Residual Dynamic

Residual dynamics modeling focuses on learning and compensating for the discrepancies between simplified models of dynamic systems and their real-world behavior. Current research emphasizes using machine learning, particularly deep neural networks and Gaussian processes, to learn these residual dynamics, often incorporating techniques like meta-learning and variational integrators to improve model accuracy and efficiency. This approach is crucial for enhancing the performance and robustness of control systems in diverse applications, ranging from robotics and autonomous vehicles to image processing and scientific modeling, by improving the accuracy and reliability of predictions and control actions. The ability to accurately model and compensate for residual dynamics is key to achieving safe and effective operation in complex environments.