The field of autonomous systems and robotic control is witnessing significant advancements, driven by innovations in areas such as path planning, diffusion models, and reinforcement learning. Researchers are exploring new approaches to enable robots to navigate complex environments, manipulate objects, and make decisions in uncertain conditions. A key trend is the integration of cognitive reasoning and end-to-end planning, allowing robots to better understand their surroundings and adapt to new situations. Another area of focus is the development of more efficient and robust trajectory planning algorithms, capable of handling high-dimensional spaces and uncertain dynamics. Notable papers in this area include: VDRive, which introduces a novel pipeline for end-to-end autonomous driving that leverages reinforced VLA and diffusion policy to achieve state-of-the-art performance. VO-DP, which proposes a vision-only diffusion policy learning method that achieves effective fusion of semantic and geometric features for robotic manipulation. DiffVLA++, which enhances autonomous driving by bridging cognitive reasoning and end-to-end planning through metric-guided alignment. These advancements have the potential to significantly impact various applications, from autonomous driving and robotic manipulation to search and rescue operations.