# Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring

## TL;DR

This paper introduces PouringNet, a deep neural network that estimates liquid height during robotic pouring using audio vibration data, overcoming limitations of visual and haptic methods and improving robustness and accuracy.

## Contribution

We develop a novel audio-based perception method with PouringNet trained on a large multimodal dataset, enhancing liquid height estimation in robotic pouring tasks.

## Key findings

- PouringNet generalizes well across different containers and liquids.
- Audio vibration sensing improves robustness over visual and haptic methods.
- The approach achieves accurate liquid height estimation in diverse conditions.

## Abstract

In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring.