Robotics: Science and Systems XV
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
Benoit Landry, Zachary Manchester, Marco PavoneAbstract:
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.
Bibtex:
@INPROCEEDINGS{Pavone-RSS-19, AUTHOR = {Benoit Landry AND Zachary Manchester AND Marco Pavone}, TITLE = {A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2019}, ADDRESS = {FreiburgimBreisgau, Germany}, MONTH = {June}, DOI = {10.15607/RSS.2019.XV.012} }