2026-04-07T14:52:34Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/59682024-03-04T13:11:46Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Elguea, Íñigo author Arana-Arexolaleiba, Nestor author Serrano Muñoz, Antonio author 2023 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. 1879-2537 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=171346 https://hdl.handle.net/20.500.11984/5968 Reinforcement learning Contact-rich manipulation Industrial manipulators Rigid object manipulation Deformable object manipulation A review on reinforcement learning for contact-rich robotic manipulation tasks