This thesis aims to develop a data-driven diffusion model that elevates realism and controllability in simulations and intricately models the complex interactions between multiple agents for safe planning

The thesis focuses on learning-based dynamic modeling of deformable objects for manipulation tasks.

The goal of this master thesis is to integrate a large vision-language model (VLM) with a manipulation policy in order to control a robotic hand for predefined manipulation tasks, such as grasping or pushing.

The goal of this master thesis is to explore existing approaches, datasets and models that provide textual explanations of driving situations, to implement a state-of-the-art model and to validate it on predefined driving conflict situations.

This thesis aims to develop data-driven driver models using expert data. Many real-world driving datasets with diverse driving scenarios and closed-loop evaluation frameworks are currently available from Waymo, NuPlan, and Lyft.

In many real-world problems, there are multiple conflicting objective functions that need to be optimized simultaneously. For example, an investment company wants to create an optimum portfolio of stocks to maximize profits and minimize risk simultaneously. However, most reinforcement learning (RL) problems do not explicitly consider the tradeoff between multiple conflicting reward functions and assume a scalarized single objective reward function to be optimized. Multiobjective evolutionary optimization algorithms (MOEAs) can be used to find Pareto optimal policies by considering multiple reward functions as objectives.

The goal of this thesis is to devise an algorithm that combines the advantage of both IL and MBRL for robust and safe planning for autonomous driving. In this context, the thesis is expected to include implementations of IL and MBRL algorithms and fuse them for planning tasks.