Learning a Pile Loading Controller from Demonstrations

This work introduces a learning-based pile loading controller for autonomous robotic wheel loaders. Controller parameters are learnt from a small number of demonstrations for which low level sensor (boom angle, bucket angle and hydrostatic driving pressure), egocentric video frames and control signals are recorded. Application specific deep visual features are learnt from demonstrations using a Siamese network architecture and a combination of cross-entropy and contrastive loss. The controller is based on a Random Forest (RF) regressor that provides robustness against changes in field conditions (loading distance, soil type, weather and illumination). The controller is deployed to a real autonomous robotic wheel loader and it outperforms prior art with a clear margin.