让机器人通过视觉感知室内噪声，自动规划安静路径。
ANAVI: Audio Noise Awareness using Visuals of Indoor environments for NAVIgation
- 用视觉推断室内声音传播，预测机器人的发声响度。
- 在模拟家居环境中验证，不同动作的噪声影响可被准确预测。
- 适用于轮式与腿式机器人，适合对静音要求高的场景。
我们提出 ANAVI：利用室内视觉信息实现机器人导航中的音频噪声感知，以实现更安静的路径规划。人类天然具备对自身声响及其对周围影响的认知，但当前机器人缺乏此类意识。实现机器人音频感知的关键挑战在于估算机器人动作在听者位置的音量大小。由于声音受房间几何结构和材料特性影响，我们训练机器人通过被动视觉观察来感知响度。为此，我们在模拟住宅中生成不同听者位置上‘脉冲’声的响度数据，并训练声学噪声预测器（ANP）。随后，我们收集了多种导航动作对应的声学特征。将 ANP 与动作声学特征结合，我们在轮式（Hello Robot Stretch）和腿式（Unitree Go2）机器人上进行了实验，使它们能遵守环境的噪声约束。代码与数据见 https://anavi-corl24.github.io/
原文摘要 · Abstract (English)
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/
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