  Autonomous fabric manipulation is a challenging task due to complex dynamics
and potential self-occlusion during fabric handling. An intuitive method of
fabric folding manipulation first involves obtaining a smooth and unfolded
fabric configuration before the folding process begins. However, the
combination of quasi-static actions such as pick & place and dynamic action
like fling proves inadequate in effectively unfolding long-sleeved T-shirts
with sleeves mostly tucked inside the garment. To address this limitation, this
paper introduces an improved quasi-static action called pick & drag,
specifically designed to handle this type of fabric configuration.
Additionally, an efficient dual-arm manipulation system is designed in this
paper, which combines quasi-static (including pick & place and pick & drag) and
dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth
configurations. Subsequently, keypoints of the fabric are detected, enabling
autonomous folding. To address the scarcity of publicly available keypoint
detection datasets for real fabric, we gathered images of various fabric
configurations and types in real scenes to create a comprehensive keypoint
dataset for fabric folding. This dataset aims to enhance the success rate of
keypoint detection. Moreover, we evaluate the effectiveness of our proposed
system in real-world settings, where it consistently and reliably unfolds and
folds various types of fabrics, including challenging situations such as
long-sleeved T-shirts with most parts of sleeves tucked inside the garment.
Specifically, our method achieves a coverage rate of 0.822 and a success rate
of 0.88 for long-sleeved T-shirts folding.
