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.

Many small group activities, like working teams or study groups, have a high dependency on the skill of each group member. Differences in skill level among participants can affect not only the performance of a team but also influence the social interaction of its members. In these circumstances, an active member could balance individual participation without exerting direct pressure on specific members by using indirect means of communication, such as gaze behaviors. Similarly, in this study, we evaluate whether a social robot can balance the level of participation in a language skill-dependent game, played by a native speaker and a second language learner. In a between-subjects study (N = 72), we compared an adaptive robot gaze behavior, that was targeted to increase the level of contribution of the least active player, with a non-adaptive gaze behavior. Our results imply that, while overall levels of speech participation were influenced predominantly by personal traits of the participants, the robot’s adaptive gaze behavior could shape the interaction among participants which lead to more even participation during the game.

Formation of subgroups and thereby the problem of intergroup bias is well-studied in psychology. Already from the age of five, children can show ingroup preferences. We developed a social robot mediator to explore how a robot could help overcome these intergroup biases, especially for children newly arrived to a country. By utilizing an online evaluation of collaboration levels, we allow the robot to perceive and act upon the current group dynamics. We investigated the effectiveness of the robot’s mediating behavior in a between-subject study with 39 children, of whom 13 children had arrived in Sweden within the last 2 years. Results indicate that the robot could help the process of inclusion by mediating the activity. The robot succeeds in encouraging the newly arrived children to act more outgoing and in increasing collaboration among ingroup children. Further, children show a higher level of prosociality after interacting with the robot. In line with prior work, this study demonstrates the ability of social robotic technology to assist group processes.

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and selfesteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a usercentered approach to explore how teenagers envisage a social robot <feminine ordinal indicator>group assistant degrees. We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers' views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

A crucial part of traditional reinforcement learning (RL) is the initial exploration phase, in which trying available actions randomly is a critical element. As random behavior might be detrimental to a social interaction, this work proposes a novel paradigm for learning social robot behavior--the use of shielding to ensure socially appropriate behavior during exploration and learning. We explore how a data-driven approach for shielding could be used to generate listening behavior. In a video-based user study (N=110), we compare shielded exploration to two other exploration methods. We show that the shielded exploration is perceived as more comforting and appropriate than a straightforward random approach. Based on our findings, we discuss the potential for future work using shielded and socially guided approaches for learning idiosyncratic social robot behaviors through RL.   

Robots can affect group dynamics. In particular, prior work has shown that robots that use hand-crafted gaze heuristics can influence human participation in group interactions. However, hand-crafting robot behaviors can be difficult and might have unexpected results in groups. Thus, this work explores learning robot gaze behaviors that balance human participation in conversational interactions. More specifically, we examine two techniques for learning a gaze policy from data: imitation learning (IL) and batch reinforcement learning (RL). First, we formulate the problem of learning a gaze policy as a sequential decision-making task focused on human turn-taking. Second, we experimentally show that IL can be used to combine strategies from hand-crafted gaze behaviors, and we formulate a novel reward function to achieve a similar result using batch RL. Finally, we conduct an offline evaluation of IL and RL policies and compare them via a user study (N=50). The results from the study show that the learned behavior policies did not compromise the interaction. Interestingly, the proposed reward for the RL formulation enabled the robot to encourage participants to take more turns during group human-robot interactions than one of the gaze heuristic behaviors from prior work. Also, the imitation learning policy led to more active participation from human participants than another prior heuristic behavior.