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

2025-02-04

AI Summary

Robot-generated acoustic emissions—such as motor whine and wheel friction—are inevitable during operation, yet their impact on human perception in shared environments remains underexplored, hindering long-term acceptance and sustained interaction. Method: A video-based experiment with 182 participants employed structured questionnaires, multi-robot motion stimuli, qualitative thematic analysis, and quantitative acoustic feature extraction (e.g., pitch, loudness, rhythmic regularity, semantic association). Contribution/Results: This study is the first to empirically identify that humans prefer robot sounds conveying informational content, rhythmic structure, and natural semantic associations (e.g., wind, purring)—challenging the prevailing “silence-as-friendly” paradigm. High-pitched or high-loudness sounds significantly reduced perceived acceptability, whereas moderately loud, rhythmically distinct, and semantically interpretable sounds enhanced behavioral predictability, trustworthiness, and anthropomorphism. The findings yield the first evidence-driven framework for robot auditory interface design, directly informing human-centered acoustic interaction strategies.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Knowledge Representation and Reasoning: Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• Impact of robot sounds on human perception
• Identifying preferred properties of robot sounds
• Enhancing human-robot interaction through sound design

Innovation

• Analyzed human responses to robot sounds
• Identified preferred sound properties
• Suggested natural sounds over machine noise