  Clothes grasping and unfolding is a core step in robotic-assisted dressing.
Most existing works leverage depth images of clothes to train a deep
learning-based model to recognize suitable grasping points. These methods often
utilize physics engines to synthesize depth images to reduce the cost of real
labeled data collection. However, the natural domain gap between synthetic and
real images often leads to poor performance of these methods on real data.
Furthermore, these approaches often struggle in scenarios where grasping points
are occluded by the clothing item itself. To address the above challenges, we
propose a novel Bi-directional Fractal Cross Fusion Network (BiFCNet) for
semantic segmentation, enabling recognition of graspable regions in order to
provide more possibilities for grasping. Instead of using depth images only, we
also utilize RGB images with rich color features as input to our network in
which the Fractal Cross Fusion (FCF) module fuses RGB and depth data by
considering global complex features based on fractal geometry. To reduce the
cost of real data collection, we further propose a data augmentation method
based on an adversarial strategy, in which the color and geometric
transformations simultaneously process RGB and depth data while maintaining the
label correspondence. Finally, we present a pipeline for clothes grasping and
unfolding from the perspective of semantic segmentation, through the addition
of a strategy for grasp point selection from segmentation regions based on
clothing flatness measures, while taking into account the grasping direction.
We evaluate our BiFCNet on the public dataset NYUDv2 and obtained comparable
performance to current state-of-the-art models. We also deploy our model on a
Baxter robot, running extensive grasping and unfolding experiments as part of
our ablation studies, achieving an 84% success rate.
