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Abstract Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users’ break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study (N= 7) to begin to understand the robot’s influence beyond the point of novelty, and we followed up with a week-long data collection (N= 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems. 

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. 

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. 

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.