Virtual collaboration has transformed how people in mixed-ability teams, composed of disabled and non-disabled people, work together by offering greater flexibility. In these settings, accessibility practices, such as accommodations and inclusive norms, are essential for providing access to disabled people. However, we do not yet know how these practices shape broader facets of teamwork, such as productivity, participation, and camaraderie. To address this gap, we interviewed 18 participants (12 disabled, 6 non-disabled) who are part of mixed-ability teams. We found that beyond providing access, accessibility practices shaped how all participants coordinated tasks, sustained rapport, and negotiated responsibilities. Accessibility practices also introduced camaraderie challenges, such as balancing empathy and accountability. Non-disabled participants described allyship as a learning process and skill shaped by their disabled team members and team culture. Based on our findings, we present recommendations for team practices and design opportunities for virtual collaboration tools that reframe accessibility practices as a foundation for strong teamwork.

This paper examines the community-driven sustenance of Moxie, a social robot that faced discontinuation when its parent company failed to secure funding. Through interviews and investigative digital ethnography, we study how users performed extensive articulation work to transition from corporate support to an open-source platform. Our findings reveal that invisible labor and value negotiation were central to Moxie's continued operation, simultaneously opening access for some users while excluding others. These processes also fundamentally reshaped the robot's desirability and meaning within the user community. This work demonstrates how socio-technical infrastructures, articulation work, and value renegotiation can sustain robots beyond their commercial lifecycles, while revealing the uneven distribution of both labor and access in community-driven technology repair and maintenance.

As robots achieve product longevity and increasingly enter intimate spaces of human life, from healthcare facilities to homes, they inevitably encounter contexts involving death, dying, grief, and loss. Nevertheless, mortality as a fundamental relational dimension has received limited systematic attention within Human-Robot Interaction (HRI) research. Drawing from empirical vignettes across multiple research projects, from robots at deathbeds and participants who passed away, to owners planning for robots they will leave behind, we demonstrate how death already surfaces in HRI, even when not designed for it. Through analysis of these encounters, we develop a framework for mortality-aware HRI that identifies key dimensions for designing and researching robots that acknowledge human and robotic finitude. We argue that as robots achieve long-term integration into people's lives, creating intertwined lifespans of humans and machines, the field must move beyond treating mortality as an edge case and instead recognize it as a core principle shaping how people relate to and make meaning with robots.

The question “what is a robot?” has long been contested as automated embodied systems encompass many forms. We examine this fundamental question in Human-Robot Interaction through the case of Major League Baseball’s “robot umpire,” officially known as the Automated Ball-Strike System (ABS). Drawing on the concept of “fluid technology,” we analyze how the robot umpire is not a fixed technological artifact but a fluid sociotechnical assemblage whose definition and function are continuously negotiated. Through ethnographic fieldwork and interviews with stakeholders across the baseball ecosystem, we demonstrate that the robot umpire’s physical boundaries, operational parameters, and authorship remain contested and evolving, shaped by ongoing interactions between technology developers, league officials, umpires, players, and fans. Our findings reveal that treating robots as fluid technologies—rather than as discrete objects—opens new possibilities for understanding human-robot relationships. We contribute both theoretical insights regarding the ontological flexibility of “robots” and methodological approaches for studying and designing robots as sociotechnical assemblages.

Many human activities are performed in groups—making decisions in workplace meetings, cooperating on a sports team, or meeting with friends for dinner. All these activities involve complex conditions and interaction processes that influence their outcomes in terms of performance, personal goals, and group objectives. As robots are increasingly being positioned within groups, improving these outcomes has emerged as an important application area in social robotics, particularly through robotic facilitation. Robot facilitators aim to elicit positive changes by deliberately influencing group processes. While research in this field has demonstrated that robots can effectively influence interpersonal dynamics, there remains a notable gap in consolidating these insights into a coherent understanding that can guide the design and development of better facilitators. We present a scoping review of literature targeting changes in interactions between multiple humans that are driven by intentional actions from robotic agents. To identify key considerations for the design of robot facilitators, we take inspiration from human group research theories to organize existing approaches. Our review includes 108 publications that meet our inclusion criteria, yielding 85 distinct application targets for group facilitation using robots. Based on the identified instances, we extract categories of possible application targets and a set of design concepts that can guide future work on robotic group facilitators.

Sustenance of long-term human-robot relationships relies on a socio-technical infrastructure of care and repair, in which both technical maintenance and social practices work together to sustain robots as relational objects. This infrastructure is, however, often invisible and taken for granted while working well. It is in moments of breakdown and repair that the necessary infrastructure that supports long-term human-robot relationships is exposed. In this paper, we present ethnographic fieldwork and stakeholder interviews with eight users and eight producers and repair staff of successful consumer robots in Japan, that highlight the often-overlooked moments of technological breakdown and the meaning-making that surrounds them. This allows us to shed light on the evolving components of the socio-technical infrastructure-such as technical processes, cultural norms, shared language, emotional labor, and interpersonal relationships-that are essential for sustaining the functionality and relevance of robots in people's everyday lives.

In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting and space exploration, autonomous robots that aid high-stakes teamwork remain underexplored. To address this gap, we conducted a rapid prototyping process to develop a series of seemingly autonomous robots designed to assist clinical teams in the Emergency Room. We transformed a standard crash cart-which stores medical equipment and emergency supplies into a medical robotic crash cart (MCCR). The MCCR was evaluated through field deployments to assess its impact on team workload and usability, identified taxonomies of failure, and refined the MCCR in collaboration with healthcare professionals. Our work advances the understanding of robot design for high-stakes, time-sensitive settings, providing insights into useful MCCR capabilities and considerations for effective human-robot collaboration. By publicly disseminating our MCCR tutorial, we hope to encourage HRI researchers to explore the design of robots for high-stakes teamwork.

This paper explores the case study of Kota, Japan, as "aibo’s furusato" – or hometown – through ethnographic observation and interviews to investigate how diverse stake-holders, including local government, industry partners, and robot owners, collaboratively construct the town’s identity as a tourist destination centered around the social robot aibo. Our findings reveal how new robotic technologies are incorporated into existing frameworks of local economic production while also creating new opportunities for social interaction, as well as cultural and economic production that connect robotics to traditional products and practices. This work more broadly supports an understanding of robot design that goes beyond the artifacts itself to encompass a broader socio-technical infrastructure that supports robots’ meaningful use.

In this work, we examine robots "asking for help" in unpredictable human spaces. We focus on an open question particularly relevant for robots deployed in public–"how do people help robots?" We present an elicitation study that shows how asking for help in a real-world field study yields valuable and sometimes unexpected information. From our study, we examine strangers’ responses toward a robot asking for spatial directions and extract valuable themes that can inform future asking-for-help systems. Our analysis provides a wide range of information, from geometric and topological information in natural language to details about rejection during an interaction. Further, we also provide anecdotes of valuable outlier behavior that can only be captured through a study in a real public space. Through our work, we highlight the importance of in-the-wild studies and discuss how the rich information they contribute will help robots effectively ask for help.

From ELIZA to Alexa, Conversational Agents (CAs) have been deliberately designed to elicit or project empathy. Although empathy can help technology better serve human needs, it can also be deceptive and potentially exploitative. In this work, we characterize empathy in interactions with CAs, highlighting the importance of distinguishing evocations of empathy between two humans from ones between a human and a CA. To this end, we systematically prompt CAs backed by large language models (LLMs) to display empathy while conversing with, or about, 65 distinct human identities, and also compare how different LLMs display or model empathy. We find that CAs make value judgments about certain identities, and can be encouraging of identities related to harmful ideologies (e.g., Nazism and xenophobia). Moreover, a computational approach to understanding empathy reveals that despite their ability to display empathy, CAs do poorly when interpreting and exploring a user’s experience, contrasting with their human counterparts.

Successful entrainment during collaboration positively affects trust, willingness to collaborate, and likeability towards collaborators. In this paper, we present a mixed-method study to investigate characteristics of successful entrainment leading to pair and group-based synchronisation. Drawing inspiration from industrial settings, we designed a fast-paced, short-cycle repetitive task. Using motion tracking, we investigated entrainment in both dyadic and triadic task completion. Furthermore, we utilise audio-video recordings and semi-structured interviews to contextualise participants’ experiences. This paper contributes to the Human-Computer/Robot Interaction (HCI/HRI) literature using a human-centred approach to identify characteristics of entrainment during pair- and group-based collaboration. We present five characteristics related to successful entrainment. These are related to the occurrence of entrainment, leader-follower patterns, interpersonal communication, the importance of the point-of-assembly, and the value of acoustic feedback. Finally, we present three design considerations for future research and design on collaboration with robots.

Power is a fundamental determinant of social life, yet it remains elusive in Human-Robot Interaction (HRI). This paper unveils power's pervasive but largely unexplored role in HRI by systematically investigating its varied manifestations across HRI literature. We first introduce definitions of power and then delve into the existing HRI literature through a lens of power, examining studies that directly address power and those exploring power-related social configurations and concepts such as authority, dominance, and status. Leveraging Fiske and Berdahl's model and French and Raven's bases of power framework, we also explore the nuances of power in many HRI studies where power is not explicitly addressed. Finally, we propose power as a core concept to advance HRI--- explaining fragmented existing findings through a coherent theory and delineating a cohesive theoretical trajectory for future investigations.

Robot designers commonly conceptualize robot sociality as a collection of features and capabilities. In contrast, sociologists define sociality as continuously constructed through interpersonal interactions. Based on the latter perspective, we trace how robots are incorporated into emerging social interaction ritual chains by robot companies and their staff and by robot owners across diverse contexts: homes, cafes, robot stores, user-organized meetups, and company events for robot users. Our empirical findings from ethnographic field work in Japan relating to three robots -- aibo, RoboHon, and LOVOT -- show how companies create positive interactions between people and robots by incorporating familiar design patterns into robots, modeling successful interactions in person and online, and bringing owners together in events that establish common values of acceptance of social robots as artifacts to live with and nurture. Owners, for their part, develop interaction rituals that include robots in their daily activities, make interpersonal connections, and experience emotionally resonant interactions, around robots in public meetups and events. Through these emerging interaction ritual chains, companies and owners construct the notion of robots as social agents to live with as a meaningful component of their emotional experiences and broader social relationships. Our work suggests that social robot design should consider this broader framing of sociality and create affordances for establishing interaction ritual chains more explicitly.

Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions, encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors, to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.

In this paper, we set out to explore how people judge the personality of a non-anthropomorphic virtual agent during group interactions. Using a Wizard of Oz (WoZ) based approach, we observed that people judged the acceptability of a virtual agent’s behavior from a tool-based lens, that is, if this robot and its behavior are useful to the team or not. Furthermore, we found that while people were able to acknowledge the virtual agent’s personality and recognize its identity through social cues, the tool-based framing impacts these perceptions into a normative judgment of the robots’ utility. We present two case studies that we think highlight this tool-based interpretation of robotic personality: robots expressing non-factual opinions and robots expressing humor. Finally, we suggest that researchers should consider the impact of this tool-based framing on people’s perceptions of a robot’s identity when designing robots for social interaction.

This paper introduces a "lifecycle perspective" on social robot design and human-robot interaction, and explores the practices of maintenance, repair, and letting go of social robots. Drawing on interviews with robot owners and representatives of robot development and repair companies, we argue that these previously disregarded aspects of everyday use provide a context for negotiating the social value and meaning of interactions with robots. We discuss owner concerns about robot obsolescence, as well as company support for long term human-robot interaction through repair, reuse, and giving owners closure in letting go of robots they can no longer use. Our work expands the purview of HRI study and design beyond the common focus on initial design and adoption and to perceptions and practices that can foster more enduring relationships with social robots, support sustainability in robot design, and address owners’ emotional attachment to robots.

Driven by the vision of future responsive environments, where everyday surroundings can perceive human behaviors and respond through intelligent robotic actuation, we propose Wizard of Props (WoP): a human-centered design workflow for creating expressive, implicit, and meaningful interactions. This collaborative experience prototyping approach integrates full-scale physical props with Mixed Reality (MR) to support ideation, prototyping, and rapid testing of responsive environments. We present two design explorations that showcase our investigations of diverse design solutions based on varying technology resources, contextual considerations, and target audiences. Design Exploration One focuses on mixed environment building, where we observe fluid prototyping methods. In Design Exploration Two, we explore how novice designers approach WoP, and illustrate their design ideas and behaviors. Our findings reveal that WoP complements conventional design methods, enabling intuitive body-storming, supporting flexible prototyping fidelity, and fostering expressive environment-human interactions through in-situ improvisational performance.

Augmented Reality (AR) glasses separate dyadic interactions on different sides of the lens, where the person wearing the glasses (primary user) sees an AR world overlaid on their partner (secondary actor). The secondary actor interacts with the primary user understanding they are seeing both physical and virtual worlds. We use grounded theory to study interaction tasks, participatory design sessions, and in-depth interviews of 10 participants and explore how AR real-time modifications affect them. We observe a power imbalance attributed to the: (1) lack of transparency of the primary user’s view, (2) violation of agency over self-presentation, and (3) discreet recording capabilities of AR glasses. This information asymmetry leads to a negotiation of behaviors to reach a silently understood equilibrium. This paper addresses underlying design issues that contribute to power imbalances in dyadic interactions and offers nuanced insights into the dynamics between primary users and secondary actors.

This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool’s effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals’ perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions.

Artificially intelligent (AI) agents such as robots are increasingly delegated power in work settings, yet it remains unclear how power functions in interactions with both humans and robots, especially when they directly compete for influence. Here we present an experiment where every participant was matched with one human and one robot to perform decision-making tasks. By manipulating who has power, we created three conditions: human as leader, robot as leader, and a no-power-difference control. The results showed that the participants were significantly more influenced by the leader, regardless of whether the leader was a human or a robot. However, they generally held a more positive attitude toward the human than the robot, although they considered whichever was in power as more competent. This study illustrates the importance of power for future Human-Robot Interaction (HRI) and Human-AI Interaction (HAI) research, as it addresses pressing concerns of society about AI-powered intelligent agents.

Design activities, such as brainstorming or critique, often take place in open spaces combining whiteboards and tables to present artefacts. In co-located settings, peripheral awareness enables participants to understand each other’s locus of attention with ease. However, these spatial cues are mostly lost while using videoconferencing tools. Telepresence robots could bring back a sense of presence, but controlling them is distracting. To address this problem, we present ReMotion, a fully automatic robotic proxy designed to explore a new way of supporting non-collocated open-space design activities. ReMotion combines a commodity body tracker (Kinect) to capture a user’s location and orientation over a wide area with a minimally invasive wearable system (NeckFace) to capture facial expressions. Due to its omnidirectional platform, ReMotion embodiment can render a wide range of body movements. A formative evaluation indicated that our system enhances the sharing of attention and the sense of co-presence enabling seamless movement-in-space during a design review task.

Horns and sirens are important tools for communicating on the road, which are still understudied in autonomous vehicles. While HRI has explored different ways in which robots could sound, we focus on the range of actions that a single sound can accomplish in interaction. In a Research through Design study involving autonomous shuttle buses in public transport, we explored sound design with the help of voice-overs to video recordings of the buses on the road and Wizard-of-Oz tests in live traffic. The buses are slowed down by (unnecessary) braking in response to people getting close. We found that prolonged jingles draw attention to the bus and invite interaction, while repeated short beeps and bell sounds can instruct the movement of others away from the bus. We highlight the importance of designing sound in sequential interaction and describe a new method for embedding video interaction analysis in the design process.

Robotic appliances are continually being adopted into private homes. However, users have yet to exhibit the same acceptance towards domestic social robots. In this paper, we seek to bridge this issue by augmenting already-existing home appliances with capabilities mimicking social robots. We present a robotic toaster designed with animated movements to enhance and personalize the toast-making experience. Not only does the robotic toaster assist in completing the task itself, it also acts as a conscious agent with whom users may interact in a social and playful manner. Using a series of video vignettes, we identify three key themes of the robotic toaster that influence its relationship with users: these are related to (1) context awareness, (2) increased interactivity through initiative action, and (3) expression of personality despite limited degrees of freedom. Lastly, we discuss how the portrayal of home appliances with social characteristics can potentially serve as an introductory step for social robots in the home.

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible.

As robots become more ubiquitous across human spaces, it is becoming increasingly relevant for researchers to ask the question, “how can we ensure that we are designing robots to be sufficiently equipped to treat people fairly?”. This workshop brings together researchers across the fields of Human-Robot Interaction (HRI), fairness in machine learning, design, and transparency in AI to shed light on the relevant methodological challenges surrounding issues of fairness and transparency in HRI. In our workshop, we will attempt to identify synergies between these various fields. In particular, we will focus on how HRI can leverage these existing rich body of work to guide the formalization of fairness metrics and methodologies. Another goal of the workshop is to foster a community of interdisciplinary researchers to encourage collaboration. The complexity in defining fairness lies in its context sensitive nature, as such we look to the influx of definitions from the field of fairness in artificial intelligence, design, and organizational psychology to derive a set of definitions that could serve as guidelines for researchers in HRI.

Research in joint action focuses on the psychological, neurological, and physical mechanisms by which humans collabo-rate with other agents, and overlaps with several domains related to human-robot interaction. The development of artificial systems that can support or emulate the requisite aspects of joint action could lead to improved human-robot team performance as well as improvements in subjective metrics (e.g., trust). This workshop highlights theoretical and technical considerations about human-robot joint action and real-time adaptation, with a particular focus on socio-motor entrainment, showing how the emulation of psychological mechanisms (e.g., emotion, intention signaling, mirroring) can lead to improved performance. We will invite speakers with backgrounds in robotics, neuroscience and psychol-ogy, as well as speakers with a focus in adjacent works, such as in human-robot coordinated dance, alignment, or synchronization. We will call for papers that utilize the theory of joint-action in an interactive human-robot context. We will also call for position papers on the application of the theory of joint action to robotics, with a heavy focus on psychological mechanisms that could potentially be emulated or adapted to a human-robot context. Participants will have the opportunity to brainstorm considerations and techniques that would be applicable to joint action inspired works through breakout sessions with the aim to lead to new and improved collaborations across fields.

We propose a workshop stemming from ongoing conversations about the role of design methods and designed artefacts within the field of Human-Robot Interaction (HRI). Given the growing interest in understanding what the field can learn from design explorations, the workshop focuses on hands-on annotating activity where participants (researchers and practitioners from HRI, Human-Computer Interaction, and Design Research) will analyze and reflect upon selected collections of robotic artefacts. Ultimate goal of the workshop is to explicate values, concepts and perspectives that usually remain tacitly embedded in the designed artefacts and, as such, hard to appreciate as proper HRI contributions. The expected outcome of the workshop is a set of methodological recommendations and concrete examples of what kind of knowledge can be generated through robotic artefacts.

Older adults around the world lack access to a wide range of potentially life-changing digital applications, services, and information that could be provided by voice assistants (such as Amazon’s Alexa, Google’s Assistant, or Apple’s Siri). However, older adults’ needs are underrepresented in the design of voice assistants. Because of this, we are missing opportunities for digital inclusion, and increasing risks of excluding older adults as these devices permeate public settings. In this work, we video record older adults (n=26) interacting with a multi-modal voice assistants while waiting in line at food pantries, and use Interaction Analysis to draw insights from these recordings. We find that by being agnostic to body language, audio-prosodic features, and other contextual factors, voice assistants fail to capture and react to some important aspects of interactions. We discuss design (e.g, interpreting users’ posture as a cue to wake the device when they are leaning towards the device) and research (e.g., surveillance trade-offs) implications, and argue for the use of multi-modal inputs with attention to privacy. Designing and training voice assistants to take in and appropriately respond to non-verbal cues may increase their inclusivity, helping them fulfill important needs of our aging population.

In this paper, we investigate how to elicit new perspectives in research-through-design (RtD) studies through annotated portfolios. Situating the usage in human-robot interaction (HRI), we used two robotic artefacts as a case study: we first created our own annotated portfolio and subsequently ran online workshops during which we asked HRI experts to annotate our robotic artefacts. We report on the different aspects revealed about the value, use, and further improvements of the robotic artefacts through using the annotated portfolio technique ourselves versus using it with experts. We suggest that annotated portfolios – when performed by external experts – allow design researchers to obtain a form of creative and generative peer critique. Our paper offers methodological considerations for conducting expert annotation sessions. Further, we discuss the use of annotated portfolios to unveil designerly HRI knowledge in RtD studies.

We propose the use of interactive vignettes as an alternative to traditional text- and video-based vignettes for conducting large-scale Human-Robot Interaction (HRI) studies. Interactive vignettes maintain the advantages of traditional vignettes while offering additional affordances for participant interaction and data collection through interactive elements. We discuss the core affordances of interactive vignettes, including explorability, responsiveness, and non-linearity, and look into how these affordances can enable HRI research with more complex scenarios. To demonstrate the strength of the approach, we present a case study of our own research project with N=87 participants and show the data we collect through interactive vignettes. We suggest that the use of interactive vignettes can benefit HRI researchers in learning how participants interact with, respond to, and perceive a robot’s behavior in pre-defined scenarios.

The increased use of algorithms to support decision making raises questions about whether people prefer algorithmic or human input when making decisions. Two streams of research on algorithm aversion and algorithm appreciation have yielded contradicting results. Our work attempts to reconcile these contradictory findings by focusing on the framings of humans and algorithms as a mechanism. In three decision making experiments, we created an algorithm appreciation result (Experiment 1) as well as an algorithm aversion result (Experiment 2) by manipulating only the description of the human agent and the algorithmic agent, and we demonstrated how different choices of framings can lead to inconsistent outcomes in previous studies (Experiment 3). We also showed that these results were mediated by the agent's perceived competence, i.e., expert power. The results provide insights into the divergence of the algorithm aversion and algorithm appreciation literature. We hope to shift the attention from these two contradicting phenomena to how we can better design the framing of algorithms. We also call the attention of the community to the theory of power sources, as it is a systemic framework that can open up new possibilities for designing algorithmic decision support systems.

AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should allocate resources among group members and about what the consequences of such allocation are for a group’s social dynamics and outcomes. Methodological advances are needed to answer these questions allow researchers to collect data about a robot’s impact not only on interactions with the robot but also on interactions of people with each other. This paper presents Robot Assisted Tower Construction, a novel task that allows researchers to examine the impact of a robot’s allocation behavior on the dynamics of a group or team collaborating on a task. By focusing on the question of whether and how a robot’s allocation of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, a case is provided of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves a group of people. We highlight the kinds of insights the task can yield and how it can be adapted to various human robot collaboration contexts.

How should a robot that collaborates with multiple people decide upon the distribution of resources (e.g. social attention, or parts needed for an assembly)? People are uniquely attuned to how resources are distributed. A decision to distribute more resources to one team member than another might be perceived as unfair with potentially detrimental effects for trust. We introduce a multi-armed bandit algorithm with fairness constraints, where a robot distributes resources to human teammates of different skill levels. In this problem, the robot does not know the skill level of each human teammate, but learns it by observing their performance over time. We define fairness as a constraint on the minimum rate that each human teammate is selected throughout the task. We provide theoretical guarantees on performance and perform a large-scale user study, where we adjust the level of fairness in our algorithm. Results show that fairness in resource distribution has a significant effect on users' trust in the system.

Conversational agents are increasingly becoming integrated into everyday technologies and can collect large amounts of data about users. As these agents mimic interpersonal interactions, we draw on communication privacy management theory to explore people's privacy expectations with conversational agents. We conducted a 3x3 factorial experiment in which we manipulated agents' social interactivity and data sharing practices to understand how these factors influence people's judgments about potential privacy violations and their evaluations of agents. Participants perceived agents that shared response data with advertisers more negatively compared to agents that shared such data with only their companies; perceptions of privacy violations did not differ between agents that shared data with their companies and agents that did not share information at all. Participants also perceived the socially interactive agent's sharing practices less negatively than those of the other agents, highlighting a potential privacy vulnerability that users are exposed to in interactions with socially interactive conversational agents.

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.

We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts.

In this paper, we draw attention to the social functions of emotional display in interaction. A review of HRI papers on emotion suggests that this perspective is rarely taken in the field, but that it is useful to account for the context- and culture-dependency of emotional expression. We show in two case studies that emotional display is expected to occur at very specific places in interaction and rather independently from general emotional states, and that different cultures have different conventions regarding emotional expression. Based on conversation analytic work and the results from our case studies, we present design recommendations which allow the implementation of specific emotional signals for different human-robot interaction situations.

Fast-paced contemporary life full of planned interaction usually makes people miss out on wonderful moments. We here present BubbleBot, a speculative robot designed to support serendipity of interactions in public space. After observations in public spaces and embodied design workshops, we have designed BubbleBot to be a peripheral public-space robot, bursting bubbles at passersby to invite serendipitous interactions. BubbleBot is a speculative robot to create magical moments among people with minimal peripheral social interaction. With this project, we aim at generating a conversation about the future roles and interaction paradigms of robots in public space.

Many of the problems we face are solved in small groups. Using decades of research from psychology, HRI research is increasingly trying to understand how robots impact the dynamics and outcomes of these small groups. Current work almost exclusively uses humanoid robots that take on the role of an active group participant to influence interpersonal dynamics. We argue that this has limitations and propose an alternative design approach of using a peripheral robotic object. This paper presents Micbot, a peripheral robotic object designed to promote participant engagement and ultimately performance using nonverbal implicit interactions. The robot is evaluated in a 3 condition (no movement, engagement behaviour, random movement) laboratory experiment with 36 three-person groups (N=108). Results showed that the robot was effective in promoting not only increased group engagement but also improved problem solving performance. In the engagement condition, participants displayed more even backchanneling toward one another, compared to no movement, but not to the random movement. This more even distribution of backchanneling predicted better problem solving performance.

Drawing on ethnographic fieldwork in two different teaching hospitals that deployed the da Vinci surgical robot, this paper traces how the introduction of robotics reconfigures the sensory environment of surgery and how surgeons and their teams recalibrate their work in response. We explore the entangled and mutually supportive nature of sensing within and between individual actors and the broader world of people and things (with emphasis on vision and touch) and illustrate how such inter-sensory dependencies are challenged and sometimes extended under the conditions of robotic surgery. We illustrate how sensory (re)articulations and compensations allow the surgeon and surgical teams to adapt to a more-than-human sensorium and conclude by advocating new forms of sensory-aware design capable of enhancing and supporting embodied sensory conditions both individually and across teams.

Emotions play a major role in how interpersonal conflicts unfold. Although several strategies and technological approaches have been proposed for emotion regulation, they often require conscious attention and effort. This often limits their efficacy in practice. In this paper, we propose a different approach inspired by self-perception theory: noticing that people are often reacting to the perception of their own behavior, we artificially change their perceptions to influence their emotions. We conducted two studies to evaluate the potential of this approach by automatically and subtly altering how people perceive their own voice. In one study, participants that received voice feedback with a calmer tone during relationship conflicts felt less anxious. In the other study, participants who listened to their own voices with a lower pitch during contentious debates felt more powerful. We discuss the implications of our findings and the opportunities for designing automatic and less perceptible emotion regulation systems.

Interpersonal distance behaviors can vary significantly across countries and impact human social interaction. Do these cross-cultural differences play out when one of the interaction partners participates through a teleoperated robot? Emerging research shows that when being approached by a robot, people tend to hold similar cultural preferences as they would for an approaching human. However, no work yet has investigated this question from a robot teleoperator's perspective. Toward answering this, we conducted an online study (N = 774) using a novel simulation paradigm across two countries (U.S. and India). Results show that in the role of a telepresence robot operator, participants exhibited cross-cultural differences in interpersonal distance behavior in line with human-human proxemic research, indicating that culture-specific distance behavior can manifest in the way a robot operator controls a robot. We discuss implications for designers who seek to automate path planning and navigation for teleoperated robots.

Foreign language students must learn to use language creatively to overcome knowledge gaps and keep readers or listeners interested. However, few tools exist to support practicing this skill. Therefore, we set out to explore design of storytelling games for practicing creative language use. Through an iterative design process, we identified narrative grounding (establishing common ground for collaborative narrative) as key to student engagement and learning. However, designing games for narrative grounding while keeping the game flexible enough to easily accommodate teacher goals is challenging. Considering this challenge, we designed a collaborative storytelling game where students help scaffold the narrative and teachers can easily integrate language goals with "language cards". In an in-classroom evaluation with 36 students, we show the importance of narrative grounding for learning. Qualitative evidence also suggests narrative grounding makes the game more engaging for players. We conclude with discussion of design implications for digital language learning tools.

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.

The ability to constructively resolve interpersonal conflicts is a crucial set of social skills people need to effectively work and live well together. Is it possible to design social robots to support the early development of children's interpersonal conflict resolution skills? To investigate this question, 64 (32 pairs of) children ages 3-6 years engaged in a 50-minute play session consisting of 5 activities facilitated by the robot Keepon. Children were randomly assigned to 1 of 2 conditions. In the mediation condition, Keepon directed the play session flow by indicating when and which activity to switch to, and whenever possible, signaled the onset of object possession conflicts that occurred between the pair and offered prompts for constructive conflict resolution. In the control condition, Keepon only facilitated and directed the play session and did not intervene during children's conflicts. Results show that children were more likely to resolve conflicts constructively in the mediation condition than in the control condition, and that a key function for a robot mediator within the conflict process is to successfully flag the conflict onset. Drawing from these findings, we discuss design recommendation for a robot mediator.

Telepresence robots provide remote team members with embodied presence, but whether this improves remote teammate participation, remote users' perceptions of team collaboration, or collocated members' perceptions of remote teammates is an open question. We conducted an experiment in which teams of two collocated members and one telepresent (remote) member solved a word puzzle requiring a translation key. We varied who had access to the key to examine effects of resource accessibility in distributed groups: in the Robot Information condition, the remote pilot (RP) possessed the key; in the Shared Information condition, all team members possessed the key; in the Local Information condition, only collocated participants (CPs) possessed the key. Audio transcripts were analyzed for differences in the number of words spoken by each team member. RPs spoke significantly less than CPs, especially when they lacked the translation key. RPs perceived greater task difficulty and less ease of communication than CPs. CPs rated other CPs as more trustworthy than RPs. This suggests an imbalance between collocated and remote collaborators that can negatively affect collaboration. We discuss implications for the design and use of telepresence robots in the workplace.

This paper investigates how a teleoperated surgical robot reconfigures teamwork in the operating room by spatially redistributing team members. We report on findings from two years of fieldwork at two hospitals, including interviews and video data. We find that while in non-robotic cases team members huddle together, physically touching, introduction of a surgical robot increases physical and sensory distance between team members. This spatial rearrangement has implications for both cognitive and affective dimensions of collaborative surgical work. Cognitive distance is increased, necessitating new efforts to maintain situation awareness and common ground. Moreover, affective distance is introduced, decreasing sensitivity to shared and non-shared affective states and leading to new practices aimed at restoring affective connection within the team. We describe new forms of physical, cognitive, and affective distance associated with teleoperated robotic surgery, and the effects these have on power distribution, practice, and collaborative experience within the surgical team.

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

Authentic foreign language videos are effective for developing pragmatic competence, or sensitivity to meanings expressed by tone and word choice, and the ability to effectively express these meanings. However, established methods for learning from foreign language videos are primarily text-based (e.g.captioning). Using text, learners do not practice aspects of oral performance (e.g. intonation, pausing, and pitch) that are important to pragmatic competence. In this paper we present a voice-driven system where learners practice and learn a foreign language by repeating phrases out loud from any video. Utterances are transcribed and translated and, if captions are available, the system indicates the correctness of the utterance. In an evaluation with 27 participants, we show that participants more frequently used the voice-driven system than a comparison text-based system. Furthermore, ina field study of 130 independent learners, we show potential for community-driven resource collection.

Learning from captioned foreign language videos is highly effective, but the availability of such videos is limited. By using speech-to-text technology to generate partially correct transcripts as a starting point, we see an opportunity for learners to build accurate foreign language captions while learning at the same time. We present a system where learners correct captions using automatic transcription and machine-generated suggested alternative words for scaffolding. In a lab study of 49 participants, we found that compared to watching the video with accurate caption, learning and quality of experience were not significantly impaired by the secondary caption correction task using interface designs either with or without scaffolding from speech-to-text generated alternative words. Nevertheless, aggregating corrections reduced word error rate from 19% to 5.5% without scaffolding from suggested-alternatives, and 1.8% with scaffolding. Feedback from participants suggest that emphasizing the learning community contribution aspect is important for motivating learners and reducing frustration.

Participating in interaction requires not only coordination on content and process, as previously proposed, but also on affect. The term affective grounding is introduced to refer to the coordination of affect in interaction with the purpose of building shared understanding about what behavior can be exhibited, and how behavior is interpreted emotionally and responded to. Affective Ground is achieved when interactants have reached shared understanding about how behavior should be interpreted emotionally. The paper contributes a review and critique of current perspectives on emotion in HRI. Further it outlines how research on emotion in HRI can benefit from taking an affective grounding perspective and outlines implications for the design of robots capable of participating in the coordination on affect in interaction.

How does a robot's sound shape our perception of it? We overlaid sound from high-end and low-end robot arms on videos of the high-end KUKA youBot desktop robotic arm moving a small block in functional (working in isolation) and social (interacting with a human) contexts. The low-end audio was sourced from an inexpensive OWI arm. Crowdsourced participants watched one video each and rated the robot along dimensions of competence, trust, aesthetic, and human-likeness. We found that the presence and quality of sound shapes subjective perception of the KUKA arm. The presence of any sound reduced human-likeness and aesthetic ratings, however the high-end sound rated better in the competence evaluation in the social context measures when compared to no sound. Overall, the social context increased the perceived competence, trust, aesthetic and human-likeness of the robot. Based on motor sound's significant mixed impact on visual perception of robots, we discuss implications for sound design of interactive systems.

When people communicate, their messages convey affect alongside informational content. The affective dimension of messages is often unclear and open to multiple interpretations especially in an intercultural context. Thus, interlocutors may or may not achieve a state of affective grounding in which each person's affective behaviors are correctly interpreted by his/her partners. The current study examines the effects of culture on affective grounding. We conducted a laboratory experiment in which pairs of participants, half from America (A) and half from China (C), collaborated over instant messaging (IM). We found that affective grounding was harder to achieve for AA and AC pairs, but easier for CC pairs. We propose several design solutions to facilitate affective grounding in remote collaborations.

Learning a second language is challenging. Becoming fluent requires learning contextual information about how language should be used as well as word meanings and grammar. The majority of existing language learning applications provide only thin context around content. In this paper, we present work in Crystallize, a language learning game that combines traditional learning approaches with a situated learning paradigm by integrating a spaced-repetition system within a language learning roleplaying game. To facilitate long-term engagement with the game, we added a new quest paradigm, "jobs," that allow a small amount of design effort to generate a large set of highly-scaffolded tasks that grow iteratively. A large-scale evaluation of the language learning game "in the wild" with a diverse set of 186 people revealed that the game was not only engaging players for extended amounts of time but that players learned an average of 8.7 words in an average of 40.5 minutes.

Learning a second language is challenging. Becoming fluent requires learning contextual information about how language should be used as well as word meanings and grammar. The majority of existing language learning applications provide only thin context around content. In this paper, we present Crystallize, a collaborative 3D game that provides rich context along with scaffolded learning and engaging gameplay mechanics. Players collaborate through joint tasks, or quests. We present a user study with 42 participants that examined the impact of low and high levels of task interdependence on language learning experience and outcomes. We found that requiring players to help each other led to improved collaborative partner interactions, learning outcomes, and gameplay. A detailed analysis of the chat-logs further revealed that changes in task interdependence affected learning behaviors.

A key assumption that drives much of HRI research is that human robot collaboration can be improved by advancing a robot's capabilities. We argue that this assumption has potentially negative implications, as increasing social capabilities in robots can produce an expectations gap where humans develop unrealistically high expectations of social robots due to generalization from human mental models. By conducting two studies with 674 participants, we examine how people develop and adjust mental models of robots. We find that both a robot's physical appearance and its behavior influence how we form these models. This suggests it is possible for a robot to unintentionally manipulate a human into building an inaccurate mental model of its overall abilities simply by displaying a few capabilities that humans possess, such as speaking and turn-taking. We conclude that this expectations gap, if not corrected for, could ironically result in less effective collaborations as robot capabilities improve.

In this paper we demonstrate that it is possible to help individuals regulate their emotions with mobile interventions that leverage the way we naturally react to our bodily signals. Previous studies demonstrate that the awareness of our bodily signals, such as our heart rate, directly influences the way we feel. By leveraging these findings we designed a wearable device to regulate user's anxiety by providing a false feedback of a slow heart rate. The results of an experiment with 67 participants show that the device kept the anxiety of the individuals in low levels when compared to the control group and the other conditions. We discuss the implications of our findings and present some promising directions for designing and developing this type of intervention for emotion regulation.

Do teams show stable conflict interaction patterns that predict their performance hours, weeks, or even months in advance? Two studies demonstrate that two of the same patterns of emotional interaction dynamics that distinguish functional from dysfunctional marriages also distinguish high from low-performance design teams in the field, up to 6 months in advance, with up to 91% accuracy, and based on just 15minutes of interaction data: Group Affective Balance, the balance of positive to negative affect during an interaction, and Hostile Affect, the expression of a set of specific negative behaviors were both found as predictors of team performance. The research also contributes a novel method to obtain a representative sample of a team's conflict interaction. Implications for our understanding of design work in teams and for the design of groupware and feedback intervention systems are discussed.

We explore the impact of the displayed precision of instrumentation estimates of range and state-of-charge on drivers' attitudes towards an all-electric vehicle (EV), on their driving experience, and driving behavior under varying conditions of resource availability. Participants (N=73) completed a 19-mile long drive through highway, rural town and mountain road conditions in an EV that displayed high vs. low remaining range, and gave estimates of that range with high and low information ambiguity. We found that an ambiguous display of range preserved drivers' feelings of trust towards the vehicle, despite encountering situations intended to induce severe range anxiety. Furthermore, compared to drivers facing an unambiguous display of range, drivers presented with an ambiguous range display reported improved driving experience, and exhibited driving behavior better adapted to road and remaining range conditions.

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.

Persuasive technologies aim to influence user's behaviors. In order to be effective, many of the persuasive technologies de-veloped so far relies on user's motivation and ability, which is highly variable and often the reason behind the failure of such technology. In this paper, we present the concept of Mindless Computing, which is a new approach to persuasive technology design. Mindless Computing leverages theories and concepts from psychology and behavioral economics into the design of technologies for behavior change. We show through a systematic review that most of the current persuasive technologies do not utilize the fast and automatic mental processes for behavioral change and there is an opportunity for persuasive technology designers to develop systems that are less reliant on user's motivation and ability. We describe two examples of mindless technologies and present pilot studies with encouraging results. Finally, we discuss design guidelines and considerations for developing this type of persuasive technology.

The digitization of educational course content has proved to be problematic for math instructors due to the lack of quality feedback tools that can accommodate the commenter to efficiently express math formulae and convey descriptions about complex ideas contextualized in situ. This paper proposes that RichReview, a document annotation system which creates inking, voice and deictic gestures on top of the student's submitted work, is a possible formative math feedback solution, because it enables face-to-face like commentary within the contexts of the document at hand. A preliminary qualitative evaluation study conducted while having students receive RichReview feedback showed promise to our approach to enhance the quality of feedback, with the implication that incorporating multi-modal feedback into workflows can be an effective method to address elements of feedback submissions lacking in coursework that has moved online.

Designing and building mechatronic systems has gradually ceased to be the domain of only highly trained professionals and has become broadly accessible. Drawing from a notion of designing as a conversation with the materials of the situation we built an artifact that could actively engage in its own making by embedding a Wizard of Oz operated animated agent into an Arduino prototyping platform. In a 2x2 between-participants Wizard of Oz laboratory experiment with (N=68) high-school students we specifically examined how this prototyping agent's expression of interest affected perceptions of the agent and learning outcomes dependent on the embodiment of the agent as embedded in the prototyping material itself or as an external entity. We found evidence that embedding an agent into the prototyping material can positively influence learning processes and outcomes while not harming perceptions of the agent.

Behavioral researchers spend considerable amount of time coding video data to systematically extract meaning from subtle human actions and emotions. In this paper, we present Glance, a tool that allows researchers to rapidly query, sample, and analyze large video datasets for behavioral events that are hard to detect automatically. Glance takes advantage of the parallelism available in paid online crowds to interpret natural language queries and then aggregates responses in a summary view of the video data. Glance provides analysts with rapid responses when initially exploring a dataset, and reliable codings when refining an analysis. Our experiments show that Glance can code nearly 50 minutes of video in 5 minutes by recruiting over 60 workers simultaneously, and can get initial feedback to analysts in under 10 seconds for most clips. We present and compare new methods for accurately aggregating the input of multiple workers marking the spans of events in video data, and for measuring the quality of their coding in real-time before a baseline is established by measuring the variance between workers. Glance's rapid responses to natural language queries, feedback regarding question ambiguity and anomalies in the data, and ability to build on prior context in followup queries allow users to have a conversation-like interaction with their data - opening up new possibilities for naturally exploring video data.

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.

Supporting a wide variety of interaction styles across a diverse set of people is a significant challenge in human-robot interaction (HRI). In this work, we explore a data-driven approach that relies on crowdsourcing as a rich source of interactions that cover a wide repertoire of human behavior. We first develop an online game that requires two players to collaborate to solve a task. One player takes the role of a robot avatar and the other a human avatar, each with a different set of capabilities that must be coordinated to overcome challenges and complete the task. Leveraging the interaction data recorded in the online game, we present a novel technique for data-driven behavior generation using case-based planning for a real robot. We compare the resulting autonomous robot behavior against a Wizard of Oz base case condition in a real-world reproduction of the online game that was conducted at the Boston Museum of Science. Results of a post-study survey of participants indicate that the autonomous robot behavior matched the performance of the human-operated robot in several important measures. We examined video recordings of the real-world game to draw additional insights as to how the novice participants attempted to interact with the robot in a loosely structured collaborative task. We discovered that many of the collaborative interactions were generated in the moment and were driven by interpersonal dynamics, not necessarily by the task design. We explored using bids analysis as a meaningful construct to tap into affective qualities of HRI. An important lesson from this work is that in loosely structured collaborative tasks, robots need to be skillful in handling these in-the-moment interpersonal dynamics, as these dynamics have an important impact on the affective quality of the interaction for people. How such interactions dovetail with more task-oriented policies is an important area for future work, as we anticipate such interactions becoming commonplace in situations where personal robots perform loosely structured tasks in interaction with people in human living spaces.

Inspired by research on the role of affect in marital interactions, the authors examined whether affective interaction dynamics occurring within a 5-minute slice can predict pair programming performance. In a laboratory experiment with professional programmers, Group Hedonic Balance, a measure of the balance between positive and negative expressed affect, accounted for up to 35% of the variance in not only subjective but also objective pair programming performance. Implications include a new set of methods to study pair programming interactions and recommendations to improve pair programming performance.