The field of control and robotics is witnessing significant advancements, driven by innovative applications of machine learning, data-driven approaches, and novel control strategies. Researchers are exploring the integration of techniques like Gaussian Process regression, reinforcement learning, and event-triggered control to improve the precision, stability, and adaptability of control systems. Notably, the development of soft robotic modules with enhanced controllability and the design of adaptive event-triggered control strategies for soft robots are paving the way for more compliant and adaptive robotic systems. Furthermore, the application of deep reinforcement learning to motion planning and control of flexible manipulators is demonstrating superior vibration suppression and tracking accuracy. These advancements have the potential to enhance the performance and versatility of control systems in various applications, including robotics, process control, and mechatronics. Noteworthy papers include: The paper on a soft robotic module with pneumatic actuation and enhanced controllability using a shape memory alloy wire, which achieves more precise control of bending in the vertical plane. The paper on direct integration of recursive Gaussian process regression into extended Kalman filters, which outperforms alternative implementations, especially in the presence of high measurement noise.