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

arXiv: https://arxiv.org/abs/1903.00650 · HTML: https://mlsanity.com/paper/1903.00650/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations](https://mlsanity.com/paper/2109.07991/) (2021)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation](https://mlsanity.com/paper/2210.01116/) (2022)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio](https://mlsanity.com/paper/2507.02864/) (2025)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)
- [The Un-Kidnappable Robot: Acoustic Localization of Sneaking People](https://mlsanity.com/paper/2310.03743/) (2023)
- [Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications](https://mlsanity.com/paper/2410.23554/) (2024)
- [Hierarchical Audio-Visual-Proprioceptive Fusion for Precise Robotic Manipulation](https://mlsanity.com/paper/2602.13640/) (2026)
- [S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information](https://mlsanity.com/paper/2607.26047/) (2026)

## Abstract (arXiv)

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.