  Control systems are at the core of every real-world robot. They are deployed
in an ever-increasing number of applications, ranging from autonomous racing
and search-and-rescue missions to industrial inspections and space exploration.
To achieve peak performance, certain tasks require pushing the robot to its
maximum agility. How can we design control algorithms that enhance the agility
of autonomous robots and maintain robustness against unforeseen disturbances?
This paper addresses this question by leveraging fundamental principles in
optimal control, reinforcement learning, and differentiable simulation.
