This preprint is under consideration at The International Journal of Advanced Manufacturing Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Antonio Serrano Muñoz
Mondragon Unibertsitatea
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9938-673X
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Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the process's planning and operation. Machine learning, particularly reinforcement learning, methods are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the State-of-the-Art of contact-rich disassembly using reinforcement learning algorithms and a study about the object extraction skill's generalisation when applied to contact-rich disassembly tasks. The generalisation capabilities of two State-of-the-Art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation and real-world while perform a disassembly task. Results shows that, at least, one of the agents can generalise the contact-rich extraction skill. Also, this work identifies key concepts and gaps for the reinforcement learning algorithms' research and application on disassembly tasks.
Keywords
Circular economy, Remanufacturing, Disassembly, Robotics, Reinforcement Learning, Contact-rich Manipulation
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This preprint is under consideration at The International Journal of Advanced Manufacturing Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Antonio Serrano Muñoz
Mondragon Unibertsitatea
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9938-673X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 24 Mar, 2021
No community comments so far
Reviewers invited
Invitations sent on 17 Mar, 2021
Reviews received
Received 17 Mar, 2021
Editor assigned
On 16 Mar, 2021
First submitted
On 15 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the process's planning and operation. Machine learning, particularly reinforcement learning, methods are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the State-of-the-Art of contact-rich disassembly using reinforcement learning algorithms and a study about the object extraction skill's generalisation when applied to contact-rich disassembly tasks. The generalisation capabilities of two State-of-the-Art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation and real-world while perform a disassembly task. Results shows that, at least, one of the agents can generalise the contact-rich extraction skill. Also, this work identifies key concepts and gaps for the reinforcement learning algorithms' research and application on disassembly tasks.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
The full text of this article is available to read as a PDF.
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