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1. The Influence of Robot Verbal Support on Human Team Members: Encouraging Outgroup Contributions and Suppressing Ingroup Supportive Behavior

   https://doi.org/10.3389/fpsyg.2020.590181  

   Sebo, Sarah; Dong, Ling Liang; Chang, Nicholas; Lewkowicz, Michal; Schutzman, Michael; Scassellati, Brian (December 2020, Frontiers in Psychology)

   null (Ed.)

   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. 

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2. Strategies for the Inclusion of Human Members within Human-Robot Teams

   https://doi.org/10.1145/3319502.3374808  

   Strohkorb Sebo, Sarah; Dong, Ling Liang; Chang, Nicholas; Scassellati, Brian (March 2020, Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction)

   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. 

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3. Entrepreneurial Team Formation: An Exploration of New Member Addition

   https://doi.org/10.1111/j.1540-6520.2006.00119.x  

   Forbes, Daniel P.; Borchert, Patricia S.; Zellmer–Bruhn, Mary E.; Sapienza, Harry J. (March 2006, Entrepreneurship Theory and Practice)

   We explored in this article the process of entrepreneurial team formation. As theory specific to this topic is scant, we drew first on disparate views of team formation and its correlates; we then called upon in–depth interviews to provide deeper, nuanced insights into this dynamic process of creation. Our focus is team member addition. We identified resource–seeking and interpersonal attraction as primary alternative motivators for new teammate addition; however, we also illustrated how these motivations may be complementary in practice. Finally, we considered in some depth how new member identification and selection processes may unfold as new ventures are formed. 

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4. Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration

   De Langis, Karin Johanna; Sattar, J (June 2020, 2020 IEEE International Conference on Robotics and Automation)

   Autonomous underwater robots working with teams of human divers may need to distinguish between different divers, e.g., to recognize a lead diver or to follow a specific team member. This paper describes a technique that enables autonomous underwater robots to track divers in real time as well as to reidentify them. The approach is an extension of Simple Online Realtime Tracking (SORT) with an appearance metric (deep SORT). Initial diver detection is performed with a custom CNN designed for realtime diver detection, and appearance features are subsequently extracted for each detected diver. Next, realtime tracking by-detection is performed with an extension of the deep SORT algorithm. We evaluate this technique on a series of videos of divers performing human-robot collaborative tasks and show that our methods result in more divers being accurately identified during tracking. We also discuss the practical considerations of applying multi-person tracking to on-board autonomous robot operations, and we consider how failure cases can be addressed during on-board tracking. 

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5. Adaptation to Team Composition Changes for Heterogeneous Multi-Robot Sensor Coverage

   https://doi.org/10.1109/ICRA48506.2021.9560960  

   Reily, Brian; Mott, Terran; Zhang, Hao (May 2021, IEEE International Conference on Robotics and Automation (ICRA))

   We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches. 

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