  Cloth folding is a widespread domestic task that is seemingly performed by
humans but which is highly challenging for autonomous robots to execute due to
the highly deformable nature of textiles; It is hard to engineer and learn
manipulation pipelines to efficiently execute it. In this paper, we propose a
new solution for robotic cloth folding (using a standard folding board) via
learning from demonstrations. Our demonstration video encoding is based on a
high-level abstraction, namely, a refined optical flow-based spatiotemporal
graph, as opposed to a low-level encoding such as image pixels. By constructing
a new spatiotemporal graph with an advanced visual corresponding descriptor,
the policy learning can focus on key points and relations with a 3D spatial
configuration, which allows to quickly generalize across different
environments. To further boost the policy searching, we combine optical flow
and static motion saliency maps to discriminate the dominant motions for better
handling the system dynamics in real-time, which aligns with the attentional
motion mechanism that dominates the human imitation process. To validate the
proposed approach, we analyze the manual folding procedure and developed a
custom-made end-effector to efficiently interact with the folding board.
Multiple experiments on a real robotic platform were conducted to validate the
effectiveness and robustness of the proposed method.
