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Explores safe learning in robotics, covering the state of the art, open challenges, and vision in the field, emphasizing the importance of interdisciplinary collaboration.
Covers the fundamentals of optimal control theory, focusing on defining OCPs, existence of solutions, performance criteria, physical constraints, and the principle of optimality.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Explores protein aggregation control through optimal strategies, inhibitors, and spatial regulation using liquid compartments, shedding light on drug interventions and aggregate dynamics.
Covers the basics of multivariable control, including system modeling, temperature control, and optimal strategies, emphasizing the importance of considering all inputs and outputs simultaneously.
