# Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy

arXiv: https://arxiv.org/abs/2609.16186 · HTML: https://mlsanity.com/paper/2609.16186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [Yell At Your Robot: Improving On-the-Fly from Language Corrections](https://mlsanity.com/paper/2403.12910/) (2024)
- [DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting](https://mlsanity.com/paper/2105.12244/) (2021)
- [Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery](https://mlsanity.com/paper/2406.06092/) (2024)
- [SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning](https://mlsanity.com/paper/2505.10251/) (2025)
- [Learning from Demonstrations for Autonomous Soft-tissue Retraction](https://mlsanity.com/paper/2110.00336/) (2021)
- [Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model](https://mlsanity.com/paper/2005.14695/) (2020)
- [From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction](https://mlsanity.com/paper/2503.16263/) (2025)
- [Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud](https://mlsanity.com/paper/2311.07357/) (2023)
- [LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images](https://mlsanity.com/paper/2406.12407/) (2024)
- [LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions](https://mlsanity.com/paper/2411.08777/) (2024)
- [Autonomous Vision-Guided Resection of Central Airway Obstruction](https://mlsanity.com/paper/2502.18586/) (2025)
- [Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks](https://mlsanity.com/paper/2411.02619/) (2024)
- [Multi-tap Resistive Sensing and FEM Modeling enables Shape and Force Estimation in Soft Robots](https://mlsanity.com/paper/2311.14566/) (2023)
- [CRESSim-MPM: A Material Point Method Library for Surgical Soft Body Simulation with Cutting and Suturing](https://mlsanity.com/paper/2502.18437/) (2025)
- [Towards Fluorescence-Guided Autonomous Robotic Partial Nephrectomy on Novel Tissue-Mimicking Hydrogel Phantoms](https://mlsanity.com/paper/2503.02265/) (2025)

## Abstract (arXiv)

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 ± 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.