ANAVI: Audio Noise Awareness using Visuals of Indoor environments for NAVIgation

TL;DR

This paper introduces ANAVI, a system enabling robots to estimate and minimize noise pollution indoors by using visual cues to predict sound levels, enhancing quiet navigation.

Contribution

It presents a novel visual-based approach for robots to assess and control their noise output in indoor environments, which was previously lacking.

Findings

Robots can predict loudness of actions using visual data.

The system effectively reduces noise during navigation.

Demonstrated on both wheeled and legged robots.

Abstract

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/