The concepts developed for STRAS are validated and could bring new tools for surgeons to improve comfort, ease, and performances for intraluminal surgical endoscopy.
Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real-world applications.
Flexible endoscopes are used in many surgical procedures and diagnostic exams, like in gastroscopy or colonoscopy. They have also been used recently for endoscopic surgical procedures using natural orifices (NOTES). Flexible endoscopes allow to access operating areas which are not easily reachable, with one small or no incision. However, their manipulation is complex especially for surgical interventions. In order to help the physicians during interventions, we propose to motorize the flexible endoscope and to partially robotize its movements. This paper explains how the endoscope can be actively stabilized with respect to an area of interest despite physiological motions by using visual servoing and repetitive control. In vivo experiments show the validity of the proposed solution for improving the manipulation of the endoscope.
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