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 will focus on how to combine RL methods with Graph Neural Networks for solving cloth manipulation tasks.
This thesis will focus on proposing principled ways to increase the fidelity of cloth simulations using real-world observations from different types/sizes of cloths.
The Intelligent Robotics group will attend IROS 2021 with two accepted papers and a workshop mainly organised byPhD. Fares Abu-Dakka
This repository contains data and code for manuscript J. Kinnari, F. Verdoja, V. Kyrki “Season-invariant GNSS-denied visual localization for UAVs”.