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.

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.