Zobrazeno 1 - 10
of 519
pro vyhledávání: '"Kazanzides, P."'
Autor:
Wang, Junxiang, Barragan, Juan Antonio, Ishida, Hisashi, Guo, Jingkai, Ku, Yu-Chun, Kazanzides, Peter
Telesurgery is an effective way to deliver service from expert surgeons to areas without immediate access to specialized resources. However, many of these areas, such as rural districts or battlefields, might be subject to different problems in commu
Externí odkaz:
http://arxiv.org/abs/2411.13449
Autor:
Allison, Christopher John, Zhou, Haoying, Munawar, Adnan, Kazanzides, Peter, Barragan, Juan Antonio
Interactive dynamic simulators are an accelerator for developing novel robotic control algorithms and complex systems involving humans and robots. In user training and synthetic data generation applications, high-fidelity visualizations from the simu
Externí odkaz:
http://arxiv.org/abs/2410.05095
Autor:
Ding, Hao, Seenivasan, Lalithkumar, Shu, Hongchao, Byrd, Grayson, Zhang, Han, Xiao, Pu, Barragan, Juan Antonio, Taylor, Russell H., Kazanzides, Peter, Unberath, Mathias
Large language model-based (LLM) agents are emerging as a powerful enabler of robust embodied intelligence due to their capability of planning complex action sequences. Sound planning ability is necessary for robust automation in many task domains, b
Externí odkaz:
http://arxiv.org/abs/2409.13107
Despite advancements in robotic-assisted surgery, automating complex tasks like suturing remain challenging due to the need for adaptability and precision. Learning-based approaches, particularly reinforcement learning (RL) and imitation learning (IL
Externí odkaz:
http://arxiv.org/abs/2406.13865
Publikováno v:
2024 International Symposium on Medical Robotics (ISMR)
The development of algorithms for automation of subtasks during robotic surgery can be accelerated by the availability of realistic simulation environments. In this work, we focus on one aspect of the realism of a surgical simulator, which is the pos
Externí odkaz:
http://arxiv.org/abs/2406.07375
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA)
Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critica
Externí odkaz:
http://arxiv.org/abs/2406.07328
In this study, we further investigate the robustness and generalization ability of an neural network (NN) based force estimation method, using the da Vinci Research Kit Si (dVRK-Si). To evaluate our method's performance, we compare the force estimati
Externí odkaz:
http://arxiv.org/abs/2405.07453
The Segment Anything Model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assi
Externí odkaz:
http://arxiv.org/abs/2403.08003
Autor:
Zhou, Haoying, Jiang, Yiwei, Gao, Shang, Wang, Shiyue, Kazanzides, Peter, Fischer, Gregory S.
Publikováno v:
2024 International Symposium on Medical Robotics (ISMR) IEEE
In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomo
Externí odkaz:
http://arxiv.org/abs/2403.00956
Autor:
Ishida, Hisashi, Galaiya, Deepa, Nagururu, Nimesh, Creighton, Francis, Kazanzides, Peter, Taylor, Russell, Sahu, Manish
Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly
Externí odkaz:
http://arxiv.org/abs/2401.11721