  Developing autonomous assistants to help with domestic tasks is a vital topic
in robotics research. Among these tasks, garment folding is one of them that is
still far from being achieved mainly due to the large number of possible
configurations that a crumpled piece of clothing may exhibit. Research has been
done on either estimating the pose of the garment as a whole or detecting the
landmarks for grasping separately. However, such works constrain the capability
of the robots to perceive the states of the garment by limiting the
representations for one single task. In this paper, we propose a novel
end-to-end deep learning model named GarmNet that is able to simultaneously
localize the garment and detect landmarks for grasping. The localization of the
garment represents the global information for recognising the category of the
garment, whereas the detection of landmarks can facilitate subsequent grasping
actions. We train and evaluate our proposed GarmNet model using the CloPeMa
Garment dataset that contains 3,330 images of different garment types in
different poses. The experiments show that the inclusion of landmark detection
(GarmNet-B) can largely improve the garment localization, with an error rate of
24.7% lower. Solutions as ours are important for robotics applications, as
these offer scalable to many classes, memory and processing efficient
solutions.
