Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies

Understand

Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics, and tendency to self-occlude.

• Analytical controllers often fail in the presence of dense configurations, due to the difficulty of grasping between adjacent cable segments.
• We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work.
• LOKI uses a learned model of manipulation features to refine a coarse grasp keypoint prediction to a precise, optimized location and orientation, while SPiDERMan uses a learned model to sense task progress and apply recovery actions.