8 citations · 12 across the 11 of their papers we have counts for
14 papers
Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy
Ethan Kilmer, Pit Henrich, Jiawei Ge +13
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. W…
SurgVIL: Scaling Surgical Robot Imitation Learning with Open-source Surgical Videos
Xinhao Chen, JuoTung Chen, Nigel Nelson +4
Learning-based surgical robot autonomy requires large-scale demonstrations with synchronized videos and robot actions, but such data are exceedingly rare in clinical or realistic t…
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
SurgiPose: Estimating Surgical Tool Kinematics from Monocular Video for Surgical Robot Learning
Juo-Tung Chen, XinHao Chen, Ji Woong Kim +3
Imitation learning (IL) has shown immense promise in enabling autonomous dexterous manipulation, including learning surgical tasks. To fully unlock the potential of IL for surgery,…
Cosmos-Surg-dVRK: World Foundation Model-based Automated Online Evaluation of Surgical Robot Policy Learning
Lukas Zbinden, Nigel Nelson, Juo-Tung Chen +5
The rise of surgical robots and vision-language-action models has accelerated the development of autonomous surgical policies and efficient assessment strategies. However, evaluati…
SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing
Jesse Haworth, Juo-Tung Chen, Nigel Nelson +4
Robotic suturing is a prototypical long-horizon dexterous manipulation task, requiring coordinated needle grasping, precise tissue penetration, and secure knot tying. Despite numer…
SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning
Ji Woong Kim, Juo-Tung Chen, Pascal Hansen +11
Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation ove…
Towards Fluorescence-Guided Autonomous Robotic Partial Nephrectomy on Novel Tissue-Mimicking Hydrogel Phantoms
Ethan Kilmer, Joseph Chen, Jiawei Ge +7
Autonomous robotic systems hold potential for improving renal tumor resection accuracy and patient outcomes. We present a fluorescence-guided robotic system capable of planning and…
Surgical Gaussian Surfels: Highly Accurate Real-time Surgical Scene Rendering using Gaussian Surfels
Idris O. Sunmola, Zhenjun Zhao, Samuel Schmidgall +4
Accurate geometric reconstruction of deformable tissues in monocular endoscopic video remains a fundamental challenge in robot-assisted minimally invasive surgery. Although recent…
Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks
Pit Henrich, Jiawei Liu, Jiawei Ge +5
To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presen…
Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin Representation
Hao Ding, Yuqian Zhang, Wenzheng Cheng +7
Surgical phase recognition (SPR) is an integral component of surgical data science, enabling high-level surgical analysis. End-to-end trained neural networks that predict surgical…
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall +4
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder str…
General surgery vision transformer: A video pre-trained foundation model for general surgery
Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling +1
The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest datase…
General-purpose foundation models for increased autonomy in robot-assisted surgery
Samuel Schmidgall, Ji Woong Kim, Alan Kuntz +2
The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a ta…