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.
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 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.