Abstract: Dynamic manipulation of deformable objects is challenging for humans and robots because they have infinite degrees of freedom and exhibit underactuated dynamics. This thesis introduces a Task-Level Iterative Learning Control method for dynamic manipulation of deformable objects and demonstrates this method on a non-planar rope manipulation task called the flying knot. Using a single human demonstration and a simplified rope model, the method learns directly on hardware without reliance on large amounts of demonstration data or massive amounts of simulation. At each iteration, the algorithm constructs a local inverse model of the robot and rope by solving a quadratic program to propagate task-space errors into action updates. We evaluate performance across 7 different kinds of ropes, including chain, latex surgical tubing, and braided and twisted ropes, ranging in thicknesses of 7-25mm and densities of 0.013-0.5 kg/m. Learning achieves a 100\% success rate within 10 trials on all ropes. Furthermore, the method can successfully transfer between most rope types in approximately 2-5 trials. Project page: https://flying-knots.github.io

Chris Atkeson (advisor)
Oliver Kroemer
Jeff Ichnowski
Arun Bishop