The goal of this thesis is to develop model-based approaches for the dynamic manipulation of a deformable object. Particularly, the focus will be on the task of throwing a lasso around a target (a bollard) with a robotic arm.
This thesis will focus on how to use adversarial learning for improving existing methods in robotic cloth manipulation.
We are happy to announce that our work “Learning Visual Feedback Control for Dynamic Cloth Folding” was accepted to IROS 2022 and nomitated to both the IROS Best Student Paper award and the IROS Best RoboCup Paper Award.
In this work we propose to use a Graph Neural Network (GNN) surrogate model to learn the particle interactions of granular materials. We perform planning of manipulation trajectories with the learnt surrogate model to arrange the material into a desired configuration.