2017

Conference Paper

am

ics

pn

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

@conference{MaHeScTr17,
  title = {On the Design of {LQR} Kernels for Efficient Controller Learning},
  author = {Marco, Alonso and Hennig, Philipp and Schaal, Stefan and Trimpe, Sebastian},
  booktitle = {Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC)},
  pages = {5193--5200},
  publisher = {IEEE},
  month = dec,
  year = {2017},
  month_numeric = {12}
}