The field of robotics is witnessing significant developments in motion planning and control, with a focus on enhancing probabilistic decision-making and hierarchical task control. Researchers are exploring innovative approaches to address the limitations of existing methods, such as the use of heavy-tailed distributions and null-space projection techniques. These advancements have the potential to improve the efficiency and safety of robotic systems in various applications, including autonomous vehicles and manipulators. Noteworthy papers in this area include: Bayesian Inferential Motion Planning Using Heavy-Tailed Distributions, which proposes a novel approach to motion planning using Student's-t distributions, and Task Hierarchical Control via Null-Space Projection and Path Integral Approach, which presents a framework for hierarchical task control leveraging Monte Carlo simulations. Value Iteration for Learning Concurrently Executable Robotic Control Tasks is another notable work, introducing a method to train redundant robots to execute multiple tasks concurrently using reinforcement learning.