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Izenburua
A review on reinforcement learning for contact-rich robotic manipulation tasksArgitalpen data
2023Argitaratzailea
ElsevierGako-hitzak
Reinforcement learning
Contact-rich manipulation
Industrial manipulators
Rigid object manipulation ... [+]
Contact-rich manipulation
Industrial manipulators
Rigid object manipulation ... [+]
Reinforcement learning
Contact-rich manipulation
Industrial manipulators
Rigid object manipulation
Deformable object manipulation [-]
Contact-rich manipulation
Industrial manipulators
Rigid object manipulation
Deformable object manipulation [-]
Laburpena
Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard ... [+]
Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain. [-]
Argitaratzailearen bertsioa
https://doi.org/10.1016/j.rcim.2022.102517ISSN
1879-2537Non argitaratua
Robotics and Computer-Integrated Manufacturing Vol. 81. N. artículo 102517Dokumentu-mota
Artikulua
Bertsioa
Argitaratua
Eskubideak
© 2023 The AuthorsSarbidea
Sarbide irekiaBildumak
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