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 “Constrained Differential Dynamic Programming”

In this thesis, an extensive investigation of constrained DDP methods will be performed and the major selected ones will be implemented in simulation environment for trajectory optimizations of different robots such as a simple point robot, 2D car-like robot, 3D quadrotor robot and cart-pole system. In this context, the methods will be compared in terms of convergence speed, computational complexity, sensitivity to initializations and parameter selections.

Master Thesis on “Dynamic Cloth Manipulation Using Reinforcement Learning”

The goal of this thesis is to develop reinforcement learning-based approaches for dynamic cloth manipulation. In this context, the thesis is expected to include high-fidelity cloth simulations, training of an agent in that simulation environment to perform dynamic manipulation tasks, and transferring the learned policies to a real robot in testing its generalization capabilities with different sizes of clothes.

Master Thesis on “Slow Features for Representation Learning and Brain-Inspired Robot Navigation”

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.

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.