73 citations · 177 across the 11 of their papers we have counts for
11 papers · 1 filter
Velocity Optimization via Thermal Imaging Enhances Autonomous Tumor Resection
Naveed D. Riaziat, Jiawei Ge, Ethan Kilmer +6
Objective: To demonstrate and evaluate THERMO, a thermal-velocity optimization algorithm for autonomous electrosurgery, in ex vivo porcine tissue using the UR10e-based ASTR system.…
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…
Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom
Balázs Gyenes, Nikolai Franke, Paul Maria Scheikl +5
High-risk applications in robotics, such as robot-assisted surgery, present unique challenges. These systems must be both highly precise and interpretable in order to be deployed i…
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,…
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…
LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions
Pit Henrich, Franziska Mathis-Ullrich, Paul Maria Scheikl
Accurately determining the shape of deformable objects and the location of their internal structures is crucial for medical tasks that require precise targeting, such as robotic bi…
Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
Paul Maria Scheikl, Eleonora Tagliabue, Balázs Gyenes +4
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement lea…
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas +4
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end,…
LapGym -- An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery
Paul Maria Scheikl, Balázs Gyenes, Rayan Younis +4
Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However,…
Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
Paul Maria Scheikl, Balázs Gyenes, Tornike Davitashvili +6
Cognitive cooperative assistance in robot-assisted surgery holds the potential to increase quality of care in minimally invasive interventions. Automation of surgical tasks promise…