Untangling Dense Knots by Learning Task-Relevant Keypoints

Understand

Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics.

• We consider dense (tight) knots that lack space between self-intersections and present an iterative approach that uses learned geometric structure in configurations.
• We instantiate this into an algorithm, HULK: Hierarchical Untangling from Learned Keypoints, which combines learning-based perception with a geometric planner into a policy that guides a bilateral robot to untangle knots.
• To evaluate the policy, we perform experiments both in a novel simulation environment modelling cables with varied knot types and textures and in a physical system using the da Vinci surgical robot.