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/

本研究解决了机器人在路径规划中缺乏音频噪声意识的问题。通过将视觉观察与环境几何和材料特性结合，提出了一种新的训练方法以预测机器人行动产生的噪声。实验结果表明，该方法能够有效帮助机器人遵循环境噪声约束，提升导航效率。

ANAVI：利用室内环境视觉实现音频噪声感知用于导航