  Due to the high dimensionality of object states, a garment flattening
pipeline requires recognising the configurations of garments for a robot to
produce/select manipulation plans to flatten garments. In this paper, we
propose a data-centric approach to identify known configurations of garments
based on a known configuration network (KCNet) trained on depth images that
capture the known configurations of garments and prior knowledge of garment
shapes. In this paper, we propose a data-centric approach to identify the known
configurations of garments based on a known configuration network (KCNet)
trained on the depth images that capture the known configurations of garments
and prior knowledge of garment shapes. The known configurations of garments are
the configurations of garments when a robot hangs garments in the middle of the
air. We found that it is possible to achieve 92\% accuracy if we let the robot
recognise the common hanging configurations (the known configurations) of
garments. We also demonstrate an effective robot garment flattening pipeline
with our proposed approach on a dual-arm Baxter robot. The robot achieved an
average operating time of 221.6 seconds and successfully manipulated garments
of five different shapes.
