stress buffering, and painting. These interventions occur in
playrooms, homes, and labs.

Among the 198 papers, the most widely used methods are user studies (44%),
which included field studies and long-term studies. This was followed by
clinical or lab interventions and trials (28%), new robot designs or
prototypes (15%), or review and survey papers (13%). Notable family-centered
robot prototypes include Arpi (Sarmiento Calderón and Gómez Hormaza, 2023),
Emobie (Arnold, 2016), Buddy (Kim et al., 2017), Edu (Talami et al., 2021),
and Pabi (Dickstein-Fischer et al., 2011). For example, Edu (Talami et al.,
2021), is designed as a companion for children in protective isolation units
to connect and play with family.

##### Roles of Families in Child-Robot Interaction Research

The screened papers typically mentioned insights only from children, or
reported findings from children accompanied by: a parent; a sibling; a parent
and other stakeholders such as classmates, educators, teachers, clinicians,
nurses, or therapists. Rarely, papers included infant-mother pairs,
adolescents, or child-grandparent pairs. We identified roles of family members
categorized as: informer; feedback provider; interpreter; consent provider;
survey filler; diary keeper; reporter; passive observer; active participant.
We captured that, family members, typically parents, functioned as “survey
fillers” on behalf of their children and selves. In longitudinal studies, they
have acted as “diary keepers” or “reporters,” responsible of tracking
interactions and metrics for behavioral insights. Most often, family members
were either “consent providers” or “passive observers” quietly observing
interactions between their child and robot without actively influencing them.
Family members were rarely “active participants” that partake in the
interaction with the robot and child.

###  3.4. Identifying the Research Gap

We identified several gaps that limit a family-centered approach in human-
robot interaction research. First, the context predominantly includes robots
for healthcare applications and social interventions for children with neuro-
developmental disorders. Within these child-robot interaction studies, there
are limited investigation on family dynamics and social robot interactions in
different contexts. Second, family members in child-robot interaction studies
mostly involve parents, and specifically mothers. Siblings, grandparents, or
other diverse forms of family structures are underrepresented. Third, there is
limited research examining design needs for how robots can support long-term
interactions in continuously evolving family roles and dynamics. The evolution
of roles over time are not captured in these studies. In fact, family members
often take an indirect role in research and have passive involvement with the
robot.

##  4\. Synthesis of Sampled Papers

We provide an in-depth review of the 12 selected papers synthesized in two
categories: robot interactions that support family life; and family
characteristics that inform robot design (See Table 1).

###  4.1. Robot Interactions That Impact Family Life

Robot interactions in contexts such as education, companionship, storytelling,
and social assistance can support family connections, family dynamics, child
development, and learning.

####  4.1.1. Robots for Eliciting Family Connections

Chen et al. (2022) studied the design of a robot-assisted storytelling
interaction with 12 families of 3-7-year-old children in the context of
parent-child-robot interaction. Robots were deployed in family homes for 3-6
weeks and remotely teleoperated for six 25-minute sessions of parent-child-
robot story reading. The findings show that social robots can improve parent-
child interactions. Particularly, the robot’s participation in parent-child-
robot interactions fostered togetherness, “three of us” feeling, and a sense
of belonging during long-term storytelling. This suggests that the robot’s
storytelling facilitated parent-child bonding and created a unique sense of
shared experiences and unity in this triadic setting. This study shows the
potential of robots that can foster family bonding and lasting memories
through collaborative activities.

Abe et al. (2018) investigated a remote-operated mobile telepresence robot for
supporting interactions between toddlers and their non-co-located families. In
a playroom field trial, researchers tested the system’s acceptance and
usefulness. The study included 36 participants, including 19 toddlers (aged 0
to 3) and adult family members (parents or grandparents). Eight toddlers
refused to play with the robot in the playroom, while eleven toddlers
interacted with the robot. The remote teleoperated system increased
opportunities for grandparent-grandchild interactions and promoted the sharing
of daily life. Motivated by these interactions, grandparents paid special
attention to their appearance during video interactions. This phenomenon
highlights the potential of utilizing robotic telepresence to strengthen
toddler-grandparent relationships and foster meaningful connections.

####  4.1.2. Robots for Special Education and Family Bonding

In a three-month autoethnographic study (Armstrong and Huh, 2021), a parent of
a 10-year-old child with autism describes their long-term interactions and
experiences with an in-home socially assistive robot. The robot’s engagement
in simple social interactions, such as greetings, fostered direct and indirect
bonding between the parent and child and supported improvement in the child’s
social skills. Notably, the child exhibited knowledge transfer from the robot
after five weeks of sustained engagement. To support this period, the parent
and a speech pathologist created a customized curriculum to maximize the
robot’s educational potential. During the study, the child showed the robot
affection, noted as a personal milestone for the parent. After two months of
interaction, the child could express “love” by reciprocating the phrase “I
love you” to their mother through the robot. These interactions show how
therapeutic robot interventions can facilitate parent-child communication and
emotional expressions.

####  4.1.3. Robots for Supporting Family Co-Learning

In a one-month in-home study with eight families (sixteen parents and sixteen
children aged 6-15 years old) in Finland, Ahtinen et al. (2023) explored how
family members collaboratively learn about social robots at their homes.
Family members interacted with a robot collaboratively by completing many
open-ended, hands-on tasks to learn about social robots, their potential roles
and tasks, programming, and design. Study methods included online family
interviews and diaries, where parents and children filled the diaries
together. Notably, the authors emphasize parents’ access to data throughout
the study. For example, researcher communications with children occurred
through the supervision of parents, and families were provided transparency in
privacy-preserving options for the robot. Families perceived the co-learning
as a positive experience, where the findings suggest that the co-learning
experience created the feeling of collaboration and “togetherness” between
family members.

####  4.1.4. Robot Roles in Family Dynamics

A one-week in-home study by Pelikan (2019) examined how families react to non-
lexical sounds from a Cozmo robot and explored how families with at least one
8-14-year-old child interacted with the robot. The preliminary findings show
that families simplified their sentences to mimic the robot’s sounds. This
adaptation suggests that the robot’s communication style influenced family
members’ language. Additionally, some families portrayed the robot
differently, with parents calling it a “stubborn-child” and children calling
it a “buddy.” These dual characterizations demonstrate potential in the
robot’s multifaceted role within the family dynamic, ranging from a
challenging yet endearing agent to a friendly figure.

###  4.2. Family Characteristics That Inform Robot Design

Family members’ characteristics, beliefs, values, or conflicts may impact
their perceptions, interactions, and attitudes toward robots.

####  4.2.1. Family Characteristics on Robot Perceptions

McHugh et al. (2021) examined how family characteristics affect children’s
perceptions of robots through a goal-oriented play study in a museum. The
study findings identified a connection between parents’ characteristics and
children’s perceptions toward robots. Specifically, the results showed that
children whose parents had STEM backgrounds were more likely to attribute
higher animacy to the robot. The study also found a link between parent-child
communication and children’s perceptions. Specifically, the amount that
parents communicated about the robot directly influenced a child’s perception
of robot animacy. For the children of parents with STEM backgrounds, children
gave commands to the robot assuming the robot had advanced perceptual and
cognitive abilities. These findings suggest that family characteristics,
communication dynamics, and children’s perceptions are interconnected factors
that affect human-robot interaction.

####  4.2.2. Family Characteristics on Long-Term Robot Adoption

In a study of hospitalized children using a telepresence robot for two weeks
or more, Henry et al. (2020) explored the robot’s role in helping children
maintain connections with their parents and siblings and retain their sense of
belonging within the family structure. This exploratory study interviewed 17
onco-hematology patients (ages 7-25) and their parents about their home
telepresence robot experiences. The robot enabled family members to remotely
communicate with the hospitalized child, supporting patients and families.
Patients primarily valued the robot’s ability to bridge connections with
siblings, “assume their role at home”, and “continue to have a life that is as
normal as possible.” However, this robot connection became less effective for
some families as their daily lives became more complicated. When the
hospitalized child was sick or tired, they sometimes avoided robot contact
with the family. This, in turn, led to feelings of sadness among the siblings,
who were unable to connect during such moments. The changes in these dynamics
demonstrate the complex relationship between technology, family relationships,
and family members’ emotional well-being, emphasizing the need for nuanced
approaches to family-centered human-robot interactions in healthcare.

A 21-day multi-session robot-assisted autism therapy (RAAT) study by Amirova
et al. (2023) included 16 children aged 5-12 and their parents in a Kazakhstan
hospital therapy center. The study examined whether parental involvement
during autism interventions affected the efficacy of RAAT interventions.
Findings suggest that, parental presence may encourage exploratory and playful
behaviors for non-verbal children with severe autism, but may decrease verbal
children’s compliance and increase aggression. Proposed guidelines for RAAT
suggest that researchers should consult with parents before therapy, define
their roles, and allow their presence in initial sessions, especially for
severe cases of autism. Parent-involved data collection measures may include
questionnaires, parent-child relationship assessments, skill benchmarks, and
pre- and post-tests to ensure effective therapy and long-term progress
monitoring. This study shows how parental presence, children’s
characteristics, and child-parent relationships affect RAAT outcomes and can
inform robot design for long-term parent-involved therapy sessions.

In a two-week study by Levinson et al. (2022), seven families with children
aged 6-13 shared their experiences and concerns about living with a social
robot. Children engaged in entertaining activities with the robot, such as
storytelling and word-guessing games. The activities encouraged collaborative
play and turn-taking between siblings, however children faced challenges in
sustaining long-term engagement, which also created a burden on parents. The
results suggest that contextual factors such as changes in family routines
during summer vacation, may hinder long-term engagement.

####  4.2.3. Family Characteristics on Robot Acceptance

A study by de Jong et al. (2021) examined children’s views on robots and their
acceptance or rejection of them. 87 children (7–11-year-olds) interacted with
a social robot during class and completed a questionnaire about their
experiences. The findings show that three types of beliefs influenced
children’s decision-making. Utilitarian beliefs, such as the robot’s ability
to help with homework, chores, and teach jokes, contributed to fostering
acceptance. Hedonic beliefs –the desire for companionship, emotional support,
and play when no one else was available– were identified as strong motivators
for acceptance. Social beliefs, such as children who view robots as “cool” and
“unique” were more likely to accept them in their homes. In contrast,
normative beliefs about family members’ disapproval or fear of siblings were
identified as reasons for rejection. This study shows how family and
surroundings influence children’s normative beliefs about robots in the home.
This nuanced understanding of children’s beliefs reflects the multifaceted
nature of their decision-making processes for accepting robots in home.

In a qualitative design fiction study, Lin et al. (2021) explored predictors
for parents’ acceptance of storytelling robots in their families, including 14
mothers and 4 fathers with children aged 2-5. The context of use affected the
perceived appropriateness of introducing such a robot into their family.
Parents’ acceptance also was reliant on the robot’s perceived agency and
intelligence. Notably, parents were ambivalent about the robot’s emotional
expression, which may hinder adoption. Although this work did not include
insights directly from children, it demonstrates the factors that parents
consider when deciding whether to incorporate storytelling robots into their
family interactions.

Lee et al. (2022) surveyed working parents regarding their expectations and
preferences for social robots’ in childcare functions, such as socialization,
education, entertainment, and consultation. The survey included dual-income
mothers ($N=351$) and fathers ($N=273$) with single or multiple children
(early childhood, aged 3-7, $N=204$; middle childhood, aged 8-12, $N=210$).
Parents favored robots for childcare functions like socialization,
entertainment, and consultation, but not education. Additionally, working
parents’ parenting styles (e.g., family-oriented, work-oriented,
noninterventional, dominant) influenced their expectations regarding social
robots. Children’s characteristics also influenced parents’ preferences for a
social robot, such that families with younger children were more likely to
prefer social robots with counseling functions. However, parents of older
children were more positive towards an entertainment robot. These findings
illustrate how child and parent characteristics influence acceptance of robots
at the home.

##  5\. Toward a Family-Centered Framework in HRI

We employed a deductive approach to guide the framework development grounded
by (1) the ecological theories described in Section 2.2, (2) the gaps noted in
Section 3.4, and (3) the synthesis of the sampled 12 papers in Section 4.

First, by incorporating family-systems theories, specifically Bowen’s Family
Systems Theory (FST), into our theoretical framework, we aim to capture the
dynamics of family interactions with robots. FST’s perspective on families as
complex social systems provides insights into the potential role of social
robots as integral members of these systems. This lens enables the exploration
of diverse interactions and relationships formed between social robots and
family members. Through FST, we identified three systems for the proposed
framework: Family System, Robot System, and Contextual System.

Second, we translated practical insights grounded in the theoretical
background, research gaps, and paper synthesis, to identify the multifaceted
roles of social robots within family systems. The synthesis served as a guide
for developing the proposed framework that represents the complexities of
family-robot interactions. From robots fostering togetherness through
storytelling to robots strengthening family bonds via telepresence, these
studies offered tangible examples of social robot characteristics that might
contribute positively to family dynamics. The system components aim to inform
the design and deployment of social robots, ensuring their meaningful
integration into diverse family structures and sustained interactions over
time. We describe these systems below.

###  5.1. Family-Robot Interaction Systems

At a high level, family-robot interactions consist of three systems: the
family system, robot system, and contextual system (See Fig 3).

####  5.1.1. Family System

The family system includes all members involved in the family and the
relationships between each member. It considers the unique characteristics,
beliefs, values, and conflicts within the family that influence their
perceptions and interactions with robots. These members may be co-located or
located in different contextual systems. The family system is dynamic and may
evolve over time, impacting the long-term nature of robot interactions. For
example, new members may join or leave the family system for various reasons
(short-term changes: work, vacation, school; long-term changes: birth, death,
marriage, divorce). From the literature review, we identified that several
family members’ characteristics play a significant role in shaping how family
members perceive and engage with robots, these include: family routines
(Levinson et al., 2022); parents’ STEM backgrounds (McHugh et al., 2021); dual
parent employment and parenting styles (Lee et al., 2022); family resilience
towards changes in children’s health conditions (Henry et al., 2020); the
spectrum of children’s diagnosis and parents physical presence (Amirova et
al., 2023); children’s hedonic, social, and utilitarian beliefs (de Jong et
al., 2021); and parents’ perceptions towards the agency, intelligence, and
expressivity of robots (Lin et al., 2021).

####  5.1.2. Robot System

The robot system considers the technical and interactive attributes of the
robot itself, including its design, capabilities, and functions. We identified
varying use cases of robots that have distinct impacts on family life, such as
those designed for storytelling (Chen et al., 2022; Lin et al., 2021), play
(Abe et al., 2018; McHugh et al., 2021; Levinson et al., 2022), learning
(Ahtinen et al., 2023; de Jong et al., 2021), therapy (Armstrong and Huh,
2021; Amirova et al., 2023), communication (Pelikan, 2019; Henry et al.,
2020), and childcare (Lee et al., 2022). Several robot characteristics may
support interactions between family members, for example, robot’s ability to
convey emotion (Armstrong and Huh, 2021), hold multi-party interactions (Chen
et al., 2022), assist with homework, chores (de Jong et al., 2021),
entertainment (Levinson et al., 2022), help maintain long-distance connections
(Henry et al., 2020), as well as its communication style (Pelikan, 2019),
perceived intelligence and agency (Lin et al., 2021).

####  5.1.3. Contextual System

The contextual system captures the physical environments in which HRI takes
place, such as homes, museums, libraries, or any other relevant setting. This
system sets the stage for HRI interactions and shapes the nature of robot use
within specific environments. Examples from our review include: robots in the
home (Chen et al., 2022; Armstrong and Huh, 2021; Ahtinen et al., 2023;
Pelikan, 2019; Levinson et al., 2022) or surveys for in-home use (Lin et al.,
2021; Lee et al., 2022); museums (McHugh et al., 2021); hospital (Amirova et
al., 2023); or robots used in multiple contexts, between a playroom and non-
co-located family members (Abe et al., 2018), a hospital and home (Henry et
al., 2020), or classroom and home (de Jong et al., 2021).

###  5.2. Influences on Family-Robot Systems

All three of these systems are bounded by four dimensions: roles assumed
within a system; goals set within a system; processes followed within and
between systems; and the changes in time.

####  5.2.1. Roles

The roles of family members in child-robot interactions span across varying
levels of involvement such as: direct participants ((Chen et al., 2022; Abe et
al., 2018; Armstrong and Huh, 2021; Ahtinen et al., 2023; Pelikan, 2019;
McHugh et al., 2021; Henry et al., 2020; Amirova et al., 2023; Levinson et
al., 2022)), for example in parent-child co-learning and co-diary keeping
(Ahtinen et al., 2023), parent-child storytelling (Chen et al., 2022), or
parent-child goal-oriented play (McHugh et al., 2021); or indirect influences
on children’s perceptions towards robots (e.g., (de Jong et al., 2021)),
including roles such as an informer, feedback provider, interpreter, consent
provider, survey filler (e.g., (Lin et al., 2021; Lee et al., 2022)), diary
keeper, reporter, passive observer. Robots can assume various roles within the
family, such as a companion for the child or an assistant for the parents
(Cagiltay et al., 2020). In the reviewed work, robot roles span across
storytelling companions (Chen et al., 2022; Lin et al., 2021), playmates (Abe
et al., 2018; Pelikan, 2019; McHugh et al., 2021; Levinson et al., 2022),
learning companions (Armstrong and Huh, 2021; Ahtinen et al., 2023; de Jong et
al., 2021; Amirova et al., 2023), childcare assistant (Lee et al., 2022), and
telepresence agent (Henry et al., 2020).

Figure 3. Systems for Family-Robot Interaction

This figure illustrates that Family-Robot Interactions consist of three
systems: the contextual system, family system, and robot system. The papers
that fall into each system and their categories are listed. There is an arrow
from the family system to the robot system saying “informs design” and an
arrow from the robot system to family system saying “supports interactions”

####  5.2.2. Goals

The goals set by individual family members, for example, parents’ desires for
childcare support (Lee et al., 2022) or children’s need for companionship (de
Jong et al., 2021), may influence the broader family acceptance and engagement
with robots. As robots become more prevalent among family homes, conflicts and
tensions regarding the use of these technologies will likely emerge due to the
family members’ multi-faceted needs (e.g., as seen in family interactions with
smart home devices (Beneteau et al., 2019, 2020; Sun et al., 2021)). The goals
of robots are inherently shaped by its use cases (e.g., storytelling, play,
education) and may include goals such as sustaining long-term engagement,
facilitating child-parent co-play, or a medium to connect distant family
members.

####  5.2.3. Processes

The processes represent the actions that occur between systems or within a
system. Take the example of a storytelling robot (robot system) placed in a
library (contextual system) interacting with children and grandparents (family
system). The processes between systems may include the direct interactions
between the child-grandparent-robot and library staff (e.g., turn-taking), as
well as the indirect interactions between the library context and robot (e.g.,
the library’s noise level impacting the robot’s ability for speech
recognition). The processes within a system may capture the direct actions
between the child and grandparent, or indirect actions such as the robot’s
connection to a cloud server. Inherently, the processes capture beyond a
single dimension, and are shaped by the roles, goals, and affordances of all
three systems.

####  5.2.4. Time

The factor of time is crucial for a holistic understanding of family-centered
HRI. It accounts for changes that happen over time and reflects the importance
of long-term interactions. Our review included several examples that captured
families’ long-term interactions with robots, which typically ranged between
one week to one month (Amirova et al., 2023; Henry et al., 2020; Levinson et
al., 2022; Chen et al., 2022; Ahtinen et al., 2023; Pelikan, 2019) and in one
case up to three months (Armstrong and Huh, 2021). The time dimension cuts
across all three systems. The indirect impact of time on the family system
becomes visible in long-term interactions with robots, for example, uncovering
improvements in family connections, changes in child development and learning,
and shifts in family dynamics. Similarly, this dimension can capture the
evolution of the robot’s role and capabilities within the family over extended
periods. For example, a robot’s role may initially be an assistant for the
family and, over time, may evolve into a companion for an older adult in the
household. On the contrary, families may become less attached to the robot
over time and have decreased engagement due to changes in the family system.
In response, this may call for proposing novel and adaptive robot behaviors.
Nonetheless, contextual changes that happen over time may inevitably impact
the robot and family system. For example, it may be easier for families to
maintain routines with robots during the school year compared to the summer
vacation.

##  6\. Discussion

In this work, we introduced a theoretically motivated framework as a step
toward family-robot interactions. We propose that, a family-robot interaction
involves three systems: family system, robot system, and contextual system.
Within this framework, it is necessary to explore, identify, and define the
roles, goals, and processes between members involved in each system to
understand the holistic nature of family-robot interactions. This framework
was constructed given insights drawn from reviewed literature in child-robot
interaction and insights from family theories. In our narrative review, we
explored the current space of child-robot interactions that expand to broader
family involvement. We found three gaps in the field: first, the limited body
of work that focuses on contexts beyond therapy and healthcare; second, the
lack of representation of family members involved in child-robot interaction;
and third, limited exploration of long-term social robot use that can adapt to
changes in roles and dynamics within the family.

With further synthesis, we identified two core themes that capture the high-
level interplay between family members and robots. First, robots have the
potential to support the needs and preferences of children and their
surrounding care ecosystems, embodying roles that could enhance family
connections. They can serve as catalysts for family togetherness, create
shared experiences, and promote lasting memories through collaborative
activities. Second, family characteristics, such as parents’ STEM backgrounds,
employment, individual beliefs, values, and communication patterns, play a
pivotal role in shaping how family members perceive and interact with robots,
potentially influencing long-term acceptance and adoption. By understanding
the diversity among family characteristics and the unique needs of individuals
within the family, researchers can inform design decisions for robot
capabilities that are more suitable to real-world use-cases. Finally, we
translated these insights to construct a family-centered framework for family-
robot interactions.

###  6.1. Practical Applications of the Framework

Here, we discuss strategies for validating and applying the family-centered
framework for family-robot interactions and ways in which the framework may
provide value to its practitioners. First, to ensure the holistic
understanding of family-robot interactions, it becomes imperative to unravel
and define the roles, goals, and processes within each system. To validate the
framework, we propose engaging in empirical studies to assess the extent to
which the framework explains observed family-robot interactions. For example,
a study may explore how family members’ characteristics and perceived trust
may influence turn-taking in family-robot group interactions and inform robot
design. For validation, one should demonstrate the emergence of systems and
boundaries as outlined in the framework, providing empirical support and
enhancing our comprehension of family-robot interactions. Once validated, the
framework aims to propose value as a versatile tool for HRI researchers
seeking to take a family-centered lens. This would enable applying the
framework, which would involve guiding researchers in the early stages of
their work, aiding them in making informed technical design decisions,
structuring study designs, formulating hypotheses, selecting study sites, and
recruiting participants. By integrating this family-centered perspective into
the research process, the framework should ideally ensure that the study
captures the nuanced interplay between family members and robots. For example,
the framework might serve as a template to create a family-robot integration
plan, specifying design requirements tailored to family needs and robot
affordances. Moreover, there is potential for customization and extension of
the framework based on specific research contexts and new insights.
Researchers are encouraged to adapt the framework to suit the unique
characteristics of their study populations and contexts. As insights emerge
from varying family structures, cultural contexts, and long-term implications,
the framework will likely serve as a basis to be customized and expanded to
align with the evolving landscape of family-robot interactions.

###  6.2. Limitations and Future Directions

We acknowledge several limitations in our work. First, our review process for
constructing the framework does not constitute as a comprehensive examination
of existing literature. Thus, our synthesis may exclude some studies,
viewpoints, or nuanced findings. The selected papers for synthesis were
included as a first step toward a framework demonstrating how child-robot
interaction can involve other family members. Future research must prioritize
design, user evaluations, and extensive scoping reviews of relevant family-
robot interaction phenomena to better understand the landscape and expand our
framework. Furthermore, it is worth noting that many of the discussion points
included in this framework are already known challenges within HRI research.
Long-term interaction, adaptation, personalization, cultural diversity, and
ethical and social implications for children have long been recognized as
critical areas of exploration. Our framework situates these dimensions in the
broader family-robot interaction context. We believe that focusing on families
as a whole may provide a more holistic approach to addressing broader
challenges in HRI research. The framework aims to increase HRI researchers’
participation in these important conversations around robots tailored for
families. Despite these limitations, we aim for our work to serve as a
valuable starting point for future research directions toward family-centered
HRI, fostering innovation and collaboration within the field.

##  7\. Conclusion

We presented a theoretically motivated framework informed by family theories
and the growing literature in HRI focusing on children and families. We argue
that a family-centered approach in HRI should encapsulate the multifaceted
interactions between family members and robots. We aim that our proposed
framework may serve as a first step toward designing for family-robot
interactions.

###### Acknowledgements.

This work was supported by NSF awards #2312354 and #2247381. We would like to
thank Pragathi Praveena, Hailey Johnson, Casey Wong, and the anonymous
reviewers for their constructive and actionable feedback that helped improve
the quality of our paper.

## References

  * (1)
  * Abe et al. (2018) Kasumi Abe, Masahiro Shiomi, Yachao Pei, Tingyi Zhang, Narumitsu Ikeda, and Takayuki Nagai. 2018\.  ChiCaRo: tele-presence robot for interacting with babies and toddlers.  _Advanced Robotics_ 32, 4 (2018), 176–190.
  * Ahtinen et al. (2023) Aino Ahtinen, Nasim Beheshtian, and Kaisa Väänänen. 2023\.  Robocamp at home: Exploring families’ co-learning with a social robot: Findings from a one-month study in the wild. In _Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction_. 331–340.
  * Alves-Oliveira et al. (2022) Patrícia Alves-Oliveira, Elin A Björling, Patriya Wiesmann, Heba Dwikat, Simran Bhatia, Kai Mihata, and Maya Cakmak. 2022\.  Robots for Connection: A Co-Design Study with Adolescents. In _2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)_. IEEE, 578–583.
  * Amirova et al. (2023) Aida Amirova, Nazerke Rakhymbayeva, Aida Zhanatkyzy, Zhansaule Telisheva, and Anara Sandygulova. 2023\.  Effects of parental involvement in robot-assisted autism therapy.  _Journal of Autism and Developmental Disorders_ 53, 1 (2023), 438–455.
  * Andrews et al. (1981) Mary P Andrews, Margaret M Bubolz, and Beatrice Paolucci. 1981\.  An ecological approach to study of the family.  _Marriage & Family Review_ 3, 1-2 (1981), 29–49.
  * Armstrong and Huh (2021) Lisa Armstrong and Yeol Huh. 2021.  Longing to connect: Could social robots improve social bonding, attachment, and communication among children with autism and their parents?. In _Social Robotics: 13th International Conference, ICSR 2021, Singapore, Singapore, November 10–13, 2021, Proceedings 13_. Springer, 650–659.
  * Arnold (2016) Lindsey Arnold. 2016\.  Emobie™: A robot companion for children with anxiety. In _2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_. IEEE, 413–414.
  * Belpaeme et al. (2012) Tony Belpaeme, Paul Baxter, Robin Read, Rachel Wood, Heriberto Cuayáhuitl, Bernd Kiefer, Stefania Racioppa, Ivana Kruijff-Korbayová, Georgios Athanasopoulos, Valentin Enescu, et al. 2012\.  Multimodal child-robot interaction: Building social bonds.  _Journal of Human-Robot Interaction_ 1, 2 (2012).
  * Belpaeme et al. (2018) Tony Belpaeme, James Kennedy, Aditi Ramachandran, Brian Scassellati, and Fumihide Tanaka. 2018\.  Social robots for education: A review.  _Science robotics_ 3, 21 (2018), eaat5954.
  * Beneteau et al. (2020) Erin Beneteau, Ashley Boone, Yuxing Wu, Julie A Kientz, Jason Yip, and Alexis Hiniker. 2020\.  Parenting with Alexa: exploring the introduction of smart speakers on family dynamics. In _Proceedings of the 2020 CHI conference on human factors in computing systems_. 1–13.
  * Beneteau et al. (2019) Erin Beneteau, Olivia K Richards, Mingrui Zhang, Julie A Kientz, Jason Yip, and Alexis Hiniker. 2019\.  Communication breakdowns between families and Alexa. In _Proceedings of the 2019 CHI conference on human factors in computing systems_. 1–13.
  * Bowen (1966) Murray Bowen. 1966\.  The use of family theory in clinical practice.  _Comprehensive psychiatry_ 7, 5 (1966), 345–374.
  * Bronfenbrenner (1979) Urie Bronfenbrenner. 1979\.  _The ecology of human development: Experiments by nature and design_.  Harvard university press.
  * Bronfenbrenner and Morris (1998) Urie Bronfenbrenner and Pamela A Morris. 1998.  The ecology of developmental processes.  (1998).
  * Bronfenbrenner and Morris (2007) Urie Bronfenbrenner and Pamela A Morris. 2007.  The bioecological model of human development.  _Handbook of child psychology_ 1 (2007).
  * Cagiltay et al. (2020) Bengisu Cagiltay, Hui-Ru Ho, Joseph E. Michaelis, and Bilge Mutlu. 2020.  Investigating family perceptions and design preferences for an in-home robot. In _Proceedings of the Interaction Design and Children Conference_. 229–242.
  * Cagiltay et al. (2023) Bengisu Cagiltay, Bilge Mutlu, and Margaret L Kerr. 2023\.  Family Theories in Child-Robot Interactions: Understanding Families as a Whole for Child-Robot Interaction Design. In _Proceedings of the 22nd Annual ACM Interaction Design and Children Conference_. 367–374.
  * Cagiltay et al. (2022) Bengisu Cagiltay, Nathan Thomas White, Rabia Ibtasar, Bilge Mutlu, and Joseph Michaelis. 2022\.  Understanding Factors That Shape Children’s Long Term Engagement with an In-Home Learning Companion Robot. In _Proceedings of the Interaction Design and Children Conference_ (Braga, Portugal) _(IDC ’22)_. Association for Computing Machinery, 362–373.  https://doi.org/10.1145/3501712.3529747
  * Chambers (2012) Deborah Chambers. 2012\.  ‘Wii play as a family’: The rise in family-centred video gaming.  _Leisure Studies_ 31, 1 (2012), 69–82.
  * Chen et al. (2022) Huili Chen, Anastasia K Ostrowski, Soo Jung Jang, Cynthia Breazeal, and Hae Won Park. 2022\.  Designing Long-term Parent-child-robot Triadic Interaction at Home through Lived Technology Experiences and Interviews. In _2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)_. IEEE, 401–408.
  * Cox (2010) Martha J Cox. 2010\.  Family systems and sibling relationships.  _Child Development Perspectives_ 4, 2 (2010), 95–96.
  * Cox and Paley (1997) Martha J Cox and Blair Paley. 1997.  Families as systems.  _Annual review of psychology_ 48, 1 (1997), 243–267.
  * Dautenhahn et al. (2005) Kerstin Dautenhahn, Sarah Woods, Christina Kaouri, Michael L Walters, Kheng Lee Koay, and Iain Werry. 2005.  What is a robot companion-friend, assistant or butler?. In _2005 IEEE/RSJ international conference on intelligent robots and systems_. IEEE, 1192–1197.
  * Davison et al. (2013) Kirsten K Davison, Janine M Jurkowski, and Hal A Lawson. 2013\.  Reframing family-centred obesity prevention using the Family Ecological Model.  _Public health nutrition_ 16, 10 (2013), 1861–1869.
  * de Graaf et al. (2016) Maartje MA de Graaf, Somaya Ben Allouch, and Jan AGM van Dijk. 2016.  Long-term evaluation of a social robot in real homes.  _Interaction studies_ 17, 3 (2016), 462–491.
  * de Graaf (2015) Maartje Margaretha Allegonda de Graaf. 2015\.  Living with robots: investigating the user acceptance of social robots in domestic environments.  (2015).
  * de Jong et al. (2021) Chiara de Jong, Jochen Peter, Rinaldo Kühne, Caroline van Straten, and Alex Barco. 2021\.  Exploring children’s beliefs for adoption or rejection of domestic social robots. In _2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)_. IEEE, 598–603.
  * De Ruyter et al. (2005) Boris De Ruyter, Privender Saini, Panos Markopoulos, and Albert Van Breemen. 2005.  Assessing the effects of building social intelligence in a robotic interface for the home.  _Interacting with computers_ 17, 5 (2005), 522–541.
  * Dickstein-Fischer et al. (2011) Laurie Dickstein-Fischer, Elizabeth Alexander, Xiaoan Yan, Hao Su, Kevin Harrington, and Gregory S Fischer. 2011.  An affordable compact humanoid robot for autism spectrum disorder interventions in children. In _2011 Annual international conference of the ieee engineering in medicine and biology society_. IEEE, 5319–5322.
  * Fink et al. (2014) Julia Fink, Séverin Lemaignan, Pierre Dillenbourg, Philippe Rétornaz, Florian Vaussard, Alain Berthoud, Francesco Mondada, Florian Wille, and Karmen Franinović. 2014\.  Which robot behavior can motivate children to tidy up their toys? Design and Evaluation of” Ranger”. In _Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction_. 439–446.
  * Forlizzi (2013) Jodi Forlizzi. 2013\.  The product service ecology: using a systems approach in design.  (2013).
  * Forlizzi and DiSalvo (2006) Jodi Forlizzi and Carl DiSalvo. 2006.  Service robots in the domestic environment: a study of the roomba vacuum in the home. In _Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction_. 258–265.
  * Forlizzi et al. (2004) Jodi Forlizzi, Carl DiSalvo, and Francine Gemperle. 2004\.  Assistive robotics and an ecology of elders living independently in their homes.  _Human–Computer Interaction_ 19, 1-2 (2004), 25–59.
  * Henry et al. (2020) Julie Henry, Tanguy Leprince, Sandra Garcia Robles, Alexandra Famery, Helen Boyle, Lila Gilis, Christine Witz, Jean-Christophe Barland, Jean-Yves Blay, and Perrine Marec-Bérard. 2020.  Qualitative, exploratory, and multidimensional study of telepresence robots for overcoming social isolation of children and adolescents hospitalized in onco-hematology.  _Journal of Adolescent and Young Adult Oncology_ 9, 1 (2020), 90–95.
  * Holloway et al. (2019) Donell Holloway, Leslie Haddon, Lelia Green, and Kylie J Stevenson. 2019.  Scaffolding early childhood learning through app use in the family home.  _The Routledge international handbook of learning with technology in early childhood_ (2019), 210–9.
  * Isbister et al. (2022) Katherine Isbister, Peter Cottrell, Alessia Cecchet, Ella Dagan, Nikki Theofanopoulou, Ferran Altarriba Bertran, Aaron J Horowitz, Nick Mead, Joel B Schwartz, and Petr Slovak. 2022\.  Design (Not) Lost in Translation: A Case Study of an Intimate-Space Socially Assistive “Robot” for Emotion Regulation.  _ACM Transactions on Computer-Human Interaction (TOCHI)_ 29, 4 (2022), 1–36.
  * Kim et al. (2017) Jiyong Kim, Hyunsu Jeong, Changhyeon Lee, Azure Yvette Pham, Thiha Soe, Pilwoo Lee, Mingu Lee, Seung Woo Kim, and Juhyun Eune. 2017.  Buddy: Interactive toy that can play, grow, and remember with baby. In _Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems_. 467–467.
  * Kim et al. (2022) SunKyoung Kim, Masakazu Hirokawa, Atsushi Funahashi, and Kenji Suzuki. 2022.  What Can We Do with a Robot for Family Playtime?. In _2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_. IEEE, 847–849.
  * Lee et al. (2022) Jieon Lee, Daeho Lee, and Jae-gil Lee. 2022.  Can robots help working parents with childcare? optimizing childcare functions for different parenting characteristics.  _International Journal of Social Robotics_ (2022), 1–19.
  * Levinson et al. (2022) Leigh Levinson, Gonzalo A Garcia, Guillermo Perez, Gloria Alvarez-Benito, J Gabriel Amores, Mario Castaño-Ocaña, Manuel Castro-Malet, Randy Gomez, and Selma Šabanović. 2022.  Living with Haru4Kids: Child and Parent Perceptions of a Co-Habitation Robot for Children. In _International Conference on Social Robotics_. Springer, 54–63.
  * Lin et al. (2020) Chaolan Lin, Karl F MacDorman, Selma Šabanović, Andrew D Miller, and Erin Brady. 2020\.  Parental expectations, concerns, and acceptance of storytelling robots for children. In _Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction_. 346–348.
  * Lin et al. (2021) Chaolan Lin, Selma Šabanović, Lynn Dombrowski, Andrew D Miller, Erin Brady, and Karl F MacDorman. 2021.  Parental acceptance of children’s storytelling robots: A projection of the uncanny valley of AI.  _Frontiers in Robotics and AI_ 8 (2021), 579993.
  * McHugh et al. (2021) Sam R McHugh, Maureen A Callanan, Kevin Weatherwax, Jennifer L Jipson, and Leila Takayama. 2021\.  Unusual artifacts: Linking parents’ STEM background and children’s animacy judgments to parent–child play with robots.  _Human Behavior and Emerging Technologies_ 3, 4 (2021), 525–539.
  * Michaelis and Mutlu (2018) Joseph E Michaelis and Bilge Mutlu. 2018.  Reading socially: Transforming the in-home reading experience with a learning-companion robot.  _Science Robotics_ 3, 21 (2018), eaat5999.
  * Michaelis and Mutlu (2019) Joseph E Michaelis and Bilge Mutlu. 2019.  Supporting interest in science learning with a social robot. In _Proceedings of the 18th ACM International Conference on Interaction Design and Children_. 71–82.
  * Pelikan (2019) Hannah Pelikan. 2019\.  ” A Stubborn Child”-How Robot Sounds are Oriented to in Everyday Situated Interaction at Home.  _Mensch und Computer 2019-Workshopband_ (2019).
  * Sarmiento Calderón and Gómez Hormaza (2023) Lucía Gabriela Sarmiento Calderón and Leonardo Fabio Gómez Hormaza. 2023\.  Arpi, a Social Robot for Children with Epilepsy. In _Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction_. 857–860.
  * Scassellati (2007) Brian Scassellati. 2007\.  How social robots will help us to diagnose, treat, and understand autism. In _Robotics research: Results of the 12th international symposium ISRR_. Springer, 552–563.
  * Scassellati et al. (2018) Brian Scassellati, Laura Boccanfuso, Chien-Ming Huang, Marilena Mademtzi, Meiying Qin, Nicole Salomons, Pamela Ventola, and Frederick Shic. 2018\.  Improving social skills in children with ASD using a long-term, in-home social robot.  _Science Robotics_ 3, 21 (2018), eaat7544.
  * Slovák et al. (2018) Petr Slovák, Nikki Theofanopoulou, Alessia Cecchet, Peter Cottrell, Ferran Altarriba Bertran, Ella Dagan, Julian Childs, and Katherine Isbister. 2018.  ” I just let him cry… Designing Socio-Technical Interventions in Families to Prevent Mental Health Disorders.  _Proceedings of the ACM on Human-Computer Interaction_ 2, CSCW (2018), 1–34.
  * Sonne et al. (2016) Tobias Sonne, Jörg Müller, Paul Marshall, Carsten Obel, and Kaj Grønbæk. 2016.  Changing family practices with assistive technology: MOBERO improves morning and bedtime routines for children with ADHD. In _Proceedings of the 2016 CHI conference on human factors in computing systems_. 152–164.
  * Sun et al. (2021) Kaiwen Sun, Yixin Zou, Jenny Radesky, Christopher Brooks, and Florian Schaub. 2021.  Child safety in the smart home: parents’ perceptions, needs, and mitigation strategies.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW2 (2021), 1–41.
  * Sung et al. (2010) JaYoung Sung, Rebecca E Grinter, and Henrik I Christensen. 2010\.  Domestic robot ecology.  _International Journal of Social Robotics_ 2, 4 (2010), 417–429.
  * Talami et al. (2021) Filippo Talami, Maximiliano Romero, and Giovanni Borga. 2021.  Edù, a robotic companion in pediatric protective isolation units. In _Educational Robotics International Conference_. Springer, 103–107.


#  Neutron skin thickness of 116,118,120,122,124Sn determined from reaction
cross sections of proton scattering

Shingo Tagami  Department of Physics, Kyushu University, Fukuoka 819-0395,
Japan  Tomotsugu Wakasa  Department of Physics, Kyushu University, Fukuoka
819-0395, Japan  Masanobu Yahiro<EMAIL_ADDRESS>Department of Physics,
Kyushu University, Fukuoka 819-0395, Japan

###### Abstract

* Background

The cross sections of the isovector spin-dipole resonances (SDR) in the Sb
isotopes have been measured. Within the model used, the neutron-skin
thicknesses $r_{\rm skin}({\rm exp})$ deduced $0.12\pm 0.06$ fm for 116Sn,
$0.13\pm 0.06$ fm for 118Sn, $0.18\pm 0.07$ fm for 120Sn, $0.22\pm 0.07$ fm
for 122Sn, $0.19\pm 0.07$ fm for 124Sn. We tested the chiral (Kyushu)
$g$-matrix folding model for 12C+12C scattering, and found that the Kyushu
$g$-matrix folding model is reliable for reaction cross sections $\sigma_{\rm
R}$ in $30~{}\,$ E_in $\stackrel{{\scriptstyle<}}{{\widetilde{}}}$100
$~{}MeVand$250 $\stackrel{{\scriptstyle<}}{{\widetilde{}}}$E_in
$\stackrel{{\scriptstyle<}}{{\widetilde{}}}$400$~{}MeV.Wedetermineneutronskinthickness$r_skin(exp)$,usingmeasured$σ_R$of$^4$He+$^116,120,224$Snscattering.Theresultsare$r_skin(exp)=0.242
±0.140$~{}fmfor$^116$Sn,$r_skin(exp)=0.377
±0.140$~{}fmfor$^120$Sn,$r_skin(exp)=0.180
±0.142$~{}fmfor$^124$Sn.The$σ_R$areavailableforprotonscatteringon$^116,118,120,122,124$Snwithhighaccuracyof$2
∼3%$asafunctionofincidentenergy$E_in$.\par\description@item@[Purpose]Ouraimistodetermine$r_skin(exp)$for$^116,118,120,122,124$SnwithsmallerrorsbyusingtheKyushu$g$-matrixfoldingmodel.\par\description@item@[Methods]OurmodelistheKyushu$g$-matrixfoldingmodelwiththedensitiesscaledfromtheD1S-GHFB+AMPneutrondensity,whereD1S-GHFB+AMPstandsforGogny-D1SHFB(D1S-GHFB)withtheangularmomentumprojection(AMP).\par\description@item@[Results]TheprotonradiiofD1S-GHFB+AMPagreewiththosecalculatedwiththeisotopeshiftmethodbasedontheelectronscattering.WethenscaletheD1S-GHFB+AMPneutrondensitiessoastoreproducethe$σ_R(exp)$.In$30
$\stackrel{{\scriptstyle<}}{{\widetilde{}}}$E_in
$\stackrel{{\scriptstyle<}}{{\widetilde{}}}$65$~{}MeV,wedetermine$r_skin(exp)$frommeasured$σ_R$.Thevaluesare$r_skin(exp)=0.118
±0.021$~{}fmfor$^116$Sn,$0.112 ±0.021$~{}fmfor$^118$Sn,$0.124
±0.021$~{}fmfor$^120$Sn,$0.156
±0.022$~{}fmfor$^124$Sn.Asfor$^122$Sn,theskinvaluein$30
$\stackrel{{\scriptstyle<}}{{\widetilde{}}}$E_in
$\stackrel{{\scriptstyle<}}{{\widetilde{}}}$50$~{}MeVis$0.122
±0.024$~{}fm.\par\description<EMAIL_ADDRESS>