Safe Active Learning and Probabilistic Design of Experiment for Autonomous Hydraulic Excavators

Recently, data-driven and hybrid control of hydraulic cylinders for excavator assistance functions have been in the focus of many research papers. To ensure an accurate behavior, data-driven controllers and models need a large amount of data to cover all relevant operation regions, which requires a time-consuming data generation process. In this work, we introduce two learning-based methods to enhance the efficiency of this procedure: a static learning method and an active learning method. Both methods reduce the amount of required data to learn a hydraulic inverse actuation model. Compared to previous collection methods, the required data was reduced by factor 7.5, while the information content of the dataset remains nearly the same.