Hi, my name is Sarah Sebo and in this video I will present the work that I’ve done in
collaboration with Ling Dong, Nicholas Chang, and Brian Scassellati at Yale University.
Our work is titled “Strategies for the Inclusion of Human Members within Human-Robot Teams.”
Now to begin, inclusion is an incredibly important component of collaborative teaming. When studying
human teams, work has found that the perceived inclusion of human team members, how included
a person feels within a group, is positively correlated with both their commitment as well
as the team’s overall performance.
As robots increasingly join collaborative teams of people, it is important for us to
consider how robots may influence people’s perceived inclusion within human-robot groups.
So in this work, we investigated two strategies that a robot could use to hopefully increase
the inclusion of human team members.
The first strategy we examined is being given a specialized role to interact with the robot.
This specialized role emulates much of what we see today when a robot is incorporated
into a human team, where one human team member is designated as the robot “operator”
or “controller.”
The second strategy that we explored is supportive utterances from the robot.
We investigated these two strategies in a human-subjects experiment that involved participants
completing two rounds of a collaborative task.
In the first round, two people and one Jibo robot interact together for 15 minutes. At
the same time, a third participant and another Jibo robot interact together for the same
15 minutes. We did this in order to create an “ingroup” and “outgroup.” Then
we brought the outgroup member to join the room with the two participants and that Jibo
robot for a second round of the task, lasting 30 minutes.
This two round design allowed us to create an ingroup and outgroup in the first round
and to test our strategies for inclusion in the second round. So in order to test our
first strategy of improving human team member inclusion, we designated one of the participants
as the ‘robot liaison’ at the beginning of the second round. The role of the robot
liaison was to gather information by asking questions to the robot related to the task.
In our first between subjects condition, we designated an ingroup member as the robot
liaison. And in our second between subjects condition, we designated the outgroup member
as the robot liaison.
In order to test our second strategy of improving human team member inclusion, supportive utterances
from the robot, we had the robot say utterances such as “Good idea, Sarah” to participants
during round 2 of the task. These supportive utterances were directed at all three human
participants.
For this human-subjects study, we recruited and ran 13 groups in each experimental condition.
I’ll now describe our hypotheses related to these two strategies to improve human team
member inclusion, in the context of this experimental design. Our first hypothesis is that robot
liaisons will positively influence inclusion. We thought that the robot liaison’s role
to provide the group with task-relevant information would be a valuable contribution to the group,
increasing that team member’s feelings of inclusion.
Additionally, we thought that the supportive utterances from the robot would positively
influence inclusion.
We thought that both the robot liaison role and supportive utterances from the robot would
be especially helpful for the inclusion of the outgroup team member.
The task that we had participants complete was a modified version of the Desert Survival
Problem. In the first round, participants were given a list of 25 common household items,
and were asked to rank them with respect to how useful they thought they were for survival.
This task was completed both by the two ingroup members as well as the outgroup member, in
their separate rooms.
In the second round of the task, when all three participants were together, the participants
were given information about the environment where they were to be ‘stranded.’ And
then, as a group of three, they were asked to produce one final ranked list of 8 survival
items. We introduced information about the environment in round two of the task in order
to provide participants with additional material for further discussion and to encourage questioning
of prior assumptions.
The robot liaison’s role was to gather information about the survival items and the environment.
They did this by verbally querying the robot. Here are some examples: “hey Jibo, tell
me about the chocolate,” hey Jibo, tell me about the season.”
Next, I’ll show a video example where a robot liaison in the experiment asks Jibo
about the weather.
Hey Jibo, tell me about the weather.
Weather, life threatening temperature is rare, but does occur. Make sure you save up supplies
to survive a 3 day long blizzard. There’s also lots of heavy rain.
The robot also delivered targeted supportive utterances during the task. Here are some
examples.
Let’s say Melissa says, “I think we need a coffee pot.” The robot might rephrase
her comment to say, “We need a coffee pot, good idea Melissa.” If Robert were to say,
“a camera could reflect light.” The robot might pick up on the mention of ‘camera’
in Robert’s utterance and respond with, “camera, Robert, I think that’s worth
considering.” Lastly, let’s say Jennifer said, “rabbits are good, you can eat them.”
The robot could respond with a general statement of “Okay, Jennifer.”
Each participant received approximately 6 of these targeted supportive utterances over
the the 30 minutes of the second part of the task. I’ll now share some video examples
of the robot’s targeted supportive utterances during the experiment.
“But we need newspaper for a fire.” “Newspaper, oh where should that be?”
“Just put it in 6.” “Newspaper, good idea.”
“From where we are, it’s 15 miles away, but we can get closer.”
“Yeah we can just…” “I mean, I assume that we know where the
stream is.” “Get closer, good idea.”
“Thanks”
We used the following measures in this experiment: the talking time of each participant, the
initial survival item rankings compared with the final survival item ranking, participants’
psychological safety, participants’ perceptions of group cohesion, participants’ perceived
inclusion, and participants’ perceptions of the robot according to the RoSAS scale.
Now, for our results. We found that the perceived inclusion of the robot liaisons was significantly
less than other group members. These findings have important implications for the increasingly
prevalent number of human-robot teams today, as robot members are frequently incorporated
with a human “liaison,” such as factory robot operators, robot controllers in search
and rescue, and surgeons controlling robots in surgical procedures. These results indicate
that having someone with a robot liaison role in a human-robot team may actually be detrimental
to the perceived inclusion of those liaisons.
Our next result examined the survival item selections by ingroup and outgroup members.
At the end of round 1, the two ingroup members produced one ranked list of 25 survival items
and the outgroup member also produced one ranked list of the 25 survival items. And
then, when they came together for part 2 of the experiment, they produced one final list
of 8 items.
From these ranked lists of survival items, we examined the items that were initially
ranked ‘low’ on the ingroup list and ‘high’ on the outgroup list, and looked at how many
of these items made it onto the final list of 8. For example, let’s say that the ingroup
members rated chocolate low on their initial list, however, the outgroup member thought
that chocolate was extremely necessary and rated it high on their initial list. What
was the likelihood that chocolate would end up on the final list?
We found that when an ingroup member was the robot liaison, 47% of the items ranked low
by the ingroup and high by the outgroup ended up on the final list of 8. However, if an
outgroup member was the robot liaison, only 32% of these items made it to the final list
of 8. This difference was statistically significant.
This result highlights the dangers of having an already-excluded team member take on a
specialized role, as the outgroup participants had less of their ideas incorporated into
the team’s final decision when they were given a specialized role to interact with
the robot.
Finally, we examined the impact of the robot’s targeted supportive utterances. We did this
by looking at the amount of time participants spent talking in the 1 minute after the robot
delivered a targeted supportive utterance to them, compared with two controls. We wanted
to see if the targeted supportive utterances had an immediate effect on the verbal contribution
of the participant being supported by the robot.
First we examined the amount of time that a person spent talking after the robot supported
them with an utterance such as, “good idea Sarah”, and compared that with the time
a participant spent talking after the robot supported someone else, such as, “good idea
Tom”. We did this by subtracting one from the other. We found that outgroup members,
more than ingroup members, had a significantly positive change in the amount that they talked
after the robot supported them as opposed to someone else.
We also examined the amount of time that a person spent talking after the robot supported
them with a second control, the time a participant spent talking after an undirected utterance
from the robot, such as “okay.” We found the same result as before, where outgroup
members, compared with ingroup members, talk more after the robot specifically supports
them as opposed to when the robot made undirected utterances.
These results provide evidence that outgroup members talked more after robot targeted support.
Thus, it seems likely that targeted supportive utterances are helpful in encouraging and
including participation from excluded team members.
To conclude, we examined these two strategies to improve the inclusion of human members
within human-robot teams: the first, being given a specialized role to interact with
the robot and 2 - supportive utterances from the robot. The results from our human-subjects
experiment indicate that a specialized role to interact with the robot has a negative
impact on perceived inclusion, and especially the adoption of ideas from outgroup team members.
In contrast, the supportive utterances from the robot in our experiment had a positive
impact on the verbal contribution from outgroup members, providing evidence in favor of using
robot verbal support to include the opinions and ideas of marginalized team members.
We hope you enjoyed watching this video. And we encourage you to read our paper and watch
our supplementary video to learn more about this work. Thank you.