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.

In addition to transportation comfort and efficiency, autonomous vehicles provide a vital improvement in traffic safety by minimizing impact of human factor. In this project, the data- and model-based approaches will be combined to develop a safety-oriented decision-making algorithm for autonomous driving systems. The main assessment criteria for the vehicle performance actuation is traffic safety, […]

Driverless cars and autonomous driving have shown major progress recently with the use of machine learning to learn driving behaviors from human demonstrations. However, the uptake of these is still limited, especially since the safety of such data-driven solutions is difficult to guarantee or even assess. Our work in autonomous driving targets the question how […]

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 […]