# Encouraging Bystander Assistance for Urban Robots: Introducing Playful Robot
Help-Seeking as a Strategy

Xinyan Yu<EMAIL_ADDRESS>0000-0001-8299-3381 Design Lab, The
University of SydneySydneyNSWAustralia ,  Marius Hoggenmüller
<EMAIL_ADDRESS>0000-0002-8893-5729 Design Lab, The
University of SydneySydneyNSWAustralia and  Martin Tomitsch
<EMAIL_ADDRESS>0000-0003-1998-2975 Transdisciplinary School,
University of Technology SydneySydneyNSWAustralia

###### Abstract.

Robots in urban environments will inevitably encounter situations beyond their
capabilities (e.g., delivery robots unable to press traffic light buttons),
necessitating bystander assistance. These spontaneous collaborations possess
challenges distinct from traditional human-robot collaboration, requiring
design investigation and tailored interaction strategies. This study
investigates playful help-seeking as a strategy to encourage such bystander
assistance. We compared our designed playful help-seeking concepts against two
existing robot help-seeking strategies: verbal speech and emotional
expression. To assess these strategies and their impact on bystanders’
experience and attitudes towards urban robots, we conducted a virtual reality
evaluation study with 24 participants. Playful help-seeking enhanced people’s
willingness to help robots, a tendency more pronounced in scenarios requiring
greater physical effort. Verbal help-seeking was perceived less polite,
raising stronger discomfort assessments. Emotional expression help-seeking
elicited empathy while leading to lower cognitive trust. The triangulation of
quantitative and qualitative results highlights considerations for robot help-
seeking from bystanders.

urban robots; casual bystanders; robot help-seeking; human-robot collaboration

††ccs: Human-centered computing Empirical studies in interaction design

##  1\. Introduction

Robots are evolving beyond their traditional roles in semi-controlled
environments, such as industrial and domestic settings, and are increasingly
being deployed in more dynamic and unpredictable urban spaces. In these urban
spaces primarily designed for human use, robots may encounter situations that
extend beyond their pre-programmed abilities, necessitating human intervention
for effective operation (Nanavati et al., 2021). This challenge is evident in
recent field observations, which reveal instances where urban robots require
human assistance for tasks like pressing traffic light buttons (Pelikan et
al., 2024), navigating unpredictable obstacles (Weinberg et al., 2023; Pelikan
et al., 2024) and temporarily altered streetscapes (Pelikan et al., 2024;
Dobrosovestnova et al., 2022), or managing conflicts with other road users
(Gehrke et al., 2023).

While the primary focus of robotics has long been on developing robots that
can autonomously complete tasks without or with little human intervention, the
challenges encountered by urban robots highlight that technology alone may not
always suffice (Veloso, 2018). This has led to increased interest in robots
designed to seek human assistance when necessary (Cila, 2022; Nanavati et al.,
2021). Furthermore, exploratory projects (Weiss et al., 2010; Smith and
Zeller, 2017; Kinzer, 2009) and emerging design research perspectives (Lupetti
et al., 2019; Kuijer and Giaccardi, 2018; Marenko and Van Allen, 2016)
increasingly frame human-robot interaction as a symbiotic relationship,
focusing less on a robot’s individual capabilities and more on the mutual
dependency between humans and robots.

Efforts in human-robot collaboration research have been directed towards
enabling intuitive and seamless interactions between humans and robots, with
humans predominantly cast in the role of collaborative working partners
(Ajoudani et al., 2018). In urban environments, however, the people urban
robots typically encounter and would approach for assistance are often
bystanders, or as defined in research, _‘incidentally copresent persons’_
(InCoPs) (Rosenthal-von der Pütten et al., 2020), people who do not have the
deliberate intention of engaging in interactions with the robot. This shift in
how humans relate to nearby robots calls for the re-examination of existing
strategies and the development of new interaction approaches that cater to the
spontaneous nature of casual human-robot collaborations in urban settings.

Previous research focusing on the implications of bystander assistance for
commercially-deployed urban robots has identified two broad strategies:
‘helping-as-work’, which considers assistance as invisible labour, and
‘helping-as-care’, emphasising the emotional and relational aspects of
providing help (Hakli et al., 2023). Extending this discussion, our study
introduces a novel dimension: ‘helping-as-play’, which focuses on encouraging
bystander assistance through playful interactions.

Playful strategies and the use of game elements has been found to be effective
in various human-robot interaction contexts, from motivating children’s
learning (Donnermann et al., 2021; Chen et al., 2023) to enhancing
collaboration between factory workers and robots (Chowdhury et al., 2021;
Venås et al., 2024). Furthermore, it has been shown to effectively engage the
public and create enjoyable experiences among bystanders (Lee et al., 2020;
Hoggenmueller et al., 2020). A major challenge for urban robots in soliciting
assistance from bystanders lies in persuading them to invest time and manage
potential disruptions (Nanavati et al., 2021). Leveraging the inherent
playfulness in humans has the potential to entice bystanders into assisting
robots by offering a moment of joy within urban environments.

To investigate this opportunity, we developed a series of playful help-seeking
design concepts that implement the ‘helping-as-play’ strategy. To test their
effectiveness, we compared our playful concepts with concepts that implement
the aforementioned strategies: verbal help-seeking, aligning with ‘helping-as-
work’ as evidenced by its prevalent use in human-robot teamwork settings
(Knepper et al., 2015; Srinivasan and Takayama, 2016; Budde et al., 2018), and
emotional expression help-seeking, aligning with ‘helping-as-care’ for its
effectiveness in eliciting human empathy (Backhaus et al., 2018; Zhou and
Tian, 2020; Urakami, 2023; Daly et al., 2020). These strategies were
prototyped and evaluated through a virtual reality (VR) experiment involving
24 participants. Our comparative analysis assesses the quality of these help-
seeking strategies in terms of their unambiguity, politeness, appropriateness,
and effectiveness. Additionally, it examines their impact on people’s
experiences and moods, as well as their influence on people’s attitudes
towards the robot. Through our design process and evaluation study, we aim to
thoroughly investigate the effectiveness of various help-seeking strategies in
eliciting bystander assistance. Additionally, we seek to investigate their
broader implications, thus advancing our understanding of how such spontaneous
collaborations should be facilitated.

This paper makes the following contributions: (1) It introduces playful
engagement as a novel strategy for urban robots to seek help from bystanders,
along with three exemplary design concepts implementing this strategy; (2) It
provides insights into different robot help-seeking strategies and their
potential influence on bystanders’ experience and attitudes towards robots.
This understanding further leads to design considerations of how such robot
help-seeking should be facilitated, as well as reflections on the design of
game-inspired playful help-seeking strategy. Our study extends the application
of playful engagement beyond merely creating enjoyable experiences,
demonstrating its effectiveness in supporting the increasingly prevalent
casual collaboration between urban robots and bystanders, thus promoting
reciprocal co-existence.

##  2\. Related Work

###  2.1. Service robots in public spaces

Robots, once predominantly deployed in controlled and semi-controlled
configurations such as industrial settings (Sauppé and Mutlu, 2015) and
domestic environments (Schneiders et al., 2021), are now expanding their
presence into public urban spaces, thereby increasingly becoming integral
components of our urban landscapes. These robots provide services in various
aspects of society, including sectors such as transportation and logistics,
infrastructure maintenance, cleaning, and surveillance (Salvini, 2018).

Despite technological advancements endowing robots with increasing autonomous
capabilities, the inherent dynamism and complexity of urban environments pose
significant challenges to their operation. This was vividly demonstrated in
several viral videos on social media, showing, for example, delivery robots
struggling in Estonia’s heavy snowfall (Palmipuu, 2021). Further, recent field
observation studies of urban service robots have also documented instances
where their operations were hindered by unexpected obstacles (Weinberg et al.,
2023), the inability to manipulate traffic infrastructure (i.e., traffic light
button (Pelikan et al., 2024), human activities occurring on the streets
(Pelikan et al., 2024; Babel et al., 2022), and bullying behaviours (Brščić et
al., 2015; Salvini et al., 2010).

Beyond the evident operational difficulties, these instances intriguingly
highlighted the spontaneous and supportive behaviours demonstrated by
passersby, showcasing a compelling facet of casually formed human-robot
interaction. In the observation study conducted by Dobrosovestnova et al.
(2022), passersby were observed clearing snow in front of the robot or giving
it a gentle push to return it to its path. In (Weinberg et al., 2023),
pedestrians voluntarily assisted immobilised robots by removing obstacles. In
some cases, people have even interrupted their own activities to assist the
robot, as exemplified by a window cleaner pausing their work to allow delivery
robots to pass through (Pelikan et al., 2024). These observations underscore
the potential of leveraging bystander assistance to enhance the operation of
urban robots, resonating with the emerging perspectives in HRI that emphasise
relational collaboration over solely technological independence (Weiss et al.,
2010; Kinzer, 2009; Lupetti et al., 2019).

###  2.2. Robot help-seeking

In human-robot collaboration settings, robot help-seeking has been explored as
an intentionally designed strategy to recover from inevitable situations that
exceed the robot’s inherent capabilities. Tested in a human-robot team
assembling context, Knepper et al. (2015) equipped the robot with verbal
communication ability to generate help-seeking requests for tasks like
handling unreachable objects. To advance the understanding of verbal help-
seeking, research further evaluated factors like ambiguity (Budde et al.,
2018) and politeness (Srinivasan and Takayama, 2016; Budde et al., 2018) in
framing requests, and their impact on the effectiveness of help-seeking. In
addition to explicit and spoken-language help-seeking requests, some studies
have successfully tested the use of implicit and non-verbal cues, such as
movement (Kwon et al., 2018), as well as light and sound (Cha and Matarić,
2016), to elicit assistance from human collaborators. Furthermore, employing
emotional expressions to elicit empathy has been explored as another strategy
for encouraging collaborative assistance (Backhaus et al., 2018; Zhou and
Tian, 2020; Urakami, 2023; Daly et al., 2020) or inducing bystander prosocial
interventions during robot abuse (Tan et al., 2018; Connolly et al., 2020).
For example, research has shown that when robots exhibited sad emotional
expressions, people were more inclined to assist them, leading to quicker
success in a collaborative game (Zhou and Tian, 2020).

Unlike structured human-robot team settings with shared goals between both
parties, help-seeking in public spaces poses unique challenges due to
misaligned objectives between robot and bystanders. Furthermore, research has
shown that diverse factors such as activities bystanders are currently engaged
in (Huttenrauch and Eklundh, 2003; Fischer et al., 2014; Rosenthal et al.,
2012), the robot’s apparent legitimacy and perceived risk (Booth et al.,
2017), and the bystander’s trust in and perceived competence of robots
(Cameron et al., 2015), collectively influence their willingness to offer
help. However, tailored design strategies or investigations that specifically
focus on robot help-seeking from bystanders remain limited. Some studies have
adopted verbal help-seeking (Rosenthal et al., 2012; Liang et al., 2023) which
has been predominantly used in traditional human-robot collaboration settings,
and a few have explored broader communication modalities, examining the
interplay of movement with speech (Holm et al., 2022; Fischer et al., 2014).
Thus, the increasing need for assistive behaviors towards robots in public
spaces calls for designers to imagine new interaction strategies to engage
bystanders in casual collaboration with robots (Cila, 2022).

###  2.3. Playful and gameful design in human-robot interaction

Over the past two decades, technology has become ubiquitous, expanding beyond
the context of the workplace. This has marked a significant paradigm shift in
the field of interaction design (Harrison et al., 2007), challenging previous
values such as efficiency and placing a stronger focus on meaning-making and
enhancing the experiential qualities of interaction. This shift has also
resulted in the rise of commercial social robots, as well as numerous examples
from research, designed to promote playful engagement while striving for
higher-level goals. Examples include playful robots that provide companionship
to people in domestic contexts (Zuckerman et al., 2020; Ye et al., 2023;
Sirkin et al., 2015), support children’s learning (Lupetti, 2020), promote
creative and critical thinking (Lee and Jung, 2020; Lupetti and Van Mechelen,
2022), or trigger social interactions in the context of the city
(Hoggenmueller et al., 2020; Lee et al., 2020; Bu et al., 2023; Yamaji et al.,
2010). Many of these robotic artefacts are intentionally designed to be open-
ended in terms of their form factor and interactions, aiming to foster
exploration and self-directed play.

On the other hand, a more structured approach to play encompasses the use of
social robots as players or facilitators in games (Lupetti et al., 2018;
Lupetti, 2016; Zaga et al., 2016), thereby adhering to specific rules and
objectives. Lupetti et al. (2018), for example, designed and evaluated a
mixed-reality playground in which children can play physical games with or
against a robot. Furthermore, there have been examples where gameful design
elements and principles have been applied to social robots that are deployed
in a non-game context, such as education (Donnermann et al., 2021; Chen et
al., 2023; Riedmann et al., 2022), labor (Chowdhury et al., 2021; Venås et
al., 2024), and healthcare (Feingold-Polak et al., 2021). In addition, game
elements have been used in robots to promote positive behaviour change in
public spaces, such as waste sorting (Castellano et al., 2019). In the broader
interaction design community, the practice of adding gameful design elements
and principles to non-game contexts is commonly also referred to as
‘gamification’ (Tondello, 2016). This approach leverages people’s intrinsic
and/or extrinsic motivation to engage in an activity due to the enjoyment of
the task itself or the possibility to attaining a goal (e.g., implemented
through a reward system).

While previous observation studies have reported on people’s playful attitudes
towards delivery robots (e.g., (Dobrosovestnova et al., 2022; Weinberg et al.,
2023; Pelikan et al., 2024)) and design researchers have documented case
studies of urban robotic artefacts designed solely for play (Hoggenmüller et
al., 2021a), there is a gap on leveraging playful engagement through gameful
design to encourage casual bystanders to assist urban robots in situations of
failure. Importantly, our research contrasts with most existing
implementations of gameful design in HRI, which primarily use robots to engage
users in a full game in order to support higher-level goals. Instead, we are
investigating whether and how gameful design can be used in specific
interaction scenarios (e.g., robot failure), with the robot pursuing its own
primary function (e.g., delivery).

###  2.4. Summary

In summary, robots operating in urban environments will inevitably encounter
situations that exceed their inherent capabilities, necessitating bystander
assistance. The involvement of bystanders and the complexity of contextual
factors set these casual collaborations apart from traditional human-robot
collaborations. This distinction highlights the need for design investigation
into strategies that can effectively facilitate such help-seeking in dynamic
and unpredictable urban settings. Drawing inspiration from playful robots and
the use of gameful design elements in robotics to achieve certain goals, we
propose ‘helping-as-play,’ a novel strategy that likens robot help-seeking to
playful interactions.

##  3\. Design Concept Development

In our prior research (Yu et al., 2024) on encouraging bystander assistance
for urban robots, playfulness was identified as a key incentive for fostering
bystander help. Building upon these findings, this study advances the
exploration of using playful engagement as a strategy for robots to seek help
from bystanders. Following an iterative design process, we first conducted a
design workshop with members of our research group to engage in collaborative
brainstorming and receive feedback on a set of initial concepts. These
insights were then incorporated into the final design concepts.

###  3.1. Help-seeking scenario

Our design investigation is contextualised in real-world scenarios in which
urban robots might encounter operational difficulties. These scenarios were
drawn from a comprehensive online ethnography study we previously conducted.
In this study, we analysed 177 user-generated videos that captured road users’
casual encounters with delivery robots on TikTok 111https://www.tiktok.com/.
We identified three typical scenarios where an urban robot may face
operational difficulties, including (1) The robot is blocked and needs people
to make way for it, (2) The robot is unable to cross the road and needs people
to press the traffic light button for it, and (3) The robot is stuck and needs
people to push it out. We chose these varied representative scenarios because
they encompass different levels of engagement required from bystanders,
ranging from simply making way for the robot, to manipulating city
infrastructure on the robot’s behalf, and to physically pushing the robot when
it gets stuck which further requires more effort. At the same time, developing
and evaluating design concepts across different scenarios can help validate if
the playful help-seeking strategy applies to a broad range of situations that
robots may encounter in urban environments.

Figure 1. The design process. Left: Initial sketches with bubble-blasting
(top) and PacMan (bottom) style game elements for promoting helpful
behaviours. Middle: Photo from the design workshop brainstorming session.
Right: Example sketch generated during the workshop, which was sketched on
printed a bird’s-eye view illustration of the scenario including the robot,
bystanders, and text descriptions.

A composite image depicting three stages of the design process. On the left,
two initial sketches feature game elements for promoting helpful behaviors:
the top sketch shows a bubble-blasting mechanism, and the bottom sketch
illustrates a PacMan-style game. The center of the image shows a photo from a
design workshop, with several participants actively brainstorming and
sketching ideas. On the right, an example sketch generated during the workshop
is displayed, presenting a bird’s-eye view illustration of an urban scenario
with a delivery robot at a pedestrian crosswalk.

###  3.2. Design workshop

The workshop involved five members of our research group (two interaction
designers, two engineers, and one urban geographer). It started with a
screening of selected TikTok videos, showcasing typical challenges urban
robots may face, selected from the dataset of our prior online ethnography
study. This provided the workshop participants with a contextual understanding
of the scenarios where robots seek assistance. Subsequently, participants were
provided with paper representations of the scenarios (see Fig. 1 Right) to
facilitate brainstorming and idea sketching. They were encouraged to
incorporate game-inspired elements with specific rules to promote helping
behaviours among bystanders. This focus was chosen over more open-ended
playful interactions, as urban robots are functionally oriented and such
unstructured interactions could potentially interfere with their operations.
Following the sketching session, all participants presented and commented on
each design idea, including the initial design concepts developed by the first
author before the workshop.

The ideation session and discussion were consolidated into two key
considerations: (1) drawing inspiration from well-known games to ensure
intuitiveness in engaging bystanders, and (2) using digital content (i.e.
projections) to augment elements in the urban environment. For example, in one
of the design ideas generated during the workshop, a traffic light button was
reimagined as part of a shooting game (see Fig. 1 Right). Pedestrians would
press it to launch a projectile to break a ‘brick wall’ that blocked the
robot’s path. (3) Given the functionality-oriented purpose of urban robots
operating in urban environments, we decided to make minimal alterations to the
robots themselves (e.g., adding lights, transformations). This approach also
ensures the applicability of our design concepts across various types of
robots.

Figure 2. The design concepts. Left: Games that inspired the design concepts;
Right: Illustration of the process of bystanders engaging in playful helping-
seeking.

Figure 2 is a composite image illustrating design concepts for a robot-helping
scenario, divided into two main sections. On the left, three panels display
the design inspirations for each scenario, each drawing from a classic video
game. Design concept for scenario A, inspired by Tetris, shows a robot blocked
and needing people to clear the way. Design concept for scenario B, influenced
by Space Invader, depicts a robot that requires people to press a crosswalk
button. Design concept for scenario C, based on PacMan, illustrates a robot
stuck and needing people to push it out. Each panel includes icons inspired by
the respective games.

On the right, three sets of three images demonstrate the sequence of bystander
interactions in engaging with the three playful help-seeking design concepts
in each scenario within a VR simulation. The first set shows a person clearing
a path for the robot, the second set displays a person pressing the crosswalk
button, and the third set illustrates a person pushing the stuck robot.

###  3.3. Design Concepts

Building on the ideas and insights generated from the workshop, we further
developed final design concepts for robot help-seeking across the three
scenarios. For each concept, we drew inspiration from a classic game,
utilising its game mechanics to foster an intuitive understanding of the
scenarios, motivate bystanders to assist the robot, and elicit interaction.

Scenario A: Robot blocked. In this scenario, we incorporated mechanics
reminiscent of the retro game ‘Tetris’. We also drew inspiration from an
existing research prototype, TetraBIN, which employed similar game mechanics
to encourage people to deposit rubbish into a bin, thereby controlling light
blocks on an integrated screen to complete the game (Tomitsch, 2014;
Hoggenmueller et al., 2018). A block is projected in front of the bystander,
requiring them to move to control and align the block with a corresponding
Tetris-like shape projected in front of the robot (see Fig.2 Top). Once the
block and shape match, the block ‘drops’ from the bystander side towards the
robot, completing the Tetris line. This action destroys the line in the game,
simultaneously creating enough space for the robot to pass through.

Scenario B: Traffic light button. To motivate passersby to press the traffic
light button for the robot, we transformed this button into a trigger for a
projected shooting game, reminiscent of classic arcade games like ‘Space
Invader’ (see Fig.2 Middle). A virtual projectile was projected in front of
the robot, continuously rotating to aim at ‘enemies’, accompanied by a
projected arrow pointing at the traffic light pole. This arrow serves as an
indicator, prompting passersby to press the button. When the robot’s
perception system detects this button-pressing behaviour, it boosts the
projectile, enabling the robot to shoot ‘enemies’.

Scenario C: Robot stuck. We incorporated mechanics reminiscent of the classic
game ‘Pac-Man’, where players collect items to earn points or chase ‘ghosts’
for the bonus. Specifically, we projected several ‘ghost’ images in the
intended direction of the stuck robot and a Pac-Man image in front of it (see
Fig.2 Bottom). The game mechanics encouraged participants to ‘chase’ the
‘ghosts’ by pushing the robot towards its intended direction, thereby
simultaneously aiding the robot out of its stuck position.

##  4\. Study Design

To gain in-depth insights into our designed playful help-seeking concepts, and
to explore their distinct characteristics and those of other robot help-
seeking approaches that correspond to the existing perspectives of ‘helping-
as-work’ and ‘helping-as-care’, we conducted a within-subject study. For
‘helping-as-work’, we opted for a spoken-language robot help-seeking request
as previously tested in work environments for task-oriented assistance
(Srinivasan and Takayama, 2016; Budde et al., 2018). For ‘helping-as-care’, we
decided on emotional expression as robot help-seeking approach, emphasising
the affective and relational aspects of interaction, previously tested in
(Backhaus et al., 2018; Zhou and Tian, 2020; Urakami, 2023).

We decided on an evaluation study in VR that allows participants to experience
encounters with and help-seeking requests from a delivery robot in a simulated
urban space. VR simulations, now widely used for prototyping and evaluating
interactions with robots (e.g., (Yu et al., 2023; Milde et al., 2023;
Grzeskowiak et al., 2020; Legler et al., 2023)), have been validated for
reproducing authentic interaction experiences (Villani et al., 2018;
Hoggenmüller et al., 2021b). Our study focuses on unpredictable scenarios
involving urban robots needing assistance, which are difficult to replicate in
public spaces. Moreover, conducting the study in VR not only minimises
potential risks to participants but also reduces both the implementation time
and costs associated with real-world prototypes, thus facilitating a more
efficient evaluation method in design research.

Our study is specifically guided by the following research questions:

  * •

RQ1: How do different robot help-seeking strategies vary in terms of (1)
unambiguity, (2) politeness, (3) appropriateness, and (4) effectiveness?

  * •

RQ2: How do different robot help-seeking strategies affect the mood and user
experience of bystanders?

  * •

RQ3: How do different robot help-seeking strategies influence bystanders’
attitudes towards the robot in terms of (1) acceptance, (2) trust, (3)
perceived social attributes, and (4) likability?

###  4.1. Experiment conditions

In order to compare our playful help-seeking concepts with existing
strategies, we conducted a comparative study with three conditions. The
conditions mapped respectively to our proposed ‘helping-as-play,’ and the
existing strategies ‘helping-as-work’ and ‘helping-as-care.’ In the _Play_
condition, we implemented our final design concepts for playful robot help-
seeking (introduced in Section 3.3, see Fig. 2). It is worth noting that our
primary objective is to compare these strategies on a conceptual level rather
than specific attributes that manifest them, such as audio or visual
modalities.

For the _Work_ condition, we aligned the verbal help-seeking approach with
‘helping-as-work’ strategy, as it is commonly employed in the context of
human-robot teamwork (Knepper et al., 2015; Srinivasan and Takayama, 2016;
Budde et al., 2018). To compose the verbal help-seeking content, we followed
the structure of _Justification_ (i.e., interpreting the situation regarding
the help needed) + _Introduction_ (i.e., communicating a definitive
instruction on how an individual should provide help) as proposed in (Budde et
al., 2018) and has also been employed in the setting where a delivery robot
asks bystanders for help (Boos et al., 2022) (see Table 1 for detail request
content). The robot’s help request was recorded using the ‘Alex’ voice of the
macOS text-to-speech engine, as per the method in (Srinivasan and Takayama,
2016).

Table 1. Speech help-seeking in each scenario

The table titled ’Speech help-seeking in each scenario’ lists verbal help-
seeking strategies used by a robot in three different scenarios. Each scenario
is outlined with two columns: Introduction and Justification. In Scenario A,
labeled ’Robot Blocked’, the robot’s introduction is ’Please let me pass’ with
the justification ’I am going to be late’. Scenario B, titled ’Traffic Light’,
has the introduction ’Please help me press the traffic light button’ with the
justification ’I can’t reach it’. Scenario C, ’Robot Stuck’, includes the
introduction ’Please push me out of this spot’ and the justification ’I am
stuck’.  Scenario Introduction Justification A: Robot Blocked Please let me
pass I am going to be late B: Traffic Light Please help me press the traffic
light button I can’t reach it C: Robot Stuck Please push me out of this spot I
am stuck

In the _Care_ condition, we aligned emotional expression help-seeking with
‘helping-as-care’ strategy due to its effectiveness in eliciting human
empathy, thus encouraging assistive behaviours (Backhaus et al., 2018; Zhou
and Tian, 2020; Urakami, 2023; Daly et al., 2020). We implemented sad facial
expressions, as existing research suggests that robots displaying sad
emotional expressions can elicit an increased willingness to help from people
(van der Grinten et al., 2020; Hakli et al., 2023; Herdel et al., 2021). We
used the same sad facial expression as in (Herdel et al., 2021), because of
its proven effectiveness in eliciting prosocial responses from bystanders in
similar casual encounter settings. Following their approach, we created a set
of faces as key-frames (See example key-frames in Fig. 3) and blended them
into an animation sequence.

Figure 3. Key frames of robot facial expression

Key frames of robot facial expression. The image displays three consecutive
frames of a robot’s face designed to show different expressions. From left to
right: the first frame shows the robot with a neutral expression,
characterised by a flat mouth and circular eyes. The second frame shows a
slightly saddened expression, with the mouth turned downward. The third frame
retains the sad expression but with a slightly more pronounced downturn of the
mouth. All expressions are depicted using simple line drawings on a black
background, emphasizing the changes in the mouth to convey different emotions.

###  4.2. Study apparatus and implementation

We simulated the three scenarios in Virtual Reality using
Unity3D222https://unity.com/, implementing the three different help-seeking
strategies for a delivery robot for each scenario. We used off-the-shelf 3D
models from the Unity Asset Store to construct scenarios, such as sidewalks
and pedestrian crossings with traffic lights. For the mobile robot, we used a
3D model of the delivery robot Starship, obtained from the modeling platform
Sketch Fab333https://sketchfab.com/. We employed the HTC Vive
headset444https://www.vive.com/ for participants to engage in the various
robot help-seeking scenarios. This setup enabled them to move freely within a
3m x 3m tracked area and interact with the virtual objects (e.g., traffic
light button, robot) using simulated hands by holding the controllers.

###  4.3. Participants

We recruited a total of 24 participants in the age range of 18 to 74 years.
The majority (n=17) were between 25 and 34 years old, with three aged 35-44
and two aged 18-24. Our participant cohort also included two representatives
from older demographics, including one participant each from the age groups of
55-64 and 65-74, respectively. Thirteen of our participants self-identified as
female, ten as male, and one preferred not to disclose their gender.
Participants were recruited from our university’s mailing lists, flyers and
social networks. All participants voluntarily took part in the experiment and
initial contact had to be made by them, following the study protocol approved
by our university’s human research ethics committee.

###  4.4. Procedure

After participants arrived at the study site, they were first given a brief
introduction about the study background and procedure. They were then asked to
sign a consent form, followed by a demographic questionnaire that obtained
their basic information, including age group, gender, occupation, nationality,
and previous experience with AR/VR and robots. Following this, we briefly
introduced the VR headset and its basic operations, and notified participants
that the HMD VR was used to simulate the experience of interacting with urban
robots.

Before the experiment commenced, each participant went through a
familiarisation session to practice walking and interacting with the
controllers in the VR environment (i.e., push a rack on wheels). We ensured
that participants did not experience motion sickness and were willing to
proceed with the study. By the end of the familiarisation session,
participants were asked to fill out a single-item questionnaire that measured
their mood.

During the experiment, each participant experienced the three experimental
conditions in a different order (counterbalanced using a balanced Latin Square
design which one condition precedes another exactly twice (Bradley, 1958)) to
minimise carryover effects. Each experimental condition encompassed all three
introduced scenarios: _Robot blocked_ , where the robot is blocked by
participants and needs them to make way for it; _Traffic light_ , where the
robot requires participants to press the traffic light button for it; and
_Robot stuck_ , where the robot is immobilised and needs participants to help
it out of its stuck position. The order in which each participant experienced
the scenarios was also randomised.

Before each scenario, participants were provided with a context to aid their
immersion, such as waiting for a bus at a bus station. Participants were not
informed about the robot’s intention to seek help before the study; they were
simply instructed to respond spontaneously to any cues from the robot. This
aimed to reduce any sense of obligation to assist the robot resulting from the
study’s purpose. Each scenario concluded with participants either providing
help to the robot or indicating their refusal to engage in further
interaction. Subsequent to each scenario, participants filled out two single-
item questionnaires that measured their mood and willingness to assist the
robot through an in-headset interface. Upon completing all three scenarios
within each condition, participants removed the headset and were requested to
fill out several standardised questionnaires that assessed help-seeking
quality, user experience, and their perceptions of the robot. After
participants completed all three conditions, we conducted a post-study semi-
structured interview to gain in-depth insights into their experiences. The
whole study took approximately 60 minutes.

Figure 4. Study procedure

Figure 4: Study procedure. The flowchart outlines five main stages of the
study process. Starting with ”Introduction,” where study background
information is provided, consent forms are signed, and demographic
questionnaires are completed. Next is ”Familiarisation,” where participants
practice walking and physical interactions in a virtual reality (VR) setting,
including using a face scale. The third stage is the ”VR Experiment,” divided
into three conditions: Work, Care, and Play. Under each condition, three
scenarios are tested: Robot blocked, Traffic light, and Robot stuck, where
participants’ willingness and mood are assessed. The fourth stage is
”Questionnaires,” covering help-seeking appropriateness, user experience,
trust, acceptance, likeability, and social attributes of robots. The final
stage is ”Interview,” discussing help-seeking strategies and perceptions of
robots.

###  4.5. Data Collection

We collected both quantitative and qualitative data through questionnaires,
observations, and interviews, following a mixed-methods approach (Creswell,
2014). The data collection was structured to address each of the three
research questions correspondingly.

Help-seeking quality: To assess the quality of help-seeking strategies, we
used items from previous HRI studies investigating robot help-seeking to
measure the perceived ambiguity, politeness (Budde et al., 2018) and
appropriateness (Srinivasan and Takayama, 2016). In addition, we assessed
participants’ willingness to help the robot after each scenario using the same
item as in (Budde et al., 2018). Each dimension was rated on a 7-point Likert
scale ranging from 1 to 7.

Experience and mood: To measure participants’ experience of their interaction
with the urban robot within the simulated VR environment, we used the short
version of the widely adopted User Experience Questionnaire (UEQ-S) (Schrepp
et al., 2017) on a 7-stage scale from -3 to +3. Helper’s high refers to the
phenomenon that helping someone or something else can lead to psychological
benefits such as mood improvement (Kwak et al., 2018). To investigate its
relevance in the context of helping public space robots, we also assessed
participants’ moods before the study and after each scenario. We used the face
scale developed by (Lorish and Maisiak, 1986), which was previously used in
another HRI study (Chirapornchai et al., 2021) to evaluate mood changes after
helping a robot. The scale features faces numbered 1 to 20 in descending mood
order, with 1 indicating the most positive and 20 the most negative mood. It
is worth noting that mood assessment results from instances where participants
did not offer help were excluded from our analysis, as our focus was on
assessing mood changes after helping behaviour.

Attitudes towards the robot: We used the Robotic Social Attributes Scale
(RoSAS) (Carpinella et al., 2017) to measure participants’ perceptions of the
robot, which comprises three factors: warmth, competence, and discomfort. In
addition, we used the likeability subscale from the Godspeed Questionnaire
(Bartneck et al., 2009) to measure the perceived likeability of robots. For
evaluating trust, we utilised the trust scale developed by McAllister (1995),
which has been employed in assessing human trust towards robots (Zieger et
al., 2023). This scale consists of two subscales: cognitive trust and
affective trust. We selected this scale over other trust scales (Jian et al.,
2000; Malle and Ullman, 2023) as its two subscales help differentiate the
effects of help-seeking strategies for two types of trust: trust based on
rational assessment of the robot’s capabilities and trust based on emotional
connections to the robot. This differentiation allows for a more nuanced
understanding of how various help-seeking strategies influence trust-building
between bystanders and robots. Lastly, to assess the impact of robot help-
seeking strategies on participants’ acceptance towards urban robots, the
System Acceptance Scale developed by Van Der Laan et al. (1997) was used. Each
measurement was rated on a 7-point Likert scale ranging from 1 to 7.

Semi-structured interviews: Following each experimental condition,
participants were asked to provide brief feedback on their experiences in the
scenarios, including their reasons for deciding to assist or not assist the
robot. After finishing all experimental conditions, we conducted a semi-
structured interview with participants regarding their preferences for and
opinions on the different strategies and design concepts. The aim was to gain
an in-depth understanding of how the various strategies influenced their
willingness to offer help and shaped their overall perceptions of the robots.

###  4.6. Data Analysis

_Questionnaires:_ We first assessed the internal reliability of all multi-item
scales by calculating Cronbach’s alpha. The internal reliability for the help-
seeking assessments of _unambiguity_ ($\alpha$=0.864) and _appropriateness_
($\alpha$=0.804) was found to be good. The UEQ-S scales showed good
reliability for _pragmatic_ quality ($\alpha$=0.815) and excellent reliability
for _hedonic_ quality ($\alpha$=0.928). The system acceptance scale
demonstrated excellent reliability with $\alpha$=0.918. For the RoSAS, the
_competence_ and _warmth_ subscales showed good ($\alpha$=0.899) and excellent
($\alpha$=0.900) reliability, respectively, while the _discomfort_ subscale
had acceptable reliability of $\alpha$=0.771. Lastly, the likability
assessment of the robot received excellent reliability with an $\alpha$ value
of 0.948.

We then proceeded to conduct descriptive and inferential analyses of the
questionnaire data. Given the non-parametric nature of the data, we used the
Friedman test to identify any statistically significant differences. In cases
where significant differences were found, we performed pairwise comparisons
with Bonferroni corrections. A p-value of less than 0.05 was considered
indicative of a significant effect.

_Interviews:_ All interviews were transcribed by the first author, employing a
mixed thematic analysis approach (Braun and Clarke, 2006). First, all
interviews were coded inductively and initial codes were sorted into sub-
themes. The sub-themes were then deductively grouped into final themes,
structured around the three main aspects of our research questions, aligning
with the Results section’s organisation.

##  5\. Results

###  5.1. Received help

In our study, each participant experienced nine instances of help-seeking (3
conditions x 3 scenarios), totaling 216 help-seeking instances. The robot
received help in the majority of these instances. Exceptions where
participants refused to help were few: In _‘Work’_ condition, p18 and p8 did
not help in two instances where the robot got stuck, due to the required
physical effort to push the robot. In _‘Care’_ condition, p10, p4, and p17 did
not assist in three instances, with two involving a stuck robot and one in the
traffic light button scenario. In _‘Play’_ , p1 did not provide assistance in
two cases, one with the robot stuck and another involving pressing the traffic
light button. The lack of assistance in both the _‘Care’_ and _‘Play’_
conditions was attributed to participants not fully understanding the robot’s
help request.

###  5.2. Help-seeking assessments: unambiguity, politeness, appropriateness,
and effectiveness

####  5.2.1. Quantitative results

Figure 5. Qualitative assessment of help-seeking strategy (Left), Box plot of
participants’ Willingness to Help the robot across each scenario (Right). M:
Mean, SD: Standard Deviations, *: p ¡ .05, **: p ¡ .01, ***: p ¡ .001

Figure 5 is composed of two graphical representations assessing participant
responses in a study on help-seeking strategies. On the left, a radar chart
displays qualitative assessments for three strategies: Work, Care, and Play,
each evaluated on Unambiguity, Politeness, and Appropriateness. Scores are
given as follows: Work scores 4.32 on Unambiguity, 4.13 on Politeness, and
4.59 on Appropriateness; Care scores 4.41, 4.96, and 5.31; and Play scores
4.50, 4.54, and 5.33 respectively. Care and Play strategies rate higher on
Unambiguity and Appropriateness, while Work is highest on Politeness.

On the right, a box plot illustrates participants’ willingness to help the
robot across three scenarios: Robot Blocked, Traffic Light, and Robot Stuck.
Willingness to help is numerically represented with means and standard
deviations: Robot Blocked at 5.17 (SD 1.57), Traffic Light at 5.67 (SD 1.24),
and Robot Stuck at 4.96 (SD 1.60). Statistically significant differences in
willingness are noted, with the highest willingness observed in the Traffic
Light scenario and significantly lower willingness in the Robot Stuck
scenario.

In the descriptive analysis (see Fig. 5), help-seeking unambiguity was highest
in the _Work_ , whereas assessments of politeness and appropriateness were
notably the lowest in the same condition. _Care_ and _Play_ showed similar
levels of unambiguity, with _Play_ receiving the highest scores in both
politeness and appropriateness. Friedman’s ANOVA revealed significant
differences in both unambiguity ($\chi^{2}(3)$=27.179, p¡0.001) and politeness
($\chi^{2}(3)$=6.659, p=0.036), but not in appropriateness
($\chi^{2}(3)$=5.37, p=0.068). Post-hoc tests revealed that _Work_ received
significantly higher unambiguity ratings compared to both _Care_ ($p<0.001$)
and _Play_ ($p<0.001$), with no significant differences between _Care_ and
_Play_. Conversely, participants’ politeness ratings in _Work_ were
significantly lower than in _Care_ ($p=0.04$) and _Play_ ($p=0.002$), with
_Play_ further exhibiting a significantly higher politeness score than _Care_
($p=0.03$).

Participants’ willingness to help the robot was highest in _Play_ , and was
lowest in _Work_. Friedman’s ANOVA revealed significant differences
($\chi^{2}(3)$=8.47, p=0.015). Subsequent post-hoc tests revealed a
significant increase in willingness to help only when comparing _Play_ to
_Work_ ($p=0.004$). We further analysed participants’ willingness to help the
robot across different scenarios. The results showed that the willingness
ranking remained consistent with the overall assessment for both the robot
stuck and traffic light scenarios. However, in the robot block scenario,
willingness to help was slightly higher in _Care_ than in _Play_. Friedman’s
ANOVA revealed significant differences only in the robot stuck scenario, which
requires the most physical effort for participants to push the robot out of
the stuck, ($\chi^{2}(3)$=7.136, p=0.028). Post-hoc tests revealed that
participants exhibited a significantly higher willingness to help the robot in
_Play_ compared to both _Care_ ($p=0.03$) and _Work_ ($p=0.002$), with _Care_
also demonstrating significantly higher willingness than _Work_ ($p=0.03$).

####  5.2.2. Qualitative feedback on help-seeking quality

Unambiguity: Most participants (n=21) perceived verbal help-seeking as clear
and straightforward, while some considered the helping-seeking strategies used
in _Care_ (n=10) and in _Play_ (n=12) as lacking in unambiguity, thus
necessitating closer observation of the situation to understand the robot’s
requests (n=7). The unambiguity and efficiency of speech communication were
indicated by five participants as the primary reason for rating verbal help-
seeking as their most preferred strategy. Seven participants also found the
playful help-seeking approach easy to understand. Some attributed this to
recognising the game from which the help-seeking originated (n=3), while
others mentioned cues from the visualisation that helped infer the robot’s
intention (n=7), such as the ‘Pac-Man’ projection indicating the robot’s
intended direction. P7, who belonged to the older age group of over 65 and was
not acquainted with some of the games, managed to engage successfully, albeit
after a brief period of contemplation, as expressed in their remark, _‘it did
take me a minute to think’_. This learning curve in grasping the help-seeking
request was also mentioned by the other nine participants, with seven of them
stating that their comprehension was developing during the interactive
process, such as seeing _‘projections, kind of responding to my movements.’_
(p12)

Regarding emotional help-seeking, nine participants easily inferred the
robot’s need for help from its sad face, yet were unsure of the specific
assistance required, as p3 highlighted: _‘[…] you know that it needs help, but
it’s not clear that it wants to cross the road and it wants you to press the
button.’_ Furthermore, participants indicated several instances of
misunderstanding in both _Care_ (n=4) and _Play_ (n=7) conditions. For
instance, some misconstrued the projection in _Play_ as merely
_‘advertisements’_ (p5, p13), while others misinterpreted the robot’s sad
facial expression as being _‘out of battery’_ (p4) or _‘malfunctioning’_ (p5).

Politeness: Rudeness and impoliteness were prominent in participants’ comments
about verbal help-seeking (n=16), with p3 and p8 notably exclaiming _‘How
rude!’_ immediately after hearing the robot’s request during the study. This
aversion impression can be attributed to participants’ perception of verbal
help-seeking as resembling _‘commands’_ (n=8), being _‘demanding’_ (n=3), or
giving them the feeling of being _‘ordered around’_ (n=6). The impression of
an authoritative directive further led seven participants to express that they
were not helping the robot out of their own will but felt obligated and even
having a feeling of being _‘forced’_ (p2, p9, p11). P16’s statement
exemplified this sentiment: ‘I still feel like I have to help it even though I
didn’t like helping it as much. […] being given the instruction, it almost
feels like you have to help it, kind of thing.’ P11 even expressed concern
that the robot might harm them if they did not comply with its requests,
stating, ‘Maybe if I did not follow the sound commands, it would hurt me.’ In
contrast, participants commented that _Care_ (n=4) and _Play_ (n=3) help-
seeking strategies were less assertive and demanding. This perception could
stem from the feeling that they have an option not to engage if they lack
interest in these two conditions (n=9). For instance, as p1 indicated, _‘If
you don’t have the desire to help, you can just walk away. […] It’s not
demanding you to do anything.’_

Appropriateness: Furthermore, five participants found the robot’s verbal help-
seeking abrupt or unexpected, as p5 indicated:_‘I was a bit surprised when it
first spoke to me […] you wouldn’t expect a robot to randomly ask a stranger
for help._ While the verbal help-seeking approach is direct, it was also
perceived as interruptive by four participants. In contrast, the more
implicit, playful strategies (n=6) and emotional expressions (n=2), though
lacking unambiguity, were deemed less invasive and less likely to cause
interruptions.

Five participants expressed feelings of aversion towards the robot verbally
asking for help. P14 highlighted this sentiment: _‘I feel like it is blaming
me for being in his way’._ Four participants conveyed their discomfort with
the robot displaying negative emotions in the _‘Care’_ condition. This
sentiment was echoed by p17, who stated, _‘I don’t think anyone should be
responsible for their [the robots’] personal negative energy.’_ Moreover, P17
elaborated that their decision to help the robot was because they _‘just
wanted to finish this [the interaction]. I don’t want to see that sad face.’_

Willingness to help the robot For the factors motivating participants, eleven
participants expressed a neutral sentiment in _Work_ condition. They did not
cite specific reasons for their willingness to help, suggesting it was a
natural response for them, especially if it didn’t require _‘too much effort’_
(p1) or if they were not _‘in a rush.’_(p7, p17) In the _Care_ condition, the
predominant factor motivating participants to offer help was the empathy
evoked by the robot’s sad face, as indicated by nine participants. Regarding
the playful strategies, four participants indicated that the projections
sparked their curiosity to engage further. This intrigue was indicated by p18,
who expressed the intention to _‘take on a challenge’_ and _‘to see if I could
work it out’_. Furthermore, some participants also raised concerns about the
effectiveness of help-seeking strategies for long-term deployment. Four
participants indicated that they might not help the robot again in the _Work_
scenario if they encountered it frequently in their daily lives. As p3 noted,
they could _‘accept it (verbal help-seeking) […] if it’s a one-time or two-
time thing,’_ but would be reluctant to offer help if they ‘encounter this
every day on the road’. Conversely, four participants indicated their
willingness to assist the robot again if it used playful help-seeking
strategies, as p9 noted it made them _‘delighted and more willing to help
again’._

It is noteworthy that in scenarios where the robot was stuck and needed people
to push it, six participants reported a decreased willingness to help due to
the increased physical effort involved. However, p14 and p21 highlighted that
the playful strategies helped to mitigate the perceived exertion involved in
assisting the robot, as p14 suggested: _‘ distracting me from the process of
helping it’_. This observation aligns with quantitative results, showing that
participants’ enhanced willingness to help robots in the _Play_ condition was
more pronounced in the robot stuck scenario than in scenarios requiring less
physical involvement (See Fig. 5, Right).

###  5.3. Participant experience and mood change

Figure 6. Box plot of UEQ results (Left), and mood assessment before and after
each scenario (Right) M: Mean, SD: Standard Deviations, *: p ¡ .05, **: p ¡
.01, ***: p ¡ .001

Figure 6 contains two sets of box plots. On the left, the box plots depict the
results of the User Experience Questionnaire (UEQ) across two dimensions:
Pragmatic and Hedonic, analyzed for three strategies: Work (gray), Care
(green), and Play (purple). The Pragmatic dimension scores are Work at 1.05
(SD 0.31), Care at -0.38 (SD 0.69), and Play at -1.36 (SD 1.52). The Hedonic
dimension scores are Work at 1.35 (SD 1.18), Care at 1.36 (SD 1.50), and Play
at 1.05 (SD 1.02). Statistical significance is noted between Work and Play in
both dimensions, with Work scoring significantly higher.

On the right, two sets of box plots represent participants’ mood assessments
across three scenarios—Robot Blocked, Traffic Light, and Robot Stuck—both
before and after each scenario (colored in gray for before and orange, green,
and purple respectively for after). Mood is measured on a scale from 0 to 20,
with means shown for each: Before study, mood averages around 4.73 to 6.76,
while after, mood varies more significantly, especially after the Traffic
Light scenario where mood improves to 6.33 from 4.20 before.

####  5.3.1. Quantitative Results

Descriptive data analysis (see Fig. 6) of UEQ ratings revealed _Work_ as
highest in pragmatic but lowest in hedonic quality, with _Play_ exhibiting the
inverse pattern. Friedman’s ANOVA showed significant differences in both
pragmatic ($\chi^{2}(3)$=8.25, p=0.016) and hedonic quality
($\chi^{2}(3)$=16.795, p¡0.001). Post-hoc tests revealed that _Work_ received
significantly higher ratings for pragmatic quality than both _Care_
($p<0.001$) and _Play_ ($p=0.03$), with no significant differences between
_Care_ and _Play_. Hedonic ratings were significantly higher in _Play_
compared to both _Work_ ($p<0.001$) and _Care_ ($p=0.0013$), with _Care_ also
scoring significantly higher than _Work_ ($p=0.03$).

Participants reported better mood levels before the experiment than after
assisting the robot in all three conditions, with the worst mood assessment
after assisting the robot in the _Work_ condition. Participants’ moods after
helping the robot in different scenarios showed similar patterns to the
overall mood assessment, with _work_ consistently rated the worst across all
three scenarios. _Play_ was rated higher than _Care_ in robot stuck and robot
block scenarios, but slightly lower in the robot traffic light scenario.
Friedman’s ANOVA showed no significant differences in the overall mood
assessment, nor in comparisons within each scenario.

####  5.3.2. Qualitative feedback:

In the _Work_ condition, participants generally appreciated the efficiency of
the speech strategies used for requesting help (n=16), even though it’s _‘less
interesting’_ compared to the other two approaches (n=5). Four participants
reported a neutral feeling after helping the robot, with P1 indicating, _‘It
feels like I’m just fulfilling a task, like doing a job’_. Two participants
found speech help-seeking ‘less rewarding’ compared to the other two methods,
and five participants reported feeling unpleasant after assisting the robot.
For example, p21 described a decline in mood after helping the verbally help-
seeking robot, stating their _‘mood went down’_ after having to repeatedly
help the robot three times. Five participants attributed their neutral or
unhappy feelings to the robot’s lack of response after assistance. P14
exemplified this sentiment: _‘I was expecting it to say thank you or something
like that, but it doesn’t provide any feedback, which makes me a bit
unhappy’_. Furthermore, six participants expressed concerns about the
accessibility of the auditory help-seeking approach, worrying that it might
not be heard in noisy urban environments or by people wearing headsets.

For the _Care_ condition, eight participants reported positive feelings after
assisting the robot. Four of these participants noted that their positive
response was similar to the feeling of helping other people or small animals
in need. P20 encapsulated this sentiment: _‘I would say that I felt the best
when I helped the one with the sad face because it felt like I was, […]
helping out a person, almost.’_ However, due to the robot’s human or animal-
like expressions, more participants – compared to the _Work_ condition –
expected a response (n=9) or wished for more interactions with the robot
(n=3). For example, some participants expressed a wish to see positive
reactions, such as a smiling face (n=5) or behaviours indicating excitement,
like a _‘dance to show some excitement’_ as described by p17. Five
participants reported negative feelings due to the lack of response from the
robot after assisting it. Furthermore, five participants noted difficulties in
seeing the robot’s face when standing at the side of the robot or when pushing
the robot from behind, factors which could negatively impact their experience.

Participants generally feel that their experience of helping the robot in
_Play_ was _‘fun’_ (n=8), _‘enjoyable_ (n=8), and _‘interesting’_ (n=6).
Additionally, two participants expressed excitement due to the novelty of the
approach. However, p14 also noted that the sense of _‘freshness will
disappear’_ with long-term deployment. Furthermore, in the _Play_ condition,
half of the participants expressed a _‘sense of accomplishment’_ or
achievement following their assistance to the robot. This feeling was
exemplified by p15, who reflected, _‘I felt more fulfilled, like, oh, I did
something.’_ Six participants noted a disconnect between game-playing and
assisting a robot, with p16 describing it as _‘more like having a game with
the robot rather than helping it._ Furthermore, five participants highlighted
the additional enjoyment that such games could bring to urban life, thus
_‘adding colour to the city’_ (p6). Another participant, p10, suggested that
they would like to see more of these types of games, as they provide a good
way _‘to kill the time […] while waiting for the bus’_.

The most common negative feedback regarding the playful help-seeking
experience centred on the extra effort required beyond simply assisting the
robot (n=8). For example, in the traffic light scenario where participants
were invited to press the button multiple times to complete a shooting game,
four participants questioned the necessity of repeatedly pressing the button
to complete the game. Additionally, five participants expressed a desire to
skip the game and help the robot directly.

###  5.4. Attitudes towards the robot: acceptance, trust, and social
attributes

Figure 7. Qualitative results of participants’ attitudes towards the robot. *:
p ¡ .05, **: p ¡ .01, ***: p ¡ .001

Figure 7 shows a radar chart illustrating qualitative results of participants’
attitudes towards a robot, measured across seven attributes: Acceptance,
Likability, Competence, Warmth, Cognitive Trust, Affective Trust, and
Discomfort. Three strategies are evaluated: Work (gray), Care (green), and
Play (purple). Each vertex of the radar represents one of the attributes with
scores plotted and connected to form three distinct polygons. Notable scores
include high Competence for Work at 4.31, high Warmth for Care at 4.26, and
low Discomfort for Play at 2.24.

####  5.4.1. Quantitative results

Following descriptive data analysis (see Fig. 7), participants’ acceptance of
the robot was highest in _Play_ and lowest in _Work_. Friedman’s ANOVA showed
significant differences ($\chi^{2}(3)$=9.156, p=0.01). Post-hoc tests
indicated the acceptance scores in both _Play_ ($p=0.001$) and _Care_
($p=0.04$) were significantly higher than in _Work_ , with no significant
differences between _Care_ and _Play_.

Regarding the robot’s social attributes, it received the lowest competence
rating in _Care_ and the highest in _Play_. The robot’s warmth attributes
received the highest rating in _Care_ , slightly lower in _Play_ , and the
lowest in _Work_. The robot received the lowest discomfort rating in _Care_
and the highest in _Work_. Significant differences were found in Friedman’s
ANOVA test across all three dimensions (competence: $\chi^{2}(3)$=12.413,
p=0.002; warmth: $\chi^{2}(3)$=13.130, p=0.001; discomfort:
$\chi^{2}(3)$=13.505, p=0.001). Post-hoc tests indicated that the robot was
rated significantly less competent in _Care_ compared to both _Work_
($p=0.01$) and _Play_ ($p=0.001$), with _Play_ also scoring significantly
higher than _Work_ ($p=0.01$). Warmth ratings were significantly higher in
both _Care_ ($p=0.001$) and _Play_ ($p<0.001$) compared to _Work_ , with no
significant differences between _Care_ ($p=0.03$) and _Play_. Robots in both
_Care_ ($p<0.001$) and _Play_ ($p=0.006$) were rated significantly lower for
discomfort than those in _Work_ , and _Care_ also scoring significantly lower
than _Play_ ($p=0.03$).

Participants’ trust was highest in _Play_ and lowest in _Work_ across both
cognitive trust and affective trust subscales. _Care_ received the lowest
cognitive trust rating, while _Work_ received the lowest affective trust
rating. Friedman’s ANOVA showed significant differences in cognitive trust
($\chi^{2}(3)$=7.670, p=0.022), but not in affective trust
($\chi^{2}(3)$=5.365, p=0.068) subscales. Post-hoc tests revealed that
participants’ cognitive trust ratings in _Play_ were significantly higher than
in _Work_ ($p=0.01$) and _Care_ ($p=0.001$). There were no significant
differences between _Care_ and _Work_ in the cognitive trust subscale.

At last, the robot in _Work_ was rated lowest in likability, followed by
_Care_ and _Play_. Friedman’s ANOVA showed significant differences
($\chi^{2}(3)$=15.438, p¡0.001). Post-hoc tests indicated the likability
scores in both _Play_ ($p=0.001$) and _Care_ ($p=0.001$) were significantly
higher than in _Work_ , with no significant differences between _Care_ and
_Play_.

####  5.4.2. Qualitative feedback on impressions of the robot

The synthesised voice in _Work_ condition was perceived by six participants as
machine-like or artificial. Three participants mentioned that the speech
functionality aligned with their expectations of a robot, leading them to
perceive the robot as _‘competitive’_ (p14) and _‘professional’_ (p17), while
on the other hand three participants described the robot as _‘scary’_ or
_‘creepy’_.

In the _Care_ condition, _‘cute’_ was the impression most frequently
attributed to the robot by participants (n=9). Additionally, we observed that
two participants petted the robot upon their first encounter, as exemplified
by p1, who expressed a desire to comfort it due to its sad facial expression.
Many participants also indicated that the facial expression made the robot
more human-like (n=7) or animal-like (n=6). However, the sad emotion also
raised people’s questions towards the robot’s capability (n=6), making it less
trustworthy (n=5) or even _‘stupid’_ (n=2). P12 exemplified this sentiment by
describing the robot with the sad face as _‘useless’_ , likening its
helplessness to the impression of _‘a baby’_ and commenting, _‘It’s like a
baby, so I don’t think I can trust the robot to do a lot of things.’_
Furthermore, three participants felt manipulated by the robot displaying a sad
face to prompt them to offer help. P3 noted, _‘The one with expressions feels
like it’s a robot using tactics, like pretending to be pitiable.’_ Another
intriguing response from p1 suggested they felt somewhat _‘being scammed’_ for
helping the robot.

In the _Play_ condition, five participants described the robot as _‘smart’_ or
_‘intelligent’_. Three participants indicated that they have more confidence
in the robot’s problem-solving abilities. P14 further elaborated on this
point, indicating that the robot appeared _‘to have predicted that it would
encounter such difficulties’_ and was able to use a playful strategy _‘to
overcome a particular scenario’_. Two participants described the robot as
being _‘active’_ (p16) and _‘sociable’_ (p17), noting that it consistently
sought the attention of passersby to engage them in game-playing. Furthermore,
while four participants perceived the robot projecting a game onto the ground
as less human-like, even comparing it to a _‘game console’_ (P3), two other
participants felt as though they were playing a game with a _‘kid’_.

##  6\. Discussion

The section starts with three key considerations for facilitating robot help-
seeking from bystanders generated from study results, then delves into a
focused reflection on the game-inspired playful help-seeking strategy.

###  6.1. Casting bystander help as voluntary engagement over obligated
response

Achieving task efficiency has long been one of the primary objectives in
human-robot collaboration (Ajoudani et al., 2018). Thus, verbal speech help-
seeking has become the most common method for robots to express their need for
assistance, due to its rich and intuitive communication, widely adopted in
both research (Backhaus et al., 2018; Budde et al., 2018; Cameron et al.,
2015; Knepper et al., 2015; Srinivasan and Takayama, 2016) and commercially-
deployed urban robots (Mail, 2023). However, our study results raise questions
about whether such explicit, direct, and objective-oriented approaches are
suitable for situations where robots request help from bystanders. Despite its
clarity and communication efficiency, verbal help-seeking was perceived
negatively in terms of politeness and appropriateness, casting an unfavourable
impression of the robot as also evidenced through the lowest levels of liking
and acceptance among all tested conditions. Participants’ comments revealed a
conflict that emerged from their perceived role as passive bystanders without
responsibility towards the robot, as opposed to the sense of command and
obligation that they felt when receiving direct requests from the robot. This
conflict in turn resulted in aversion and their reported reduced willingness
to provide assistance.

In contrast, implicit methods such as emotional expression and playful
engagement, while less efficient in terms of clear communication, were
perceived as more polite and socially acceptable. For individuals who casually
encounter urban robots in public spaces while being involved in a variety of
activities and having different agendas, there is often a misalignment of
goals with those of urban robots (Pelikan et al., 2024; Huttenrauch and
Eklundh, 2003). Consequently, it becomes crucial that when robots seek
assistance from these bystanders, they do so in a manner that minimises
disruption to the activities these individuals are currently engaged in.
Therefore, participants generally favoured the indirect and implicit nature of
emotional expression or playful help-seeking, as these approaches engendered a
feeling of reduced obligation, conveying to the bystander that they have the
freedom to ignore the request and opt not to assist. Urban robot help-seeking
strategies should therefore consider implicit and less persuasive approaches,
presenting bystander assistance as a voluntary, spontaneous act driven by
personal kindness or playful interest, rather than as an obligation, which is
often associated with direct verbal requests.

###  6.2. Incentivising bystander efforts through playful help-seeking
strategy

Prior research suggests that more laborious requests from robots tend to
reduce people’s willingness to assist (Srinivasan and Takayama, 2016). Our
study explored this phenomenon across three distinct help-seeking scenarios,
ranging from minimal (such as stepping aside to allow the robot to pass) to
such that required more physical effort (like bending over to push a stuck
robot). We observed a clear trend in _Work_ and _Care_ conditions, where
participants demonstrated a reluctance to engage in the more physically
demanding task of pushing the robot. Interestingly, participants’ willingness
to help in _Play_ conditions was consistently stable across all scenarios.
Furthermore, the disparity between the playful strategy and the two other
help-seeking strategies in terms of willingness to help was even more
pronounced in the robot stuck scenario that had the highest request demand.

This trend was also mirrored in participants’ mood changes. In _Work_ and
_Care_ conditions, more laborious requests (i.e., in the robot stuck scenario
that required participants to bend over to push the robot) led to a decrease
in their mood after helping the robot. Conversely, in _Play_ condition, the
most effort-intensive robot stuck scenario resulted in the highest reported
mood levels. Although various game-inspired concepts may have influenced the
outcomes, participants primarily attributed the elevated mood levels in the
effort-intensive scenarios to an increased sense of achievement and
competence, which is the key motivational impact of gaming elements (Tondello,
2016). This experience effectively neutralised the perceived effort and the
accompanying sense of fatigue.

Drawing upon the principles of reciprocity and altruism in social exchange
theory (Cropanzano and Mitchell, 2005), our study highlights that bystanders,
who do not directly benefit from urban robot services, often need a form of
incentives for their time and effort in assisting robots they encounter
casually. Such needs are particularly significant when the physical effort
from helpers increases, highlighting the lack of direct mutual benefits. While
playful help-seeking strategy, leveraging gameful experiences, offers a
potential solution, using game elements as motivators could also raise ethical
considerations about invisible labour (Dubbell, 2015) – a concern also noted
in socio-technical research on bystander assistance for commercially deployed
robots (Hakli et al., 2023). While participants in our study generally
expressed autonomy in deciding whether to engage in gameplay, it is important
to be mindful of the potential for creating invisible labour when designing
gameful incentives.

###  6.3. Avoiding helpless robot portrayal

Public trust and acceptance are crucial for the successful integration of
robots into urban environments. There have been instances where distrust by
the general public even led to regulatory bans on urban robots (Hoffmann and
Prause, 2018). Despite this, the specific impact of robot help-seeking
behaviour on people’s additutdes towards robots has not been explored in
existing research.

Previous research has shown that robots displaying sad emotions are more
likely to receive human assistance, both in collaborative settings (Zhou and
Tian, 2020; Urakami, 2023) and when used in robot pets seeking help (Daly et
al., 2020). Our research expands upon the investigation of using emotional
expressions to elicit help from bystanders, uncovering patterns that diverge
from those in more traditional human-robot collaboration settings. While
emotional expressions are generally effective in eliciting help in human-robot
teaming (Urakami, 2023), our findings suggest that in casual bystander
contexts, a robot displaying sadness was perceived as needy and less
competent, which at the same time resulted in a decrease in people’s cognitive
trust. These expressions even further engendered a perception of emotional
blackmail, especially without an established human-robot relationship between
bystanders. This aligns with prior findings (Yu et al., 2024) that bystanders
tend to view urban service robots as professional entities, thereby expecting
them to exhibit proficiency and dependability.

In contrast, robots employing playful strategies were perceived as more
capable, as they were perceived as actively helping themselves out of
situations using well-prepared and dedicated strategies. This approach further
resulted in enhanced trust and acceptance towards the robot. We therefore
conclude that urban robot help-seeking should avoid portraying robots as
helpless or needy in order to minimise the negative impact on bystander trust
in the robot.

###  6.4. Reflections on the game-inspired playful help-seeking design
concepts

Our user study demonstrated that integrating mechanics and visual cues from
classic games has the potential to effectively engage bystanders in playful
human-robot collaborations as a means to provide assistance to a robot.
Notably, this was also true for the two participants (p7 and p13) from the
older demographic group. Analysis of their willingness to help results in
_Play_ condition revealed that neither participant was statistically deviant
from the overall sample, with standard deviations of 1.352 and 0.0854,
respectively. Particularly, p7, who is over 65 and was not familiar with some
of the games but managed to comprehend and engage successfully. However, while
most participants could recognise and comprehend the game’s functions, there
was some ambiguity about whether _they_ were invited by the robot to
participate. Noteworthy, this issue did not arise in the Tetris-inspired
concept, as the movement of the tetra block in response to the participants’
actions made it immediately clear that they had full control over the game
elements. Conversely, in the other scenarios requiring mediation of other
objects for game element manipulation (i.e. pressing the traffic light button
to shoot the projectile or pushing the robot to chase the ghosts),
participants faced a steeper learning curve and expressed uncertainty
regarding their involvement. These findings suggest that when designing game-
inspired playful help-seeking requests, game elements should be designed in a
way that they respond instantly to bystander movement or incorporate a feed-
forward mechanism (Nguyen et al., 2023).

Repetition is a fundamental aspect of game design, serving as a key
reinforcement of game mechanics and thereby enhancing player engagement
(Robson et al., 2015). Our design concepts also employed repetitive elements;
for instance, bystanders were required to press the traffic light button
multiple times to eliminate all enemies. While enjoyable for some
participants, others noted a disconnect between the game context, requiring
repetitive action, and the real-life context, where a single press of the
traffic light button is typically sufficient. This feedback highlights a
potential mismatch between game mechanics and real-world expectations.
Furthermore, several participants indicated a preference for omitting
repetitive game-play actions if they were in a rush. Considering the rapid
pace of urban life, it is evident that designing for game-inspired playful
help-seeking must be succinct and allow for flexibility in engagement,
accommodating the varying preferences and time constraints of bystanders.

Moreover, it’s crucial to consider the broader urban environment where such
playful engagements for robot help-seeking are situated. The novelty effect
observed in our study, if implemented in the real world, necessitates
consideration of the potential impact of people crowding, as people may gather
around the interaction area. Furthermore, since the game-playing interaction
is attention-demanding, it could distract bystanders from cautiously observing
their surrounding traffic environment, potentially leading to safety concerns.
Additionally, the urban environment’s rhythm, busy on workdays and relaxed on
weekends, could affect bystander engagement levels. Game-inspired playful
help-seeking design should thus adapt to these varying urban rhythms to better
align with people’s availability and willingness to engage.

###  6.5. Limitations

First, the controlled lab study design may not fully reflect spontaneous real-
life helping behaviour, as some participants noted potential differences in
their decisions. In addition, the scenarios included in our study could be
subject to the potential bias of social media posts, as they are derived from
a previous online ethnography study. Conducting field studies with real robots
and naive bystanders across more diverse scenarios could further validate our
findings. Second, the ecological validity of this work is limited by the use
of a VR simulation. The novelty of the VR experience influenced participants’
mood assessments, potentially leading to inflated mood ratings after the VR
familiarisation session. This may explain why the ‘helper’s high’ phenomenon –
helping someone or something else can lead to psychological benefits – was not
validated in our study. Furthermore, implementing the playful design concepts
in VR overlooks potential technical challenges in real-world implementations.
For instance, limitations exist in projecting in outdoor environments due to
variations in lighting and distance constraints. While the VR study validates
the playful help-seeking strategy as a proof of concept, further technological
refinement and adaptation are required for practical real-world application.
Third, our study findings are based on a small group of participants with
Western cultural or educational backgrounds, which might have facilitated
their understanding of such game-playing interactions. To ensure the
generalisability of these findings, further validation is necessary with
participants from a more diverse range of cultural backgrounds.

##  7\. Conclusion

Our study extended the use case of playful engagement in HRI from creating
enjoyable experiences to facilitating casual collaboration. The evaluation
study in VR with 24 participants found that playful engagement has the
potential to enhance bystanders’ willingness to assist and improve their mood
after helping the robot. In our study, this effect was particularly noticeable
when the assistance required more effort. By comparing the helping-as-play
strategy with helping-as-work (using verbal help-seeking) and helping-as-care
(using emotional expression) strategies, we found that participants perceived
verbal help-seeking to be impolite and that there was a decline in trust
towards the robot when using sad emotions to request help. These findings
prompt broader considerations for robot help-seeking in urban contexts and
offer insights into playful help-seeking as a strategy to obtain assistance
from casual bystanders in dynamic and unpredictable urban environments.

###### Acknowledgements.

This study is funded by the Australian Research Council through the ARC
Discovery Project DP220102019 Shared-Space Interactions Between People and
Autonomous Vehicles. We thank all the participants for taking part in this
research. We also thank the anonymous DIS’24 reviewers and ACs for their
constructive feedback and suggestions to make this contribution stronger.

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