A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
Robotics
2019-07-03 v2 Optimization and Control
Abstract
Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.
Cite
@article{arxiv.1902.03319,
title = {A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization},
author = {Benoit Landry and Zachary Manchester and Marco Pavone},
journal= {arXiv preprint arXiv:1902.03319},
year = {2019}
}
Comments
Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019