DiffTune: Auto-Tuning through Auto-Differentiation
share
The performance of a robot controller depends on the choice of its parameters, which require careful tuning. In this paper, we present DiffTune, a novel, gradient-based automatic tuning framework. Our method unrolls the dynamical system and controller as a computational graph and updates the controller parameters through gradient-based optimization. Unlike the commonly used back-propagation scheme, the gradient in DiffTune is obtained through sensitivity propagation, a forward-mode auto differentiation technique that runs parallel to the system's evolution. We validate the proposed auto-tune approach on a Dubin's car and a quadrotor in challenging simulation environments. Simulation experiments show that the approach is robust to uncertainties in the system dynamics and environment and generalizes well to unseen trajectories in tuning.
READ FULL TEXT