Effects of Robot Sound on Auditory Localization in Human-Robot Collaboration

• Published in: 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 434 - 442
• Main Authors: Cha, Elizabeth, Fitter, Naomi T., Kim, Yunkyung, Fong, Terrence, Matarić, Maja J.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 26.02.2018
• Series: ACM Conferences
• Subjects: auditory localization; Broadband communication; Collaboration; Computer systems organization > Embedded and cyber-physical systems > Robotics; Computing methodologies > Artificial intelligence > Planning and scheduling > Robotic planning; coordination; Data collection; Human-centered computing > Human computer interaction (HCI) > Empirical studies in HCI; Human-centered computing > Human computer interaction (HCI) > Interaction techniques > Auditory feedback; Human-robot interaction; Location awareness; nonverbal communication; Robots; sound; Task analysis; human-robot interaction; nonverbal communication; auditory localization; collaboration; sound; coordination
• ISBN: 9781450349536; 1450349536
• ISSN: 2167-2148
• DOI: 10.1145/3171221.3171285

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.