Learning Task Parameterised Dynamical Systems with Gaussian Process Regression and Classification

Recent research has shown that a Learning from Demonstration (LfD) approach is useful for teaching robots flexible skills efficiently, and it opens the possibility for non-expert users to program these skills. When learning from demonstration data, learning frameworks should learn representations that are flexible and can generalize to unseen...

ADS-B Based Trajectory Prediction for Aerial Vehicles

The evolution of aerial vehicle technology necessitates robust trajectory prediction models. These models are crucial for maintaining safe airspace and enabling autonomous operations. Automatic dependent surveillance–broadcast (ADS-B) is a surveillance system that enables aircraft to receive data from navigation satellites and periodically...

Control of Dynamical Systems via Deep Kernel Learning

In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...

Data-driven optimal control for safe quadrotor navigation in windy environments

Creating autonomous Micro Aerial Vehicles for executing complex missions poses various challenges, including safe navigation in the presence of external wind disturbances. Most current navigation methods handle external wind disturbances through real-time estimation and rejection algorithms in the control stage, but lack safety guarantees in...

Distributed Gaussian Process for Multi-agent Systems

This work is focused on the distributed system, i.e. Multi-agent Systems (MAS), with application in environmental monitoring and learning. The specific task is to develop algorithms, i.e. Gaussian Process (GP), that are robust, accurate and fully-distributed to learn the unknown spatial environmental field. The two main problems are (1). how to...

Ionic Force Field Parameterization with Bayesian Optimization

Force field is widely used to model the potential energy in atomistic simulation systems. Despite force fields have a concise mathematical form, a good set of force field parameters usually requires extra care of calibration. Besides, numerous ionic force field parameters are reported from various sources as researchers have specific target...

Surrogate Constitutive Models with Multi-fidelity Gaussian Processes for Composite Micromodels

Various engineering applications rely on efficient, high performance materials to overcome design challenges. This high performance can be achieved by engineering micro-heterogenous materials also known as composites. Since the behavior of composites relies heavily on micro-scale interactions between different components, modeling...

Reduced Order Model Base Creation with Bayesian Optimization

This research considers the offline training stage of the Reduced Order Models (ROM), that has been getting attention recently on the endeavor to come up with efficient solutions for the highly complex numerical models. In this work, a simply supported beam problem has been considered, for which a reduced basis creation has been investigated....

Quantum Gaussian Processes for Data-Driven Design of Metamaterials

The data-driven approach shows great potential for designing new materials with unprecedented properties by using machine learning and optimization. Recently, a data-driven framework was successfully applied to design a unit cell of metamaterial achieving super-compressibility, despite being built out of brittle base material. The key element of...

Safe Reinforcement Learning Applications

Reinforcement Learning (RL) focuses on maximizing the returns (discounted rewards) throughout the episodes, one of the main challenges when using it is that it is inadequate for safety-critical tasks due to the possibility of transitioning into critical states while exploring. Safe Reinforcement Learning (SafeRL) is a subset of RL that focuses...

Gaussian Process Regression Models for Predicting Water Retention Curves: Application of Machine Learning Techniques for Modelling Uncertainty in Hydraulic Curves

An accurate representation of water retention curves is important for various reasons. Traditional models already exist for the representation of these curves, with one of them being the van Genuchten model. When soil parameters are available, the van Genuchten model can be used to plot water retention curves. However, when these soil parameters...

Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework

A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...

Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control

This thesis investigates the applicability of the Probabilistic Inference for Learning COntrol (PILCO) algorithm to large systems and systems with time varying measurement noise. PILCO is a state-of-the-art model-learning Reinforcement Learning (RL) algorithm that uses a Gaussian Process (GP) model to average over uncertainties during learning....