  In this paper, our aim is to highlight Tactile Perceptual Aliasing as a
problem when using deep neural networks and other discriminative models.
Perceptual aliasing will arise wherever a physical variable extracted from
tactile data is subject to ambiguity between stimuli that are physically
distinct. Here we address this problem using a probabilistic discriminative
model implemented as a 5-component mixture density network comprised of a deep
neural network that predicts the parameters of a Gaussian mixture model. We
show that discriminative regression models such as deep neural networks and
Gaussian process regression perform poorly on aliased data, only making
accurate predictions when the sources of aliasing are removed. In contrast, the
mixture density network identifies aliased data with improved prediction
accuracy. The uncertain predictions of the model form patterns that are
consistent with the various sources of perceptual ambiguity. In our view,
perceptual aliasing will become an unavoidable issue for robot touch as the
field progresses to training robots that act in uncertain and unstructured
environments, such as with deep reinforcement learning.
