The field of robotics is witnessing significant advancements in motion planning and control, driven by the integration of machine learning and optimization techniques. Researchers are exploring innovative approaches to improve the efficiency and safety of motion planning in complex environments. Notably, the use of diffusion models and reinforcement learning is gaining traction, enabling robots to adapt to dynamic environments and learn from experience. Furthermore, the development of frameworks that align diffusion models with problem-specific constraints is reducing constraint violations and improving trajectory optimization. The application of quality-diversity algorithms is also showing promise in overcoming deceptive fitness landscapes and discovering novel solutions. Overall, these advancements are paving the way for more efficient, safe, and autonomous robotic systems. Noteworthy papers include: A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators, which proposes a hybrid learning and optimization framework for reactive whole-body motion planning. Dynamics-aware Diffusion Models for Planning and Control, which integrates system dynamics into the diffusion model's denoising process for generating dynamically admissible trajectories.