k次マルコフ制約付き運動問題に対するニュートン法
Newton methods for k-order Markov Constrained Motion Problems
ロボット運動最適化のためのフレームワークを提案し、Gauss-Newtonや拡張ラグランジュ法などの古典的制約最適化手法を効率的に適用できる定式化と新しいany-time拡張ラグランジュ法を導入した。
著者: Marc Toussaint
分類: cs.RO
原文アブストラクト
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.
