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:
In this thesis, model predictive control-based emergency corridor building algorithms will be developed for autonomous vehicles in simulation. In this context, the thesis is expected to include varying simulated scenarios in terms of lane numbers, autonomous/human drivers amount, road types, traffic densities.
The goal of this thesis to bridge the gap between slow representation learning and brain-inspired navigation. After reviewing the relevant literature from both domains, an experiment will investigate if slow representations can lead to the self organization of structures similar to complex cells in the brain. If the results confirm the hypothesis, another experiment will investigate if the emerging structures can be used for navigation similar to the hand-crafted place-cell network in ViTa-SLAM.
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.
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.
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.
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.