Learning Deformable Object Manipulation Using Task-Level Iterative Learning Control

• URL: http://arxiv.org/abs/2602.21302v1
• Date: Tue, 24 Feb 2026 19:13:25 GMT
• Title: Learning Deformable Object Manipulation Using Task-Level Iterative Learning Control
• Abstract summary: We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of deformable objects.<n>We demonstrate this method on a non-planar rope manipulation task called the flying knot.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic manipulation of deformable objects is challenging for humans and robots because they have infinite degrees of freedom and exhibit underactuated dynamics. We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of deformable objects. We demonstrate 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. https://flying-knots.github.io

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