  In this paper we present a Deep Reinforcement Learning approach to solve
dynamic cloth manipulation tasks. Differing from the case of rigid objects, we
stress that the followed trajectory (including speed and acceleration) has a
decisive influence on the final state of cloth, which can greatly vary even if
the positions reached by the grasped points are the same. We explore how goal
positions for non-grasped points can be attained through learning adequate
trajectories for the grasped points. Our approach uses few demonstrations to
improve control policy learning, and a sparse reward approach to avoid
engineering complex reward functions. Since perception of textiles is
challenging, we also study different state representations to assess the
minimum observation space required for learning to succeed. Finally, we compare
different combinations of control policy encodings, demonstrations, and sparse
reward learning techniques, and show that our proposed approach can learn
dynamic cloth manipulation in an efficient way, i.e., using a reduced
observation space, a few demonstrations, and a sparse reward.
