Open Loop

Open-loop control systems operate without feedback, relying on pre-programmed instructions to achieve a desired outcome. Current research focuses on improving the robustness and efficiency of open-loop control in various applications, including robotics, autonomous driving, and soft robotics, often employing techniques like model predictive control (MPC), trajectory optimization, and machine learning models (e.g., neural networks) to generate effective open-loop plans. These advancements aim to address challenges such as unpredictable dynamics, uncertain environments, and the need for data-efficient planning, ultimately leading to more reliable and adaptable systems in diverse fields.