A tutorial on Newton methods for constrained trajectory optimization and relations to SLAM, Gaussian Process smoothing, optimal control, and probabilistic inference

Many state-of-the-art approaches to trajectory optimization and optimal control are intimately related to standard Newton methods. For researchers that work in the intersections of machine learning, robotics, control, and optimization, such relations are highly relevant but sometimes hard to see across disciplines, due also to the different notations and conventions used in the disciplines. The aim of this tutorial is to introduce to constrained trajectory optimization in a manner that allows us to establish these relations. We consider a basic but general formalization of the problem and discuss the structure of Newton steps in this setting. The computation of Newton steps can then be related to dynamic programming, establishing relations to DDP, iLQG, and AICO. We can also clarify how inverting a banded symmetric matrix is related to dynamic programming as well as message passing in Markov chains and factor graphs. Further, for a machine learner, path optimization and Gaussian Processes seem intuitively related problems. We establish such a relation and show how to solve a Gaussian Process-regularized path optimization problem efficiently. Further topics include how to derive an optimal controller around the path, model predictive control in constrained k-order control processes, and the pullback metric interpretation of the Gauss-Newton approximation.