Optimization Methods for Robotics

Catalog description

Robotics requires translating high-level goals into precise physical motion under real-world dynamics, sensing, and constraints. Optimization offers a principled mathematical framework for this challenge, unifying methods for planning, control, and state estimation. This course develops the theory and practice of numerical optimization, spanning sampling-based methods, Newton-style algorithms, and constrained nonlinear programming. Students will implement these methods in core robotics applications such as trajectory optimization and state estimation on manifolds. Coursework will emphasize hands-on experience: problem sets will guide students through implementing core algorithms and applying them to realistic robotics tasks, while a semester-long project allows students to explore the application of optimization-based techniques in robotics, AI, or other domains of interest.