Autonomously Untangling Long Cables
Vainavi Viswanath, Kaushik Shivakumar, Justin Kerr, Brijen, Thananjeyan, Ellen Novoseller, Jeffrey Ichnowski, Alejandro Escontrela,, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg

TL;DR
This paper presents a robotic system that autonomously untangles long cables using specialized perception, motion primitives, and an iterative algorithm, achieving moderate success rates on various knot configurations.
Contribution
It introduces a novel algorithm, SGTM, combining perception and motion primitives for autonomous cable untangling with a bilateral robot.
Findings
67% success on simple knots
50% success on complex knots
Effective for cables up to 3 meters long
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Soft Robotics and Applications · Hand Gesture Recognition Systems
