Haptic Classification and Recognition of Objects Using a Tactile Sensing Forearm
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Abstract
In this paper, we demonstrate data-driven inference of mechanical properties of objects using a tactile sensor
array (skin) covering a robot’s forearm. We focus on the
mobility (sliding vs. fixed), compliance (soft vs. hard), and
identity of objects in the environment, as this information could
be useful for efficient manipulation and search. By using the
large surface area of the forearm, a robot could potentially
search and map a cluttered volume more efficiently, and be informed by incidental contact during other manipulation tasks.
Our approach tracks a contact region on the forearm over time in order to generate time series of select features, such as the
maximum force, contact area, and contact motion. We then process and reduce the dimensionality of these time series to
generate a feature vector to characterize the contact. Finally,
we use the k-nearest neighbor algorithm (k-NN) to classify a new feature vector based on a set of previously collected feature vectors. Our results show a high cross-validation accuracy in
both classification of mechanical properties and object recognition. In addition, we analyze the effect of taxel resolution,
duration of observation, feature selection, and feature scaling
on the classification accuracy.
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2012-10
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