This lecture presents Robust-RRT, a planner for motion planning under uncertainty in nonlinear systems. It addresses challenges such as diverse uncertainty types, underactuation, and unknown uncertainty distributions. The Robust-RRT planner is generalizable, works with various system dynamics, and provides probabilistic completeness guarantees. It plans with reachable sets estimated online, ensuring safe solution families are chosen. The lecture discusses how Robust-RRT deals with obstacles, initial state beliefs, and goal regions, emphasizing the importance of planning in uncertain environments.