The recent advancements in control systems for autonomous and robotic applications have shown a significant shift towards more adaptive and data-driven approaches. There is a notable emphasis on improving the efficiency and robustness of model predictive control (MPC) schemes, with innovations such as variable-horizon MPC and adaptive terminal constraints leading to reduced conservatism and enhanced performance in tasks like orbital rendezvous. Additionally, the field is witnessing a push towards proactive motion planning methods that avoid local minima in complex environments, leveraging techniques such as repulsive potential augmentation in MPC to achieve global optimality without compromising computational efficiency. Data-driven approaches are also being integrated into trajectory planning for autonomous racing, where velocity prediction models are optimized using Bayesian methods to enhance performance at high-curvature tracks. Nonlinear control systems, particularly in hydraulic actuators, are benefiting from dynamic input mapping inversion to avoid algebraic loops and improve tracking performance. Lastly, there is a growing interest in developing unitless and normalized measures of nonlinearity for state estimation, which can adaptively select or parameterize estimation algorithms based on the system's nonlinearity.