Analyzing Liquid Pouring Sequences via Audio-Visual Neural Networks

Auston Sterling
Auston Sterling

IROS, pp. 7702-7709, 2019.

Cited by: 1|Bibtex|Views39|Links
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Abstract:

Existing work to estimate the weight of a liquid poured into a target container often require predefined source weights or visual data. We present novel audio-based and audio-augmented techniques, in the form of multimodal convolutional neural networks (CNNs), to estimate poured weight, perform overflow detection, and classify liquid and ...More

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