Master thesis topics
To have more information on any of the proposals, please contact either Prof. Ville Kyrki or one of the advisors indicated in the proposal of interest.
At present, the following master thesis proposals are available in the group:
Master Thesis on “Anomaly detection in people flow through maps of dynamics”
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.
Master Thesis on “Imitation Learning from Human Demonstrations for Manipulation of Deformable Objects”
The thesis focuses on learning-based dynamic modeling of deformable objects for manipulation tasks.
Master Thesis on “Predictive World Models for End-to-End Autonomous Driving”
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.
Master Thesis on “Implementing and evaluating a Vision-Language-Action model for Robotic Manipulation“
The goal of the thesis is for the student to implement and evaluate a recent Visual-Language-Action model for robotic manipulation, OpenVLA, in a real robot-laboratory setting.
Master Thesis on “Fine-tuning Open-source LLMs for Processing Open-vocabulary Commands for Robotic Navigation”
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.
Master Thesis on “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.
Master’s Thesis Topic on Discriminative Filtering/Feature Extraction for Classification of Tactile Signals
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.
Master Thesis on “Learning From Demonstrations (LfDs) for Robotic Manipulators”
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.
Master Thesis on “Teleoperation and Assistive Systems”
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.
Master Thesis on “Data-Driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations”
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
Master Thesis on “Controlling a Robotic Arm with Instructions in Natural Language”
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.
Master Thesis on “Explaining Driving Situations with Natural Language”
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.
Master thesis on “Development of data-driven driver model”
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.
Master Thesis on “Deep Imitative Models for Safe Planning of Autonomous Driving”
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.