Learning a Pile Loading Controller from Demonstrations

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Number of pages7
ISBN (Electronic)9781728173955
ISBN (Print)978-1-7281-7396-2
Publication statusPublished - 2020
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation - Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729
ISSN (Electronic)2577-087X


ConferenceIEEE International Conference on Robotics and Automation

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Learning a Pile Loading Controller from Demonstrations'. Together they form a unique fingerprint.

Cite this