A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization
This provides a general-purpose solver for bilevel optimization problems in robotics, which is incremental as it builds on existing augmented Lagrangian and differentiation techniques.
The authors tackled bilevel nonlinear optimization problems in robotics by developing a general-purpose solver using augmented Lagrangian methods and automatic differentiation, demonstrating its validity and scalability on robust control and parameter estimation tasks.
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.