16-707 Robotic Foundations: Optimization

6 units, Carnegie Mellon University

Numerical optimization is the foundation of contemporary robotics. Optimization serves as the decision-making process that makes autonomy possible for complex robots acting in constrained environments with imperfect representations of the world surrounding them. This course focuses heavily on the optimization methods that underpin model-based robotics, striking a balance between theory and practical implementation on “real” robotic systems. Throughout the course, you will learn how to formulate and solve robotics problems such as numerical inverse kinematics, trajectory optimization, model predictive control, and optimal estimation using modern optimization tools. By the end of the course, you will be able to peer into the black box of contemporary optimization routines to understand their inner workings and to augment as necessary for your own research.

Prerequisites: none listed

Offered: Fall 2026

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