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 basics of multivariable control, including system modeling, temperature control, and optimal strategies, emphasizing the importance of considering all inputs and outputs simultaneously.
Explores the Extended Kalman Predictor algorithm and the linearized Kalman Filter for multivariable control systems, discussing the challenges and applications.
Explores the application of control theory to manage protein aggregation processes, focusing on amyloid fibers and their implications in various diseases.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
