A Novel Gradient Feature Importance Method for Neural Networks: An Application to Controller Gain Tuning for Mobile Robots

Abstract

In the paper, a novel gradient-based feature importance method for neural networks is described. This method is compared to the existing feature importance method using a trained neural network, which predicts the optimal gains in real time, for a steering controller on a mobile robot. The neural network is trained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to minimize an objective function. From an analysis using the feature importance methods, key inputs are determined, and their contribution to the neural network’s prediction are observed. Furthermore, using a first-order Taylor approximation of the neural network, an improved control law is determined and tested based on the results of the gradient-based feature importance method. This analysis is then applied to an existing neural network using real-world experiments, in order to determine the behavior of the gains with respect to each input, and allows for a glimpse into the neural network’s inner workings in order to improve its explainability.

Ashley W.D. Hill

PhD Researcher Engineer specialized in machine learning applied to robotics

My research interests include Machine Learning, Robotics, Electronics, and other oddities.