Robotics: Science and Systems XXII

Learning Deformable Object Manipulation Using Task-Level Iterative Learning Control

Krishna Suresh, Christopher G. Atkeson

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.

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Bibtex:

  
@INPROCEEDINGS{SureshK-RSS-26, 
    AUTHOR    = {Krishna Suresh AND Christopher G. Atkeson}, 
    TITLE     = {{Learning Deformable Object Manipulation Using Task-Level Iterative Learning Control}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.126} 
}