Robust Nonlinear Trajectory Optimization
Robust Nonlinear Trajectory Optimization
Project Overview
It is often challenging to get nonlinear trajectory optimization to converge reliably without getting stuck in spurious local minima. Better methods for handling constraints in the optimization problem and improved design of objective (cost) functions can help alleviate this problem. Leveraging new methods of data-efficient machine learning and mathematical insights from optimization theory, we are working to design robust trajectory optimization methods that can be used for real-time nonlinear model predictive control.
Publications
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[Advised Thesis] (2020). Reinforcement Learning to Enable Robust Robotic Model Predictive Control. Harvard University Bachelors of Arts Thesis.
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(2018). Parallel and Constrained Differential Dynamic Programming for Model Predictive Control. Harvard University Masters of Engineering Thesis.
