  Manipulating deformable objects, such as fabric, is a long standing problem
in robotics, with state estimation and control posing a significant challenge
for traditional methods. In this paper, we show that it is possible to learn
fabric folding skills in only an hour of self-supervised real robot experience,
without human supervision or simulation. Our approach relies on fully
convolutional networks and the manipulation of visual inputs to exploit learned
features, allowing us to create an expressive goal-conditioned pick and place
policy that can be trained efficiently with real world robot data only. Folding
skills are learned with only a sparse reward function and thus do not require
reward function engineering, merely an image of the goal configuration. We
demonstrate our method on a set of towel-folding tasks, and show that our
approach is able to discover sequential folding strategies, purely from
trial-and-error. We achieve state-of-the-art results without the need for
demonstrations or simulation, used in prior approaches. Videos available at:
https://sites.google.com/view/learningtofold
