Sound Judgment: Properties of Consequential Sounds Affecting Human-Perception of Robots

TL;DR

The paper investigates how robot consequential sounds shape human perception in shared environments. It employs an online Qualtrics study with 182 participants, 5 robot-video trials, and a sound versus no-sound design, complemented by qualitative topic modelling to extract 16 sound-property codes and 4 valence categories. Key findings show that high-pitched, loud, and acute noises are commonly disliked, while rhythmic, informative, and natural sounds are preferred, with some participants desiring robotic or human-like voices. The study provides practical design guidelines for enhancing robot sound profiles through augmentation or dampening to improve human-robot interaction and proxemics. Overall, the large, diverse sample and mixed-methods approach yield concrete, actionable insights for sound design in human-centered robotics.

Abstract

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.

Figures (6)

• Figure 1: What properties of robot consequential sounds affect human-perceptions of robots, either negatively or positively?
• Figure 2: Sound primed responses - properties of existing consequential sounds which participants 'like' and 'dislike'.
• Figure 3: Sound primed responses - preferences for robot sounds, properties to 'avoid' and what people 'want instead', separated by sound (CS) versus no-sound (NS) participant condition.
• Figure 4: General (non-primed) responses - properties of existing consequential sounds which participants 'like' and 'dislike'.
• Figure 5: General (non-primed) responses - preferences for robot sounds, properties to 'avoid' and what people 'want instead', separated by participant sound condition (CS) versus (NS).