Publication
Deep reinforcement learning applied to a robotic pick-and-place application
| dc.contributor.author | Gomes, Natanael Magno | |
| dc.contributor.author | Martins, Felipe N. | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Wörtche, Heinrich | |
| dc.date.accessioned | 2022-04-05T14:21:52Z | |
| dc.date.available | 2022-04-05T14:21:52Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/ unknown positions. This can be achieved by off-the-shelf visionbased solutions, but most require prior knowledge about each object to be manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a ϵ- greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pretrained CNN models (RexNext, MobileNet, MNASNet and DenseNet). Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment. | pt_PT |
| dc.description.sponsorship | This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020 and by the Innovation Cluster Dracten (ICD), project Collaborative Connected Robots (Cobots) 2.0. The authors also thank the support from the Research Centre Biobased Economy from the Hanze University of Applied Sciences. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Gomes, Natanael Magno; Martins, Felipe N.; Lima, José; Wörtche, Heinrich (2021). Deep reinforcement learning applied to a robotic pick-and-place application. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 251-265. ISBN 978-3-030-91884-2 | pt_PT |
| dc.identifier.doi | 10.1007/978-3-030-91885-9_18 | pt_PT |
| dc.identifier.isbn | 978-3-030-91884-2 | |
| dc.identifier.uri | http://hdl.handle.net/10198/25357 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer Nature | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Cobots | pt_PT |
| dc.subject | Reinforcement learning | pt_PT |
| dc.subject | Computer vision | pt_PT |
| dc.subject | Pick-and-place | pt_PT |
| dc.subject | Grasping | pt_PT |
| dc.title | Deep reinforcement learning applied to a robotic pick-and-place application | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.citation.conferencePlace | Bragança | pt_PT |
| oaire.citation.endPage | 265 | pt_PT |
| oaire.citation.startPage | 251 | pt_PT |
| oaire.citation.title | Optimization, learning algorithms and applications: first International Conference, OL2A 2021 | pt_PT |
| oaire.citation.volume | 1488 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Lima | |
| person.givenName | José | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 55851941311 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | d88c2b2a-efc2-48ef-b1fd-1145475e0055 | |
| relation.isAuthorOfPublication.latestForDiscovery | d88c2b2a-efc2-48ef-b1fd-1145475e0055 | |
| relation.isProjectOfPublication | 6e01ddc8-6a82-4131-bca6-84789fa234bd | |
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