Author: Gökhan Alcan
Master Thesis on “Reinforcement Learning and Graph Neural Networks for deformable object manipulation”
This thesis will focus on how to combine RL methods with Graph Neural Networks for solving cloth manipulation tasks.
Master Thesis on “Real2Sim Transfer for Cloth Manipulation”
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.
Master Thesis on “Emergency Corridor Building by Autonomous Vehicles”
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.
Safe Model Predictive Control
Safe Model Predictive Control (Safe MPC) aims to ensure that a physical system’s safety constraints are satisfied with high probability. Our research is on extending constrained MPC methods to cope with probabilistic safety constraints. We further research modeling uncertainty of dynamics to ensure safe exploration when combined with safety constraints learned in simulation, and learning powerful data-efficient surrogate models for complex dynamics.
Data Driven Nonlinear Dynamic Models for Predicting Heavy-Duty Diesel Engine Torque and Combustion Emissions
Robotic Seminar Series – 21st February 2020. Speaker: Gökhan Alcan.
Robot Control
Robotic tasks in real-world applications generally involve uncertain, stochastic and dynamic environments. Pre-programming based solutions either do not work or give unsatisfactory results in such environments. This requires to generate cautious control strategies that provide optimum actions to perform the desired task while considering the effects of the uncertainties in the environment. Robot control aims […]