Fast re-learning of a controller from sparse data
Charles E. Martin, Heiko Hoffmann · 2014
We address the problem of adapting a controller of a dynamical system to an unexpected change in dynamics. Such a system can be controlled using model predictive control if the model of the dynamics (forward model) is sufficiently accurate. The challenge is to adapt the forward model quickly. We motivate the requirement to achieve this adaptation given only sparse training data. To solve this challenge, we introduce the concept of preserving any a priori learned functional relationship in the dynamics, while adapting solely to the relatively simple functional relationship describing the change in dynamics. We show that this concept can be realized by augmenting a forward model with a simple corrector network and demonstrate feasibility on a challenging control problem in simulation.