In human-robot interaction, nonverbal robot cues such as gaze and emotional expression can potentially enhance coordination with nearby people via advantages like conveying a shared focus. At the same time, although laboratory studies have shown that expressive eye behavior increases the overall task performance while improving social perception of the robot, it is unclear whether these effects resoundingly generalize beyond the controlled laboratory environment into an unstructured public environment. We conducted an in-the-wild study to replicate the results of a previous in-lab study on expressive eye behavior (i.e., gaze and emotional expression) using a Hello Robot Stretch mobile manipulator. N = 55 participants interacted with the robot in a guided block placement task under one of four experiences: control, gaze only, emotion only, and both gaze and emotion combined. Participants in the gaze condition required significantly fewer attempts to select the correct block and reported the robot to be significantly more socially warm, suggesting that gaze may be a strong cue for directing collaboration as well as social perception in natural settings, while emotional expression could have more subtle impacts. These results can help robot practitioners create systems that fit more seamlessly and effectively into real-world settings.

One challenge in human-robot interaction is selecting communication methods that fit a given robotic system and avoid overpromising. For example, verbal speech provides a clear and easy-to-understand communication method, but can inflate expectations of robot abilities. Is verbal speech the ultimate option? Might other tactics provide similar advantages with fewer downsides? The presented work focuses on addressing these important questions by 1) quantifying any inflated opinions of robots that use verbal speech and 2) gathering perspectives on alternative nonverbal sound-based communication tactics (as a means to potentially shrink gaps between expected and actual robot performance). We conducted a within-subjects online study that varied robot communication modes in videos of successful and unsuccessful mock tasks by a modern commercial robot. Assessments of robot competence and trust after an observed robot failure were higher for verbal robots, but we observed less decline in competence and trust ratings due to the failure for a nonverbal robot using character-like sound (compared to a robot using verbal communication). Human-robot interaction practitioners can use our results to design effective and robust communication strategies for robots.

Mobile manipulators — robots with a moving base and an arm for grasping objects — are becoming more common in human-populated environments, such as hospitals, warehouses, and even homes. Yet most mobile manipulators lack clear ways to communicate intent to human interlocutors in a continuous, socially acceptable, and easy-to-interpret way. One possible solution for improving mobile manipulator communication is the addition of expressive eyes. This paper presents the design and evaluation of a custom expressive LED eye module for mobile manipulators, which can display both gaze and emotional expressions. Our evaluation study$(N=32)$involved a mock teamwork task alongside a Hello Robot Stretch RE2 mobile manipulator with the custom LED eye module. The results showed that both gaze and emotional expressions supported better participant performance in the task and more feelings of social closeness. Emotional eye expressions also yielded higher ratings of robot social warmth and competence. This work can inform mobile manipulator design for smoother integration into human-populated spaces.

Profanity is nearly as old as language itself, and cursing has become particularly ubiquitous within the last century. At the same time, robots in personal and service applications are often overly polite, even though past work demonstrates the potential benefits of robot norm-breaking. Thus, we became curious about robots using curse words in error scenarios as a means for improving social perceptions by human users. We investigated this idea using three phases of exploratory work: an online video-based study (N = 76) with a student pool, an online video-based study (N = 98) in the general U.S. population, and an in-person proof-of-concept deployment (N =52) in a campus space, each of which included the following conditions: no-speech, non-expletive error response, and expletive error response. A surprising result in the outcomes for all three studies was that although verbal acknowledgment of an error was typically beneficial (as expected based on prior work), few significant differences appeared between the non-expletive and expletive error acknowledgment conditions (counter to our expectations). Within the cultural context of our work, the U.S., it seems that many users would likely not mind if robots curse, and may even find it relatable and humorous. This work signals a promising and mischievous design space that challenges typical robot character design.

In early motor interventions from clinical rehabilitation to physical activity encouragement, one major challenge is maintaining child engagement and motivation. Robots show unique promise for addressing this challenge, but providing robots with new types of autonomous functionality is vital for promoting robot integration and usefulness in the clinic and home spaces. To provide needed autonomy capabilities for GoBot, our assistive robot for child–robot motion interventions, we propose a behavior tree framework. Within our framework, we build two trees: one manually designed based on expert knowledge of the child–robot interaction domain, and a second automatically synthesized and requiring minimal human input and time to construct. We tested each behavior tree with N = 11 children who interacted with GoBot during two behavior tree phases and a stationary-robot control phase. Our results show that both behavior tree phases tended to yield more child motion and significantly higher parent perception of child engagement, compared to the control phase. We showed that GoBot, equipped with our framework, has the potential to encourage movement and interaction in children and that a synthesized tree can be competitive with a manually designed tree. The products of this work can benefit researchers of behavior trees and child–robot interaction.

Devices that deliver deep pressure sensations (DPS) are common, but how well do these systems actually work, and are DPS experiences different across devices? To help address these questions, we previously designed a portable and fast-acting soft-robotic DPS alternative: the AID Vest. In this work, we evaluate the AID Vest’s effect on individuals with moderate or high anxiety specifically. We conducted a study with N = 10 participants, providing experiences with a weighted blanket and the AID Vest, and measuring biosignals, one-shot self-reports, and exploratory continuous self-reports related to these experiences. The results show reductions in established biosignal and self-report methods for measuring anxiety for both DPS experiences. The continuous self-report results were mixed, but may be useful for future hypothesis generation. This work shows more positive AID Vest effects compared to our past work on convenience population users, and our results can inform others with interest in DPS applications such as anxiety management.

With the increasing prevalence of robots in everyday life, there is a growing need for the general public to understand the capabilities and limitations of these technologies. For example, in the case of sidewalk delivery robots, humans ranging from nearby pedestrians to intended end users may miscalculate robot states, plans, and capabilities without this type of intuition. This paper explores the effectiveness of a relatively brief video-based intervention on enhancing public understanding of current delivery robots. The presented study (N = 100) assessed participants’ perceptions of their own knowledge and characteristics of the studied robotic system before and after watching the video. Results indicated significant changes in participants’ confidence levels, robot trust ratings, robot competence ratings, and attachment to the robots. The study can help to inform future techniques for improving the general public’s understanding of day-to-day robotic technologies, including rapid and relatively entertaining tactics like the video compilation considered in this work.

The rapid emergence of food delivery robots in public spaces has raised important questions regarding public perceptions and policy creation. One method for addressing these questions is examining the relationship between delivery robots and the communities they already serve. We assessed our university community’s experiences with, and perceptions of, the Starship Technologies robots (robots that currently operate on campus) using two efforts: analysis of online posting board content related to the robots and interviews of campus community members about the robots. Perspectives captured in the online post analysis tended to be negative, while views tended to be positive in the interview results. At the same time, both results showed differing opinions and complexity; one tension that emerged in both efforts, for example, is the potential of the robots to both benefit and impede disability communities on campus. Further, there were fundamental misunderstandings about what data the robots can and do record. This research can help to inform roboticists and policymakers whose work relates to autonomous robots in public spaces.

Children around the world are growing more sedentary over time, which leads to considerable accompanying wellness challenges. Pilot results from our research group have shown that robots may offer something different or better than other developmentally appropriate toys when it comes to motivating physical activity. However, the foundations of this work involved larger-group interactions in which it was difficult to tease apart potential causes of motion, or one-time sessions during which the impact of the robot may have been due to novelty. Accordingly, the work in this paper covers more controlled interactions focused on one robot and one child participant, in addition to considering interactions over longitudinal observation. We discuss the results of a deployment during which$N=8$participants interacted with our custom GoBot robot over two months of weekly sessions. Within each session, the child users experienced a teleoperated robot mode, a semi-autonomous robot mode, and a control condition during which the robot was present but inactive. Results showed that children tended to be more active when the robot was active and the teleoperated mode did not yield significantly different results than the semi-autonomous mode. These insights can guide future application of assistive robots in child motor interventions, in addition to informing how these robots can be equipped to assist busy human clinicians.

Mobility aids such as the white cane provide close-range information to help people with vision disabilities navigate the world. However, this technology has a limited sensing range and does not provide long-distance scene awareness. This paper proposes a vibrotactile feedback device to fill this gap: the OptiBand, which was developed based on design criteria from a blind stakeholder. The presented user study ($N=27$) compared the OptiBand to a proxy for existing shorter-range mobility aids, considered two potential sensed distance-to-vibration mapping strategies, and covered the use cases of locating and approaching objects of interest. Results of the object-locating trials showed that using the OptiBand led to faster and more successful performance, as well as lower task load and more satisfaction with the device, compared to using a proxy state-of-the-art device. A final trial with the original stakeholder demonstrated that the design criteria were met and supplied insights for the next iteration of participatory design for the OptiBand. Those who are interested in assistive devices for people with vision disabilities can benefit from this work.

Sound can benefit human-robot interaction, but little work has explored questions on the design of nonverbal sound for robots. The unique confluence of sound design and robotics expertise complicates these questions, as most roboticists do not have sound design expertise, necessitating collaborations with sound designers. We sought to understand how roboticists and sound designers approach the problem of robot sound design through two qualitative studies. The first study followed discussions by robotics researchers in focus groups, where these experts described motivations to add robot sound for various purposes. The second study guided music technology students through a generative activity for robot sound design; these sound designers in-training demonstrated high variability in design intent, processes, and inspiration. To unify the two perspectives, we structured recommendations through the design thinking framework, a popular design process. The insights provided in this work may aid roboticists in implementing helpful sounds in their robots, encourage sound designers to enter into collaborations on robot sound, and give key tips and warnings to both.

Nonverbal sound offers great potential to enhance robots’ interactions with humans, and a growing body of research has begun to explore nonverbal sound for tasks such as sound source localization, explicit communication, and improving sociability. However, nonverbal sound has a broad interpretation and design space that can draw from areas such as machine learning, music theory, and foley. We sought to identify and compare use cases and approaches for nonverbal sound in human-robot interaction through a systematic review. A search of sound and robotics-related publisher databases yielded 148 peer-reviewed articles presenting systems, studies, and taxonomies. Differences in taxonomy and overlap of terminology with adjacent research fields such as speech, gaze, and gesture posed difficulties for the search, which we attempted to address through a multi-stage search process. Based on the reviewed articles, we developed a pair of taxonomies using scientific communication principles and analyzed study designs and measures for the creation of nonverbal robot sound. We discuss recommendations for the field, including the use of the new taxonomies; methods for design, generation, and validation; and paths for future research. Roboticists may benefit from incorporating nonverbal sound as a key component in multimodal human-robot interaction.

HRI is an interdisciplinary field that requires researchers to be knowledgeable in broad areas ranging from social sciences to engineering. Study design is a multifaceted aspect of HRI that is hard to develop and perfect. Thus, the second edition of the “Workshop Your Study Design” workshop aims to improve the quality of future HRI studies by training researchers and boosting the accessibility of HRI as a field. Participants will have the opportunity to receive guidance and feedback on their study from an expert mentor. Researchers from all avenues of HRI will be invited to submit a 2–4 page paper on an HRI study they are currently designing, including a brief introduction and a complete methods section. Accepted submissions will be discussed in small groups led by mentors with relevant expertise. Prior to the workshop, papers will be shared within each group. Participants will be encouraged to read other submissions. During the workshop, attendees will work within their menteementor groups to discuss each paper and provide feedback. There will also be a session where mentors lead mini discussions on topics important to study design, such as balancing qualitative and quantitative design, power analysis, and research ethics. The workshop will end with a session where all participants can share important lessons that they learned with fellow attendees.

Robot-mediated interventions are one promising and novel approach for encouraging motor exploration in young children, but knowledge about the effectiveness of toy-like features for child-robot interaction is limited. We were interested in understanding the characteristics of current toys to inform the design of interactive abilities for assistive robots. This work first provides a systematic review of toy characteristics in$n=154$Fisher-Price products and then analyzes the effectiveness of common and uncommon toy-like behaviors from our custom assistive robot. Toy review results showed that light and sound features were significantly more common than bubbles, wheels, and self-propulsion. Exploratory play sessions with our assistive robot showed that bubbles were significantly more successful at encouraging child motion than other robot behaviors. Further, all studied robot behaviors demonstrated the capability to encourage child motion. The products of this work can inform the efforts of human-robot interaction and child development experts who study child mobility interventions.

Robotic comedians (and social robots generally) need to recognize and adapt to human responses during playful dialog. To support this ability, we determined design guidelines via a survey of 20 human comedians and developed a machine learning pipeline to support comedian-like behaviors by our robotic system. Based on comedian input, we identified that discerning laughter vs. no laughter during a joke setup and big laugh vs. so-so response vs. no laugh after a punchline were important skills for a comedian. To enable these abilities in a robotic system, we used an existing dataset of robot comedy performance audio to train classifiers for audience responses during the setup and after the punchline of jokes. Top-performing models for the above types of discernment performed similarly to human raters who completed the same classification task. Comparison of the current results to our past efforts of a similar nature reveal repeatability of top-performing approaches and generalizability of the approaches to new parts of robot comedy routines. The social intelligence supported by this work can promote the likability and acceptance of robots.

Transformative robot sound can improve perceptions of robots, but its implementation will likely require more hardware and cost. Does the addition of transformative sound yield an increase in value to offset this cost? Using the van Westendorp Price Sensitivity Meter, a questionnaire from marketing research, n = 97 participants measured acceptable price points for a robot with (and without) transformative sound. Results showed similar perceptual improvements as past studies, as well as a significant increase in perceived value, when transformative sound was included. These increases in social and value perceptions of robots confirm the utility of adding transformative sound to robots. This work benefits the broader human-robot interaction research community by sharing more ways to understand and validate the incorporation of transformative robot sound and other robot features.

With their ability to embody users in physically distant spaces, telepresence robots have gained popularity in environments including hospitals, schools, and offices. However, with platforms lacking in individuation and social presence, users often personalize telepresence robots with clothing and accessories to increase their recognizability and sense of embodiment. Toward understanding personalization preferences, as well as perceptions of personalized platforms, we conducted a series of five studies that investigate patterns in personalization of a telepresence robot and evaluate the impacts of common personalizations along five dimensions (robot uniqueness, humanness, pleasantness/unpleasantness, and people's willingness to interact with it). Finding a strong preference for the use of clothing and headwear in Studies 1-2 (N=52), we systematically manipulated a robot's appearance using these items and evaluated the qualitative and quantitative impacts on observer perceptions in Studies 3-4 (N=160). Observing that personalization increased perceptions of uniqueness and humanness, but also decreased positive responding, we then investigated the associations between personalization preferences and perceptions via a fifth study (N=100). Across the five studies, tensions emerged between operators' interest in using wigs and interlocutors' dislike of wigs. This result highlights a need to consider both operator and interlocutor perspectives when personalizing telepresence robots.

Effective social robots should leverage humor’s unique ability to improve relationship connections and dispel stress, but current robots possess limited (if any) humorous abilities. In this paper, we aim to supplement one aspect of autonomous robots by giving robotic systems the ability to "read the room" to assess how their humorous statements are received by nearby people in real time. Using a dataset of the audio of crowd responses to a robotic comedian over multiple performances (first presented in past work), we establish human-labeled joke success ground truths and compare individual human rater accuracy against the outputs of lightweight Machine Learning (ML) approaches that are easy to deploy in real-time joke assessment. Our results indicate that all three ML approaches (naïve Bayes, support vector machines, and single-hidden-layer feedforward neural networks) performed significantly better than the baseline approach used in our past work. In particular, support vector machines and neural network approaches are comparable to a human rater in the task of assessing if a joke failed or not in certain cases. The products of this work will inform self-assessment techniques for robots and help social robotics researchers test their own assessment methods on realistic data from human crowds.

Lovable robots in movies regularly beep, chirp, and whirr, yet robots in the real world rarely deploy such sounds. Despite preliminary work supporting the perceptual and objective benefits of intentionally-produced robot sound, relatively little research is ongoing in this area. In this paper, we systematically evaluate transformative robot sound across multiple robot archetypes and behaviors. We conducted a series of five online video-based surveys, each with N≈ 100 participants, to better understand the effects of musician-designed transformative sounds on perceptions of personal, service, and industrial robots. Participants rated robot videos with transformative sound as significantly happier, warmer, and more competent in all five studies, as more energetic in four studies, and as less discomforting in one study. Overall, results confirmed that transformative sounds consistently improve subjective ratings but may convey affect contrary to the intent of affective robot behaviors. In future work, we will investigate the repeatability of these results through in-person studies and develop methods to automatically generate transformative robot sound. This work may benefit researchers and designers who aim to make robots more favorable to human users.

All robots create consequential sound—sound produced as a result of the robot’s mechanisms—yet little work has explored how sound impacts human-robot interaction. Recent work shows that the sound of different robot mechanisms affects perceived competence, trust, human-likeness, and discomfort. However, the physical sound characteristics responsible for these perceptions have not been clearly identified. In this paper, we aim to explore key characteristics of robot sound that might influence perceptions. A pilot study from our past work showed that quieter and higher-pitched robots may be perceived as more competent and less discomforting. To better understand how variance in these attributes affects perception, we performed audio manipulations on two sets of industrial robot arm videos within a series of four new studies presented in this paper. Results confirmed that quieter robots were perceived as less discomforting. In addition, higher-pitched robots were perceived as more energetic, happy, warm, and competent. Despite the robot’s industrial purpose and appearance, participants seemed to prefer more "cute" (or "kawaii") sound profiles, which could have implications for the design of more acceptable and fulfilling sound profiles for human-robot interactions with practical collaborative robots.

Robots have unique abilities to influence people, but when deploying robotic systems in assistive applications, roboticists must understand how users perceive these systems’ behaviors. As part of an ongoing project to use robots as motivational break-taking aids, we present Cozmo behaviors that could function as the action space of a future robot learning strategy. Before deploying these behaviors in the wild, we evaluated them using an online video-based study with N = 113 participants. Results show that participant perceptions of Cozmo behaviors tend to match the intended valence and energy level. Furthermore, behavior valence in particular has a strong bearing on other perceived characteristics such as interaction appeal, trustworthiness, and safety. Facial expression and loudness acted as important covariates, which may help generalize these results to other behaviors and robots. The products of this work can benefit those who are interested in robot emotional expression and assistive robot applications.

Childhood ambulatory disabilities detract from not only the physical development, but also the social engagement of young children. Commercial mobility aids can help improve the autonomy of children with disabilities, but affordability issues, policy challenges, and uncertainty about training standards limit early use of these devices. In this paper, we build on affordable research-grade mobility aids for young children and consider how to design and evaluate an assistive robot that can support the use of these devices. With young children’s contingency learning abilities in mind, we designed an assistive mobile robot capable of supplying age-appropriate light, sound, and bubble rewards. We conducted a first evaluation of the robot’s ability to support driving practice with N = 5 typically developing infants. The results indicate mixed success of the robot rewards; driving distances uniformly tended to fall over the course of the study, but children did tend to look at the robot. In a second exploratory study involving N = 6 children in free ambulatory play, we see clearer differences in gaze and behavior from the introduction of an assistive robot. Generally, this research can inform others interested in assistive robotic interventions for young children.

Telepresence robots act as the remote embodiments of human operators, enabling people to stay connected to friends, family, and coworkers over lengthy physical separations. However, the factors affecting how humans can best make use of such systems are not yet well understood. This paper explores the effects of personalization and relationship closeness on telepresence via two studies. Study 1 was a between-participants experiment that investigated telepresence robot personalization. 32 pairs of friends (N = 64) participated in the study's team-building-style activities and answered questions about robot operator presence. The results unexpectedly indicated that relationship closeness influenced the interaction experience more than any other considered predictor variable. To study closeness more rigorously as the central manipulation, we conducted Study 2, a between-participants experiment with 24 pairs (N = 48) and a similar procedure. Robot operators who reported a closer relationship with their teammate felt more present in this investigation. These findings can inform the design and application of telepresence robot systems to increase a remote operator's feelings of presence via robot.

Social robots and autonomous social agents are becoming more ingrained in our everyday lives. Interactive agents from Siri to Anki's Cozmo robot include the ability to tell jokes to engage users. This ability will build in importance as in-home social agents take on more intimate roles, so it is important to gain a greater understanding of how robots can best use humor. Stand-up comedy provides a naturally-structured experimental context for initial studies of robot humor. In this preliminary work, we aimed to compare audience responses to a robotic stand-up comedian over multiple performances that varied robot timing and adaptivity. Our first study of 22 performances in the wild showed that a robot with good timing was significantly funnier. A second study of 10 performances found that an adaptive performance was not necessarily funnier, although adaptations almost always improved audience perception of individual jokes. The end result of this research provides key clues for how social robots can best engage people with humor.

More than ever, people spend the workday seated in front of a computer, which contributes to health issues caused by excess sedentary behavior. While breaking up long periods of sitting can alleviate these issues, no scalable interventions have had long-term success in motivating activity breaks at work. We believe that socially assistive robotics (SAR), which combines the scalability of e-health interventions with the motivational social ability of a companion or coach, may offer a solution for changing sedentary habits. To begin this work, we designed a SAR system and conducted a within-subjects study with N = 19 participants to compare their experiences taking breaks using the SAR system versus an alarm-like device for one day each in participants' normal workplaces. Results indicate that both systems had similar effects on sedentary behavior, but the SAR system led to greater feelings of pleasure, enjoyment, and engagement. Interviews yielded design recommendations for future systems. We find that SAR systems hold promise for further investigations of aiding healthy habit formation in work settings.

Although nonverbal expressive abilities are an essential element of human-to-human communication, telepresence robots support only select nonverbal behaviors. As a result, telepresence users can experience difficulties taking turns in conversation and using various cues to obtain the attention of others. To expand telepresence robot users' abilities to hold the floor during conversation, this work proposes and evaluates new types of expressive telepresence robot hardware. The described within-subjects study compared robot user and co-present person experiences during teamwork activity conditions involving basic robot functions, expressive LED lights, and an expressive robot arm. We found that among participants who preferred the arm-based expressiveness, individuals in both study roles felt the robot operator to be more in control of the robot during the arm condition, and participants co-located with the robot felt closer to their teammate during the arm phase. Participants also noted advantages of the LED lights for notification-type information and advantages of the arm for increasing perceptions of the robot as a human-like entity. Overall, these findings can inform future work on augmenting the nonverbal expressiveness of telepresence robots.

Telepresence robots can help to connect people by providing videoconferencing and navigation abilities in faraway environments. Despite this potential, current commercial telepresence robots lack certain nonverbal expressive abilities that are important for permitting the operator to communicate effectively in the remote environment. To help improve the utility of telepresence robots, we added an expressive, non-manipulating arm to our custom telepresence robot system and developed three user interfaces to control deictic gesturing by the arm: onscreen, dial-based, and skeleton tracking methods. A usability study helped us to evaluate user presence feelings, task load, preferences, and opinions while performing deictic gestures with the robot arm during a mock order packing task. The majority of participants preferred the dial-based method of controlling the robot, and survey responses revealed differences in physical demand and effort level across user interfaces. These results can guide robotics researchers interested in extending the nonverbal communication abilities of telepresence robots.

Auditory cues facilitate situational awareness by enabling humans to infer what is happening in the nearby environment. Unlike humans, many robots do not continuously produce perceivable state-expressive sounds. In this work, we propose the use of iconic auditory signals that mimic the sounds produced by a robot»s operations. In contrast to artificial sounds (e.g., beeps and whistles), these signals are primarily functional, providing information about the robot»s actions and state. We analyze the effects of two variations of robot sound, tonal and broadband, on auditory localization during a human-robot collaboration task. Results from 24 participants show that both signals significantly improve auditory localization, but the broadband variation is preferred by participants. We then present a computational formulation for auditory signaling and apply it to the problem of auditory localization using a human-subjects data collection with 18 participants to learn optimal signaling policies.

All over the world, people find joy and amusement in playing hand-clapping games such as “Pat-a-cake” and “Slide.” Thus, as robots enter everyday human spaces and work together with people, we see potential for them to entertain, engage, and assist humans through cooperative clapping games. This paper explores how data recorded from a pair of commonly available inertial measurement units (IMUs) worn on a human's hands can contribute to the teaching of a hand-clapping robot. We identified representative hand-clapping activities, considered approaches to classify games, and conducted a study to record hand-clapping motion data. Analysis of data from fifteen participants indicates that support vector machines and Markov chain analysis can correctly classify 95.5% of the demonstrated hand-clapping motions (from ten discrete actions) and 92.3% of the hand-clapping game demonstrations recorded in the study. These results were calculated by withholding a participant's entire dataset for testing, so these results should represent general system behavior for new users. Overall, this research lays the groundwork for a simple and efficient method that people could use to demonstrate hand-clapping games to robots.

Human friends and teammates commonly connect through handshakes, high fives, fist bumps, and other forms of hand-to-hand contact. As robots enter everyday human spaces, they will have the opportunity to join in such physical interactions, but few current robots are intended to touch humans. To begin investigating this topic, we sought to discover precisely how robots should move and react in hand-clapping games, which we define as interactions involving repeated hand-to-hand contacts between two agents. We conducted an experiment to observe seven pairs of people performing a variety of hand-clapping activities. Their recorded hand movements were accurately described by sinusoids that have a constant participant-specific maximum velocity across clapping tempos. Behaviorally, people struggled most with hand clapping at fast tempos, but they also smiled and laughed most often during fast trials. We used the human-human experiment findings to select, modify, and program a Rethink Robotics Baxter Research Robot to clap hands with a human partner. Preliminary tests have demonstrated that this robot can move like our participants and reliably detect human hand impacts through its wrist-mounted accelerometers, thereby exhibiting promise as a safe and engaging interaction partner.

Creating a robot that can teach humans simple interactive tasks such as high-fiving requires research at the intersection of physical human-robot interaction (PHRI) and socially assistive robotics. This paper shows how observation of natural human-human interaction can improve the design of requirements for social-physical robots and form a framework for autonomous execution of interactive physical tasks. Eleven pairs of human subjects were recruited to perform a set of high-fiving games; a magnetic motion tracker and an accelerometer were mounted to each person's hand for the duration of the experiment, and each subject completed several questionnaires about the experience. The results reveal valuable clues about the generally positive feelings of the participants and the movement of their hands during play. We discuss how we plan to use these results to create a robot that can teach humans similar high-fiving games.

Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.