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Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning
This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportatio...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309906/ https://www.ncbi.nlm.nih.gov/pubmed/34300518 http://dx.doi.org/10.3390/s21144780 |
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author | Eoh, Gyuho Park, Tae-Hyoung |
author_facet | Eoh, Gyuho Park, Tae-Hyoung |
author_sort | Eoh, Gyuho |
collection | PubMed |
description | This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques. |
format | Online Article Text |
id | pubmed-8309906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83099062021-07-25 Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning Eoh, Gyuho Park, Tae-Hyoung Sensors (Basel) Article This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques. MDPI 2021-07-13 /pmc/articles/PMC8309906/ /pubmed/34300518 http://dx.doi.org/10.3390/s21144780 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Eoh, Gyuho Park, Tae-Hyoung Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title | Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title_full | Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title_fullStr | Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title_full_unstemmed | Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title_short | Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning |
title_sort | cooperative object transportation using curriculum-based deep reinforcement learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309906/ https://www.ncbi.nlm.nih.gov/pubmed/34300518 http://dx.doi.org/10.3390/s21144780 |
work_keys_str_mv | AT eohgyuho cooperativeobjecttransportationusingcurriculumbaseddeepreinforcementlearning AT parktaehyoung cooperativeobjecttransportationusingcurriculumbaseddeepreinforcementlearning |