Newton methods for k-order Markov Constrained Motion Problems

Understand

This is a documentation of a framework for robot motion optimization that aims to draw on classical constrained optimization methods.

• With one exception the underlying algorithms are classical ones: Gauss-Newton (with adaptive step size and damping), Augmented Lagrangian, log-barrier, etc.
• The exception is a novel any-time version of the Augmented Lagrangian.
• The contribution of this framework is to frame motion optimization problems in a way that makes the application of these methods efficient, especially by defining a very general class of robot motion problems while at the same time introducing abstractions that directly reflect the API of the source code.