In this project we research on how to manipulate more efficiently deformable objects by using dynamic manipulation as well as the modeling deformable objects via graph structures. Our applications range from manipulation of granular materials such as ground coffee to cloth manipulation.
This thesis will focus on how to use adversarial learning for improving existing methods in robotic cloth manipulation.
The Intelligent Robotics Group got recently accepted four journal articles to IEEE T-RO, IEEE RA-L, RAS, and JMLR.
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.