Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …

The learning-based model predictive control (LB-MPC) is an effective and critical method to
solve the path tracking problem in mobile platforms under uncertain disturbances. It is well …

This letter details the theory and implementation behind practically ensuring safety of
remotely piloted racing drones. We demonstrate robust and practical safety guarantees on a …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

Mobile manipulation in robotics is challenging due to the need to solve many diverse tasks,
such as opening a door or picking-and-placing an object. Typically, a basic first-principles …

From both an educational and research point of view, experiments on hardware are a key
aspect of robotics and control. In the last decade, many open-source hardware and software …

In this study, we are concerned with autonomous driving missions when a static obstacle
blocks a given reference trajectory. To provide a realistic control design, we employ a model …

Learning-based controllers have demonstrated su-perior performance compared to classical
controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the …

The interest in using reinforcement learning (RL) controllers in safety-critical applications
such as robot navigation around pedestrians motivates the development of additional safety …

The problem of coverage control, ie, of coordinating multiple agents to optimally cover an
area, arises in various applications. However, coverage applications face two major …