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arXiv · 2004.01160

Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging

Zackory Erickson·Eliot Xing·Bharat Srirangam·Sonia Chernova·Charles C. Kemp
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Abstract

Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging, that enables robots to estimate the materials of household objects. We release a dataset of high resolution texture images and spectral measurements collected from a mobile manipulator that interacted with 144 household objects. We then present a neural network architecture that learns a compact multimodal representation of spectral measurements and texture images. When generalizing material classification to new objects, we show that this multimodal representation enables a robot to recognize materials with greater performance as compared to prior state-of-the-art approaches. Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.

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Zackory Erickson, Eliot Xing, Bharat Srirangam, Sonia Chernova, Charles C. Kemp. 2020-04-02. Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging. https://arxiv.org/abs/2004.01160

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