The field of robotic control is moving towards the integration of learning-based methods and traditional control techniques to improve safety, stability, and performance. Researchers are exploring the use of deep learning algorithms, such as Deep Reinforcement Learning (DRL) and neural networks, to enhance the control of robots and autonomous vehicles. However, ensuring the safety and stability of these systems remains a key challenge. To address this, researchers are developing new frameworks that combine the strengths of model predictive control (MPC) and deep learning, such as the use of Control Barrier Functions (CBFs) for collision avoidance and set-based state estimation for robustness. Notable papers in this area include: Gray-Box Computed Torque Control for Differential-Drive Mobile Robot Tracking, which proposes a learning-based nonlinear algorithm for tracking control of differential-drive mobile robots. Parallel-Constraint Model Predictive Control: Exploiting Parallel Computation for Improving Safety, which presents a method to improve safety in robotic systems by exploiting parallel computation. Lyapunov-Based Deep Learning Control for Robots with Unknown Jacobian, which develops a theoretical framework for end-to-end deep learning control that ensures system stability. Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers, which presents a robust MPC framework that addresses non-Gaussian noise in deep learning-based perception modules. Safety Meets Speed: Accelerated Neural MPC with Safety Guarantees and No Retraining, which proposes a framework that synergizes neural networks' fast computation with MPC's constraint-handling capability.