Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network. (arXiv:2203.06183v1 [cs.RO])

For humans, our “senses of touch” have always been necessary for our ability
to precisely and efficiently manipulate objects of all shapes in any
environment, but until recently, not many works have been done to fully
understand haptic feedback. This work proposed a novel method for getting a
better shape descriptor than existing methods for classifying an object from
multiple tactile data collected from a tactile glove. It focuses on improving
previous works on object classification using tactile data. The major problem
for object classification from multiple tactile data is to find a good way to
aggregate features extracted from multiple tactile images. We propose a novel
method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile
features considering relations among different features by using Graph
Convolutional Network. Our model outperforms previous methods on the STAG
dataset with an accuracy of 81.82%.

Source: https://arxiv.org/abs/2203.06183


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