音響振動で液体の高さを推定するロボット注ぎ技術
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring
ロボット注ぎ作業において、音響振動センシングと深層学習モデルPouringNetを用いて液体の高さを推定し、不透明容器でも高精度に汎化できることを示した論文。
著者: Hongzhuo Liang, Shuang Li, Xiaojian Ma, Norman Hendrich, Timo Gerkmann, Fuchun Sun, Jianwei Zhang
分類: cs.RO, cs.SD, eess.AS
原文アブストラクト
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.
