ANAVI: Audio Noise Awareness using Visuals of Indoor environments for NAVIgation
This addresses the issue of noise disturbance caused by robots in indoor settings, such as homes or offices, for improved human-robot coexistence, though it is incremental as it builds on existing navigation and acoustic prediction techniques.
The paper tackles the problem of robots lacking awareness of the noise they generate during navigation, which can disturb humans in indoor environments, by developing a method to predict and plan quieter paths, resulting in robots adhering to noise constraints with demonstrated experiments on wheeled and legged robots.
We propose Audio Noise Awareness using Visuals of Indoors for NAVIgation for quieter robot path planning. While humans are naturally aware of the noise they make and its impact on those around them, robots currently lack this awareness. A key challenge in achieving audio awareness for robots is estimating how loud will the robot's actions be at a listener's location? Since sound depends upon the geometry and material composition of rooms, we train the robot to passively perceive loudness using visual observations of indoor environments. To this end, we generate data on how loud an 'impulse' sounds at different listener locations in simulated homes, and train our Acoustic Noise Predictor (ANP). Next, we collect acoustic profiles corresponding to different actions for navigation. Unifying ANP with action acoustics, we demonstrate experiments with wheeled (Hello Robot Stretch) and legged (Unitree Go2) robots so that these robots adhere to the noise constraints of the environment. See code and data at https://anavi-corl24.github.io/