{"ID":2843539,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08490","arxiv_id":"2511.08490","title":"A Supervised Autonomous Resection and Retraction Framework for Transurethral Enucleation of the Prostatic Median Lobe","abstract":"Concentric tube robots (CTRs) offer dexterous motion at millimeter scales, enabling minimally invasive procedures through natural orifices. This work presents a coordinated model-based resection planner and learning-based retraction network that work together to enable semi-autonomous tissue resection using a dual-arm transurethral concentric tube robot (the Virtuoso). The resection planner operates directly on segmented CT volumes of prostate phantoms, automatically generating tool trajectories for a three-phase median lobe resection workflow: left/median trough resection, right/median trough resection, and median blunt dissection. The retraction network, PushCVAE, trained on surgeon demonstrations, generates retractions according to the procedural phase. The procedure is executed under Level-3 (supervised) autonomy on a prostate phantom composed of hydrogel materials that replicate the mechanical and cutting properties of tissue. As a feasibility study, we demonstrate that our combined autonomous system achieves a 97.1% resection of the targeted volume of the median lobe. Our study establishes a foundation for image-guided autonomy in transurethral robotic surgery and represents a first step toward fully automated minimally-invasive prostate enucleation.","short_abstract":"Concentric tube robots (CTRs) offer dexterous motion at millimeter scales, enabling minimally invasive procedures through natural orifices. This work presents a coordinated model-based resection planner and learning-based retraction network that work together to enable semi-autonomous tissue resection using a dual-arm...","url_abs":"https://arxiv.org/abs/2511.08490","url_pdf":"https://arxiv.org/pdf/2511.08490v1","authors":"[\"Mariana Smith\",\"Tanner Watts\",\"Susheela Sharma Stern\",\"Brendan Burkhart\",\"Hao Li\",\"Alejandro O. Chara\",\"Nithesh Kumar\",\"James Ferguson\",\"Ayberk Acar\",\"Jesse F. d'Almeida\",\"Lauren Branscombe\",\"Lauren Shepard\",\"Ahmed Ghazi\",\"Ipek Oguz\",\"Jie Ying Wu\",\"Robert J. Webster\",\"Axel Krieger\",\"Alan Kuntz\"]","published":"2025-11-11T17:31:20Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}