Classification of Household Materials via Spectroscopy
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Recognizing an object's material can inform a robot on how hard it may grasp the object during manipulation, or if the object may be safely heated up. To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing. In this paper, we explore a technique for robots to estimate the materials of objects using spectroscopy. We demonstrate that spectrometers provide several benefits for material recognition, including fast sensing times and accurate measurements with low noise. Furthermore, spectrometers do not require direct contact with an object. To illustrate this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 distinct objects, and we show that a residual neural network can accurately analyze these measurements. Due to the low variance in consecutive spectral measurements, our model achieved a material classification accuracy of 97.7 when given only one spectral sample per object. Similar to prior works with haptic sensors, we found that generalizing material recognition to new objects posed a greater challenge, for which we achieved an accuracy of 81.4 leave-one-object-out cross-validation. From this work, we find that spectroscopy poses a promising approach for further research in material classification during robotic manipulation.
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