Category: Deformable Object Manipulation
Master Thesis on “Adversarial Robotic Cloth Manipulation”
This thesis will focus on how to use adversarial learning for improving existing methods in robotic cloth manipulation.
Our work “Learning Visual Feedback Control for Dynamic Cloth Folding” was accepted to IROS 2022!
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.
Manipulation of Granular Materials by Learning Particle Interactions
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.
Evolving-Graph Gaussian Processes poster at the Time Series Workshop at ICML 2021
This work extends the current SotA of Graph Gaussian Processes (GGPs) to dynamic graphs and asses the performance of the proposed evolving-Graph Gaussian Process (e-GGP) in two simulated tasks where deformable objects are represented as a graph that evolves over time.