Automatic Loading of Unknown Material with a Wheel Loader Using Reinforcement Learning

Daniel Eriksson, Reza Ghabcheloo, Marcus Geimer

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

2 Citations (Scopus)
6 Downloads (Pure)

Abstract

Loading multiple different materials with wheel loaders is a challenging task because various materials require different loading techniques. It’s, therefore, difficult to find a single controller capable of handling them all. One solution is to use a base controller and fine-tune it for different materials. Reinforcement Learning (RL) automates this process without the need for collecting additional human-annotated data. We investigated the feasibility of this approach using a full-size 24-tonnes wheel loader in the real world and demonstrated that it’s possible to fine-tune a neural network controller that was originally trained with imitation learning on blasted rock for use with an unknown gravel material, requiring 20 bucket fillings. Additionally, we showcased the adaptability of a controller pre-trained on woodchips for an unknown gravel material, requiring 40 bucket fillings. We also proposed a novel reward function for the material loading task. Finally, we examined how the sampling time of the reinforcement learning algorithm affects convergence speed and adaptability. Our results demonstrate that it’s optimal to match the sampling time of the RL algorithm to the delays of the wheel loader’s hydraulic actuators.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages3646-3652
Number of pages7
ISBN (Electronic)979-8-3503-8457-4
ISBN (Print)979-8-3503-8458-1
DOIs
Publication statusPublished - 2024
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Robotics and Automation - Yokohama, Japan
Duration: 13 May 202417 May 2024

Publication series

NameIEEE international conference on robotics and automation
ISSN (Print)2152-4092
ISSN (Electronic)2379-9552

Conference

ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA
Country/TerritoryJapan
CityYokohama
Period13/05/2417/05/24

Publication forum classification

  • Publication forum level 2

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