Hello.
My name is Sarah Sebo, and in this video,
I'm going to present an overview of our paper titled,
"Robots in Groups and Teams: A Literature Review."
Robots are increasingly interacting
with people in a variety of work and public settings, delivering
food; helping customers find products in retail stores;
assisting in manufacturing tasks;
delivering supplies in hospitals;
and scanning for spills and empty shelves in supermarkets.
From these examples, we see that robots are often
interacting with multiple people at the same time, which
makes sense, because people rarely live or work alone.
The CSCW research community has cultivated a rich body
of work examining the impact of computers
and computer-mediated interactions on human groups
and teams.
Existing frameworks and CSCW have predominantly theorized
these technologies as tools or infrastructure
to support teamwork.
However, these theories do not well
extend to robots with high levels of perceived agency
that can take on roles within groups as teammates
or constituent members.
Within the human-robot interaction community,
research has been transitioning away from dyadic interactions
with one human and one robot to interactions with groups.
As researchers begin exploring robot interactions
with multiple people, it has become clear
that several aspects of the interaction
change when a robot engages with multiple people as
opposed to a single person.
Notably, groups of people are more
likely to engage with robots.
For example, Booth et al.
Showed that college students were
more willing to let a robot enter their dormitory building
when they were in a group as opposed to if they were alone.
Groups of people are also more likely to exhibit
more aggressive behavior towards robots,
as shown in Brscic et al's observations of children
bullying a robot in a shopping mall.
Groups of people also have within-group, human-to-human
interactions, which Leite et al. believes
may be one contributing factor to why individual children show
increased recall of educational content
compared to groups of children.
And finally, Leite et al. also demonstrated
that individual children and children in groups
express their internal states differently,
indicating that models of human behavior
must take into consideration whether a person is alone
or interacting with other people.
The additional complexity and emergent behaviors present
in groups of people constitutes a fundamental change
in complexity that is neither captured
by current theory in human-robot interaction,
nor do existing technical approaches
for human-robot interaction readily scale to groups.
For these reasons, we reviewed studies
focusing on robot interactions with groups of people
to provide both an objective insight
into where the field stands to date and further push
researchers towards areas of potential need and interest.
Our review included peer-review research studies
that satisfied the following criteria.
One, the study must include at least one
physically-embodied robot.
Two, at least two locally-present people
must interact with a robot simultaneously.
Three, the robots must be autonomous or perceived
to be autonomous interactants.
Four, the study must explore group-level phenomena
and provide a direct contribution
to our understanding of how robots interact with
and influence groups of people.
Using our including criteria, we conducted an exhaustive search
of papers within top-tier HRI outlets,
papers of the attendees of the Robots in Groups CSCW 2017
workshop, papers that appear using Google Scholar search
terms, and papers identified through other means.
After a refinement process, our corpus
consists of 101 distinct research studies.
Next, we highlight several key findings from our review.
First, we found that both verbal and nonverbal robot
behavior powerfully shaped group interactions.
For example, Correa et al. demonstrated
that robots that verbally express group-based emotions as
opposed to individual-based emotions
lead to increased levels of group identification and trust.
With respect to nonverbal behavior, [?] et al.
showed that a robot's use of gaze cues
can significantly influence its ability to regulate turn-taking
in group interactions.
We also found that robots take on different roles
depending on the group context.
By graphically representing the studies
in our corpus on the dimensions of setting, number of studies,
and robot role, we observe that certain types of settings
seem to suit particular robot roles better than others.
For example, in settings where the desired behavior
of the robot is repetitive and consistent, like for robots
that explain museum exhibits, robots
are often put in the role of a leader.
When looking at the types of groups
that robots interact with, we found
that there has been substantial growth in the research
attention paid to robots interacting with task groups,
as shown in figure 6(b).
As robot capabilities continue to improve
and an increasing number of robots
are developed to join teams of people,
it is likely that work focused on robot teammates and task
groups will continue to grow.
Finally, we also found a focus in recent work
on how robots can influence human-to-human interaction
in groups.
For example, some of our work has demonstrated
that a robot's verbal expressions of vulnerability
can have ripple effects in a group by increasing how
likely human members of the group
are to be vulnerable with one another.
Our paper also contributes to theory describing
a robot's role and impact within a group or team
by adopting the input process output framework, previously
proposed by McGrath and Hackman and Morris.
We conceptualize the robot's behavior and role
within a group as an additional input factor
to a group's interaction process,
which in turn influences the group's outcomes.
Thank you for watching this video,
and we encourage you to read the paper
to learn more about this exciting research
area of robots interacting with groups and teams of people.