Regression-Based Linear Quadratic Regulator

Citation:

Carlos H, Hayet J-B, Murrieta-Cid R. Regression-Based Linear Quadratic Regulator, in Proc. of Int. Conf. on Robotics and Automation (ICRA). Brisbane, Australia ; 2018.

Abstract:

We present the Regression-based Linear Quadratic Regulator (R-LQR), a new approach for determining locally-optimal control feedback policies for robots with non-linear dynamics and non-quadratic cost functions. Our proposal uses a free-derivative algorithm based on local quadratic regressions to obtain the robot motion policy. In addition, our methodology allows to define a notion of scale that translates into the definition of neighborhoods of valid policy and into the exploration of larger areas of the search space to find the optimal policies. The results show that our formulation allows to reach policies with lower costs than existing algorithms and to avoid problems when the behavior of the cost function makes the optimization difficult.

Publisher's Version

Last updated on 08/04/2020