People movement within a space can be modeled through the use of Maps of Dynamics (MoDs), i.e., probabilistic spatial representations modeling the local flow of people within an environment. Goal of this thesis is to explore whether MoDs can be used to detect anomalous behaviours in people flow.

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

This research aims to explore whether learning and leveraging world models can also be beneficial in visual representation learning for autonomous driving in a closed-loop settings.

Most of the systems performing open-vocabulary navigation use closed LLMs accessible via an API. If this kind of systems are to be adopted for practical robotic applications, it is important that the information is not sent to third-party systems. The goal of the project is to evaluate fine-tuning of local open-source models for processing natural language commands for robotic navigation.

Recent breakthroughs in natural language processing are enabling robots to understand human language like never before. The thesis will address the problem of translating human language to executable robot skills.

We are currently seeking for a motivated and talented master’s student to work on discriminative filtering and feature extraction for classification of tactile signals.

We are currently seeking a motivated and talented master’s student to investigate and improve upon a developed method that learns cost and constraints explicitly as part of their master’s thesis.

We are currently seeking a motivated and talented master’s student to work on developing a Teleoperation and Assistive system for robotic systems as part of their master’s thesis.

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 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.

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.