We are happy to announce that our work “QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation” was accepted to IROS 2023.
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.
We participated at the Cloth Manipulation and Perception Track at the IEEE ICRA 2023 Robotic Grasping and Manipulation Competition.
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 Paper award, 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.
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.
Driverless cars and autonomous driving have shown major progress recently with the use of machine learning to learn driving behaviors from human demonstrations. However, the uptake of these is still limited, especially since the safety of such data-driven solutions is difficult to guarantee or even assess. Our work in autonomous driving targets the question how […]
For any mobile robotic platform, the ability to navigate its environment is important. Avoiding dangerous situations such as collisions and unsafe conditions is a crucial capability to achieve autonomy. Moreover, robots need to maintain a representation of their environment in order to properly operate inside it; this is achieved through maps, often built by the […]
Robots’ ability to interact with their surroundings is an essential capability, especially in unstructured human-inhabited environments. The knowledge of such an environment is usually obtained through sensors. The study of acquiring knowledge from sensor data is called robotic perception. Perception is the first step in many tasks such as manipulation or human-robot interaction.