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of 24
pro vyhledávání: '"Scheikl Paul Maria"'
Autor:
Scheikl, Paul Maria, Tagliabue, Eleonora, Gyenes, Balázs, Wagner, Martin, Dall'Alba, Diego, Fiorini, Paolo, Mathis-Ullrich, Franziska
Publikováno v:
IEEE Robotics and Automation Letters 8 (2023) 560-567
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor polic
Externí odkaz:
http://arxiv.org/abs/2406.06092
Autor:
Scheikl Paul Maria, Laschewski Stefan, Kisilenko Anna, Davitashvili Tornike, Müller Benjamin, Capek Manuela, Müller-Stich Beat P., Wagner Martin, Mathis-Ullrich Franziska
Publikováno v:
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 1-11 (2020)
Semantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prom
Externí odkaz:
https://doaj.org/article/1a0b98c75e114068b3b7000b5c93c2f6
Autor:
Scheikl, Paul Maria, Schreiber, Nicolas, Haas, Christoph, Freymuth, Niklas, Neumann, Gerhard, Lioutikov, Rudolf, Mathis-Ullrich, Franziska
Publikováno v:
IEEE Robotics and Automation Letters 9 (2024) 5338-5345
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a novel method for i
Externí odkaz:
http://arxiv.org/abs/2312.10008
Autor:
Henrich, Pit, Gyenes, Balázs, Scheikl, Paul Maria, Neumann, Gerhard, Mathis-Ullrich, Franziska
In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of defor
Externí odkaz:
http://arxiv.org/abs/2311.07357
Autor:
Linkerhägner, Jonas, Freymuth, Niklas, Scheikl, Paul Maria, Mathis-Ullrich, Franziska, Neumann, Gerhard
Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations whi
Externí odkaz:
http://arxiv.org/abs/2302.11864
LapGym -- An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery
Autor:
Scheikl, Paul Maria, Gyenes, Balázs, Younis, Rayan, Haas, Christoph, Neumann, Gerhard, Wagner, Martin, Mathis-Ullrich, Franziska
Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However, progress in algorithms and methods depends on the availability of sta
Externí odkaz:
http://arxiv.org/abs/2302.09606
Autor:
Scheikl, Paul Maria, Gyenes, Balázs, Davitashvili, Tornike, Younis, Rayan, Schulze, André, Müller-Stich, Beat P., Neumann, Gerhard, Wagner, Martin, Mathis-Ullrich, Franziska
Cognitive cooperative assistance in robot-assisted surgery holds the potential to increase quality of care in minimally invasive interventions. Automation of surgical tasks promises to reduce the mental exertion and fatigue of surgeons. In this work,
Externí odkaz:
http://arxiv.org/abs/2110.04857
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