  Robotic manipulation of cloth is a challenging task due to the high
dimensionality of the configuration space and the complexity of dynamics
affected by various material properties. The effect of complex dynamics is even
more pronounced in dynamic folding, for example, when a square piece of fabric
is folded in two by a single manipulator. To account for the complexity and
uncertainties, feedback of the cloth state using e.g. vision is typically
needed. However, construction of visual feedback policies for dynamic cloth
folding is an open problem. In this paper, we present a solution that learns
policies in simulation using Reinforcement Learning (RL) and transfers the
learned policies directly to the real world. In addition, to learn a single
policy that manipulates multiple materials, we randomize the material
properties in simulation. We evaluate the contributions of visual feedback and
material randomization in real-world experiments. The experimental results
demonstrate that the proposed solution can fold successfully different fabric
types using dynamic manipulation in the real world. Code, data, and videos are
available at https://sites.google.com/view/dynamic-cloth-folding
