A new neural network feature importance method: Application to mobile robots controllers gain tuning


This paper proposes a new approach for feature importance of neural networks and subsequently a methodology to determine useful sensor information in high performance controllers, using a trained neural network that predicts the quasi-optimal gain in real time. The neural network is trained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to lower a given objective function. The important sensor information for robotic control are determined using the described methodology. Then a proposed improvement to the tested control law is given, and compared with the neural network’s gain prediction method for real time gain tuning. As a results, crucial information about the importance of a given sensory information for robotic control is determined, and shown to improve the performance of existing controllers.

Intelligent Control Systems and Optimization, Robotics and Automation, Signal Processing, Sensors, Systems Modelling and Control, Industrial Informatics (ICINCO)
Ashley W.D. Hill
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.