Robotic-Arm-Based Force Control by Deep Deterministic Policy Gradient in Neurosurgical Practice

Ibai Inziarte-Hidalgo, Erik Gorospe, Ekaitz Zulueta (), Jose Manuel Lopez-Guede, Unai Fernandez-Gamiz and Saioa Etxebarria
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Ibai Inziarte-Hidalgo: Research & Development Department, Montajes Mantenimiento y Automatismos Electricos Navarra S.L., 01010 Vitoria-Gasteiz, Spain
Erik Gorospe: Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
Ekaitz Zulueta: Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Jose Manuel Lopez-Guede: Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Unai Fernandez-Gamiz: Department of Nuclear Engineering and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Saioa Etxebarria: Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain

Mathematics, 2023, vol. 11, issue 19, 1-19

Abstract: This research continues the previous work “Robotic-Arm-Based Force Control in Neurosurgical Practice”. In that study, authors acquired an optimal control arm speed shape for neurological surgery which minimized a cost function that uses an adaptive scheme to determine the brain tissue force. At the end, the authors proposed the use of reinforcement learning, more specifically Deep Deterministic Policy Gradient (DDPG), to create an agent that could obtain the optimal solution through self-training. In this article, that proposal is carried out by creating an environment, agent (actor and critic), and reward function, that obtain a solution for our problem. However, we have drawn conclusions for potential future enhancements. Additionally, we analyzed the results and identified mistakes that can be improved upon in the future, such as exploring the use of varying desired distances of retraction to enhance training.

Keywords: neurosurgical robotics; optimal control; reinforcement learning; deep deterministic policy gradient (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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