Title: Strategies for the Inclusion of Human Members within Human-Robot Teams
Team member inclusion is vital in collaborative teams. In this work, we explore two strategies to increase the inclusion of human team members in a human-robot team: 1) giving a person in the group a specialized role (the 'robot liaison') and 2) having the robot verbally support human team members. In a human subjects experiment (N = 26 teams, 78 participants), groups of three participants completed two rounds of a collaborative task. In round one, two participants (ingroup) completed a task with a robot in one room, and one participant (outgroup) completed the same task with a robot in a different room. In round two, all three participants and one robot completed a second task in the same room, where one participant was designated as the robot liaison. During round two, the robot verbally supported each participant 6 times on average. Results show that participants with the robot liaison role had a lower perceived group inclusion than the other group members. Additionally, when outgroup members were the robot liaison, the group was less likely to incorporate their ideas into the group's final decision. In response to the robot's supportive utterances, outgroup members, and not ingroup members, showed an increase in the proportion of time they spent talking to the group. Our results suggest that specialized roles may hinder human team member inclusion, whereas supportive robot utterances show promise in encouraging contributions from individuals who feel excluded. more »« less
As teams of people increasingly incorporate robot members, it is essential to consider how a robot's actions may influence the team's social dynamics and interactions. In this work, we investigated the effects of verbal support from a robot (e.g., “ good idea Salim ,” “ yeah ”) on human team members' interactions related to psychological safety and inclusion. We conducted a between-subjects experiment ( N = 39 groups, 117 participants) where the robot team member either (A) gave verbal support or (B) did not give verbal support to the human team members of a human-robot team comprised of 2 human ingroup members, 1 human outgroup member, and 1 robot. We found that targeted support from the robot (e.g., “ good idea George ”) had a positive effect on outgroup members, who increased their verbal participation after receiving targeted support from the robot. When comparing groups that did and did not have verbal support from the robot, we found that outgroup members received fewer verbal backchannels from ingroup members if their group had robot verbal support. These results suggest that verbal support from a robot may have some direct benefits to outgroup members but may also reduce the obligation ingroup members feel to support the verbal contributions of outgroup members.
Moffat, A. D.; Fowler, R. R.; Matz, R. L.; JaQuay, S.(
, American Society for Engineering Education)
Team- and project-based pedagogies are increasingly normative in engineering education and beyond. Student teamwork holds the promise of developing collaborative skills deemed essential for new engineers by professional accreditation bodies such as ABET. The emphasis on these models, furthermore, reflects developments in pedagogical theory, stressing the importance of experiential learning and the social construction of knowledge, repositioning the instructor as a facilitator and guide. Teamwork in an educational context differs from that in professional contexts in that learning outcomes for all team members – both in terms of technical knowledge and team-working skills – are a primary goal of the activity, even while more tangible task-related outcomes might be the main concern of the students themselves. However, team-based learning also holds the potential for team members to have negative experiences, of which instructors may have little or no awareness, especially in real-time. Teams may achieve team-level outcomes required for successful completion, in spite of uneven levels of participation and contribution. Reduced participation on the part of an individual team member may have many causes, pro-active or reactive: it may be a deliberate refusal to engage, a lack of self-confidence, or a response to hostility from other members, among other possibilities. Inequitable team interactions will lead to uneven uptake of desired learning outcomes. Fostering equity in interactions and identifying inequitable practices among team members is therefore an important part of implementing team-based pedagogies, and an essential first step in identifying and challenging systematic patterns of inequity with regard to members of historically marginalized groups. This paper will therefore explore ways in which equity in group decision-making may be conceptualized and observed, laying the foundations for identifying and addressing inequities in the student experience. It will begin by considering different potential manifestations of interactional equity, surveying notions derived from prior education research in the fields of health, mathematics, engineering, and the natural sciences. These notions include: equity of participation on the basis of quantified vocal contributions (in terms of words, utterances, or clausal units); distribution and evolution of interactional roles; equity of idea endorsement and uptake; distribution of inchargeness and influence; equity of access to positional identities and discourse practices; and team member citizenship. In the paper’s empirical component, we trial measures of equity taken or developed from this literature on a small dataset of transcripts showing verbal interactions between undergraduate student team members in a first-year engineering design course. Some measures will be qualitative and others quantitative, depending on the particular form and manifestation of equity they are designed to examine. Measures include manual coding of speech acts and interactional ‘bids’, statistical measures of utterance frequency and length, and computational approaches to modeling interactional features such as social impact and receptivity. Results are compared with the students’ own reflections on the interactions, taken immediately afterward. Recommendations are made for the application of the measures, both from research and practice perspectives. Keywords: Teamwork, Equity, Interaction, Design
Rheman, Jennifer; Fraune, Marlena R.(
, Not in my house!: Children Playing an Online Game with Robots Show Low Trust and Closeness with Ingroup Robots.)
null
(Ed.)
This paper presents preliminary research on whether children will accept a robot as part of their ingroup, and on how a robot's group membership affects trust, closeness, and social support. Trust is important in human-robot interactions because it affects if people will follow robots' advice. In this study, we randomly assigned 11- and 12-year-old participants to a condition such that participants were either on a team with the robot (ingroup) or were opponents of the robot (outgroup) for an online game. Thus far, we have eight participants in the ingroup condition. Our preliminary results showed that children had a low level of trust, closeness, and social support with the robot. Participants had a much more negative response than we anticipated. We speculate that there will be a more positive response with an in-person setting rather than a remote one.
Lee, Young-eun; Payir, Ayse; Heiphetz, Larisa(
, Social Cognition)
Humans behave more prosocially toward ingroup (vs. outgroup) members. This preregistered research examined the influence of God concepts and memories of past behavior on prosociality toward outgroups. In Study 1 (n = 573), participants recalled their past kind or mean behavior (between-subjects) directed toward an outgroup. Subsequently, they completed a questionnaire assessing their views of God. Our dependent measure was the number of lottery entries given to another outgroup member. Participants who recalled their kind (vs. mean) behavior perceived God as more benevolent, which in turn predicted more generous allocation to the outgroup (vs. ingroup). Study 2 (n = 281) examined the causal relation by manipulating God concepts (benevolent vs. punitive). We found that not only recalling kind behaviors but perceiving God as benevolent increased outgroup generosity. The current research extends work on morality, religion, and intergroup relations by showing that benevolent God concepts and memories of past kind behaviors jointly increase outgroup generosity.
Newman, B.A.; Biswas, A; Ahuja, S.; Girdhar, S.; Kitani, K.K.; Admoni, H.(
, International Conference on Social Robotics (ICSR))
Wagner, A.R.; null
(Ed.)
Collaborative robots that provide anticipatory assistance are able to help people complete tasks more quickly. As anticipatory assistance is provided before help is explicitly requested, there is a chance that this action itself will influence the person’s future decisions in the task. In this work, we investigate whether a robot’s anticipatory assistance can drive people to make choices different from those they would otherwise make. Such a study requires measuring intent, which itself could modify intent, resulting in an observer paradox. To combat this, we carefully designed an experiment to avoid this effect. We considered several mitigations such as the careful choice of which human behavioral signals we use to measure intent and designing unobtrusive ways to obtain these signals. We conducted a user study (𝑁=99) in which participants completed a collaborative object retrieval task: users selected an object and a robot arm retrieved it for them. The robot predicted the user’s object selection from eye gaze in advance of their explicit selection, and then provided either collaborative anticipation (moving toward the predicted object), adversarial anticipation (moving away from the predicted object), or no anticipation (no movement, control condition). We found trends and participant comments suggesting people’s decision making changes in the presence of a robot anticipatory motion and this change differs depending on the robot’s anticipation strategy.
Strohkorb Sebo, Sarah, Dong, Ling Liang, Chang, Nicholas, and Scassellati, Brian. Strategies for the Inclusion of Human Members within Human-Robot Teams. Retrieved from https://par.nsf.gov/biblio/10170650. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction . Web. doi:10.1145/3319502.3374808.
Strohkorb Sebo, Sarah, Dong, Ling Liang, Chang, Nicholas, & Scassellati, Brian. Strategies for the Inclusion of Human Members within Human-Robot Teams. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, (). Retrieved from https://par.nsf.gov/biblio/10170650. https://doi.org/10.1145/3319502.3374808
Strohkorb Sebo, Sarah, Dong, Ling Liang, Chang, Nicholas, and Scassellati, Brian.
"Strategies for the Inclusion of Human Members within Human-Robot Teams". Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (). Country unknown/Code not available. https://doi.org/10.1145/3319502.3374808.https://par.nsf.gov/biblio/10170650.
@article{osti_10170650,
place = {Country unknown/Code not available},
title = {Strategies for the Inclusion of Human Members within Human-Robot Teams},
url = {https://par.nsf.gov/biblio/10170650},
DOI = {10.1145/3319502.3374808},
abstractNote = {Team member inclusion is vital in collaborative teams. In this work, we explore two strategies to increase the inclusion of human team members in a human-robot team: 1) giving a person in the group a specialized role (the 'robot liaison') and 2) having the robot verbally support human team members. In a human subjects experiment (N = 26 teams, 78 participants), groups of three participants completed two rounds of a collaborative task. In round one, two participants (ingroup) completed a task with a robot in one room, and one participant (outgroup) completed the same task with a robot in a different room. In round two, all three participants and one robot completed a second task in the same room, where one participant was designated as the robot liaison. During round two, the robot verbally supported each participant 6 times on average. Results show that participants with the robot liaison role had a lower perceived group inclusion than the other group members. Additionally, when outgroup members were the robot liaison, the group was less likely to incorporate their ideas into the group's final decision. In response to the robot's supportive utterances, outgroup members, and not ingroup members, showed an increase in the proportion of time they spent talking to the group. Our results suggest that specialized roles may hinder human team member inclusion, whereas supportive robot utterances show promise in encouraging contributions from individuals who feel excluded.},
journal = {Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction},
author = {Strohkorb Sebo, Sarah and Dong, Ling Liang and Chang, Nicholas and Scassellati, Brian},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.
