# Robust Robotic Pouring using Audition and Haptics

arXiv: https://arxiv.org/abs/2003.00342 · HTML: https://mlsanity.com/paper/2003.00342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2408.01366/) (2024)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)
- [Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations](https://mlsanity.com/paper/2208.02680/) (2022)
- [Audio-Visual Object Classification for Human-Robot Collaboration](https://mlsanity.com/paper/2203.01977/) (2022)
- [Hierarchical Audio-Visual-Proprioceptive Fusion for Precise Robotic Manipulation](https://mlsanity.com/paper/2602.13640/) (2026)

## Abstract (arXiv)

Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container.