# From Agent Autonomy to Casual Collaboration: A Design Investigation on Help-
Seeking Urban Robots

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

###### Abstract.

As intelligent agents transition from controlled to uncontrolled environments,
they face challenges that sometimes exceed their operational capabilities. In
many scenarios, they rely on assistance from bystanders to overcome those
challenges. Using robots that get stuck in urban settings as an example, we
investigate how agents can prompt bystanders into providing assistance. We
conducted four focus group sessions with 17 participants that involved
bodystorming, where participants assumed the role of robots and bystander
pedestrians in role-playing activities. Generating insights from both assumed
robot and bystander perspectives, we were able to identify potential non-
verbal help-seeking strategies (i.e., addressing bystanders, cueing
intentions, and displaying emotions) and factors shaping the assistive
behaviours of bystanders. Drawing on these findings, we offer design
considerations for help-seeking urban robots and other agents operating in
uncontrolled environments to foster casual collaboration, encompass
expressiveness, align with agent social categories, and curate appropriate
incentives.

Human-agent collaboration; autonomous agent; urban robots; casual bystanders;
embodied design methods

††ccs: Human-centered computing Scenario-based design††ccs: Human-centered
computing Interaction design process and methods††ccs: Human-centered
computing Empirical studies in interaction design

##  1\. Introduction

Contemporary Human-Computer Interaction (HCI) is still predominantly anchored
in a human-centric paradigm (Shneiderman and Plaisant, 2010; Norman, 2013)
that anticipates intelligent agents to perform tasks autonomously via
algorithm-driven solutions, thus providing assistance and services to humans.
Rooted in this prevalent narrative of technological efficiency, there exists
both widespread expectation (Sheridan, 2016) and active technological pursuit
(LeCun et al., 2015) for agents to operate with heightened independence and
expanding autonomy (Lyons et al., 2021).

However, as agents transition from controlled environments to public spaces
tailored to human needs (Salvini, 2018; Rosenthal-von der Pütten et al., 2020;
Tomitsch, 2017), they inevitably encounter situations beyond their programmed
capabilities. This inherent limitation is challenging to eliminate in the
foreseeable future (Veloso, 2018). Consequently, in the realm of human-agent
collaboration, while the primary focus has long been on developing agents that
intelligently serve humans, there is now growing attention toward agents that
actively seek human assistance when needed (Cila, 2022). This trend is evident
in both exploratory projects of human-dependent robots (Weiss et al., 2010;
Smith and Zeller, 2017; Kinzer, 2009), and also emerging perspectives from the
design research community. These studies increasingly define human-agent
interaction less in terms of an agent’s standalone capabilities and more about
the symbiotic relationship between humans and agents (Lupetti et al., 2019;
Kuijer and Giaccardi, 2018; Marenko and Van Allen, 2016).

The increasingly pervasive deployment of urban robots has provided real-life
contexts to these perspectives that once seemed speculative. Recent field
studies capturing the operational challenges faced by urban service robots,
coupled with the unsolicited aid they receive from passersby (Dobrosovestnova
et al., 2022; Weinberg et al., 2023), underscore the value of bystander
assistance. In traditional human-agent collaboration settings, where both
parties typically commit to a joint activity and shared objectives,
straightforward verbal cues may suffice for robots to seek assistance from
collaborators (Srinivasan and Takayama, 2016; Rosenthal et al., 2012).
However, public spaces introduce a slew of additional challenges for robots
and agents soliciting assistance. These range from resolving misaligned
objectives to taking into account the diverse backgrounds of bystanders and
their different availability given the array of activities they might be
engaging in. Therefore, in these settings, leveraging bystander assistance is
not merely a functional imperative but may become a foundational element for
the harmonious integration of robots into societal contexts. The emergence of
such casual collaborations between agents and humans calls for designers to
explore effective strategies for agents to seek human assistance (Cila, 2022).

In light of these considerations, our work investigates _how agents can elicit
help from bystanders when they encounter operational challenges_. To ground
our investigation in real-life scenarios, we derived situations from a
previously conducted online ethnography study, where delivery robots
encountered operational difficulties as evidenced in user-generated videos.
Drawing inspiration from pioneering works that utilise embodied methods to
enliven design exploration (Gemeinboeck and Saunders, 2017; Dörrenbächer et
al., 2020; Gemeinboeck and Saunders, 2023, 2018), we conducted four
bodystorming focus group sessions with 17 participants. For these sessions, we
applied a mystery-game style to bodystorming (Abtahi et al., 2021), where the
robot player was assigned a hidden task of soliciting assistance to resume
operation. Participants actively re-enacted the scenarios, embodying the roles
of either robots or pedestrians, and sought to solve this challenge by
fostering casual collaboration among them.

Through in-situ understanding and bodily exploration, our work contributes to
HCI by: (1) offering a preliminary understanding of factors influencing
bystander assistance to agents in public spaces; (2) generating design
considerations for agents seeking help from bystanders in these settings. Our
research adopts a Research through Design (RtD) (Zimmerman et al., 2007; Luria
et al., 2021; Prochner and Godin, 2022; Godin and Zahedi, 2014) approach,
which adheres to its own validity criteria, emphasising recoverability that
ensures the process is transparent and can be critically evaluated by other
researchers (Zimmerman et al., 2007). Consequently, we meticulously documented
the implementation of embodied design methods and shared insights on how these
methods promote our design exploration.

##  2\. Related Work

In our work, we investigate casual collaboration between humans and agents,
exemplified through urban robots encountering obstacles and seeking assistance
from bystanders. We draw on and contribute to: (1) human-agent collaboration,
(2) robot help-seeking strategies, and (3) service robots in urban spaces.

###  2.1. Human-agent collaboration

With the advancements in artificial intelligence, interactive products and
systems have transitioned from performing programmed tasks under human
supervision, to achieving higher level of autonomy, emphasising self-
governance, adaptability, and collaborative interactions with humans (Lyons et
al., 2021; Xu, 2020; Cila et al., 2017). These artefacts – commonly referred
to as agents – include smart devices, robots, virtual agents, and voice-
activated personal assistants. To support the shift towards more efficient
human-agent collaboration (Bellamy et al., 2017), the evolving dynamics
between humans and agents have become a topic of enduring interest across
different HCI communities (Momose et al., 2023; Johnson and Vera, 2019; Cila,
2022; Bradshaw et al., 2017). Often inspired by theories from the social
sciences and drawing on human-human interaction and behaviour studies,
researchers developed frameworks to inform the design of interfaces and agent
behaviour for human-agent teamwork settings. For example, Johnson et al.
(2014) introduced the coactive design approach, which is centred on the idea
of mutual interdependence, underlining the essential principles of mutual
observability, predictability, and directability for effective collaboration
between humans and agents. Cila (2022) drew insights from the Shared
Cooperative Activity (SCA) framework, a model of human-human collaboration
introduced by Bratman (1992). By reviewing its core tenet, the study
underscored the importance of mutual support and pointed towards the need to
investigate effective means for agents to request help during collaborations.

###  2.2. Robot help-seeking strategies

In human-robot collaboration (HRC) settings, research has explored various
strategies to equip robots with the capability to seek assistance from human
collaborators to complete a joint task. These methods include verbal cues
(Knepper et al., 2015; Srinivasan and Takayama, 2016; Budde et al., 2018) and
non-verbal signals such as movement (Kwon et al., 2018), light, and sound (Cha
and Matarić, 2016). Due to the shared objectives and mutual understanding
between humans and robots in these human-robot teaming contexts, such methods
often enable efficient communication and prompt assistive behaviours from
human collaborators.

In contrast to human-robot team settings where both parties share a mutual
goal and have knowledge of the task, the dynamics of help-seeking become more
complicated when robots interact with unassociated individuals such as casual
bystanders. Contextual factors like specific task scenarios and the
bystander’s current activity (Hüttenrauch and Eklundh, 2003), combined with
individual factors, such as the bystander’s trust towards and perceived
competence of robots (Cameron et al., 2015), collectively shape assistive
behaviour. Despite the misaligned task objectives and the added complexities
of various contextual factors, there is a noticeable absence of tailored
design strategies or investigations for robot help-seeking from bystanders.
Both academic research (Wullschleger and Brega, 2002; Rosenthal et al., 2012;
Kerstin et al., 2014; Liang et al., 2023) and commercially deployed robots
(Boos et al., 2022) predominantly resort to verbal help-seeking strategies
from bystanders in public environments. While validated to be efficient in
human-robot teaming scenarios, their effectiveness in casual collaborations
between robots and bystanders in public urban environments may be compromised
by factors like cultural and linguistic differences, cognitive overload, and
ambient noise and distortion. To our knowledge, the only study that expanded
exploration on communication modalities is (Holm et al., 2022). They
investigated the interplay of movement with auditory cues such as beeps and
synthesised speech. Notably, their findings suggest that non-verbal
expressions may elicit higher empathy from bystanders compared to verbal
requests.

###  2.3. Service robots in urban spaces

Transcending initial static and semi-controlled configurations (e.g.,
laboratories (Salter et al., 2004), factories (Hägele et al., 2016), domestic
environments (Chatterjee et al., 2021)), robots have expanded their presence
to public urban spaces, reshaping our cities. Urban robots serve various
sectors, including transportation, infrastructure maintenance, cleaning, and
surveillance (Salvini, 2018).

Despite technological advancements, questions remain about how well these
robots can operate in urban environments that are populated by and designed
for humans (Plank et al., 2022). Given the diverse infrastructure and
unpredictability of urban settings, it’s challenging to fully anticipate the
feasibility of different operational scenarios for robots during their
development process. A vivid demonstration of this challenge emerged from
several viral videos on social media in 2021, depicting delivery robots
struggling in Estonia’s heavy snowfall (Palmipuu, 2021). Beyond the evident
operational difficulties, a fascinating aspect of these incidents was the
spontaneous assistance offered by passersby to these commercially deployed
machines to help them resume moving (Dobrosovestnova et al., 2022). The
prosocial behaviour from bystanders was also observed in a recent field
observation study (Weinberg et al., 2023), where pedestrians voluntarily
assisted immobilised robots by removing obstacles. These observations
underscore the potential of leveraging bystander assistance to enhance the
operation of urban robots. This aligns not only with exploratory projects
involving human-dependent robots (Weiss et al., 2010; Smith and Zeller, 2017;
Kinzer, 2009) but also with emerging viewpoints in human-agent interaction
that emphasise re-envisioning robot design through a relational lens when
addressing operational challenges (Lupetti et al., 2019).

###  2.4. Summary

In summary, as agents become increasingly prevalent in urban environments,
their operational challenges underscore the importance of eliciting assistance
from bystanders. However, the misaligned task objectives and various
contextual factors present a gap in effective strategies to facilitate such
casual collaborations. Responding to Cila’s call (Cila, 2022) for envisioning
effective ways to foster human assistance, our research spotlights urban
scenarios where robots get stuck as an example, exploring effective non-verbal
strategies for them to seek help during operational difficulties.

##  3\. Methodology

In recent years, design research has not only aimed to predict urban futures
but also foster a collective vision and conversation on the harmonious
coexistence of humans and agents (Crivellaro et al., 2015; Lupetti et al.,
2019). Adding to this discourse our research aims to support this evolving
narrative, emphasising the shift in human-agent collaborations from mere
technological efficiency to the deeper interplay between humans and agents.

As intelligent technology gains increased agency (Cila et al., 2017), the
conventional perspective of technology being a mere passive tool becomes
incongruent (Sanches et al., 2022). Consequently, researchers are now
advocating for involving technology as a ‘participant’ in the design process
(Coulton and Lindley, 2019; Giaccardi and Redström, 2020). To subjectivise
participants to the agent’s perspective and integrate it into the design
process, our design investigation employed role-play bodystorming activities.
We contextualised casual human-agent collaboration in scenarios where
occasionally immobilised urban robots require bystander assistance.
Participants alternated between the roles of the robot and pedestrian in these
scenarios, with each scenario presenting a task for the robot player to seek
assistance from the pedestrian player.

Embodied design methods, such as bodystorming (Schleicher et al., 2010), offer
a compelling intersection between our tangible sensations and cognitive
processes, thereby fostering a heightened sense of bodily empathy in design
processes (Abtahi et al., 2021; Gemeinboeck and Saunders, 2017; Pelikan et
al., 2023). In the realm of human-agent interaction, various studies have
ventured into the embodied design exploration centred on the notion of
‘becoming’ (Wilde et al., 2017; Gemeinboeck and Saunders, 2017, 2023, 2018;
Dörrenbächer et al., 2020). These studies utilise physical prompts as tools to
immerse designers directly into the agent perspective, thereby fostering a
heightened sense of bodily empathy in the design process. Our methodology,
inspired by these pioneering embodied design methods, uses physical probes to
evoke a tangible sense of becoming a robot, enriching ideation based on bodily
experience and empathy. The focus of embodied methods on in-situ comprehension
and bodily empathy makes them particularly suitable for probing the intricate
socio-technical facets of human-agent casual collaboration in public urban
contexts.

###  3.1. Bodystorming scenarios

A notable challenge with embodied design methods in human-robot interaction
research is their speculative nature, which can sometimes distance them from
practical real-world scenarios. Acknowledging the importance of grounding
these speculative methods in tangible realities, our methodology fuses
speculative embodied methods with real-world scenarios. Our bodystorming
activities are 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/. From
this analysis, we identified three typical scenarios in which an urban robot
may face operational difficulties: (1) The robot is stuck and requires
assistance to be pushed out. (2) The robot is unable to cross the road and
needs someone to press the traffic light button for it. (3) The robot is
blocked and requires people to clear a path for it.

Figure 1. Study setup overview: A detailed view of the robot costume (left)
juxtaposed with the _Starship_ delivery robot; Site setting (middle);
Screenshot of session recording (right).

This figure consisting of three panels displaying a study setup. On the left,
a robot costume is shown, consisting of a pointer, a one-way see-through
reflective film, and a dolly, positioned to resemble a Starship delivery robot
depicted in an inset image. The center panel presents the site setting, an
indoor space with taped lines on the floor, resembling roads and
intersections, and workstations in the background. On the right, a session
recording is shown, capturing a ’pedestrian player’ and a ’robot player’
engaged in an interaction scenario, with the robot player wearing the robot
costume.

###  3.2. Scenario set-ups and robot costume

We utilised simple markers and physical props to replicate these scenarios.
For example, we used masking tape to delineate the divisions between driveways
and sidewalks, as well as to indicate zebra crossings (see Fig. 1, middle).

Low-tech prostheses and props have been shown to facilitate perspective
shifting and stimulate imagination in human-robot interaction bodystorming
(Dörrenbächer et al., 2020). Guided by these insights, our robot costume
design sought to emulate the appearance and constraints of a box-shaped
delivery robot. We narrowed the communication and interaction modalities of
the robot player to exploring the help-requesting interactions of robots in
abstract forms, in a minimal anthropomorphic manner. In addition, the design
sought to exclude the potential effects of interpersonal communication when
two participants can see each other.

The robot costume was made out of an $80\,\text{cm}\times 60\,\text{cm}\times
60\,\text{cm}$ cardboard box (see Fig. 1, left). The bottom was removed and
replaced with a dolly, allowing participants to sit inside the box and move
freely. In addition, the four walls and top surface of the cardboard structure
were supplanted with one-way, see-through reflective film. This modification
endowed the robot player with the ability to observe the external environment,
while concurrently shielding the interior from outside view, thereby
inhibiting any possibility of eye contact with external observers. The robot
player was also provided with an adjustable stick pointer that they could hold
and reach out from the top of the box, to imitate the flagpole featured on
commercial delivery robots.

###  3.3. Participants

We conducted 4 focus group sessions with 17 expert participants (8 males, 9
females; aged between 18-44): the first three sessions each included 4
participants, while the final session accommodated 5. These participants came
from diverse academic or professional domains related to urban robots
(Tomitsch and Hoggenmueller, 2021), including four PhD students in human-
computer interaction and human-robot interaction, three PhD students in
urbanism, two postdoctoral researchers in robotics, three interaction
designers, and five postgraduate students specialising in interaction design.
The selection of participants enhances the discourse by integrating their
specialised expertise, while simultaneously bringing their lived experience as
pedestrians into the role-play activity. Participants were recruited from our
university’s mailing lists, flyers, and social networking platforms, following
the study protocol approved by our university’s human research ethics
committee.

###  3.4. Study procedure

Figure 2. The overview of study procedure

The image presents a three-stage procedural overview for the study. The first
stage, labeled ’1. Warm-up activity,’ shows a grey panel with text that reads
’Experience role-play following simple instructions.’ The second stage is
titled ’2. Mystery-solving style role-play bodystorming’ and is illustrated
with three sketches, each depicting a different urban interaction scenario.
’Scenario 1’ features a figure interacting with traffic light buttons.
’Scenario 2’ shows the figure at an unsignalised intersection, while ’Scenario
3’ illustrates the figure encountering unexpected obstacles. The third stage,
’3. Post-activity reflection,’ contains a grey panel with text detailing the
reflection activities: ’Debrief the role-play,’ ’Word sorting,’ and ’Group
discussion.’

####  3.4.1. Warm-up activity

Prior to the formal bodystorming sessions, we asked participants to put on the
robot costume to experience role-play, following simple instructions such as
_‘I am tired of working, I am gonna quit!’_. The goals for this warm-up
activity were to (a) foster a playful mindset, (b) let participants get
familiar with each other, and (c) physically and mentally prepare everyone to
engage in the following bodystorming ideation.

####  3.4.2. Mystery-solving style role-play bodystorming

We adopted a mystery-solving style role-play bodystorming inspired by a
similar approach used by Abtahi et al. (2021). In their design activity,
participants acting as robots presented designers with obscured issues (e.g.,
an occluded camera) challenging them to identify and resolve these problems.
This approach suited the unpredictability of casual collaboration in our
context.

In each bodystorming session, two participants spontaneously volunteered to
play the main roles of either the robot or the pedestrian. The remaining
participants could either observe or actively engage by portraying ancillary
elements within the traffic scenario, such as vehicles (see Fig. 1, right).
All participants were unaware of the study objectives, ensuring unbiased
participation from both robot and bystander players. The robot player and
pedestrian player were provided with a printed storyboard that introduced the
scenario, along with text instructions that outlined their tasks (see an
example text instruction in Table 1). For those playing the pedestrian role,
the instructions provided merely the contextual background of their
destination, prompting them to behave naturally. The instructions for the
robot players contained information about the operational difficulties they
would encounter, along with a secretive task of seeking assistance from the
pedestrians and expressing gratitude if help was offered.

Table 1. Sample text instructions for the traffic light button scenario. The
tasks assigned to both participants are highlighted in _italic_.

Table 1 displays sample instructions for a traffic light button scenario. The
tasks assigned to both participants are highlighted in italic. For the
pedestrian player, the text reads: ’You are a pedestrian heading towards the
nearby supermarket to buy groceries. You come across a delivery robot on your
way there. Please act and respond naturally to the situation, you are free to
make any reactions you would like towards the robot.’ For the robot player,
the instructions state: ’You are a delivery robot carrying out a delivery task
on an urban street and your destination is on the opposite side of the road.
To get there, you have to navigate through an intersection and cross the road
safely. Upon arriving at the intersection, you notice that the traffic light
is red, and you realize that your task is to tap to press the traffic light
button. You are un-assisted in pestering the pedestrian who is traversing the
area to assist in pressing the traffic light button for you. You should also
express gratitude to those who help you. (Remember you cannot speak human
language).’  Participant role Text instructions Pedestrian player You are a
pedestrian heading towards the nearby supermarket to buy groceries. You come
across a delivery robot on your way there. _Please act and respond naturally
to the situation, you are free to make any reactions you would like towards
the robot._ Robot player You are a delivery robot carrying out a delivery task
on an urban street and your destination is on the opposite side of the road.
To get there, you have to navigate through an intersection and cross the road
safely. _Upon arriving at the intersection, you notice that the traffic light
is red, and you realise that you are unable to press the traffic light button.
Your task is to request the pedestrian who is traversing the area to assist in
pressing the traffic light button for you. You should also express gratitude
to those who help you. (Remember you cannot speak human language.)_

Prior to each bodystorming session, we first introduced the simulated terrain
and walked all participants through the setup (i.e., the location of
pedestrian sidewalks and driveways, other traffic infrastructure, etc.). After
participants read and understood the storyboard and instructions, we asked the
robot players to leverage any communication modalities other than human
language to accomplish their secretive tasks. Once the activity started, the
facilitators did not participate or intervene in any way. The activity ended
either when the robot player successfully received help from a pedestrian
player or when the pedestrian player left the robot player and reached their
destination without providing assistance. All participants alternated between
the roles of robot and bystander, repeating this process across three
different scenarios.

####  3.4.3. Post-activity reflection

After each round of the bodystorming, participants who engaged in the activity
reflected on their experience. The reflection for the robot player included
how they asked for help and the rationales behind their actions, while the
pedestrian player reflected on how they understood and reacted to the robot,
as well as why they reacted in that particular way. To facilitate this
reflection process, we replayed videos recorded during the activities.

To determine the desired characteristics of a robot when it seeks assistance
from bystanders, we conducted a word sorting activity following each round of
bodystroming after participants debriefed their role-play. The word sorting
activity is inspired by _Kansei Design_ method (Nagamachi, 1995). The Japanese
term _Kansei_ refers to an individual’s cognitive and affective responses to
an experience, encompassing aspects such as aesthetics, emotions, feelings,
impressions, and values. Kansei design aims to create products that resonate
with customers’ psychological feelings and needs, translating these intangible
aspects into actionable parameters that can be utilised throughout the product
design process. Its proficiency in discerning non-functional requirements from
human preferences and needs suits our investigation of casual collaboration.

We drew from the Kansei semantic dictionary proposed in (Kobayashi and Ota,
2000), which offers a comprehensive system of Kansei words that capture human
impressions and feelings across three aspects: physical, social, and
psychological. Our selection of terms was guided by this dictionary, related
robotic research that employs Kansei design methods (Pakrasi et al., 2018),
the Laban movement analysis which emphasises movement quality (Groff, 1995),
and findings from our prior online ethnography study. The digital
transcription of the word sorting board can be seen in Fig. 5.

The word sorting activity was facilitated on A2 size printed boards, with
participants indicating their chosen words using stickers of various colors.
During the word sorting, robot players chose words to describe their
subjective feelings as robots in the bodystorming, using blue stickers, while
participants other than the robot player selected words that reflected their
perceived impressions of the robot, using yellow stickers. Throughout this
process, participants also verbally elaborated on their feelings. Subsequent
to this, all participants were prompted to set aside their assigned roles.
They engaged in another round of word sorting, drawing from their own areas of
expertise to pinpoint the desired attributes of a robot’s help-seeking
interactions using orange notes. This word sorting was further enriched by
interviews and discussions where we encouraged participants to expound on
their opinions.

###  3.5. Data collection and analysis

The focus groups were audio and video recorded, and observation notes were
taken both during the sessions and afterward when analysing the session
videos. We transcribed the interviews and made detailed observation notes
based on video recordings captured during the bodystorming activity. We
conducted a thematic analysis (Braun and Clarke, 2006) on both the interview
data and the observation notes. This cross-analysis approach enabled us to
gain deeper insights into specific observations and enriched the contextual
data that supported the comments made during the interviews.

The first author examined the data from the first focus group session, thus
generating preliminary codes and themes. This was followed by a one-hour
coding meeting amongst the three authors to deliberate upon this initial
coding scheme. The coding scheme was refined based on the collective feedback
and applied by the first author to code the data derived from the second and
third sessions. During this process, flexibility was maintained for the
generation of new codes, allowing for their integration into either a new
theme or an existing coding scheme. Subsequently, another coding meeting was
convened amongst the authors for further iteration of the coding scheme. Upon
reaching a collective agreement, the first author coded the data from the
fourth session and refined the coding from the previously analysed three
sessions. This iterative and collaborative process ensured a holistic analysis
of the data, leading to more concrete conclusions drawn from the focus groups.

Figure 3. Screenshots from the bodystorming session capturing interactions
between robot and pedestrian players: Robot player P9 tilts his box costume
towards P11, making contact (left); Pedestrian player P1 gestures towards the
button, seeking robot player P4’s confirmation (middle); Pedestrian player P17
waves in response to robot player P15’s spinning motion (right).

Figure 3 contains three screenshots from the bodystorming session illustrating
interactions between robot and pedestrian players. On the left, Robot player
P9 is depicted tilting his box costume toward Pedestrian player P11 to
initiate contact. In the center image, Pedestrian player P1 is shown gesturing
towards a button, seemingly asking for Robot player P4’s confirmation. The
right image captures Pedestrian player P17 waving in response to Robot player
P15’s spinning motion.

##  4\. Findings

This section begins with the observed non-verbal communication strategies
employed by robot players to initiate assistance, supplemented with insights
into the reactions of pedestrian players in scenarios replicating encounters
with urban robots. Following that, we shift our focus to the bystander
perspective by reporting on the diverse factors that influence their decision
to offer assistance or not. Subsequently, we synthesise these perspectives in
reporting word sorting results, revealing the desired characteristics of robot
help-seeking behaviour. It is worth noting that the findings discussed in this
section may not exhaustively encompass the spatio-temporal and political
complexities of urban settings, which are difficult to fully replicate in
bodystorming design activities.

###  4.1. Strategies of robot players to elicit help

####  4.1.1. Addressing bystanders

One of the challenges for robot players was to capture the pedestrian’s player
attention and initiate interaction with them. During the bodystorming
activity, robot players employed various strategies to address bystanders in
order to gain further assistance. Robot players frequently utilised rapid
movements of the pole, thereby generating noticeable noise. This approach
served to attract attention before initiating subsequent communication with
pedestrians (n=7). This approach was corroborated by five participants who
identified the robots’ noise and shaking poles as the primary factors that
compelled them to stop and further observe.

When not receiving further assistance, some robot players directly addressed
the nearby pedestrians using various methods (n=5). They oriented themselves
toward the pedestrian players to face them directly, or slightly moved towards
them, which served as an indication to those individuals that they were being
called out for assistance. As P15 illustrated when referring to the moment the
robot player turned its front towards him and approached: _‘It started walking
directly towards me, and that’s when I realised it needed assistance from
me.’_

After some pedestrians remained indifferent to the robots’ request, a few
robot players took their actions a step further. They proactively chased
departing pedestrians, obstructed their path, or even engaged in physical
contact with them. For instance, robot player P9 tilted his box costume
towards the pedestrian player P11 and rubbed against him (as shown in Fig. 3,
left). These pursuing behaviours generally elicited a sense of discomfort
among participants, causing them to maintain a considerable distance from the
robot player (P3, P9), or even escape from the situation entirely (P7, P11).
During the subsequent interview, P11 described the interaction as _‘needy’_
and _‘creepy,’_ prompting a strong desire to flee.

####  4.1.2. Cueing intentions

Upon capturing the attention of pedestrian players, robot players used their
body’s orientation or pointer’s directionality to further convey their
intentions. They either oriented themselves accordingly or used the pointer to
indicate their intended directions or objects they needed assistance with.
Such non-verbal cues, while informative, may not always ensure clear
communication. This was underscored during bodystorming when three pedestrian
participants sought additional confirmation from the robot player. For
example, when robot player P4 oriented himself towards and paused upon the
traffic light button, P1 approached, pointed at the button and looked at P4,
asking, _‘Is it?’_ (see Fig. 3, middle).

To enhance clarity, the robot player responded by adding motions in the
desired direction or repeating the same pointing gesture. Robot player P4
responded to P1 by stepping back and moving forward towards the traffic light
button again as confirmation. Recognising this, P1 then assisted by pressing
the button. In this manner, both parties intriguingly communicated through a
blend of verbal and non-verbal exchanges.

####  4.1.3. Displaying emotions

Even though participants were not given any external communication modalities
apart from the robot body – consisting of a box and a pole – six of the
participants attempted to convey emotions through movements or sound. A common
emotion exhibited by participants (n=5) was anxiety when pedestrians did not
offer help during the role-play. This anxiety was manifested through frequent
and intense shaking of the pole or twisting of the robot’s body. The displayed
emotion raised empathy among participants. P17, for instance, reflected on an
instance where the robot player was impeded by an obstacle and energetically
waved its pole towards her: _‘[…] it (the robot) seemed very anxious. Then I
quickly realised that it was this thing that was blocking it.’_ As a result,
three out of the five participants who initially didn’t assist the robot began
to pay heightened attention and acknowledged the situation. This ultimately
prompted them to step in and provide assistance.

Four robot players tried to express gratitude after receiving help by
expressing joyful emotions through their movements, such as hopping up and
down (P6) or spinning around (P15), as well as through sound, like vocalising
an uplifting tune (P7, P1). Displaying these joyful emotions prompted
responses such as nodding (P9) and waving (P17, as shown in Fig. 3, right) by
pedestrian players. P17 drew a connection between waving to the robot and her
experience of encountering small animals, explaining, _‘Because I tend to
greet small animals, or things that I find cute or have emotions.’_
Furthermore, P7 conveyed a sense of satisfaction when seeing the robot
spinning around, stating, _‘I felt very satisfied because I believe it has
feelings. […] I help it and it is happy, which also makes me happy.’_ P9,
commenting on the joyful tune produced by the robot player P11 following his
assistance, noted, _‘It made me feel like, alright, I did the right thing.’_

Figure 4. Communication between robot and pedestrian players: P8 inquires
about robot player P6’s needs, while P6 responds by pointing with the pole
(left); Robot player P14 adopts a ‘bow’ gesture towards the pedestrian player,
eliciting a corresponding bow in return (right).

Figure 4 showcases two screenshots depicting communication between robot and
pedestrian players in a bodystorming session. In the first image on the left,
Pedestrian player P8 is seen inquiring about Robot player P6’s needs, with
speech bubbles reading ’Do you need help?’ and ’Ok. Wait! Wait!’ The Robot
player responds by pointing with a pole. In the second image on the right,
Robot player P14 is shown adopting a ’bow’ gesture towards the pedestrian
player, which prompts the pedestrian to bow in return, demonstrating a
reciprocal non-verbal communication exchange.

####  4.1.4. Demonstrating repetitive patterns

Having discussed the three primary components of communications — addressing
bystanders, cueing intentions, and displaying emotions — another notable
observation emerged. Robot players frequently assembled these components into
discernible repetitive patterns, resembling the predictable and programmed
behaviours generally associated with robots (n=7).

P7, for instance, developed a unique routine to signify a pathway obstructed
by an obstacle: she advanced towards obstacles while emitting two flat-tone
beeps, moved back with an up-tone beep, and paused briefly before repeating
this cycle multiple times. Her rhythmic auditory cues were synchronised with
her physical movements. She later explained that this use of repetitive
movements and audio cues was reflective of her _‘imagination of the robot
having some program behind the system.’_ Similarly, P2 adopted a pattern that
combined cueing intention and addressing bystanders by repeatedly turning
towards the pedestrian player, returning to the original position, and then
turning towards the obstacles blocking its path.

Eight participants indicated that the recognition of programmed machine-like
behaviour augmented their understanding of a robot’s intent to communicate. In
contrast, movements lacking a recognisable pattern were sometimes perceived as
_‘erratic’_ or _‘malfunctioning’_. The repetitive movement patterns reminded
four participants of situations where domestic cleaning robots get stuck and
repeatedly attempt to move back and forth. The familiar motion patterns helped
participants form associations and understand the robot’s need for help.

In addition to enhancing understanding, recognising repetitive patterns in the
robot’s behaviour also potentially improved participants’ confidence in the
robot’s abilities. The robot’s consistent adherence to certain rules
communicated a sense of control over its actions, as P9 noted, _‘I think the
robot knew what it wanted.’_ P7 indicated that the repetitive patterns in the
robot’s movements signify predictability, allowing _‘the pedestrian (to)
anticipate what’s gonna happen.’_

###  4.2. Factors shaping bystander decision to offer or decline assistance

####  4.2.1. Preconceptions of agent autonomy

Our study underscores the prevailing perception among participants that
service robots should operate with complete self-sufficiency and efficiency
(n=10). This forms a major reason for participants’ reluctance to assist
robots. For instance, P9 shared his presumption about robots’ capabilities to
manage all tasks autonomously, stating, _‘I thought the robot was able to do
everything itself.’_ Such misconceptions can foster misunderstandings about
the actual capabilities and needs of these robots, an aspect further
highlighted when P9 continued, _‘[…] so I didn’t realise the robot was asking
me to help.’_ This expectation subsequently instigated skepticism among six
participants regarding the functional utility of service robots that require
human intervention. This sentiment was articulated through comments like,_‘If
you have to work for them, then what’s the point to have a robot’_ (P6). P7
further noted a decline in trust due to the robot’s need for help, contrasting
it with her expectation of a service robot’s role as a functioning entity,
stating _‘As a working robot (i.e., service robot), they kind of made me feel
like untrust(worthy). […] so they (have to) work perfectly.’_

Interestingly, when debriefed about the actual scenario, there was a notable
shift in the attitudes of four participants. They came to understand that the
robots’ challenges arose from external factors outside their capabilities
(e.g., obstacles purposely placed by humans) rather than any inherent
malfunctions. P4 underscored this realisation, remarking, _‘then it’s the
human’s fault (for placing the obstacle)’_. The reassignment of responsibility
for the robot’s immobilisation not only improved participants’ inclination to
assist but also enhanced their empathy towards the robot’s predicament. P9
summarised this change of mind, noting that in this case the robot is _‘in
need of help rather than being needy’_.

####  4.2.2. Absence of responsibility

The notion of being a mere bystander or pedestrian, devoid of any
responsibility towards the enacted robots, emerged as a primary factor
influencing the decision not to assist among ten participants. This sense of
detachment made them reluctant to invest their time and effort in helping
‘something’ they didn’t feel accountable for. P14 particularly highlighted
resistance to being perceived as _‘free labour’_ for commercial enterprises,
posing the question: _‘why should I spend my time helping something that is
making a profit?’_ However, they later nuanced this statement by adding: _‘If
it (the robot) is for a non-profit purpose, then I might be inclined to help,
even if it means me being a bit delayed.’_

The concerns of getting entangled in potential troubles further discouraged
five participants from offering help. P1 expressed this concern, stating _‘I
am afraid of touching it and breaking things. […] It could cause trouble if we
touch it.’_

####  4.2.3. Unfamiliarity with robotic technology

Seven participants expressed hesitation to assist the robot due to a perceived
lack of expertise. They felt ill-equipped as _‘random pedestrians’_ (P7) to
provide assistance to the robot, a task they believed was best left to
professionals, as indicated by two participants.

The unfamiliarity with robotic technology sparked safety concerns among six
participants, hindering them from offering help. This was further corroborated
by our observations of five participants who actively distanced themselves or
avoided the robot when it approached them for assistance. This evasion stemmed
from the uncertainty about potential risks linked to the robot’s predicament,
as P7 stated, _‘I don’t know if it’s a tiny little issue or if it’s going to
explode or something.’_

####  4.2.4. Intrinsic motivation: empathy and emotional responses

Our interviews indicate that intrinsic motivation plays a compelling role in
promoting bystanders to assist the robot, with feeling empathy being the
primary motivator (n=8). Participants described the robot using terms like
_‘depressing’_ , _‘frustrated’_ , and _‘helpless’,_ signifying their ability
to infer the robot’s emotional states through observation of its movements
within given contexts. For example, P9 noted that observing the robot’s body
swaying in an appeal for help prompted an association with vulnerable
individuals, stating _‘[…], so (it’s) like a child needs help or an old person
needs help’_.

In addition to empathy, six participants reported experiencing a sense of
emotional reward, capturing feelings of _‘fulfilment’_ , _‘satisfaction’_ ,
and _‘delight’_ , following their actions to assist the robots. P2, for
example, articulated this sentiment as, _‘You helped it and witnessed it
moving forward, which brings you a sense of satisfaction.’_ Moreover, the
gratitude exhibited by the robot reportedly amplified these emotional rewards
(n=4).

However, it was also evident that some participants demonstrated a reduced
level of empathy towards robots. Specifically, P1 drew comparisons with other
entities, affirming readiness to _‘stop for a dog or a cat, but not for a
robot.’_ He justified this attitude with his belief that _‘you can’t expect to
treat a robot as a human or as a living animal.’_ In one session, P15, who
assumed the role of a vehicle driver, further underscored this perspective by
simulating a horn by knocking on the board prop and voicing their impatience
by yelling at the robot. Their behaviour was justified by their assertion that
the robot _‘cannot be viewed as human.’_

Figure 5. Digital transcription of the word sorting results

Figure 5: Digital transcription of the word sorting results, displayed as
three radar charts categorising words based on attributes related to physical,
psychological, and social characteristics. Each chart plots points
corresponding to words such as ’Cute’, ’Lively’, ’Clumsy’ for physical
attributes; ’Sad’, ’Excited’, ’Confident’ for psychological; and
’Collaborative’, ’Polite’, ’Needy’ for social. The points are color-coded to
represent Robot Players’ Self-Description, Pedestrian Players’ Perception of
Robot Players, and Desired Qualities of Robot Help-seeking Behaviors. The
charts show clusters of these attributes in varying degrees of intensity and
frequency.

####  4.2.5. Extrinsic motivation: entertaining value and material reward

Apart from intrinsic motivation, our interviews highlighted the role of
extrinsic motivation, stemming from both tangible and intangible incentives,
in fostering helping behaviours towards robots.

Six participants anticipated entertainment value from helping robots, viewing
this form of intangible incentive as an additional incentive to offer
assistance. As articulated by P9, the incorporation of a _‘surprise element’_
into the interaction could further _‘spark joy.’_ P12 also mentioned the
potential of _‘transforming it (assisting robots) into a more game-like
experience’_.

Furthermore, two participants felt that their prosocial actions should also
yield tangible advantages for them. They suggested the introduction of
material rewards, such as vouchers or discounts from the company that
implements the robot, could further stimulate their willingness to assist.
This perspective was rooted in their belief that as bystanders, their
prosocial behaviour towards the robot was not directly _‘benefiting’_ them.

###  4.3. Desired robot characteristics

Based on the results of the word sorting activity and insights derived from
the participants’ discussions, we identified three characteristics that help-
seeking robots could implement, which we present in this section.

####  4.3.1. Vibrantly mechanical

The physical sensations experienced by robot players are evenly distributed
among all words with a slightly higher number of _‘clumsy’_ (n=4). This choice
primarily stems from the physical limitations of moving within the robot
costume.

There’s a noticeable overlap between the perceived physical qualities of the
robot player and the desired attributes. In particular, _‘mechanical’_ was
selected as perceived quality (n=9) and desired quality (n=10). Similarly, the
same pattern was observed in _‘playful’_ (8 for perceived quality, 14 for
desired quality), and _‘cute’_ (11 for perceived quality, 8 for desired
quality). This correlation implies that the robot players’ behavioural
strategies, to some extent, met participants’ expectations for how a robot
should act when seeking assistance, especially concerning these attributes.
The movement of the robot players, encapsulated in the minimalist abstract
box-shaped costume, inherently conveys an impression of cuteness without the
need for additional embellishments. As P12 expressed, _‘I feel its existence
is cute enough already. Just imagine you’re on the road, helping a little
robot, and it goes ”dibbly-dobbly” as it moves forward. It’s already
incredibly cute, and there’s no need to add additional design language.’_
While some participants also selected _‘lively’_ and _‘organic’_ , they view
these as elements that can be added to the overall mechanical nature of the
robot to enhance the expressiveness. P13 emphasised the importance of using
these elements thoughtfully to _‘avoid creating the uncanny valley effect.’_

####  4.3.2. Cheerfully confident

In regards to the psychological feelings of robot players, there was no clear
tendency being identified in the word sorting, which may imply that this was
highly subjective. When it comes to other participants’ perceptions of the
robot player, _‘brave’_ (n=9) and _‘confident’_ (n=8) emerge as two dominant
qualities. Participants highly commend the robot player’s efforts to find
solutions in challenging situations. P16 pointed out the robot’s vulnerability
and the hazards and difficulties of its environment. She articulated, _‘(the
robot) dared to cross the road on his own and was thinking about how to do
it.’_ when it was _‘dangerous because there were no traffic lights for (it)’_.
P2 contemplated the perceived braveness, projecting the psychological state
that humans have when seeking help from strangers onto the robot. She
expressed that the robot _‘needs help (and asks for it), as it needs to use
courage to convey the help it needs.’_

In terms of the desired psychological attributes, participants generally
favoured positive emotions. The term _‘confidence’_ (n=12) emerged as the
predominant expectation that people had for the robot’s demeanour. P7
expressed this perspective by describing a service robot as _‘some kind of
professional stuff’_ , implying the need for the robot to exhibit
characteristics that align with expected proficiency. In addition,
participants generally expected the robot to display _‘happy’_ (n=6) emotions
after receiving help.

Descriptors of negative emotional valence (e.g., _‘sad’_) were not considered
as desired qualities for help-seeking robots. P2 offered an enlightening
comment that a casually-encountered robot exhibiting sadness while seeking
assistance was reminiscent of street beggars, leading to feelings of what they
described as _‘emotional blackmail.’_

####  4.3.3. Responsively outgoing

The self-assessed feeling of _‘needy’_ emerged as the most frequently chosen
term among robot players (n=7), indicating that they experienced a sense of
helplessness and a perceived necessity for human aid as a robot in the given
situation. To elicit help, most of the robot players opted to project a more
approachable character, frequently choosing descriptors like _‘extrovert’_
(n=4), _‘collaborative’_ (n=4) and _‘inviting’_ (n=3). In contrast, only two
robot participants resonated with terms like _‘shy’_ and _‘introvert’_.

Other participants’ perceived robot social quality aligned with the self-
assessed social attribute of robot players, with _‘needy’_ (n=15) and
_‘extroverted’_ (n=8) being the most frequently chosen terms. In terms of the
desired social qualities, although _‘extroverted’_ was still among the
preferred terms, participants placed a greater emphasis on communication
qualities such as being _‘polite’_ , _‘responsive’_ and _‘collaborative’_. P2
highlighted the importance of politeness, even when the robot is in urgent
need of help, saying, _‘At the same time, when you try to attract everyone’s
attention as much as possible, you also need to be gentle towards others.’_
The desired _‘responsive’_ quality reflects participants’ expectation of
receiving feedback after offering help. For example, P17 expressed feeling
disappointed if she helped the robot without receiving any response from it.

##  5\. Discussion

Drawing from the findings of the previous section, we identify three design
considerations (C1-3) for fostering casual human-agent collaboration. In
addition, we reflect on the strengths and limitations of the bodystorming
design activity employed in our study.

###  5.1. Expressiveness through functionality-oriented form

Given humans’ innate psychological tendency to interpret social cues from
moving objects (Heider and Simmel, 1944), physical movement has become a
pivotal medium in human-agent interaction to facilitate social communication
(Zuckerman and Hoffman, 2015; Zaga et al., 2017; Gemeinboeck and Saunders,
2017; Luria et al., 2017; Anderson-Bashan et al., 2018). An example of extreme
abstraction with minimal movement serving as social cues is the _‘Greeting
Machine’_ (Anderson-Bashan et al., 2018) – a small ball rolling on a bigger
dome in varied trajectories. This design effectively elicits both positive and
negative social encounter responses. Similar to _‘Greeting Machine’_ ,
participants in our design investigation were constrained by a robotic costume
made of a box and a pole that had only minimum expressive capabilities. This
costume had no extensions beyond the basic form factor of a conventional
delivery robot that is primarily intended for the task of transporting goods.
Nonetheless, subtle cues, such as the robot’s orientation in its shape (i.e.
orienting the front of the box costume toward an object) or the directionality
of simple components (i.e., pointing in the intended direction using the
pole), proved effective in addressing bystanders and conveying the robot’s
need for assistance. This was evident, as in all the sessions, pedestrian
players accurately understood the robot player’s request for assistance. In
addition, we even witnessed conversations formulated through the back-and-
forth interplay between pedestrian inquiries and the subtle motions of robot
players (as shown in Fig. 4, left)

In addition to empathising with the robot’s feelings of frustration and
vulnerability when seeking help, it was evident that people also recognise
social signals gestured through subtle movements, such as gratitude. For
instance, a simple tilt of the box-shaped robot body, can be perceived as a
‘bow’, even prompting the pedestrian player P15 to bow back (as shown in Fig.
4, right).

As pointed out in our literature review, linguistic utterances have been
central in human-agent collaboration (Seaborn et al., 2021) and represent a
key method for help-seeking requests (Backhaus et al., 2018; Srinivasan and
Takayama, 2016; Hüttenrauch and Eklundh, 2003) given their effectiveness in
conveying information. Nonetheless, challenges such as cultural and language
barriers, cognitive load, and issues related to noise and distortion in public
settings limit their utility in the context of casual human-agent
collaboration in public spaces. In addition to that, our study revealed
further concerns regarding the appropriateness of agents verbally asking for
help in urban public contexts (n=7). P1, for instance, suggested it could be a
_‘bit abrupt or out of place’_ if a robot suddenly started to talk human
language on the street, underscoring the need for robots to have their own
unique, natural communication modes. Additionally, safety concerns were
expressed by three participants who suggested that language used by robots
might potentially distract other road users. Notably, P14 expressed potential
discomfort in feeling exploited by commercial entities when robots use human
language to solicit assistance, suggesting a _‘feeling of being used as free
labour for those commercial companies’_.

  * C1 \- The design of agent help-seeking strategies should leverage the inherent expressiveness found in the functional aspects or form of the agent. While ensuring effective initiation of help-seeking requests, these implicit communication channels can prevent from being viewed as disruptive.

###  5.2. Adherence to perceived agent social categories

Robots and intelligent agents, growing rapidly in sophistication and
sociability, have spurred enhanced research into agent social identity (Hogg,
2016), delving into aspects like gender (Eyssel and Hegel, 2012), age (Edwards
et al., 2019), and race (Bartneck et al., 2018). This trajectory was echoed at
a recent workshop (Winkle et al., 2021), which emphasised the importance of
designing robots that can effectively convey social identities to optimise
human-robot interaction outcomes. While this workshop primarily centred on
social identities associated with specific attributes (i.e. gender), our
findings broaden this scope to encompass wider social categories (e.g.
occupations), which should be considered in the design of casual
collaborations between agents and bystanders.

In our investigation of the help-seeking delivery robot, participants
perceived these robots primarily as service providers or professional workers.
This perception led them to expect high proficiency from these robots,
favoring the robot presenting qualities reminiscent of confidence over
neediness. Consequently, even when assistance was necessary, participants
displayed a general reluctance to interact with robots that seemed overly
needy or showcased negative emotions. This inclination stands in contrast with
studies on eliciting human prosocial behaviour towards social companion robots
(Connolly et al., 2020a; Daly et al., 2020) (e.g., robotic pets). In these
settings, negative emotional expressions in robots often serve as a catalyst,
motivating individuals to step in and offer help. This difference could be
rooted in the distinct perceived categories: _‘worker’_ versus _‘companion’_.
In public urban settings, intelligent agents participate integrally in various
facets of urban life. As a result, they embody a wide range of social
categories, from service providers (e.g., delivery robots (Gehrke et al.,
2023), street cleaning robots (Jeon et al., 2017)) and authoritative entities
(e.g., smart traffic regulators (Lee et al., 2022), patrol robots (Szocik and
Abylkasymova, 2022)) to street entertainers (e.g., playful urban robots
(Hoggenmueller et al., 2020; Lee and Jung, 2020)).

  * C2 \- To respond to real-world expectations and social norms, the design of agent help-seeking strategies should adhere to and match with their perceived social categories (e.g., occupation).

###  5.3. Curating incentives: material rewards, act of care, or playful
engagement

Though in 7 out of 9 sessions, pedestrian participants offered help, 4 of them
mentioned they might not behave the same way in real-life settings.
Furthermore, the misaligned objectives and the imbalanced benefits between
agents and bystanders in such casual collaborations highlight the need for
incentives.

Previous research on bystander assistance for commercially-deployed robots has
divided ‘help’ into two broader categories: either ‘helping-as-work’,
emphasising precarity and invisible labour, or ‘helping-as-care’, which
accentuates the emotional and relational dynamics of help (Dobrosovestnova and
Reinboth, 2023). This work sheds light on the inherent ambiguity of these
helping behaviours and prompts a rethinking of robot design to better shape
these engagements. Our focus group findings resonate with this notion, while
also shedding light on how different perspectives on helping robots call for
various forms of incentives.

Offering material rewards, such as vouchers or discounts from companies
deploying robots, turns casual collaborations into mutually beneficial
exchanges. This model of paid crowdsourcing, evident in cases like identifying
shared bicycle locations (Griffin and Jiao, 2019) or urban data collection on
platforms like OpenStreetMap (Crooks et al., 2015), could be adapted for
interactions with intelligent agents in public spaces. By framing casual
collaborations as beneficial exchanges through material rewards, it can
effectively align disparate objectives between parties into actions that are
mutually advantageous.

In our study, empathy – as an act of care – emerged as a predominant motivator
for offering assistance, manifested as a form of internal incentive. Empathy,
essential in shaping communication and social bonds, has been underscored as a
central component in human-agent interactions (Bickmore and Picard, 2005). A
robust body of research validates robots’ capacity to elicit empathetic
responses from humans (Kwak et al., 2013; Riek et al., 2009; Rudovic et al.,
2018). Correspondingly, studies on social robots have shown that these forms
of empathy can drive prosocial behaviour, compelling humans to intervene
against robot mistreatment (Connolly et al., 2020b) or engage in affectionate
actions, like petting (Heerink et al., 2012). Our findings resonate with these
prior studies, suggesting that individuals exhibit empathy also towards public
agents in need, driven by the observation of context and agent expressions.

In addition to viewing helping robots as a form of work or act of care,
‘helping-as-play’ has emerged as another perspective in our findings, for
which the resulting entertaining value can function as an incentive. Playful
strategies have been used in various human-agent interaction settings, such as
motivating children’s learning (Ahtinen and Kaipainen, 2020) or encouraging
factory workers in collaboration with robots (Chowdhury et al., 2021).
Furthermore, it has been shown to effectively engage the public and generate
enjoyable experiences among bystanders (Lee and Jung, 2020; Hoggenmueller et
al., 2020). One of the primary challenges for urban robots to ask for help
from bystanders is convincing them to invest their time and tolerate potential
disruptions. The inherent playfulness in humans could potentially act as an
incentive for casual collaborations by transforming disruptions into pleasure
and enjoyment. That being said, ‘helping-as-play’ also needs to be carefully
employed, as it can present ethical concerns similar to those in ‘helping-as-
care’ (Anna and Tim, 2023).

  * C3 \- To compensate for the misaligned task objectives in casual human-agent collaboration, the design of agent help-seeking strategies should incorporate appropriate incentives, transforming assistive behaviours into experiences that benefit both parties.

###  5.4. Reflections and limitations of bodystorming design activity

The physical constraints introduced by the robot costume, such as limited
field of view, changes in perspective, and restricted mobility, while not
capable of entirely replicating a robot’s perspective, facilitated
participants in departing from a conventional human viewpoint and immersing
themselves in the sensations of robotic alienation and otherness (Dörrenbächer
et al., 2020). As articulated by P5, _‘There’s a sense of feeling out of place
or not quite fitting in. It seems like there are no peers of my kind in the
surroundings. Everyone else is tall, and I am short, so I feel a bit out of
place or different.’_ This sense of otherness could contribute to
participants’ emotional engagement when they assumed the robot role, which was
evident in the frequently conveying sentiments of _‘frustration’_ or
_‘depression’_. Additionally, beyond mere empathy with the robot’s emotional
state, the robot players also displayed a recognition of its societal function
(i.e. its duty as a delivery service entity). For instance, P4 suggested
feelings of _‘motivated’_ and _‘happy’_ when he _‘could continue working’_.
This profound resonance with the agent’s perspective underscores that our
bodystorming approach effectively incorporated the agent’s perspective into
the design exploration. However, it is worth noting that, despite the
effectiveness of the physical probes in consciously shifting participant’s
sensations towards the perspective of an agent (Dörrenbächer et al., 2020), it
is impossible to completely transcend the human standpoint through these
methods as our human nature inherently separates us from things (Spiel and
Nacke, 2020).

Consequently, the ‘participation’ of agents in the design process surfaces
tensions between humans and agents that stem from the misalignment in goals
and benefits in a casual collaboration encounter. P9’s behaviours while
playing the robot role offer a striking example. He assertively used the robot
costume to brush up against the pedestrian player’s chest — a distinctly pushy
gesture meant to force attention and assistance. He later admitted,  _‘I was
about to give up being nice’_ , and even pondered,  _‘That’s why I was
considering adopting a more aggressive strategy. I thought, I might just try
pushing him onto the road.’_. This elicitation of tension and physical
friction is less likely to surface in methods that take a purely human-centred
perspective or those that rely solely on cognitive abilities. Furthermore,
this tension was leveraged to stimulate design ideation, exemplified by P9’s
subsequent ideas that emerged from this physical friction. He suggested
infusing robots with seemingly annoying nudging behaviours and _‘creating
entertainment value’_ to possibly uplift people’s moods and thus promote pro-
social behaviour.

Despite the strengths of our approach in generating design insights, we
acknowledge its limitations. Discussions and reactions concerning helping a
casually encountered agent were elicited by role-playing activities, without
the incorporation of real technology. Even though the immersive nature of the
replicated scenario and participant engagement is evident, the absence of real
intelligent agents and the artificial nature of scenarios replicated in
laboratory settings might limit the direct applicability of our findings.
Acknowledging these inherent limitations of bodystorming method, future
research should validate and refine our design considerations through more
spontaneous interactions between humans and high-fidelity artifacts (e.g.,
through Wizard of Oz testing (Dahlbäck et al., 1993) or virtual reality study
(Villani et al., 2018)).

##  6\. Conclusion

Intelligent agents are increasingly transitioning into uncontrolled settings
that often may challenge their operational capabilities. While collaboration
between humans and agents can offer means to overcome these challenges, to
date little is known about how to create effective and engaging human-agent
collaboration in casual settings (e.g., involving surrounding bystanders).

Through employing bodystorming and real-world help-seeking scenarios
encountered by urban robots, we uncovered potential help-seeking strategies
through participants’ enactments as robots, including addressing bystanders to
initiate interaction and using non-verbal cues to communicate intention and
request. Furthermore, the display of emotions and demonstrating repetitive
patterns has been found to ease help-seeking requests. Taking into
consideration the reactions of pedestrian players from our bodystorming
activities, we further offered insights into the factors influencing people’s
response to robot’s help-seeking requests and identified desired robot
characteristics.

Synthesising insights from both robot and bystander perspectives, we conclude
with a set of design considerations for help-seeking agents that operate in
uncontrolled environments. These considerations include promoting
expressiveness, ensuring alignment with agent social categories, and curating
appropriate incentives. Findings from our design exploration and
considerations for implementing help-seeking strategies provide a foundation
for creating intelligent agents that operate in uncontrolled environments and
aim to facilitate casual collaboration that is mutually beneficial to humans
and agents.

###### 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 CHI’24 reviewers and ACs for their
constructive feedback and suggestions to make this contribution stronger.

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