Conversational social robots can use backchanneling to provide real-time feedback, convey an understanding, demonstrate attention and maintain a flowing dialog. However, backchanneling implementations are often project- and robot-specific, and we do not yet have standardized backchanneling toolkits to enable robot dialog designers to easily configure a robot's back-channel behaviour. This highlights a need for high-level toolkits to enable dialog designers to engage with backchanneling and customization, allowing rapid exploration of backchanneling as a part of dialog design. To engage with this problem, we surveyed recent works on backchanneling social robots, performed an initial analysis to describe the range of backchannel techniques, and outlined preliminary criteria for high-level backchanneling design.

We propose to employ hazard labels to communicate a robot's potentially hazardous capabilities and behaviours to users. Robots can pose a range of ethical and physical safety concerns that users must be aware of when deciding whether to interact. We developed an initial set of key hazards, and a corresponding prototype of a hazard labeling scheme, to demonstrate the potential of this approach. We intend to use this prototype to support exploration of different styles of labeling, and ultimately to develop a more formalized system of robot hazard communication.

We explored the use of three robot interaction strategies to support people living with loneliness (physical comfort, social engagement, requiring care), by building these into a robot prototype and deploying the robots into homes for long-term evaluation. We placed our original prototype, SnuggleBot, unsupervised into the homes of seven people for at least 7 weeks (optionally up to 6 months), with bi-weekly interviews, to investigate how people engage with our three robot interaction strategies. Our qualitative analysis illuminated how people engaged the robot based on all three interaction strategies. Further, some participants showed signs of bonding with the robot as well as self-reported wellbeing benefits, while some participants failed to achieve sustained use over time. Our results provide strong support for future research into robots developed with our interaction strategies, and general potential for supporting wellbeing.

While children with disabilities often face barriers to play including reduced time, exclusion, and ill-suited toys, impacting their development, social robots provide the potential to help: they can motivate children, increase task engagement, and facilitate social interactions. However, social robots (and technological interventions in general) struggle to be adopted into regular use within homes by families, commonly being abandoned after a short time. Rather than focusing on the utility of these interventions, we instead look how they integrate into family needs and lifestyles. We designed and conducted a study where we engaged children living with disabilities and their families, using interactions with real robots and exploratory exercises, to learn about their perspectives, needs, and concerns regarding adopting a social companion robot in their home. We analyzed participant task engagement and feedback from the perspective of supporting play for children with disabilities and presented resulting design recommendations for addressing primary concerns and matching key expectations, and to support adoption pathways to improve the chances of success.

Eyes have been widely found to play an important role in communicating emotions and intentions for social robots. However, when introducing robots with eyes into people’s personal living spaces, it is necessary to consider how the social aspects of a robot impact perceptions of privacy. Specifically, in this paper we explored how the design of a robot’s eyes and eye-use behavior impacts use perception of privacy and related concerns. We developed a prototype robot with display-based eyes and investigated the impacts on perceptions of privacy concerns living with the robot. Our findings indicated that robot eye design does impact perceptions of privacy concerns, with the unexpected result of robots whose eyes that stay always on garnering lower concerns than eyes that close or turn off (the display) just before a person leaves. This result offer valuable insights for robot development and present empirical evidence on the influence of robot eye design on user privacy concerns.

Robots are being developed to help in various settings with young children. However, research suggests that children may overtrust robots, which can have a negative impact when such trust is unwanted or unsafe. Based on recent results from the community we suggest designing robots to use intentional errors to potentially reduce children’s trust in robots and to mitigate overtrust. We present a breakdown of robot errors that might affect children’s trust towards robots, and which could be used intentionally to mitigate overtrust. This includes accuracy errors, responsiveness errors, and error recovery strategies. We highlight how they could be used to decrease trust. We lastly provide an agenda for researchers to further investigate how the intentional use of robot errors could help to mitigate children’s overtrust in robots.

This paper presents a novel framework for describing human-robot interaction dialog, developed from a survey and analysis of existing systems and research. We collected data from 75 published systems and conducted an iterative thematic analysis to distill the broad range of work into key underlying factors de-fining them. Our framework provides a language to describe hu-man-robot dialog systems and a new way of classifying and under-standing human-robot dialog, in terms of both high-level design aspects and relevant implementation details. Our quantitative sur-vey summary further provides a detailed, contemporary snapshot of predominant approaches in the field, highlighting opportunities for further exploration.

As sex robots continue to be developed by industry, portrayed by media, and studied by researchers, it is common to conceptualize robots from a cisgender and heterosexual (cishet), or feminist perspective. We advocate for an increased shift toward the 2LGBTQIA+ community for inspiration and a path forward for more inclusive, successful, and socially responsible sex robots. In addition to the intrinsic value of being inclusive, looking to the 2LGBTQIA+ community can help us to break away from traditional ideas of gender and sexuality, to unlock the full potential of this technology to be flexible and offer new possibilities. Further, we reflect on the importance of considering how the designs of sex robots, as politically charged technological artifacts, can contribute to reinforcing ideas about heteronormativity; instead, sex robots have the potential to positively contribute to breaking down traditional barriers surrounding gender and sex. We envision a future of sex robots that reach their full potential as fluid, individualized companions that enable people to comfortably engage their interests and identity.

Bullying in schools is a widespread problem with serious consequences. We are exploring the use of social robots and role-playing to foster anti-bullying peer-support. In this paper, we present results from a co-design study with 22 children (8-12 years old) to explore how they envision a “student robot”. To understand how they conceptualize bullying in this context (e.g. whether robots can be bullied), we also investigated how they envision this robot’s various social interactions. We prompted children to imagine a fictional robot about their age and follow a stepwise process to design the robot, and make stories about its interactions. Qualitative analysis of this study suggests themes of robots being described as highly-customizable characters with predominantly positive traits, that are imperfect. We also found that children can articulate various scenarios involving robots taking roles of bullies, victims, and bystanders. These findings contribute insights for designing pedagogical robots and anti-bullying interventions for children.

Bullying in schools is a serious issue with severe and long-term consequences. We explore using social robots in anti-bullying programs to encourage children to intervene in bullying of their peers. To that end, we have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants’ perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it. This work contributes insights for understanding how people perceive bullying of robots, designing intelligent behaviors to discourage bullying of robots, and to our long-term goal of developing anti-bullying pedagogical programs that use social robots.

A growing area of human-robot interaction explores how robots, for example as companions, can be used to help people manage loneliness. However, we do not yet have research results indicating if people are ready to accept companion robots in their daily lives, and thus if companion robots can actually be successful broadly in society. We present a novel long-term in-home interaction study design that will explore how people accept these robots in their homes and how the robots impact loneliness.

Loneliness is an increasing problem in today's world, especially during the current pandemic when people are finding themselves increasingly isolated. Social robotics research has highlighted how robots can be designed to support people and improve their mood and general wellness. However, we do not yet see widespread social companion robots in homes. In this project, we took a constrained design approach to developing a novel cuddly companion robot, focusing on simple and deployable technologies, while aiming to support people's wellbeing by helping with loneliness. We had three design goals that are simultaneously feasible and align with supporting wellness: the robot should be physically comforting, socially engaging, and require people to care for it. We present a simple prototype cuddly companion robot that implements these goals using simple, off-the-shelf technologies.

Robots are increasingly becoming present in workplaces and social scenarios, where people are interacting and completing tasks with them. In this paper, we explore the importance of the physical seating arrangement or placement of the robot in relation to the human. We designed a novel collaborative human-robot task for exploring how human-robot seating arrangement may influence interaction and the person's perceptions of a robot, and present the results from a pilot and two formal experiments (total 72 participants). Drawing from proxemics literature, we compared a person sitting next to a robot to being across a table from it or beside it, with a corner between them. Our results highlight a range of impacts on participant attitudes toward the robot, as well as their behavior and interaction with it. In particular, seating arrangement impacted participant preferences for the robot, and their use of aggression or condescension during disagreements with it. Our results highlight the importance of seating arrangements for collaborative human-robot teams.

We introduce a conversational social robot designed for long-term in-home use to help with loneliness. Our simple design leverages techniques from psychology to encourage self-reflection and promote wellness. To increase feasibility, we pay special attention to simple and robust robotic interaction, avoiding complex behaviors that may be prohibitively complex (i.e., possible in lab but not deployable) or unpredictable (e.g., generated speech saying something inappropriate). We present a novel robot behavior design to have simple self-reflection conversations with people to improve wellness, while still being feasible, deployable, and safe.

AV-pedestrian interaction will impact pedestrian safety, etiquette, and overall acceptance of AV technology. Evaluating AV-pedestrian interaction is challenging given limited availability of AVs and safety concerns. These challenges are compounded by "mixed traffic" conditions: studying AV-pedestrian interaction will be difficult in traffic consisting of vehicles varying in autonomy level. We propose immersive pedestrian simulators as design tools to study AV-pedestrian interaction, allowing rapid prototyping and evaluation of future AV-pedestrian interfaces. We present OnFoot: a VR-based simulator that immerses participants in mixed traffic conditions and allows examination of their behavior while controlling vehicles' autonomy-level, traffic and street characteristics, behavior of other virtual pedestrians, and integration of novel AV-pedestrian interfaces. We validated OnFoot against prior simulators and Wizard-of-Oz studies, and conducted a user study, manipulating vehicles' autonomy level, interfaces, and pedestrian group behavior. Our findings highlight the potential to use VR simulators as powerful tools for AV-pedestrian interaction design in mixed traffic.

We present Mutation: performing arts based approach that can help decrease the cognitive load associated with cyborg transitioning. Cyborgs are human-machine hybrids with organic and mechatronic body parts that can be implanted or worn. The transition into and out of experiencing additional body parts is not fully understood. Our goal is to draw from performing arts techniques in order to help decrease the cognitive load associated with becoming and unbecoming a cyborg. Actors constantly shift between states, whether from one character to another, or from pre- to post- performance. We contribute a straightforward adaptation of classic performing art practices to cyborg transitioning, and a study where actors used these protocols in order to enter a cyborg state, perform as a cyborg, and then exit the cyborg state. Our work on Mutation suggests that classic performing art practices can be useful in cyborg transitioning, as well as in other technology augmented experiences.

Robots that interact with children are becoming more common in places such as child care and hospital environments. While such robots may mistakenly provide nonsensical information, or have mechanical malfunctions, we know little of how these robot errors are perceived by children, and how they impact trust. This is particularly important when robots provide children with information or instructions, such as in education or health care. Drawing inspiration from established psychology literature investigating how children trust entities who teach or provide them with information (informants), we designed and conducted an experiment to examine how robot errors affect how young children (3-5 years old) trust robots. Our results suggest that children utilize their understanding of people to develop their perceptions of robots, and use this to determine how to interact with robots. Specifically, we found that children developed their trust model of a robot based on the robot's previous errors, similar to how they would for a person. We however failed to replicate other prior findings with robots. Our results provide insight into how children as young as 3 years old might perceive robot errors and develop trust.

Robots are being increasingly developed as social actors, entering public and personal spaces such as airports, shopping malls, care centres, and even homes, and using human or animal-like social techniques to work with people. Some even aim to engineer social situations, or are designed specifically for an emotional response (e.g., comforting a person). However, if we consider these robots as social interventions, then it is important to recognize that the robots design - its behavior, its application, its appearance, even its marketing image - will have an impact on the society and in the spaces it enters. While in some cases this may be a positive effect, social robots can also contribute negatively, e.g., reinforcing gender stereotypes or promoting ageist views. This full-day workshop aims to offer a forum for Human-Robot Interaction (HRI) researchers to explore this issue, and to work toward potential opportunities for the field. Ultimately, we want to promote robots for social good that can contribute to positive social changes for socio-political issues (e.g., ageism, feminism, homelessness, environmental issues). The political aspects of technologies have long been scrutinized in related areas such as Science and Technology Studies (STS) and Human-Computer Interaction (HCI). In particular, critical design explicitly targets the design of technologies that can contribute to our understanding of how technology can impact society. This workshop aims to strengthen this discussion in the HRI community, with the goal of working toward initial recommendations for how HRI designers can include elements of critical design in their work.

We investigate if an on-screen agent that reacts to a teleoperator's driving performance (e.g., by showing fear during poor driving) can influence teleoperation. Serving as a kind of virtual passenger, we explore if and how this agent's reactions may impact teleoperation. Our design concept is to create an emotional response in the operator (e.g., to feel bad for the agent), with the ultimate goal of shaping driving behavior (e.g., to slow down to calm the agent). We designed and implemented two proof-of-concept agent personas that react differently to operator driving. By conducting an initial proof-of-concept study comparing our agents to a base case, we were able to observe the impact of our agent personas on operator experience, perception of the robot, and driving behavior. While our results failed to find compelling evidence of changed teleoperator behavior, we did demonstrate that emotional on-screen agents can alter teleoperator emotion. Our initial results support the plausibility of passenger agents for impacting teleoperation, and highlight potential for more targeted, ongoing work in applying social techniques to teleoperation interfaces.

Priming is the influence of external stimuli on a person's behavior and thoughts, where the effect is related to some quality of that stimuli. In this work, we explore three methods of priming teleoperator's expectations about robot capability and investigate how this may impact driving behavior and perceptions of the robot. We tested priming impressions of robot ability by the stiffness of the robot controller, additionally describing the changes in stiffness verbally to operators and simply describing the robots' abilities on paper and verbally. Our results found that all priming methods affected the perception of the robot including its speed, weight, or overall safety. Further, we confirmed that some priming methods lowered operator collisions by over 40%. Thus, interface and product designers should consider priming as a tool to leverage to improve operator performance and perception of the robot.

Social robots are being designed to use human-like communication techniques, including body language, social signals, and empathy, to work effectively with people. Just as between people, some robots learn about people and adapt to them. In this paper we present one such robot design: we developed Sam, a robot that learns minimal information about a person's background, and adapts to this background. Our in-the-wild study found that people helped Sam for significantly longer when it adapted to match their background. While initially we saw this as a success, in re-considering our study we started seeing a different angle. Our robot effectively deceived people (changed its story and text), based on some knowledge of their background, to get more work from them. There was little direct benefit to the person from this adaptation, yet the robot stood to gain free labor. We would like to pose the question to the community: is this simply good robot design, or, is our robot being manipulative? Where does the ethical line lay between a robot leveraging social techniques to improve interaction, and the more negative framing of a robot or algorithm taking advantage of people? How can we decide what is good here, and what is less desirable?

We investigate robots using infrasound, low-frequency vibrational energy at or near the human hearing threshold, as an interaction tool for working with people. Research in psychology suggests that the presence of infrasound can impact a person's emotional state and mood, even when the person is not acutely aware of the infrasound. Although often not noticed, infrasound is commonly present in many situations including factories, airports, or near motor vehicles. Further, a robot itself can produce infrasound. Thus, we examine if infrasound may impact how people interpret a robot's social communication: if the presence of infrasound makes a robot seem more or less happy, energetic, etc., as a result of impacting a person's mood. We present the results from a series of experiments that investigate how people rate a social robot's emotionally-charged gestures, and how varied levels and sources of infrasound impact these ratings. Our results show that infrasound does have a psychological effect on the person's perception of the robot's behaviors, supporting this as a technique that a robot can use as part of its interaction design toolkit. We further provide a comparison of infrasound generation methods.

We investigated interactive agents using subliminal priming - the act of exposing a person to stimuli that they may not consciously notice, but are still processed subliminally in their mind - in an attempt to shape a person's mood and behavior. We present an overview of the psychology of subliminal priming from the perspective of how it applies to human-agent interaction, including a discussion of the potential ethical and practical implications. We further present the results from two exploratory studies (one in-lab, one crowdsourced) that present potential subliminal-priming interfaces. Our results suggest that subliminal priming may impact how participants perceive an agent and how much they enjoy a task, but we failed to find any effect of priming on participant mood or agent persuasiveness. This work aims to raise awareness of the dangers of subliminal methods of priming and contributes to the discussion on the ethics of social agents.

Perceptions of a technology can shape the way the technology is used and adopted. Thus, in teleoperation, it is important to understand how a teleoperator's perceptions of a robot can be shaped, and whether those perceptions can impact how people drive robots. Priming, evoking activity in a person by exposing them to learned stimuli, is one way of shaping someone's perception. We investigate priming an operator's impression of a robot's physical capabilities in order to impact their perception of the robot and teleoperation behavior; that is, we examine if we can change operator driving behavior simply by making them believe that a robot is dangerous or safe, fast or slow, etc., without actually changing robot capability. Our results show that priming (with no change to robot behavior or capability) can impact operator perception of the robot, their teleoperation experience, and in some cases may impact teleoperation performance.

We designed and evaluated a series of teleoperation interface techniques that aim to draw operator attention while mitigating negative effects of interruption. Monitoring live teleoperation video feeds, for example to search for survivors in search and rescue, can be cognitively taxing, particularly for operators driving multiple robots or monitoring multiple cameras. To reduce workload, emerging computer vision techniques can automatically identify and indicate (cue) salient points of potential interest for the operator. However, it is not clear how to cue such points to a preoccupied operator -- whether cues would be distracting and a hindrance to operators -- and how the design of the cue may impact operator cognitive load, attention drawn, and primary task performance. In this paper, we detail our iterative design process for creating a range of visual attention-grabbing cues that are grounded in psychological literature on human attention, and two formal evaluations that measure attention-grabbing capability and impact on operator performance. Our results show that visually cueing on-screen points of interest does not distract operators, that operators perform poorly without the cues, and detail how particular cue design parameters impact operator cognitive load and task performance. Specifically, full-screen cues can lower cognitive load, but can increase response time; animated cues may improve accuracy, but increase cognitive load. Finally, from this design process we provide tested, and theoretically grounded cues for attention drawing in teleoperation.

We investigated the effects of changing the tele-operation feel of operating a robot by modifying its speed and acceleration profiles, and found that reducing a robot's maximum speed by half can reduce collisions by 32%, while only increasing navigation task time by 10%. Teleoperated robots are increasingly popular for enabling people to remotely attend meetings, explore dangerous areas, or view tourist destinations. As these robots are being designed to work in crowded areas with people, obstacles, or even unpredictable debris, interfaces that support piloting them in a safe and controlled manner are important for successful teleoperation. We investigate modifying a teleoperated robot's speed and acceleration profiles on an operator remotely navigating through an obstacle course. Our results indicate that lower maximum speeds result in lower operator workload, fewer collisions, and are only slightly slower than other profiles with a higher maximum speed. Our results raise questions about how robot designers should think about physical robot capability design and default driving software settings, the robot control interface, and the relation of robot speed to control.

Robot teleoperation, such as for search and rescue, uses multiple specialized cameras (e.g., wide environmental and sharp narrow views) to aid in task awareness. Simple display techniques, such as tiling, require ongoing mental mapping between the views; cameras that pan or tilt exacerbate the problem as the inter-view relationship changes. The detail-in-context technique bypasses this mental mapping requirement by providing a single integrated feed showing all cameras, with detail overlaid within the context. However, how this can be adapted to for robot teleoperation with multiple pan-and-tilt cameras has not yet been demonstrated. We present Monocle, an interactive detail-in-context teleoperation interface that integrates a pan-and-tilt narrow-angle first-person view into a wide-angle behind-robot view; operators can move the Monocle around a scene to obtain more resolution when and where needed. Evaluation results demonstrate Monocle's feasibility and show that it can help operators complete search and rescue tasks more effectively in comparison to simple solutions.

We present a set of mini-map alternatives for indicating the relative locations of robot team members in a tele-operation interface, and evaluation results showing that these can perform as well as mini-maps while being less intrusive. Teleoperation operators often work with a team of robots to improve task effectiveness. Maintaining awareness of where robot team members are, relative to oneself, is important for team effectiveness, such as for deciding which robot may help with a task, may be best suited to investigate a point of interest, or to determine where one should move next. We explore the use of established interface techniques from mobile computing for supporting teleoperators in maintaining peripheral awareness of robot team members' relative locations. We evaluate the nontrivial adoption of these techniques to teleoperation, comparing to an overview mini-map base case. Our results indicate that in-feed embedded indicators perform comparatively well to mini-maps, while being less obtrusive, indicating that they are a viable alternative for teleoperation interfaces.

This article illustrates how HRI can be a useful vehicle for exposing technologist students (e.g., in computer science or engineering) to social aspects of technology and to the concrete benefits that a socially aware perspective can bring to technology design, implementation, and use. The article details the strategy and layout taken with the graduate-level Human-Robot Interaction (HRI) course at the University of Manitoba, Canada, between 2011 and 2015, for the purposes of introducing students to social considerations surrounding technology. Further, this paper reflects on the results from three iterations of the course and demonstrates how students have engaged socially aware thinking and analysis in their course projects. This single, introductory HRI course can be a catalyst for encouraging socially aware thinking in technologist students.

We present the results of a pilot study that investigates if and how people judge the trustworthiness of a robot during social Human-Robot Interaction (sHRI). Current research in sHRI has observed that people tend to interact with robots socially. However, results from neuroscience suggests people use different cognitive mechanisms interacting with robots than they do with humans, leading to a debate about whether people truly perceive robots as social entities. Our paper focuses on one aspect of this debate, by examining trustworthiness between people and robots using behavioral economics' ‘Trust Game’ scenario. Our pilot study replicates a trust game scenario, where a person invests money with a robot trustee in hopes they will receive a larger sum (trusting the robot to give more back), then gets a chance to invest once more. Our qualitative analysis of investing behavior and interviews with participants suggests that people may follow a human-robot (h-r) trust model that is quite similar to the human-human trust model. Our results also suggest a possible resolution to the sHRI and Neuroscience debate: people try to interact socially with robots, but due to lack of common social cues, they draw from social experience, or create new experiences by actively exploring the robot behavior.

We investigated obedience to an authoritative robot and asked experiment participants to do a task they would rather not do. Obedience questions are increasingly important as robots participate in tasks where they give people directions. We conducted a series of HRI obedience experiments, comparing a robotic authority to other authority instances, including: (i) a human, (ii) a remote-controlled robot, and (iii) robots of variant embodiments. The results suggest that half of participants will continue to perform a tedious task under the direction of a robot, even after expressing desire to stop. Further, we failed to find an effect of robot embodiment and perceived autonomy on obedience. Instead, the robot's perceived authority status may be more strongly correlated to obedience.

We are interested in the social implications of working alongside robots. In this paper we look at a humanoid robot quality inspector, acting alongside workers in an assembly line. This setting is viable in small scale assembly lines where human assembly workers provide flexible, rapid assembly. A robotic quality inspector could enhance the quality assurance process, but places the robot in a position of relative seniority to the assembly workers. We present the results of an initial in-lab pilot study designed with our industry collaborators. In our pilot, a humanoid robot visually inspected participants' assembled products in a shared workspace and provided critiques that follow simple models of robotic social feedback. Our findings suggest that people's opinions of the robot (trust, impression of intelligence, etc.) changed based on the robot's social behaviors while it is judging the participant's work. Additionally, people rated the robot more negatively if they disagreed with the robot's opinions of their work, regardless of the robot social behavior and the value of its critique.

This paper presents an original scenario design specifically created for exploring gender-related issues surrounding collaborative human-robot teams on assembly lines. Our methodology is grounded squarely in the need for increased gender work in human-robot interaction. As with most research in social human-robot interaction, investigating and exploring gender issues relies heavily on an evaluation methodology and scenario that aims to maximize ecological validity, so that the lab results can generalize to a real-world social scenario. In this paper, we present our discussion on study elements required for ecological validity in our context, present an original study design that meets these criteria, and present initial pilot results that reflect on our approach and study design.

This work proposes the concept of shared presence, where we enable a user to "become" a co-located humanoid robot while still being able to use their real body to complete tasks. The user controls the robot and sees with its vision and sensors, while still maintaining awareness and use of their real body for tasks other than controlling the robot. This shared presence can be used to accomplish tasks that are difficult for one person alone, for example, a robot manipulating a circuit board for easier soldering by the user, lifting and manipulating heavy or unwieldy objects together, or generally having the robot conduct and complete secondary tasks while the user focuses on the primary tasks. If people are able to overcome the cognitive difficulty of maintaining presence for both themselves and a nearby remote entity, tasks that typically require the use of two people could simply require one person assisted by a humanoid robot that they control. In this work, we explore some of the challenges of creating such a system, propose research questions for shared presence, and present our initial implementation that can enable shared presence. We believe shared presence opens up a new research direction that can be applied to many fields, including manufacturing, home-assistant robotics, and education.

In designing and evaluating human-robot interactions and interfaces, researchers often use a simulated robot due to the high cost of robots and time required to program them. However, it is important to consider how interaction with a simulated robot differs from a real robot; that is, do simulated robots provide authentic interaction? We contribute to a growing body of work that explores this question and maps out simulated-versus-real differences, by explicitly investigating empathy: how people empathize with a physical or simulated robot when something bad happens to it. Our results suggest that people may empathize more with a physical robot than a simulated one, a finding that has important implications on the generalizability and applicability of simulated HRI work. Empathy is particularly relevant to social HRI and is integral to, for example, companion and care robots. Our contribution additionally includes an original and reproducible HRI experimental design to induce empathy toward robots in laboratory settings, and an experimentally validated empathy-measuring instrument from psychology for use with HRI.

We present an approach to content control where parents and children collaboratively configure restrictions and filters, an approach that focuses on education rather than simple rule setting. We conducted an initial exploratory qualitative study with results highlighting the importance that parents place on avoiding inappropriate content. Building on these findings, we designed an initial prototype which allows parents and children to work together to select appropriate applications, providing an opportunity for parents to educate their children on what is appropriate. A second qualitative study with parents and children in the six to eight year-old age group revealed a favorable response to this approach. Our results suggest that parents felt that this approach helped facilitate discussions with their children and made the education more enjoyable and approachable, and that children may have also learned from the interaction. In addition, the approach provided some parents with insights into their children's interests and understanding of their notions of appropriate and inappropriate content.

The issue of believability is core to android science, the challenge of creating a robot that can pass as a near human. While researchers are making great strides in improving the quality of androids and their likeness to people, it is simultaneously important to develop theoretical foundations behind believability, and experimental methods for exploring believability. In this paper, we explore a visual processing approach to investigating the believability of android faces, and present results from a study comparing current-generation android faces to humans' faces. We show how android faces are still not quite as believable as humans, and provide some mechanisms that may be used to investigate and compare believability in future projects.

The creation of interactive computer-controlled characters in interactive media is a challenging and multi-faceted task requiring the skills and effort of professionals from many fields. This work addresses authoring the interactive aspect of these characters' behaviors -- how characters act automatically in response to a dynamic user-controlled character. We present PaintBoard, a system that enables users to prototype and test discrete, real-time, interactive movements in a 2D grid environment simply by digitally painting a storyboard. We designed and developed a novel authoring technique for creating behaviors (painting storyboards) and a novel algorithm based on machine-learning, that analyzes a storyboard to create a behavior that works beyond situations provided in the input storyboard. We conducted two exploratory studies that grounded the prototype design, and present the results of a proof-of-concept workshop with game developers. Finally, we performed a comparison of machine learning algorithms' performance on our storyboard data.

Applying visual analytics to large software systems can help users comprehend the wealth of information produced by source repository mining. One concept of interest is the co-evolution of test code with source code, or how source and test files develop together over time. For example, understanding how the testing pace compares to the development pace can help test managers gauge the effectiveness of their testing strategy. A useful concept that has yet to be effectively incorporated into a co-evolution visualization is co-change. Co-change is a quantity that identifies correlations between software artifacts, and we propose using this to organize our visualization in order to enrich the analysis of co-evolution. In this paper, we create, implement, and study an interactive visual analytics tool that displays source and test file changes over time (co-evolution) while grouping files that change together (co-change). Our new technique improves the analyst's ability to infer information about the software development process and its relationship to testing. We discuss the development of our system and the results of a small pilot study with three participants. Our findings show that our visualization can lead to inferences that are not easily made using other techniques alone.

We present a series of projects for end-user authoring of interactive robotic behaviors, with a particular focus on the style of those behaviors: we call this approach Style-by-Demonstration (SBD). We provide an overview introduction of three different SBD platforms: SBD for animated character interactive locomotion paths, SBD for interactive robot locomotion paths, and SBD for interactive robot dance. The primary contribution of this article is a detailed cross-project SBD analysis of the interaction designs and evaluation approaches employed, with the goal of providing general guidelines stemming from our experiences, for both developing and evaluating SBD systems. In addition, we provide the first full account of our Puppet Master SBD algorithm, with an explanation of how it evolved through the projects.

Remote control robots are being found in an increasing number of application domains, including search and rescue, exploration, and reconnaissance. There is a large body of HRI research that investigates interface design for remote navigation, control, and sensor monitoring, while aiming for interface enhancements that benefit the remote operator such as improving ease of use, reducing operator mental load, and maximizing awareness of a robot's state and remote environment. Even though many remote control robots have multi-degree-of-freedom robotic manipulator arms for interacting with the environment, there is only limited research into easy-to-use remote control interfaces for such manipulators, and many commercial robotic products are still using simplistic interface technologies such as keypads or gamepads with arbitrary mappings to arm morphology. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly.

In this article we present a multipart formal design and evaluation of the style-by-demonstration (SBD) approach to creating interactive robot behaviors: enabling people to design the style of interactive robot behaviors by providing an exemplar. We first introduce our Puppet Master SBD algorithm that enables the creation of interactive robot behaviors with a focus on style: Users provide an example demonstration of human–robot interaction and Puppet Master uses this to generate real-time interactive robot output that matches the demonstrated style. We further designed and implemented original interfaces for demonstrating interactive robot style and for interacting with the resulting robot behaviors. Following, we detail a set of studies we performed to appraise users' reactions to and acceptance of the SBD interaction design approach, the effectiveness of the underlying Puppet Master algorithm, and the usability of the demonstration interfaces. Fundamentally, this article investigates the broad questions of how people respond to SBD interaction, how they engage SBD interfaces, how SBD can be practically realized, and how the SBD approach to social human–robot interaction can be employed in future interaction design.

As robots continue to enter people's everyday spaces, we argue that it will be increasingly important to consider the robots' movement style as an integral component of their interaction design. That is, aspects of the robot's movement which are not directly related to a task at hand (e.g., pick up a ball) can have a strong impact on how people perceive that action (e.g., aggressively or hesitantly). We call these elements the movement style. We believe that perceptions of this kind of style will be highly dependent on the culture, group, or individual, and so people will need to have the ability to customize their robot. Therefore, in this work we use Style by Demonstration, a style focus on the more-traditional programming by demonstration technique, and present the Puppet Dancer system, an interface for constructing paired and interactive robotic dances. In this paper we detail the Puppet Dancer interface and interaction design, explain our new algorithms for teaching dance by demonstration, and present the results from a formal qualitative study.

We present a robot augmented with an ambient display that communicates using a multi-color halo. We use this robot in a public café-style setting where people vote on which colors the robot will display: we ask people to select a color which "best represents their mood". People can vote from a mobile device (e.g., smart phone or laptop) through a web interface. Thus, the robot's display is an abstract aggregate of the current mood of the room. Our research investigates how a robot with an ambient display may integrate into a space. For example, how will the robot alter how people use or perceive the environment, or how people will interact with the robot itself? In this paper we describe our initial prototype, an iRobot Roomba augmented with lights, and highlight the research questions driving our exploration, including initial study design.

In this work we explore the development of guidelines for creating "in-the-field" interfaces for enabling a single user to remotely control multiple robots. The problem of controlling a remote team of robots is complex, requiring a user to monitor and interpret robotic state and sensor information in real time, and to simultaneously communicate direction commands to the robots. The result is that a robot controller is often seated at a console; for many relevant applications such as search and rescue or firefighting this removes the user from the field of action, rendering them unable to directly participate in a task at hand.

We present a robotic tail interface for enabling a robot to communicate its state to people. Our interface design follows an animal-inspired methodology where we map the robot's state to its tail output, leveraging people's existing knowledge of and experiences with animals for human-robot interaction. In this paper we detail our robotic-tail design approach and our prototype implementations, and outline our future steps.

The style in which a robot moves, expressed through its gait or locomotion, can convey effective messages to people. For example, a robot could move aggressively in reaction to a person's actions, or alternatively react using a set of careful, submissive movements. Designing, implementing and programming robotic interfaces that react to users' actions with properly styled movements can be a difficult, daunting, and time consuming technical task. On the other hand, most people can easily perform such stylistic tasks and movements, for example, through acting them out.

Human-robot interaction (HRI) tasks in everyday environments will require people to direct or lead a robot as they walk in close proximity to it. Tasks that exemplify this interaction include a robotic porter, carrying heavy suitcases, or a robot carrying groceries. As many users may not be robotics experts, we argue that such interaction schemes must be accessible, easy to use and understand. In this paper, we present a dog-leash interface that enables a person to lead a robot simply by holding the leash, following a dog-leash interaction metaphor. We introduce variants on dog-leash robotic interaction, present our original interface implementation, and detail our formal qualitative evaluation, exploring how users perceive and accept the dog leash robotic interaction.

Robots are poised to enter our everyday environments such as our homes and offices, contexts that present unique questions such as the style of the robot's actions. Style-oriented characteristics are difficult to define programmatically, a problem that is particularly prominent for a robot's interactive behaviors, those that must react accordingly to dynamic actions of people. In this paper, we present a technique for programming the style of how a robot should follow a person by demonstration, such that non-technical designers and users can directly create the style of following using their existing skill sets. We envision that simple physical interfaces like ours can be used by non-technical people to design the style of a wide range of robotic behaviors.

Interaction with a remote team of robots in real time is a difficult human-robot interaction (HRI) problem exacerbated by the complications of unpredictable real-world environments, with solutions often resorting to a larger-than-desirable ratio of operators to robots. We present two innovative interfaces that allow a single operator to interact with a group of remote robots. Using a tabletop computer the user can configure and manipulate groups of robots directly by either using their fingers (touch) or by manipulating a set of physical toys (tangible user interfaces). We recruited participants to partake in a user study that required them to interact with a small group of remote robots in simple tasks, and present our findings as a set of design considerations.

An ongoing issue in human robot interaction (HRI) is how people and robots communicate with one another. While there is considerable work in real-time human-robot communication, fairly little has been done in asynchronous realm. Our approach, which we call situated messages, lets humans and robots asynchronously exchange information by placing physical tokens - each representing a simple message - in meaningful physical locations of their shared environment. Using knowledge of the robot's routines, a person can place a message token at a location, where the location is typically relevant to redirecting the robot's behavior at that location. When the robot passes nearby that location, it detects the message and reacts accordingly. Similarly, robots can themselves place tokens at specific locations for people to read. Thus situated messages leverages embodied interaction, where token placement exploits the everyday practices and routines of both people and robots. We describe our working prototype, introduce application scenarios, explore message categories and usage patterns, and suggest future directions.

We present a new technique for human-robot interaction called robot expressionism through cartooning. We suggest that robots utilise cartoon-art techniques such as simplified and exaggerated facial expressions, stylised text, and icons for intuitive social interaction with humans. We discuss practical mixed reality solutions that allow robots to augment themselves or their surroundings with cartoon art content. Our effort is part of what we call robot expressionism, a conceptual approach to the design and analysis of robotic interfaces that focuses on providing intuitive insight into robotic states as well as the artistic quality of interaction. Our paper discusses a variety of ways that allow robots to use cartoon art and details a test bed design, implementation, and exploratory evaluation. We describe our test bed, Jeeves, which uses a Roomba, an iRobot vacuum cleaner robot, and a mixed-reality system as a platform for rapid prototyping of cartoon-art interfaces. Finally, we present a set of interaction content scenarios which use the Jeeves prototype: trash Roomba, the recycle police, and clean tracks, as well as initial exploratory evaluation of our approach.