The field of motion planning and control is moving towards more efficient and robust methods. Recent developments have focused on improving sampling-based motion planners, introducing novel non-uniform sampling strategies that provide probabilistically correct guarantees on the sampling regions. Additionally, there is a growing interest in model predictive control, with new approaches being developed to handle complex systems and uncertain environments. These advancements have the potential to significantly improve the performance and reliability of motion planning and control systems. Noteworthy papers include: Conformalized Non-uniform Sampling Strategies for Accelerated Sampling-based Motion Planning, which introduces a novel non-uniform sampling strategy for SBMPs. High-Altitude Balloon Station-Keeping with First Order Model Predictive Control, which develops a First-Order Model Predictive Control approach for station-keeping. Model Predictive Control via Probabilistic Inference: A Tutorial, which provides a comprehensive overview of probabilistic inference-based MPC methods.