Closed Loop

Closed-loop systems, characterized by continuous feedback between a system's output and its input, are a central focus in various fields, aiming to improve system performance, stability, and safety. Current research emphasizes the application of closed-loop principles in diverse areas, including robotics (using model predictive control, reinforcement learning, and large language models for task planning and control), autonomous driving (leveraging generative models and simulation for training and evaluation), and even biological systems (exploring homeostatic control mechanisms). This focus on closed-loop design is driving advancements in control theory, machine learning, and simulation, with significant implications for the development of robust and adaptable systems across numerous applications.