The field of robotic motion planning and control is rapidly advancing, with a focus on developing more efficient, adaptive, and robust methods for navigating complex environments. Recent research has explored the use of deep learning techniques, such as reinforcement learning and neural motion policies, to improve the performance of robotic systems in dynamic and partially observable environments. Another area of focus is the development of more effective algorithms for multi-agent path finding and collision avoidance, which is critical for applications such as warehouse logistics and autonomous vehicles. Noteworthy papers in this area include the proposal of Jacobian Exploratory Dual-Phase Reinforcement Learning for dynamic endoluminal navigation of deformable continuum robots, which demonstrates improved convergence speed and navigation efficiency. The introduction of Grasp-MPC, a closed-loop 6-DoF vision-based grasping policy, also shows promise for robust and reactive grasping of novel objects in cluttered environments. Additionally, the development of Deep Reactive Policy, a visuo-motor neural motion policy, achieves strong generalization and outperforms prior classical and neural methods in success rate across both simulated and real-world settings.