  Cables are ubiquitous in many settings and it is often useful to untangle
them. However, cables are prone to self-occlusions and knots, making them
difficult to perceive and manipulate. The challenge increases with cable
length: long cables require more complex slack management to facilitate
observability and reachability. In this paper, we focus on autonomously
untangling cables up to 3 meters in length using a bilateral robot. We develop
RGBD perception and motion primitives to efficiently untangle long cables and
novel gripper jaws specialized for this task. We present Sliding and Grasping
for Tangle Manipulation (SGTM), an algorithm that composes these primitives to
iteratively untangle cables with success rates of 67% on isolated overhand and
figure-eight knots and 50% on more complex configurations. Supplementary
material, visualizations, and videos can be found at
https://sites.google.com/view/rss-2022-untangling/home.

Robotics;Artificial Intelligence