  Robotic deformable-object manipulation is a challenge in the robotic industry
because deformable objects have complicated and various object states.
Predicting those object states and updating manipulation planning is
time-consuming and computationally expensive. In this paper, we propose
learning known configurations of garments to allow a robot to recognise garment
states and choose a pre-designed manipulation plan for garment flattening.
