editorial Open AccessRobots in the Wild: A Time for More Robust Theories of Human-Robot Interaction Share on Authors: Malte Jung Cornell University, USA, Ithaca, NY Cornell University, USA, Ithaca, NYView Profile , Pamela Hinds Stanford University, Stanford, CA, USA Stanford University, Stanford, CA, USAView Profile Authors Info & Affiliations ACM Transactions on Human-Robot InteractionVolume 7Issue 1May 2018 Article No.: 2pp 1–5https://doi.org/10.1145/3208975Published:21 May 2018 36citation1,797DownloadsMetricsTotal Citations36Total Downloads1,797Last 12 Months784Last 6 weeks86 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Based on ethnographic data collected at a global architecture firm over a one-year period, we describe the ways in which drawings are strategically created and curated by architects in an attempt to carefully craft their interactions and fulfill specific intentions with clients and contributors. We identify three strategies used by architects when creating drawings and reveal that the way these strategies are employed depends on the intended purpose of each drawing with a specific set of actors. This work builds on existing literature on the role of objects, such as drawings, for work across organizational boundaries and professional disciplines. We conclude with implications for CSCW and propose ideas for approaches and applications that better support cross-boundary and highly visual work involving multiple sets of actors and artifacts.

Previous HRI research has established that trust, disclosure, and a sense of companionship lead to positive outcomes. In this study, we extend existing work by exploring behavioral approaches to increasing these three aspects of HRI. We increased the expressivity and vulnerability of a robot and measured the effects on trust, disclosure, and companionship during human-robot interaction. We engaged (N = 61) high school aged students in a 2 (vulnerability of robot: high vs. low) × 2 (expressivity of robot: high vs. low) between-subjects study where participants engaged in a short electronics learning activity with a robotic tutor. Our results show that students had more trust and feelings of companionship with a vulnerable robot, and reported disclosing more with an expressive robot. Additionally, we found that trust mediated the relationship between vulnerability and companionship. These findings suggest that vulnerability and expressivity may improve peoples' relationships with robots, but that they each have different effects.

Previous HRI research has established that trust, disclosure, and a sense of companionship lead to positive outcomes. We explored improving these aspects of HRI through robot behavior. Specifically, we increased the expressivity and vulnerability of a robot and measured the effects on trust, disclosure, and companionship during human-robot interaction. We engaged (N = 61) high school aged students in a 2 (vulnerability of robot: high vs. low) × 2 (expressivity of robot: high vs. low) between-subjects study where participants engaged in a short electronics learning activity with a robotic tutor. Our results show that students had more trust and feelings of companionship with a vulnerable robot, and reported disclosing more with an expressive robot. This video highlights some of the qualitative interactions students had with the robot as well as a discussion of their experiences around trust, disclosure, and companionship with the robot. The video clips of student interactions show how vulnerability and expressivity can be used to engender trust, disclosure, and companionship between a person and robot.

In our qualitative study of 9 globally distributed software development teams, we found that power and how it was distributed across locations had a significant effect on team dynamics. We describe the sources of power for these teams, which include being in the flow of information, feeling that one's voice is heard by decision makers, and having opportunities for career growth and advancement. We also examine power dynamics across locations by team and show that having more balanced power was typically associated with more power struggles rather than fewer. Four of the 9 teams had ongoing power contests. Each of these teams had some sources of power at one location and other sources of power at the other location. Both sites worried about losing power, felt they should have more, and struggled against losing ground. We conclude with a discussion of the need for CSCW to expand beyond collaboration to address issues of power. We propose ideas for systems to support globally distributed teams in creating more equal access to more sources of power and alleviating unhealthy power dynamics.

We explore whether robots can positively influence conflict dynamics by repairing interpersonal violations that occur during a team-based problem-solving task. In a 2 (negative trigger: task- directed vs. personal attack) x 2 (repair: yes vs. no) between- subjects experiment (N = 57 teams, 114 participants), we studied the effect of a robot intervention on affect, perceptions of conflict, perceptions of team members' contributions, and team performance during a problem-solving task. Specifically, the robot either intervened by repairing a task-directed or personal attack by a confederate or did not intervene. Contrary to our expectations, we found that the robot's repair interventions increased the groups' awareness of conflict after the occurrence of a personal attack thereby acting against the groups' tendency to suppress the conflict. These findings suggest that repair heightened awareness of a normative violation. Overall, our results provide support for the idea that robots can aid team functioning by regulating core team processes such as conflict.

Collaboration is important to successful organizations and how coworkers are selected is crucial to the dynamics of effective collaborations. In this study we explore how people use social network information, which is increasingly accessible on enterprise systems in organizations, to choose people with whom to collaborate. We conducted a scenario-based study of 459 respondents in a global high-tech company. Our data indicate cultural differences in how social network information was valued when choosing a collaborator. The Chinese, consistent with the cultural value of Guanxi, more closely followed a closure model, whereas Americans favored neither a closure nor a structural holes model. These results provide new insights into how needs for social network information may vary between cultures and how social networking sites might support workers in choosing collaborators from within and across national cultures.

People are increasingly working with robots in teams and recent research has focused on how human-robot teams function, but little attention has yet been paid to the role of social signaling behavior in human-robot teams. In a controlled experiment, we examined the role of backchanneling and task complexity on team functioning and perceptions of the robots' engagement and competence. Based on results from 73 participants interacting with autonomous humanoid robots as part of a human-robot team (one participant, one confederate, and three robots), we found that when robots used backchanneling team functioning improved and the robots were seen as more engaged. Ironically, the robots using backchanneling were perceived as less competent than those that did not. Our results suggest that backchanneling plays an important role in human-robot teams and that the design and implementation of robots for human-robot teams may be more effective if backchanneling capability is provided.

Sociometric feedback visualizes social signals among group members to increase their awareness of their communication patterns. We deployed the Meeting Mediator, a real-time sociometric feedback system to groups participating in two rounds of a social dilemma task: in one round, all members were co-located and in the other round, the members were geographically distributed. Laboratory results show that the sociometric feedback successfully increases the speaking time and the frequency of turn transitions of groups that are initially distributed and later co-located, and also leads to a higher cooperation rate, increasing the overall earnings of these groups. In addition, the sociometric feedback helps groups have a more consistent pattern of behavior before and after a change in their geographic distribution.

The global expansion of the use of online communities, including social networking sites, necessitates a better understanding of how people self-disclose online, particularly in different cultures. In a scenario-based study of 1,064 respondents from the United States and China, we aimed to understand how self-disclosure is affected by communication mode (face-to-face vs. online), type of relationship and national culture. Our findings show that national culture interacts with communication mode and type of relationship to affect the extent of self-disclosure. Our analysis also suggests that peoples' disclosure depends on characteristics of the relationship, e.g., closeness and openness. Our results shed new light on how online communities might be designed for users in different cultures and for intercultural collaboration.

In this study, we sought to clarify the effects of users' cultural background and cultural context on human-robot team collaboration by investigating attitudes toward and the extent to which people changed their decisions based on the recommendations of a robot collaborator. We report the results of a 2×2 experiment with nationality (Chinese vs. US) and communication style (implicit vs. explicit) as dimensions. The results confirm expectations that when robots behave in more culturally normative ways, subjects are more likely to heed their recommendations. Specifically, subjects with a Chinese vs. a US cultural background changed their decisions more when collaborating with robots that communicated implicitly vs. explicitly. We also found evidence that Chinese subjects were more negative in their attitude to robots and, as a result, relied less on the robot's advice. These findings suggest that cultural values affect responses to robots in collaborative situations and reinforce the importance of culturally sensitive design in HRI.

This paper introduces Transactive Memory System (TMS) theory to the study of human-robot interaction in a complex work setting comprised of people with complementary domains of expertise. New insights regarding the development of TMS in human-robot incident response teams are presented.

This study investigates the effects of group orientation and communication style on Chinese subjects' responsiveness to robots. A 2x2 experiment was conducted with group orientation (ingroup vs. outgroup) and communication style (implicit vs. explicit) as dimensions. The results confirm expectations that subjects with a Chinese cultural background are more responsive to robots that use implicit communication styles. We also found some evidence that subjects were more responsive when they thought of the robot as an ingroup member. These findings inform the design of robots for use in China and countries with similar cultural values and reinforce the importance of culturally sensitive design in HRI.

As robots (and other technologies) increasingly make decisions on behalf of people, it is important to understand how people from diverse cultures respond to this capability. Thus far, much design of autonomous systems takes a Western view valuing individual preferences and choice. We challenge the assumption that Western values are universally optimal for robots. In this study, we sought to clarify the effects of users' cultural background on human-robot collaboration by investigating their attitudes toward and the extent to which people accepted choices made by a robot or human assistant. A 2x2x2 experiment was conducted with nationality (US vs. Chinese), in group strength (weak vs. strong) and human vs. robot assistant as dimensions. US participants reported higher trust of and compliance with the assistants (human and robot) although when the assistant was characterized as a strong ingroup member, Chinese as compared with the US subjects were more comfortable. Chinese also reported a stronger sense of control with both assistants and were more likely to anthropomorphize the robot than were US subjects. This pattern of findings confirms that people from different national cultures may respond differently to robots, but also suggests that predictions from human-human interaction do not hold universally.

We report the results of an experiment examining how an autonomous mobile robotpsilas disclosure of task versus affective information influences peoplepsilas feelings about a robot when working on a collaborative task. In a 3 times 2 experiment, we examined disclosure (task, affective and no disclosure) and the trigger that produced the disclosure (automatic vs. on-demand). We hypothesized that a robot disclosing task information would increase peoplepsilas sense of control because it provides awareness about the task situation, but that affective information would decrease peoplepsilas sense of control. We also predicted that people would like robots better when they disclose affective information. Our findings confirm that people both felt less in control and liked the robot more when the robot disclosed its affective state. We further found that providing people with the ability to control disclosure (on demand vs. automatic disclosure), increased their sense of control, but decreased their liking of the robot. This research suggests the need to weigh a potential tradeoff between designing robot collaborators that evoke perceptions of control versus liking. In the discussion, we explore design implications and how different combinations of disclosure types and triggers may optimize both feelings of control and liking in human-robot collaboration.

Scholars have recently argued for flatter, organic organizational structures that enable workers to deal more effectively with dynamic and uncertain environments. In a correlational study of 33 R&D teams, we find that although this network form is associated with more smooth coordination in collocated teams, the opposite is true for geographically distributed teams. In fact, an informal hierarchical structure was associated with more smooth coordination in distributed teams. These results add to the scant literature on networks in teams and provide insight into important differences in the structure of geographically distributed and collocated teams.

We report a study of a human-robot system composed of a science team (located in Pittsburgh), an engineering team (located in Chile), and a robot (located in Chile). We performed ethnographic observations simultaneously at both sites over two weeks as scientists collected data using the robot. Our data reveal problems in establishing and maintaining common ground between the science team and the robot due to missing contextual information about the robot. Our results have implications for the design of systems to support human-robot interaction.

As autonomous robots collaborate with people on tasks, the questions "who deserves credit?" and "who is to blame?" are no longer simple. Based on insights from an observational study of a delivery robot in a hospital, this paper deals with how robotic autonomy and transparency affect the attribution of credit and blame. In the study, we conducted a 2times2 experiment to test the effects of autonomy and transparency on attributions. We found that when a robot is more autonomous, people attribute more credit and blame to the robot and less toward themselves and other participants. When the robot explains its behavior (e.g. is transparent), people blame other participants (but not the robot) less. Finally, transparency has a greater effect in decreasing the attribution of blame when the robot is more autonomous

This ethnographic study suggests that social structures such as the sex segregation of jobs may impact how different groups of workers make sense of robots in their work environment. Designers may need to take such structures into account if mobile autonomous robots are to operate successfully alongside humans in the workplace.

We present a field study of police SWAT teams for the purpose of enabling grounded design of a system to coordinate distributed field robots. The mission-oriented, spatially distributed SWAT environment provides a rich resource for robotics designers that mirrors field robot deployments in key ways. We highlight the processes with which SWAT team leaders create and maintain common ground among team members and coordinate action in these tightly-coupled, distributed teams. We present a system for coordinating distributed robots that we designed based on our SWAT team observations.