@inproceedings{780f4a1248034e2fa36e279cc725f16e,
title = "Learning a Pile Loading Controller from Demonstrations",
abstract = "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.",
author = "Wenyan Yang and Nataliya Strokina and Nikolay Serbenyuk and Reza Ghabcheloo and Joni K{\"a}m{\"a}r{\"a}inen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; IEEE International Conference on Robotics and Automation ; Conference date: 31-05-2020 Through 31-08-2020",
year = "2020",
doi = "10.1109/ICRA40945.2020.9196907",
language = "English",
isbn = "978-1-7281-7396-2",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "IEEE",
pages = "4427--4433",
booktitle = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020",
address = "United States",
}