arXiv subjects

Jacob Rosen

Publications and source records attributed to Jacob Rosen.

5 recordsLinked to original sources

arXiv2025

High-Precision Surgical Robotic System for Intraocular Procedures

Despite the extensive demonstration of robotic systems for both cataract and vitreoretinal procedures, existing technologies or mechanisms still possess insufficient accuracy, precision, and degrees of freedom for instrument manipulation or potentially automated tool exchange during surgical procedures. A new robotic system that focuses on improving tooltip accuracy, tracking performance, and smooth instrument exchange mechanism is therefore designed and manufactured. Its tooltip accuracy, precision, and mechanical capability of maintaining small incision through remote center of motion were externally evaluated using an optical coherence tomography (OCT) system. Through robot calibration and precise coordinate registration, the accuracy of tooltip positioning was measured to be 0.053$\pm$0.031 mm, and the overall performance was demonstrated on an OCT-guided automated cataract lens extraction procedure with deep learning-based pre-operative anatomical modeling and real-time supervision.

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arXiv2025

Control Modes of Teleoperated Surgical Robotic System's Tools in Ophthalmic Surgery

The introduction of a teleoperated surgical robotic system designed for minimally invasive procedures enables the emulation of two distinct control modes through a dedicated input device of the surgical console: (1) Inside Control Mode, which emulates tool manipulation near the distal end as if the surgeon was holding the tip of the instrument inside the patient's body; (2) Outside Control Mode, which emulates manipulation near the proximal end as if the surgeon was holding the tool externally. The aim of this research is to compare the surgeon's performance on these two modes of operation along with various scaling factors in a simulated vitreoretinal surgical setting. The console of Intraocular Robotic Interventional Surgical System (IRISS) was utilized but the surgical robot itself and the human eye anatomy was simulated by a virtual environment projected microscope view of an intraocular setup to a VR headset. Five experienced vitreoretinal surgeons and five subjects with no surgical experience used the system to perform four fundamental tool/tissue tasks common to vitreoretinal surgery: touch and reset; grasp and drop; inject; circular tracking. Results indicate that Inside Control outperforms Outside Control across multiple tasks and metrics. Higher scaling factors generally performed better, particularly for reducing trajectory errors and tissue damage. This improvement suggests that larger scaling factors enable more precise control, making them the preferred option for fine manipulation. However, completion time was not consistently reduced across all conditions, indicating that surgeons need to balance speed and accuracy based on surgical requirements. By optimizing control dynamics and user interface, robotic teleoperation has the potential to reduce complications, enhance dexterity, and expand the accessibility of high precision procedures to a broader range of practitioners.

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arXiv2019

Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach

In this paper, we present a synergic learning algorithm to address the task of indirect manipulation of an unknown deformable tissue. Tissue manipulation is a common yet challenging task in various surgical interventions, which makes it a good candidate for robotic automation. We propose using a linear approximate Q-learning method in which human knowledge contributes to selecting useful yet simple features of tissue manipulation while the algorithm learns to take optimal actions and accomplish the task. The algorithm is implemented and evaluated on a simulation using the OpenCV and CHAI3D libraries. Successful simulation results for four different configurations which are based on realistic tissue manipulation scenarios are presented. Results indicate that with a careful selection of relatively simple and intuitive features, the developed Q-learning algorithm can successfully learn an optimal policy without any prior knowledge of tissue dynamics or camera intrinsic/extrinsic calibration parameters.

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arXiv2019

Raven: Open Surgical Robotic Platforms

The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

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arXiv2019

Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated that the learning from demonstration algorithm can boost the learning policy by initializing the predicted dynamics with given demonstrations. Furthermore, the learning from demonstration algorithm is implemented on a Raven IV surgical robotic system and successfully demonstrated feasibility of the proposed algorithm using an experimental approach. This study is part of a profound vision in which the role of a surgeon will be redefined as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject17.html

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