Auditory Localization and Assessment of Consequential Robot Sounds: A Multi-Method Study in Virtual Reality

2025-04-01

AI Summary

This study investigates human azimuthal localization accuracy of mobile robots—Go1 (quadrupedal), TurtleBot 2i, and HSR (wheeled)—based solely on their operational sounds in virtual reality. It systematically examines the effects of sound source type, robot velocity, motion trajectory, and Acoustic Vehicle Alerting Systems (AVAS) on auditory localization performance. Using 3D audio scene rendering, binaural localization experiments, and Likert-scale subjective evaluation, the work reveals, for the first time, a significant tension between subjective sound appraisal (e.g., trustworthiness, pleasantness) and objective localization accuracy: HSR’s sounds received the highest subjective ratings yet yielded the largest localization errors—especially without AVAS. Conversely, AVAS significantly improved azimuthal accuracy for Go1 and TurtleBot 2i, with gains modulated by platform kinematics. The findings empirically establish acoustic features as critical determinants of implicit spatial awareness in human–robot interaction, providing evidence-based guidance for audible interface design and AVAS standardization.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Interaction Techniques and Devices

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Mobile robots increasingly operate alongside humans but are often out of sight, so that humans need to rely on the sounds of the robots to recognize their presence. For successful human-robot interaction (HRI), it is therefore crucial to understand how humans perceive robots by their consequential sounds, i.e., operating noise. Prior research suggests that the sound of a quadruped Go1 is more detectable than that of a wheeled Turtlebot. This study builds on this and examines the human ability to localize consequential sounds of three robots (quadruped Go1, wheeled Turtlebot 2i, wheeled HSR) in Virtual Reality. In a within-subjects design, we assessed participants' localization performance for the robots with and without an acoustic vehicle alerting system (AVAS) for two velocities (0.3, 0.8 m/s) and two trajectories (head-on, radial). In each trial, participants were presented with the sound of a moving robot for 3~s and were tasked to point at its final position (localization task). Localization errors were measured as the absolute angular difference between the participants' estimated and the actual robot position. Results showed that the robot type significantly influenced the localization accuracy and precision, with the sound of the wheeled HSR (especially without AVAS) performing worst under all experimental conditions. Surprisingly, participants rated the HSR sound as more positive, less annoying, and more trustworthy than the Turtlebot and Go1 sound. This reveals a tension between subjective evaluation and objective auditory localization performance. Our findings highlight consequential robot sounds as a critical factor for designing intuitive and effective HRI, with implications for human-centered robot design and social navigation.

Problem

• Study human ability to localize robot sounds in VR
• Compare localization accuracy of three robot types
• Examine subjective vs objective perception of robot sounds

Innovation

• Used Virtual Reality for robot sound localization
• Assessed multiple robots with acoustic alert system
• Measured subjective and objective sound perception