Neural Network Pile Loading Controller Trained by Demonstration

Abstract

This paper presents the development and testing of end-to-end Neural Network (NN) controllers for automated pile loading with a robotic wheel loader. NNs were trained using the Learning from Demonstration approach, i.e. by first recording sensor and control signals during manually-driven pile loading actions. Training made use of three input signals: boom angle, bucket angle and hydrostatic driving pressure; and three output signals: boom control, bucket control and the gas command. Most testing was conducted using NNs with 5 neurons in a single hidden layer, which were able to fill the bucket reasonably well. Qualitative comparisons were made to ascertain how the amount of training data and number of hidden neurons affects bucket filling performance, for NNs trained using both the Levenberg-Marquardt and Bayesian Regularization backpropagation algorithms. Different NNs trained with the same data were also compared. An additional pile transfer experiment compared the performance of an NN controller with a heuristic automated controller and manual human control. By estimating the total volume of material transferred using 3D laser scans, human control was found to have the highest performance, though the NN outperformed the heuristic controller. This indicated that end-to-end NN control trained by demonstration could offer improvement over current heuristic methods for automated pile loading.