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 “Hybrid learning and control for human-robot interaction: an imitation learning perspective”

The goal of this project is to exploit more comprehensive information from humans, in order to learn as many skill patterns from humans as possible according to the tasks at hand.

Master Thesis on “Research and Development of a Decision Making and Control Method for Autonomous Vehicles Combining Advanced Driver Assistance System with Reinforcement Learning”

In this thesis a method of training the agent with RL, where traffic participants’ safety is addressed with ADAS functions, will be investigated.

Master Thesis on “Machine-learning based simulator using Graph Neural Networks”

The thesis goal is to develop a Graph Neural Network to learn to simulate multiple physical systems such as fluids, soft-bodies and rigid-body systems.

Master thesis on “Efficient learning of task-specific trajectories from demonstrations”

The goal of this thesis is to increase the efficiency of reinforcement learning when limited number of examples is available by providing a method of obtaining a large number task-specific trajectories from only a few demonstrations.

Master thesis on “Reinforcement learning with kernelized movement primitives”

The goal of this thesis is to integrate KMP with reinforcement learning to provide an automatic adaptation approach to adapt the trajectory and goal in order to optimize a desired task.

Master thesis on “People flow maps for indoor robot navigation”

This thesis goal is to develop a probabilistic approach for building and updating people flow maps able to help the robot being socially-aware while navigating by predicting people movement.

Master Thesis on “High-Level Decision Making in Self-Driving Cars using Deep Reinforcement Learning”

Decision-making is one of the most important modules in a self-driving car system and navigating an autonomous car in a dynamic and multi-agent urban scenario needs to understand the scene and the interaction between agents. One way to do the scene understanding is to use Graph Neural Networks (GNNs) to learn the interaction between agents […]