Learning Audio Feedback for Estimating Amount and Flow of Granular Material

Samuel Clarke, Travers Rhodes, Christopher G. Atkeson, Oliver Kroemer
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:529-550, 2018.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v87-clarke18a, title = {Learning Audio Feedback for Estimating Amount and Flow of Granular Material}, author = {Clarke, Samuel and Rhodes, Travers and Atkeson, Christopher G. and Kroemer, Oliver}, booktitle = {Proceedings of The 2nd Conference on Robot Learning}, pages = {529--550}, year = {2018}, editor = {Billard, Aude and Dragan, Anca and Peters, Jan and Morimoto, Jun}, volume = {87}, series = {Proceedings of Machine Learning Research}, month = {29--31 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v87/clarke18a/clarke18a.pdf}, url = {https://proceedings.mlr.press/v87/clarke18a.html}, abstract = {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. } }
Endnote
%0 Conference Paper %T Learning Audio Feedback for Estimating Amount and Flow of Granular Material %A Samuel Clarke %A Travers Rhodes %A Christopher G. Atkeson %A Oliver Kroemer %B Proceedings of The 2nd Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2018 %E Aude Billard %E Anca Dragan %E Jan Peters %E Jun Morimoto %F pmlr-v87-clarke18a %I PMLR %P 529--550 %U https://proceedings.mlr.press/v87/clarke18a.html %V 87 %X 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.
APA
Clarke, S., Rhodes, T., Atkeson, C.G. & Kroemer, O.. (2018). Learning Audio Feedback for Estimating Amount and Flow of Granular Material. Proceedings of The 2nd Conference on Robot Learning, in Proceedings of Machine Learning Research 87:529-550 Available from https://proceedings.mlr.press/v87/clarke18a.html.

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