  Robotic manipulation of deformable 1D objects such as ropes, cables, and
hoses is challenging due to the lack of high-fidelity analytic models and large
configuration spaces. Furthermore, learning end-to-end manipulation policies
directly from images and physical interaction requires significant time on a
robot and can fail to generalize across tasks. We address these challenges
using interpretable deep visual representations for rope, extending recent work
on dense object descriptors for robot manipulation. This facilitates the design
of interpretable and transferable geometric policies built on top of the
learned representations, decoupling visual reasoning and control. We present an
approach that learns point-pair correspondences between initial and goal rope
configurations, which implicitly encodes geometric structure, entirely in
simulation from synthetic depth images. We demonstrate that the learned
representation -- dense depth object descriptors (DDODs) -- can be used to
manipulate a real rope into a variety of different arrangements either by
learning from demonstrations or using interpretable geometric policies. In 50
trials of a knot-tying task with the ABB YuMi Robot, the system achieves a 66%
knot-tying success rate from previously unseen configurations. See
https://tinyurl.com/rope-learning for supplementary material and videos.
