Controlling the estimation bias in deep reinforcement learning problems with sparse rewards: Towards robust robotic object manipulation learning

Many recent robot learning problems, real and simulated, were addressed using deep reinforcement learning. The developed policies can deal with high-dimensional, continuous state and action spaces, and can also incorporate machine-generated or human demonstration data. A great number of them depend on state-action value estimates, especially the...

Distributionally Robust Abstraction and Strategy Synthesis with Formal Guarantees

There is growing interest to control cyber-physical systems under complex specifications while retaining formal performance guarantees. In this thesis we present a framework for formal control of uncertain systems under complex specifications. We consider dynamical systems with random disturbances, whose probability distribution is unknown. When...

Modelling and Control of Experimental Scale Hydrofoil Craft

Hydrofoil crafts with fully submerged foils can provide fast and economical waterway transport. However, their operation requires reliable onboard control systems to ensure the safety and comfort of their passengers, especially in rough sea conditions. This thesis project is focused on the dynamical modelling and the design...

Data-Driven Distribunally Robust Optimal Power Flow

The increased diffusion of Renewable Energy Sources (RES) into energy distribution systems gives rise to a number of issues that the Distribution Network Operators (DNOs) need to face. The dependency of RES on weather, along with their intermittent and non-dispatchable nature, urges us to develop frameworks that can guarantee a stable network,...