  In order to design haptic icons or build a haptic vocabulary, we require a
set of easily distinguishable haptic signals to avoid perceptual ambiguity,
which in turn requires a way to accurately estimate the perceptual
(dis)similarity of such signals. In this work, we present a novel method to
learn such a perceptual metric based on data from human studies. Our method is
based on a deep neural network that projects signals to an embedding space
where the natural Euclidean distance accurately models the degree of
dissimilarity between two signals. The network is trained only on non-numerical
comparisons of triplets of signals, using a novel triplet loss that considers
both types of triplets that are easy to order (inequality constraints), as well
as those that are unorderable/ambiguous (equality constraints). Unlike prior
MDS-based non-parametric approaches, our method can be trained on a partial set
of comparisons and can embed new haptic signals without retraining the model
from scratch. Extensive experimental evaluations show that our method is
significantly more effective at modeling perceptual dissimilarity than
alternatives.
