The recent advancements in the field of stochastic control and robotics have seen a significant shift towards developing more robust and scalable solutions for complex systems. A notable trend is the integration of probabilistic models and machine learning techniques to enhance the efficiency and reliability of control algorithms. Specifically, there is a growing emphasis on the use of operator-splitting methods and structural abstractions to address the challenges posed by nonlinear dynamics and chance constraints in robotics. These approaches aim to improve the exploration capabilities of algorithms, leading to better solutions under stricter safety constraints. Additionally, the field is witnessing innovations in reinforcement learning, particularly in the synthesis of controllers for safety-critical systems based on high-level specifications. The introduction of regret-free learning algorithms for temporal logic specifications represents a significant step forward in this domain. Furthermore, the use of density functions for safe navigation in dynamic environments and the development of robust probabilistic motion planning algorithms are expanding the practical applications of these theories. These developments collectively indicate a move towards more analytical and computationally efficient methods that promise to advance the state of the art in stochastic control and robotics.