SonifyIt: Towards Transformative Sound for All Robots - Zhang, Brian J.; Sigafoos, Noel; Moffit, Rabecka; Syed, Ibrahim; Adams, Lili S.; Fick, Jason; Fitter, Naomi T. (IEEE robotics and automation letters, 2022)
Transformative robot sound yields perceptual, functional, and social benefits in human-robot interactions, but broader research and implementation related to this topic is impeded by the lack of a common sound generation system for robots. Such a system could enable a wide array of situated robot sound studies, smoother collaborations with sound designers than current state of the art methods, and broader adoption of transformative robot sound. Based on other successful open-source projects in the robotics community, we integrated Robot Operating System, a popular robotics middleware, and Pure Data, a visual programming language for multimedia, to enable live sound synthesis and sample playback for robots. This sound generation system synthesized sound in an in-the-wild pilot study with positive qualitative results. Furthermore, an online within-subjects survey study with N=96 showed that the proposed sound system made the robot seem warmer, happier, and more energetic. This work benefits robotics researchers by providing the current sound system as a validated artifact and demonstrating its potential impact on broader robotics applications. We plan to develop this software into an open-source package: SonifyIt.