Introduction to Underactuated Robotics and Nonlinear Control

Traditional robotic systems often move rigidly, attempting to maintain absolute control at every joint. To build truly agile, energy-efficient machines like walking, flying, or swimming robots, we must learn to work with natural physics rather than against them. This text-only course introduces you to the core principles of underactuated systems—where robots have fewer control inputs than degrees of freedom. You will transition from studying basic mechanical dynamics to implementing advanced control strategies. By understanding how to exploit a system's natural dynamics, you will be able to design controllers that allow robots to move with the fluid, aggressive agility of biological organisms. What you'll learn: - Understand the foundational mathematics of nonlinear dynamics and underactuation. - Analyze passive dynamic walkers and biological movement patterns. - Apply energy-shaping control and partial feedback linearization to physical models. - Formulate and solve modern trajectory optimization problems. - Integrate reinforcement learning and approximate optimal control for complex maneuvers. - Evaluate how mechanical design decisions directly influence a robot's controllability. The course begins with vital terminology, system definitions, and the core mathematics of nonlinear mechanics. You will then systematically progress through energy-based control methods, trajectory optimization, and modern computational reinforcement learning algorithms. This course is built for aspiring roboticists, control engineers, and computer scientists looking for a foundational entry point into advanced control theory. No prior background in underactuated systems is required, though basic familiarity with physics and calculus will help you get the most out of the material. Start reading today to master the physics of dynamic robotic motion.