Achieving athletic intelligence in robots is one of the grand challenges of robotics and artificial intelligence. It is the pursuit to enable dynamic control of robots and produce motions that look very natural to their biological counterparts. The aim of this lecture is to provide a holistic view of athletic intelligence which combines three important insights. First, taking inspiration from analytical mechanics (in particular Lagrangian formulation), it will be shown how our universe is a natural optimizer and how our laws of classical physics are a result of this optimization process. Second, building dynamic and energy-efficient robots requires an optimal design where its natural dynamics are in harmony with the task’s dynamics. Third, it will be argued that optimal design is not enough alone, we also need optimal control (for e.g. reinforcement learning) which exploits the dynamics of the robot instead of canceling it out. It will be shown how combining these three insights has led to some of the most advanced athletically intelligent robots.