Student Theses and Projects
We often have topics for Bachelor and Master theses or student projects (e.g., project or research module in BSc and MSc study programmes of the university) available in our research area.
Please see the list of topic areas below. If you find one or several of the topic areas interesting for your thesis/project, please do not hesitate to ask us about possible topics via email (see contact details below)! Please also include your BSc/MSc transcripts so we can assess your background.
Robots need the ability to perceive objects, create maps of the environment, and localize in them. In this topic area we are interested in methods for scene perception using sensors such as cameras in the context of robotic object manipulation and autonomous navigation.
Visual 3D Scene Reconstruction and Understanding
In this topic area, we are interested in novel approaches for simultaneous localization and mapping with sensors such as cameras to
reconstruct static as well as dynamic scenes including the motion and shape of objects.
We pursue robot learning approaches for object manipulation and autonomous navigation. Possible research directions are to learn hierarchical representations or generalizable models of the effect of actions in the environment and use them for control, planning and learning.
Learning Generative Models for Scene Understanding and Planning
Generative scene models can be useful in various ways in intelligent systems, for instance, to generate training data or to forecast the effect of actions in the environment. In this topic area, we are interested in novel (deep) learning based approaches to generative scene modelling.
Differentiable Physics Simulation
Differentiable physics simulation of rigid and deformable objects could serve as a suitable prior to reduce sample complexity in physics-informed learning approaches for interactive or robotic systems. In this topic area, we are interested in novel efficient formulations of differentiable physics and there use in physics informed machine learning for such systems.