  Recognizing an object's material can inform a robot on the object's fragility
or appropriate use. 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 response times and accurate measurements with low
noise. Furthermore, spectrometers do not require direct contact with an object.
To explore this, we collected a dataset of spectral measurements from two
commercially available spectrometers during which a robotic platform interacted
with 50 flat material objects, and we show that a neural network model can
accurately analyze these measurements. Due to the similarity between
consecutive spectral measurements, our model achieved a material classification
accuracy of 94.6% 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 79.1% via leave-one-object-out cross-validation. Finally, we
demonstrate how a PR2 robot can leverage spectrometers to estimate the
materials of everyday objects found in the home. From this work, we find that
spectroscopy poses a promising approach for material classification during
robotic manipulation.
