Tangled: Learning to untangle ropes with RGB-D perception

In this paper, we address the problem of manipulating deformable objects such as ropes. Starting with an RGB-D view of a tangled rope, our goal is to infer its knot structure and then choose appropriate manipulation actions that result in the rope getting untangled. We design appropriate features and present an inference algorithm based on particle filters to infer the rope's structure. Our learning algorithm is based on max-margin learning. We then choose an appropriate manipulation action based on the current knot structure and other properties such as slack in the rope. We then repeatedly perform perception and manipulation until the rope is untangled. We evaluate our algorithm extensively on a dataset having five different types of ropes and 10 different types of knots. We then perform robotic experiments, in which our bimanual manipulator (PR2) untangles ropes successfully 76.9% of the time.