The field of robotics is witnessing significant developments in motion planning and control, with a focus on improving the efficiency, adaptability, and robustness of robotic systems. Researchers are exploring novel approaches to motion planning, such as flow field-based methods and reinforcement learning, to enable robots to navigate complex environments and perform tasks with greater precision. Additionally, there is a growing emphasis on incorporating physical constraints and dynamics into motion planning algorithms to ensure more realistic and stable robot movements. Notable papers in this area include KoopMotion, which proposes a Koopman operator-based approach to motion planning, and LIPM-Guided Reinforcement Learning, which introduces a reward design inspired by the Linear Inverted Pendulum Model to enable stable and perceptive locomotion in bipedal robots. RENet is also noteworthy for its redundant estimator network framework, which ensures robust motion performance in quadruped robots despite visual perception uncertainties. Other notable papers include Geometric Neural Distance Fields for Learning Human Motion Priors, which introduces a novel 3D generative human motion prior, and Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion, which resolves the dilemma between robustness and proactivity in humanoid locomotion. Overall, these advancements have the potential to significantly improve the capabilities of robotic systems and enable them to operate effectively in a wide range of environments and applications.