Children are interacting with robots in sophisticated ways to the extent in which they may be establishing a relationship. Forming appropriate levels of connection between children and robots could have significant impacts on the future of robot design, particularly in education. Despite this, little is known about the underlying mechanisms of such formation. In this work, we take an initial step by exploring what robot behaviors build a child-robot connection in a single interaction. Specifically, we investigated whether 6-10-year-old children feel more connected to a robot that responds to an issue by asking the child for help or simply disclosing the issue, and whether this is dependent on the valence of the response to the issue (emotional or mechanical). In a 2 x 2 between-subjects study (N=100), we found that children of all ages trusted a robot that asked for help more than a robot that simply disclosed the issue. Furthermore, children felt closer to an emotional robot that asked for help than an emotional robot that did not ask for help. Together, these findings suggest that asking for help builds trust between a robot and child and expressing relatable vulnerability, via emotional help requests, creates further feelings of connection.

Social-emotional learning (SEL) is an educational framework that helps children develop the skills necessary for academic and life success. However limited resources restrict most schools to whole-group SEL instruction which may not benefit all students. In this work, we explore using social robots to address this challenge and how a robot’s dialogue style can influence the effectiveness of one-on-one SEL lessons. The dialogue styles we investigate are (1) fictional dialogue, where the robot is human-like with emotions and discusses SEL scenarios as first person anecdotes, and (2) factual dialogue, where the robot is transparent, lacks emotions, and discusses scenarios from the third person. In a between-subjects study (N=52) at Chicago schools, students aged 9-10 were either part of a control group, receiving no robot instruction, or received four SEL lessons across two weeks from either the fictional or factual robot. We found that students who had lessons with either robot improved more in lesson skill than students in the control. We also found that during lessons students spoke to the factual robot using more lesson concepts than those talking to the fictional robot, indicating that first person storytelling and emotional disclosure from a robot may be unnecessary for, or even hinder, SEL learning with a robot.

Robots are increasingly entering the daily lives of families, yet their successful integration into domestic life remains a challenge. We explore family routines as a critical entry point for understanding how robots might find a sustainable role in everyday family settings. Together with each of the ten families, we co-designed robot interactions and behaviors, and a plan for the robot to support their chosen routines, accounting for contextual factors such as timing, participants, locations, and the activities in the environment. We then designed, prototyped, and deployed a mobile social robot in a four-day, in-home user study. Families welcomed the robot’s reminders, with parents especially appreciating the offloading of some reminding tasks. At the same time, interviews revealed tensions around timing, authority, and family dynamics, highlighting the complexity of integrating robots into households beyond the immediate task of reminders. Based on these insights, we offer design implications for robot-facilitated contextual reminders and discuss broader considerations for designing robots for family settings.

A robot's personality can shape user experience and acceptance in many social robot applications. Allowing users to customize robot personality could help them tailor robot products to their preferences, but it remains unclear whether this customization diminishes perceptions of the robot as a social agent and whether robot form factor influences these effects. We conducted a 2x2 between-subjects study (N = 79) examining robot form factor (humanoid NAO vs. non-humanoid TurtleBot) and personality customizability (customizable vs. non-customizable) during a collaborative event-planning task. Our results reveal that while customization reduced perceived social agency for both robot types, this reduction was particularly evident for humanoid robots. Conversely, personality customization significantly improved human-robot rapport, with this improvement driven primarily by non-humanoid robots. These findings reveal form factor-dependent effects in personality customization, indicating that robot form and customization capabilities yield differential impacts on perceived social agency and human-robot rapport in human-robot interaction design.

Robots such as those serving as educational tutors, healthcare supporters, and collaborative partners must develop “rapport,” a construct that encompasses mutual understanding and interpersonal connection with people, to ensure their long-term success. In our earlier work, we constructed, evaluated, and validated an 18-item Connection–Coordination Rapport (CCR) scale to measure human–robot rapport (Studies 1–3). Even though the full-length 18-item CCR scale measures rapport thoroughly, it may not always be practical for researchers to adopt given its relatively long length. Therefore, in this work, we developed a reduced-length version of the CCR scale that still effectively measures rapport using just 8 items. Following recommended practices for short-form development and validation, we leveraged the input of Human–Robot Interaction (HRI) experts (Study 4, \(N=30\) ) to shorten the CCR scale from 18 items to 8 items (4 items per factor). Then, we evaluated this reduced-length CCR scale on a new sample (Study 5, \(N=186\) ) where online participants watched a HRI video and evaluated it using both the full-length and reduced-length CCR scales. We validated the reduced-length CCR scale by showing that it has high internal reliability, high overlap with the full-length CCR scale, a consistent factor structure, high construct validity, and significant time savings.

Reinforcement learning (RL) is a powerful learning technique in robotics, where people can specify rewards that robots learn how to maximize through a process of trialanderror. Despite the numerous advantages of RL to robot programming, no approaches to our knowledge have sought to enable nontechnical users to specify RL programs for robots. In this work, we designed two novel RL-based robot programming paradigms for non-technical users: Full MDP Programming (Full-MDP) and Goal-Only MDP Programming (Goal-MDP). To evaluate the efficacy of these two approaches, we ran a between-subjects online user study ($N$= 409) where participants were asked to program a simulated robot to complete example household tasks (e.g., delivering coffee) using one of our RL programming paradigms or a commonly used baseline: Sequential Programming (Seq), or Trigger-Action Programming (TAP). While users neither performed well nor reported positive experiences with the FullMDP interface, user performance and experience with Goal-MDP was similar to the baselines (Seq and TAP) with significantly shorter programs. These results demonstrate that RL-based paradigms like Goal-MDP are a viable alternative to more traditional approaches and provide a starting point for robot programming interfaces that allow end-users to leverage the myriad benefits of RL for programming robots.

Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing “rapport,” which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named “Connection” and “Coordination.” We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.

As human-robot interactions become more social, a robot's personality plays an increasingly vital role in shaping user experience and its overall effectiveness. In this study, we examine the impact of three distinct robot personalities on user experiences during well-being exercises: a Baseline Personality that aligns with user expectations, a High Extraversion Personality, and a High Neuroticism Personality. These personalities were manifested through the robot's dialogue, which were generated using a large language model (LLM) guided by key behavioral characteristics from the Big 5 personality traits. In a between-subjects user study (N = 66), where each participant interacted with one distinct robot personality, we found that both the High Extraversion and High Neuroticism Robot Personalities significantly enhanced participants' emotional states (arousal, control, and valence). The High Extraversion Robot Personality was also rated as the most enjoyable to interact with. Additionally, evidence suggested that participants' personality traits moderated the effectiveness of specific robot personalities in eliciting positive outcomes from well-being exercises. Our findings highlight the potential benefits of designing robot personalities that deviate from users' expectations, thereby enriching human-robot interactions.

Perceived social agency-the perception of a robot as an autonomous and intelligent social other-is important for fostering meaningful and engaging human-robot interactions. While end-user programming (EUP) enables users to customize robot behavior, enhancing usability and acceptance, it can also potentially undermine the robot's perceived social agency. This study explores the trade-offs between user control over robot behavior and preserving the robot's perceived social agency, and how these factors jointly impact user experience. We conducted a between-subjects study (N = 57) where participants customized the robot's behavior using either a High-Granularity Interface with detailed block-based programming, a Low-Granularity Interface with broader input-form customizations, or no EUP at all. Results show that while both EUP interfaces improved alignment with user preferences, the Low-Granularity Interface better preserved the robot's perceived social agency and led to a more engaging interaction. These findings highlight the need to balance user control with perceived social agency, suggesting that moderate customization without excessive granularity may enhance the overall satisfaction and acceptance of robot products.

As robots become increasingly capable and widespread, they may be placed into roles where they are responsible for giving people instructions (e.g., directing human coworkers in a warehouse). It is important to better understand the factors that may influence human compliance to robot instructions, given that these instructions may undermine or invalidate the efforts of another person or robot. In this work, we investigate to what extent an established robot-robot relationship will impact a person’s choice to comply with instructions from one robot to undermine another robot’s contributions in a collaborative task. We ran a between-subjects study (N = 50) where participants collaborated with a partner robot to build a series of towers at the direction of a manager robot. These two robots were either presented as an ingroup with a shared history and preferential treatment of one another (ingroup condition) or as an outgroup without shared history and neutral treatment of one another (outgroup condition). During the experiment, the manager robot in both conditions gave the human participant instructions to undermine the efforts of the partner robot. We found that participants in the ingroup condition are significantly less likely to comply with these instructions and also view both robots more positively than those in the outgroup condition. Our results demonstrate that the presence of an ingroup relationship between robots can both lessen compliance with instructions that undermine partnerships and generate a more positive social atmosphere within a human-robot collaboration.

Robot autonomy is an influential and ubiquitous factor in human-robot interaction (HRI), but it is rarely discussed beyond a one-dimensional measure of the degree to which a robot operates without human intervention. As robots become more sophisticated, this simple view of autonomy could be expanded to capture the variety of autonomous behaviors robots can exhibit and to match the rich literature on human autonomy in philosophy, psychology, and other fields. In this paper, we conduct a systematic literature review of robot autonomy in HRI and integrate this with the broader literature into a taxonomy of six distinct forms of autonomy: those based on robot and human involvement at runtime (operational autonomy, intentional autonomy, shared autonomy), human involvement before runtime (non-deterministic autonomy), and expressions of autonomy at runtime (cognitive autonomy, physical autonomy). We discuss future considerations for autonomy in HRI that emerge from this study, including moral consequences, the idealization of "full" robot autonomy, and connections to agency and free will.

Live entertainment is moving towards a greater participatory culture, with dynamic narratives told through audience interaction. Robot characters offer a unique opportunity to mitigate the challenges of creating personalized entertainment at scale. However, robots often cannot react to audience responses, limiting opportunities for audience participation. In this work, we explore methods to increase user agency in live entertainment experiences with robot characters to improve user engagement and enjoyment. In a between-subjects study (N=60), we create an immersive story where users role-play as detectives with two distinct robot characters. Users either (1) have greater involvement and self-identification in the story by talking with the robots in-character (narrative condition), (2) have a more active role in solving puzzles (gameplay condition), or (3) follow along without being prompted by the robots for input (control condition). Our results show that increasing user agency in a role-playing experience, in either its narrative or its gameplay, improves users' flow state, sense of autonomy and competence, verbal engagement, and perceptions of the robot characters' engagement. Increasing narrative agency also led to longer unprompted reactions from participants, while gameplay agency improved feelings of immersion and relatedness with the robots. These findings suggest that creating either narrative or gameplay agency can improve user engagement, which can extend to broader robot interactions where gameplay elements and role-playing in stories can be incorporated.

Despite advances in areas such as the personalization of robots, sustaining adoption of robots for long-term use in families remains a challenge. Recent studies have identified integrating robots into families’ routines and rituals as a promising approach to support long-term adoption. However, few studies explored the integration of robots into family routines and there is a gap in systematic measures to capture family preferences for robot integration. Building upon existing routine inventories, we developed Family-Robot Routines Inventory (FRRI), with 24 family routines and 24 child routine items, to capture parents’ attitudes toward and expectations from the integration of robotic technology into their family routines. Using this inventory, we collected data from 150 parents through an online survey. Our analysis indicates that parents had varying perceptions for the utility of integrating robots into their routines. For example, parents found robot integration to be more helpful in children’s individual routines, than to the collective routines of their families. We discuss the design implications of these preliminary findings, and how they may serve as a first step toward understanding the diverse challenges and demands of designing and integrating household robots for families.

A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.

Work in Human–Robot Interaction (HRI) has investigated interactions between one human and one robot as well as human–robot group interactions. Yet the field lacks a clear definition and understanding of the influence a robot can exert on interactions between other group members (e.g., human-to-human). In this article, we define Interaction-Shaping Robotics (ISR), a subfield of HRI that investigates robots that influence the behaviors and attitudes exchanged between two (or more) other agents. We highlight key factors of interaction-shaping robots that include the role of the robot, the robot-shaping outcome, the form of robot influence, the type of robot communication, and the timeline of the robot’s influence. We also describe three distinct structures of human–robot groups to highlight the potential of ISR in different group compositions and discuss targets for a robot’s interaction-shaping behavior. Finally, we propose areas of opportunity and challenges for future research in ISR.

The goal of this workshop is to have interdisciplinary discussions on family-centered interaction design of technology as an extension to child-centered design. The workshop will discuss the potential benefits of a family-centered approach to design, as well as the challenges and open questions that designers may face when adopting this approach. Through discussions and interactive activities, participants will have the opportunity to discuss and share ideas on how to effectively incorporate a family-centered perspective into their own design processes. A family-centered approach to design has the potential to create more meaningful and contextual experiences for children and their families.

Despite the clear benefits that social connection offers to well-being, strangers in close physical proximity regularly ignore each other due to their tendency to underestimate the positive consequences of social connection. In a between-subjects study (N = 49 pairs, 98 participants), we investigated the effectiveness of a humanoid robot, a computer screen, and a poster at stimulating meaningful, face-to-face conversations between two strangers by posing progressively deeper questions. We found that the humanoid robot facilitator was able to elicit the greatest compliance with the deep conversation questions. Additionally, participants in conversations facilitated by either the humanoid robot or the computer screen reported greater happiness and connection to their conversation partner than those in conversations facilitated by a poster. These results suggest that technology-enabled conversation facilitators can be useful in breaking the ice between strangers, ultimately helping them develop closer connections through face-to-face conversations and thereby enhance their overall well-being.

Research in child-robot interactions suggests that engaging in “care-taking” of a social robot, such as tucking the robot in at night, can strengthen relationships formed between children and robots. In this work, we aim to better understand and explore the design space of caretaking activities with 10 children, aged 8–12 from eight families, involving an exploratory design session followed by a preliminary feasibility testing of robot caretaking activities. The design sessions provided insight into children’s current caretaking tasks, how they would take care of a social robot, and how these new caretaking activities could be integrated into their daily routines. The feasibility study tested two different types of robot caretaking tasks, which we call connection and utility, and measured their short term effects on children’s perceptions of and closeness to the social robot. We discuss the themes and present interaction design guidelines of robot caretaking activities for children.

Live-streamed performances, in which the perform-ers and the audience are simultaneously present in separate physical spaces, lack the emotional intensity present in in-person performances. Motivated by the social effects of robots and the potential synergy between robots and art, we conducted a between-subject study to explore robots as mediators in live-streamed performances. As a mediator between the performers and the audience, the robot can solicit audience input and direct performers according to that input. We held three interactive musical performances to compare the audiences' experiences: one with a chatbot mediator and two with a NAO robot mediator. We did not find significant differences in the audience's experiences between mediators, but survey responses and chat activity pointed to useful design considerations.

Robots are becoming increasingly prominent in the entertainment sphere, where they interact with guests in themed environments to tell stories, often in place of human characters. To evaluate the potential benefits of robots in these contexts compared to humans, we created an interactive puzzle game where either a robot or a human actor serves as a diegetic "game guide" character that is both a cooperative partner and an omniscient game master. In the game, participants solve a crime mystery by asking the game guide for information to complete tasks and for hints to solve puzzles. We conducted a between-subjects study (n = 42) to investigate how players’ game experiences differed when the game guide was a human compared to an embodied robot. Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.

Robot-initiated touch is a promising mode of expression that would allow robot caregivers to perform physical tasks (instrumental touch) and provide comfort (affective touch) in healthcare settings. To understand the factors that shape how people respond to touch from a robotic caregiver, we conducted a crowdsourced study (N=163) examining how robot-initiated touch (present or absent), the robot’s intent (instrumental or affective), robot appearance (Nao or Stretch), and robot tone (empathetic or serious) impact the perceived quality of care. Results show that participants prefer instrumental to affective touch, view the robot as having greater social attributes (higher warmth, higher competence, and lower discomfort) after robot-initiated touch, are more comfortable interacting with the human-like Nao than the more machine-like Stretch, and favor consistent robot tone and appearance. From these results, we derived three design guidelines for caregiving robots in healthcare settings.

The increasing growth of robots and other technological devices in homes makes it critical to understand child- device interactions within the home, especially given the real possibility of child aggression towards these devices. To explore factors that currently and will, in the future, shape child-robot interaction in the home related to children’s aggressive behavior, we conducted a 2 x 3 x 3 between-subjects crowdsourced study (N = 332) that examined how parents would respond and perceive their child interacting with different technological devices. Participants were shown a video clip of a person interacting with a technological device (robot, smart speaker, or tablet), exhibiting either aggressive or neutral behavior, and interacting with the device in one of three interaction modalities (audio, physical, or audio+physical). Imagining that the person in the video was their child, parents who observed aggressive behavior compared with neutral behavior indicated greater concern, a higher likelihood to intervene, distinct intervention methods, a higher perception of device mistreatment, and greater sympathy for the device. Despite hypothesizing that the robot would be seen as the most anthropomorphic, animate and, warm device, participant ratings of the robot were no different than the smart speaker, however, both devices were rated more highly on those dimensions than the tablet.

Studies have shown that people conform their answers to match those of group members even when they believe the group’s answer to be wrong [2]. In this experiment, we test whether people conform to groups of robots and whether the robots cause informational conformity (believing the group to be correct), normative conformity (feeling peer pressure), or both. We conducted an experiment in which participants (N = 63) played a subjective game with three robots. We measured humans’ conformity to robots by how many times participants changed their preliminary answers to match the group of robots’ in their final answer. Participants in conditions that were given more information about the robots’ answers conformed significantly more than those who were given less, indicating that informational conformity is present. Participants in conditions where they were aware they were a minority in their answers conformed more than those who were unaware they were a minority. Additionally, they also report feeling more pressure to change their answers from the robots, and the amount of pressure they reported was correlated to the frequency they conformed, indicating normative conformity. Therefore, we conclude that robots can cause both informational and normative conformity in people.

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.

Autonomous robots are increasingly placed in contexts that require them to interact with groups of people rather than just a single individual. Interactions with groups of people introduce nuanced challenges for robots, since robots? actions influence both individual group members and complex group dynamics. We review the unique roles robots can play in groups, finding that small changes in their nonverbal behavior and personality impacts group behavior and, by extension, influences ongoing interpersonal interactions.

Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.

When a robot breaks a person's trust by making a mistake or failing, continued interaction will depend heavily on how the robot repairs the trust that was broken. Prior work in psychology has demonstrated that both the trust violation framing and the trust repair strategy influence how effectively trust can be restored. We investigate trust repair between a human and a robot in the context of a competitive game, where a robot tries to restore a human's trust after a broken promise, using either a competence or integrity trust violation framing and either an apology or denial trust repair strategy. Results from a 2×2 between-subjects study ( n=82) show that participants interacting with a robot employing the integrity trust violation framing and the denial trust repair strategy are significantly more likely to exhibit behavioral retaliation toward the robot. In the Dyadic Trust Scale survey, an interaction between trust violation framing and trust repair strategy was observed. Our results demonstrate the importance of considering both trust violation framing and trust repair strategy choice when designing robots to repair trust. We also discuss the influence of human-to-robot promises and ethical considerations when framing and repairing trust between a human and robot.

Asch's [2] conformity experiment has shown that people are prone to adjusting their view to match those of group members even when they believe the answer of the group to be wrong. Previous studies have attempted to replicate Asch's experiment with a group of robots but have failed to observe conformity [7, 25]. One explanation can be made using Hodges and Geyers work [17], in which they propose that people consider distinct criteria (truth, trust, and social solidarity) when deciding whether to conform to others. In order to study how trust and truth affect conformity, we propose an experiment in which participants play a game with three robots, in which there are no objective answers. We measured how many times participants changed their preliminary answers to match the group of robots' in their final answer. We conducted a between-subjects study (N = 30) in which there were two conditions: one in which participants saw the group of robots' preliminary answer before deciding their final answer, and a control condition in which they did not know the robots' preliminary answer. Participants in the experimental condition conformed significantly more (29%) than participants in the control condition (6%). Therefore we have shown that groups of robots can cause people to conform to them. Additionally trust plays a role in conformity: initially, participants conformed to robots at a similar rate to Asch's participants, however, many participants stop conforming later in the game when trust is lost due to the robots choosing an incorrect answer.

Successful teams are characterized by high levels of trust between team members, allowing the team to learn from mistakes, take risks, and entertain diverse ideas. We investigated a robot's potential to shape trust within a team through the robot's expressions of vulnerability. We conducted a between-subjects experiment (N = 35 teams, 105 participants) comparing the behavior of three human teammates collaborating with either a social robot making vulnerable statements or with a social robot making neutral statements. We found that, in a group with a robot making vulnerable statements, participants responded more to the robot's comments and directed more of their gaze to the robot, displaying a higher level of engagement with the robot. Additionally, we discovered that during times of tension, human teammates in a group with a robot making vulnerable statements were more likely to explain their failure to the group, console team members who had made mistakes, and laugh together, all actions that reduce the amount of tension experienced by the team. These results suggest that a robot's vulnerable behavior can have "ripple effects" on their human team members' expressions of trust-related behavior.

As robotic technology becomes more robust and interactive, robots are increasingly stepping into the role of a collaborator to humans in various contexts. In addition to performing collaborative tasks accurately and efficiently, robots should also contribute socially by improving team effectiveness and cohesion. This work is a first step toward developing a high-level reasoning model of the motivations and strategies held by each individual of the team. With this model, social robots will be able to promote more efficient and enjoyable collaboration by suggesting improvements to specific actions, aligning diverging strategies, and encouraging actions that promote higher team cohesiveness.

Despite the growing body of research in human-robot collaboration, there has been little focus on how social robots can support human-to-human teaming. In this paper, we investigate whether a social robot can improve human-human collaboration. We conducted a between-subjects study where pairs of children play a collaborative game with a social robot. During pauses in the game, the robot either (1) asks the children questions to better focus the participants on the task they are working on, (2) asks the children questions that are targeted at developing and reinforcing the relationship between the participants, or (3) doesn't ask any questions. Our results show that participants who were asked task-focused questions had higher performance scores in the collaborative game than the other groups, however, had a lower perception of their performance than the participants who were asked relationally-focused questions. We did not find any differences between the groups in interpersonal cohesiveness. Our findings suggest that social robots can be used to improve performance measures and perception of performance in groups of children.

As social robots become more widespread in educational environments, their ability to understand group dynamics and engage multiple children in social interactions is crucial. Social dominance is a highly influential factor in social interactions, expressed through both verbal and nonverbal behaviors. In this paper, we present a method for determining whether a participant is high or low in social dominance in a group interaction with children and robots. We investigated the correlation between many verbal and nonverbal behavioral features with social dominance levels collected through teacher surveys. We additionally implemented Logistic Regression and Support Vector Machines models with classification accuracies of 81% and 89%, respectively, showing that using a small subset of nonverbal behavioral features, these models can successfully classify children's social dominance level. Our approach for classifying social dominance is novel not only for its application to children, but also for achieving high classification accuracies using a reduced set of nonverbal features that, in future work, can be automatically extracted with current sensing technology.