CoRL 2020, Spotlight Talk 166: Untangling Dense Knots by Learning Task-Relevant Keypoints

“**Untangling Dense Knots by Learning Task-Relevant Keypoints**
Jennifer Grannen (UC Berkeley)*; Priya Sundaresan (UC Berkeley)*; Brijen Thananjeyan (UC Berkeley); Jeffrey Ichnowski (University of California, Berkeley); Ashwin Balakrishna (UC Berkeley); Minho Hwang (UC Berkeley); Vainavi Viswanath (UC Berkeley); Michael Laskey (UC Berkeley); Joseph Gonzalez (UC Berkeley); Ken Goldberg (UC Berkeley)

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. We find that HULK is able to untangle cables with dense figure-eight and overhand knots and generalize to varied textures and appearances. We compare two variants of HULK to three baselines and observe that HULK achieves 43.3% higher success rates on a physical system compared to the next best baseline. HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97.9% of 378 simulation experiments with an average of 12.1 actions per trial. In physical experiments, HULK achieves 61.7% untangling success, averaging 8.48 actions per trial.

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