[Submitted on 14 Sep 2026]
Title:Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy
View a PDF of the paper titled Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy, by Ethan Kilmer and 15 other authors
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Abstract:Autonomous soft-tissue cancer surgery has been limited to interventions on organ surfaces, because current systems cannot perceive and adapt to anatomy once it deforms or is cut. We introduce the first vision-guided autonomous system capable of performing complete tumor resections for partial nephrectomy. Our system integrates conditional occupancy networks, trained entirely in a physics-based simulation, that infer full 3-D anatomy (tumor, margin tissue, and kidney) from single-view partial point clouds. These occupancy networks maintain intraoperative tracking even as tissue is cut and deformed, enabling adaptive planning and execution. The surgical platform combines a depth camera for capturing surface point clouds, dual robotic arms for electrosurgical cutting and vacuum-based tissue manipulation, and an autonomous control strategy for tumor resection. In patient-derived hydrogel phantoms under an open partial nephrectomy setting, the robot performed eight consecutive autonomous tumor resections comprising 77 electrosurgical cuts, with all cuts achieving negative surgical margins and 1.61 $\pm$ 0.48 mm mean absolute margin error. This work demonstrates, for the first time, a foundation for supervised autonomous closed-loop, imaging-driven, margin-negative tumor removal in phantoms.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.16186 [cs.RO]
(or arXiv:2609.16186v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.16186
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiawei Ge [view email] [v1] Mon, 14 Sep 2026 18:20:02 UTC (3,390 KB)
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