Learning Audio Feedback for Estimating Amount and Flow of Granular Material
Learning Audio Feedback for Estimating Amount and Flow of Granular Material
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发表时间：
2018-10
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通讯作者：
Samuel Clarke;Travers Rhodes;C. Atkeson;Oliver Kroemer
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作者：
Samuel Clarke;Travers Rhodes;C. Atkeson;Oliver Kroemer
颗粒材料在被操纵时会在空气和结构中产生音频机械振动。这些振动与事件的性质以及产生事件的材料的固有特性相关。因此，我们建议学习使用接触事件中的音频振动来估计舀取和倾倒任务期间颗粒材料的流量和数量。我们在包含五种不同颗粒材料的 13,750 个摇动和倾倒样本的数据集上评估了多个深度和浅层学习框架。我们的结果表明，音频是一种信息丰富的传感器方式，可准确估计流量和流量，五种浇注材料的平均 RMSE 为 2.8g。我们还演示了如何使用学习到的网络来注入所需数量的材料。
Granular materials produce audio-frequency mechanical vibrations in air and structures when manipulated. These vibrations correlate with both the nature of the events and the intrinsic properties of the materials producing them. We therefore propose learning to use audio-frequency vibrations from contact events to estimate the flow and amount of granular materials during scooping and pouring tasks. We evaluated multiple deep and shallow learning frameworks on a dataset of 13,750 shaking and pouring samples across five different granular materials. Our results indicate that audio is an informative sensor modality for accurately estimating flow and amounts, with a mean RMSE of 2.8g across the five materials for pouring. We also demonstrate how the learned networks can be used to pour a desired amount of material.