A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
复制标题
双层非线性优化的可微增广拉格朗日方法
DOI：
10.15607/rss.2019.xv.012
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Pavone
中科院分区：
文献类型：
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作者：
Benoit Landry;Zachary Manchester;M. Pavone
现代机器人中的许多问题可以通过将其建模为两层优化问题来解决。在这项工作中，我们利用增广拉格朗日方法和自动微分的最新进展来开发一个非常适合于双层优化的通用非线性优化求解器。然后，我们通过两个典型的机器人问题，即接触系统的鲁棒控制和参数估计，证明了该算法的有效性和可扩展性。我们强调算法的一般性及其与机器人学中许多其他问题的潜在相关性。
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.