As robots transition from confined factory floors into real-world environments amongst humans, they need the ability to quickly adapt to the changes in its environments. This, in turn, requires algorithms which are computationally faster and perform sophisticated reasoning while considering the inherent uncertainty in the environments. In this lecture, I will discuss how concepts from mathematical optimization coupled with a bit of probability theory can help us develop sophisticated algorithms for robot motion planning and controls. In particular, I will be highlighting my experiences while working on problems like Autonomous Driving, Manipulation and Human-Motion modeling.