Human-robot Group Research Articles

Interaction-Shaping Robotics: Robots That Influence Interactions between Other Agents

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.

Human-robot swarm interaction: coordinated role of human mind mindsets and robot group entitativity

ABSTRACT The one-on-one human–robot interaction has expanded to the group level; robot groups are increasingly exerting psychosocial implications on human beings. However, how people interact with robot groups, especially how human factors and robot group factors coordinate to influence people’s responses to robot groups, is underexplored. To investigate this issue, the present study examined the interaction effect between individual differences in fixed and growth mindsets about the human mind and the fundamental characteristics of robot groups (i.e. entitativity) on responses to the robots during human–robot interaction. We induced mindsets (fixed or growth) about the human mind and manipulated the level of robot group entitativity (high or low) to capture responses to robots during human–robot interaction using virtual reality (VR) technology. The results revealed that a growth (versus fixed) mindset about the human mind promoted self-disclosure toward, and reduced behavioural anxiety with respect to robot groups with high (versus low) entitativity. We found that increased psychological closeness with robots accounted for these effects. Our findings contribute to research on the determinants of human–robot relationships and present implications for human–robot interactions at the group level.

Group Norm-Aware Robot Adaptively Maintains Suitable Interpersonal Distance in Human-Robot Group

Group Norm-Aware Robot Adaptively Maintains Suitable Interpersonal Distance in Human-Robot Group

Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space

In the present paper, we propose a robotic model to help determine a robot’s position under the changing conditions of human personal space in a human-robot group. Recently, several attempts have been made to develop personal robots suitable for human communities. Determining a robot’s position is important not only to avoid collisions with humans but also to maintain a socially acceptable distance from them. Interpersonal space maintained by persons in a community depends on the particular context and situations. Therefore, robots need to determine their own positions while considering the positions of other persons and evaluating the changes made in their personal space. To address this problem, we proposed a robot navigation model and examined whether the experiment participants could distinguish the robot’s trajectory from the human’s trajectory in the experimental scenario. We prepared a scenario in which robots in a group needed to keep an appropriate distance in a three-dimensional space. The experiment participants provided their impressions on robot movements while watching the records representing the scenario. The results indicate that (1) a robot using the proposed model is able to follow the other group members and (2) the experiment participants were not sure whether the trajectories of the robots were controlled by humans and by the proposed model. Therefore, we conclude that the proposed model generates suitable trajectories in robot groups.

Should I Blame the Human or the Robot? Attribution Within a Human–Robot Group

The present research studies the attribution of credit for success and attribution of blame for failure in a human–robot group. In the experiment, two participants of the same gender and a robot composed of a group that cooperated to solve the desert survival problem. Each group completed two tasks whose outcomes were manipulated: one task succeeded, and the other task failed. The participants were asked to attribute the credit of success and blame of failure to the group members, including the robot member, the human member, and the participants themselves. The results are as follows. First, participants attributed more credit and less blame to the robot member than to themselves. Second, participants attributed less blame to the human member than to themselves, and no significant difference was observed in the attribution of credit between them. Third, the robot member was more blamed than the human member, whereas they received similar levels of credit. Finally, compared with male participants, female participants assigned more credit and less blame to the robot member. The findings in this paper shed some light on the allocation of responsibility based on outcomes in human–robot groups.

A Robot in a Human–Robot Group Learns Group Norms and Makes Decisions Through Indirect Mutual Interaction with Humans

In this study, we investigate whether group norms occur in human–robot groups. At present, there are a number of studies that examine social robots’ ways of responding, gesturing, and displaying emotion. However, sociality implies that robots not only exhibit human-like behaviors, but also display the tendency to adapt to a group of individuals. For robots to exhibit sociality, they must adapt to group norms without being told by the group members how to behave. Group norms refer to the unwritten, unspoken, and informal rules that are present in a group of individuals. In a previous study, we demonstrated that a robot model learned group norms in human groups [1]. In the present study, we investigate whether group norms occur in human–robot groups. To this end, we prepared quizzes with unclear and vague answers, and instructed participants to take the quizzes with the robot. The results of the quiz experiments demonstrated that the robot considered group norms in human–robot groups when making decisions; thus, group norms occurred in human–robot groups.

Social Influence of Group Norms Developed by Human-Robot Groups

Several studies on how social robots respond, gesture, and display emotion in human-robot interactions have been conducted. In particular, sociality of robots implies that robots do not only exhibit human-like behaviors, but also display a tendency to adapt to a group of individuals. For robots to exhibit sociality, they need to adapt to group norms without telling them how to behave by the group members. In this study, we investigated the effect of group norms on human decision-making in human-robot groups, which comprise two robots using our proposed robotic model. Furthermore, we conducted quizzes with the robots and a human participant using unclear and vague answers. We assessed this influence by making the participant and the two robots repeat a set of actions: to answer the same quiz and recognize each answer of the group members. Additionally, we evaluated the extent to which the group norms changed the opinions of humans using a questionnaire. We analyzed the results of the questionnaire and chronological change in their answers for the quiz with the same question. The quiz experimental results showed that the human participants changed their answers after they discovered the answers of the robots for the first time due to social influence from the robots assumed that the human participants were confused about the diversity of the answers in the group and were aware of the consideration of the robots of the group norm. This is to ensure that they can adjust their answers to the group norm. Moreover, the questionnaire results revealed that the group norms gave the human participants right answers to the quiz that has no correct answers. Therefore, we concluded that robots attempt to comply with a group norm affects human's decision-making.

Looking Beyond Collaboration: Socioemotional Positive, Negative and Task-Oriented Behaviors in Human–Robot Group Interactions

In this paper, we aim to increase the understanding of human–robot interaction by considering the goal orientation displayed by the robot (i.e., competitive vs. cooperative) and the role displayed by each player (partner vs. opponent) in an entertainment group scenario. Sixty participants engaged in a card game called Sueca (two robots and two humans). Each participant played with each of the other players, and the goal orientation was manipulated by the set of verbal utterances displayed by the robot. Using a coding scheme based on Bales Interaction Process Analysis, the video-recorded interactions were analysed in terms of socioemotional positive, negative and task oriented behaviours. A marginal multilevel modelling analysis yielded significant interactions between the robotic addressee and the role the robot displayed in the socioemotional and task-oriented behaviours. Overall, our main results demonstrated the following: (1) Participants directed more behaviours towards partners than opponents, although most of these behaviours occurred between humans when they were partners. (2) When comparing players in the role of opponents, participants directed more socioemotional behaviours towards robots than towards the other human player. (3) No difference in task-oriented behaviours was observed among any of the players in this condition. These results suggest the occurrence of different behavioural patterns in competitive and collaborative interactions with robots that might be useful to inform the future development of more socially effective robots.

A Convergence of Decision-making Criterions in a Human-robot Group

A Convergence of Decision-making Criterions in a Human-robot Group

Can robots be teammates?

The team has become a popular model to organize joint human–robot behavior. Robot teammates are designed with high-levels of autonomy and well-developed coordination skills to aid humans in unpredictable environments. In this paper, we challenge the assumption that robots will succeed as teammates alongside humans. Drawing from the literature on human teams, we evaluate robots’ potential to meet the requirements of successful teammates. We argue that lacking humanlike mental models and a sense of self, robots may prove untrustworthy and will be rejected from human teams. Benchmarks for evaluating human–robot teams are included, as are guidelines for defining alternative structures for human–robot groups.