  Cloth manipulation is very relevant for domestic robotic tasks, but it
presents many challenges due to the complexity of representing, recognizing and
predicting the behaviour of cloth under manipulation. In this work, we propose
a generic, compact and simplified representation of the states of cloth
manipulation that allows for representing tasks as sequences of states and
transitions. We also define a Cloth Manipulation Graph that encodes all the
strategies to accomplish a task. Our novel representation is used to encode two
different cloth manipulation tasks, learned from an experiment with human
subjects with video and motion data. We show how our simplified representation
allows to obtain a map of meaningful motion primitives.
