Comparison of machine learning methods for automatic bucket filling: An imitation learning approach
Daniel Eriksson, Reza Ghabcheloo
Abstract
This paper addresses the problem of generating controllers for automatic bucket filling using data collected during normal operations by professional operators on worksites. We introduce methods to annotate different phases of bucket fillings from long time series data and synthesize five machine learning models. The methods learn a policy that maps input signals to control commands using imitation learning from the training data. We use four different datasets as training data and first compare the model's accuracy on predicting operator commands. We then deploy and evaluate the efficacy and robustness of the policies on a real machine. The experiments shows that the Multilayer Perceptron and the Convolutional Neural Network models had the best overall performance and robustness and could achieve human level performance. Furthermore, models with high prediction accuracy are not necessarily suitable for feedback control, and using data from real worksites increased the controller's robustness.