Work

Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy

Year: 2026

Type: article

Abstract: Maintaining anatomical guidance during tissue deformation and cutting remains a challenge for autonomous softtissue tumor resection. We introduce the first vision-guided autonomous system capable of performing complete tumor resections in patient-derived hydrogel phantoms within a benchtop setup that mimics open 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 patientderived 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, imagingdriven, margin-negative tumor removal in phantoms.

Source: IEEE Transactions on Robotics

DOI: 10.1109/tro.2026.3749080