Explores safe learning in robotics, covering the state of the art, open challenges, and vision in the field, emphasizing the importance of interdisciplinary collaboration.
Explores protein aggregation control through optimal strategies, inhibitors, and spatial regulation using liquid compartments, shedding light on drug interventions and aggregate dynamics.
Covers the fundamentals and stability analysis of Networked Control Systems, including software installation, dynamical systems, equilibrium states, and stability testing.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Explores the application of control theory to manage protein aggregation processes, focusing on amyloid fibers and their implications in various diseases.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
