Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
Ivan D. Jimenez Rodriguez,Ugo Rosolia,Aaron D. Ames,Yisong Yue,Ivan D. Jimenez Rodriguez,Ugo Rosolia,Aaron D. Ames,Yisong Yue
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model. Specifically, we show how to exploit the differentiability of Gaussian Processes to create a state-dependent linearized approximation of the true continuous dynamics that can be integrated with model predictive control. Our approach is compatible with most Gaussian process approach...
