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The laboratory for Surgical Planning and Robotic Cognition (SPARC) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), led by Prof. Dr. Franziska Mathis-Ullrich, investigates cognition guided robots for surgical assistance in minimally invasive procedures, intelligent and flexible surgical instruments, and intuitive interfaces between humans and robots in the operating room.
The SPARC laboratory conducts interdisciplinary research in close collaboration with national and international partners. We aim to contribute to building a healthcare system that enables optimal and personalized treatment of patients through targeted interactions between surgical experts and the next generation of minimally invasive surgical robots and assistance systems.
We’re proud to share that our master students Ali Ghahramanizadi, Yunbing Song, Shujie Tang, Pooja Rao, and Ivan Alexander Trujillo Freire represented our group at the CURAC Conference 2025. Together, they presented three papers: two oral presentations and one poster, showcasing their research in th...
We are pleased to share that our paper, "Learning-based autonomous navigation, benchmark environments and simulation framework for endovascular interventions," has been accepted for publication in Computers in Biology and Medicine. In this work, we present stEVE (simulated EndoVascular Environment),...
On July 10th 2025, SPARC member Lennart Karstensen successfully defended his doctoral thesis on “Simulation-based Control Learning for Patient-Specific Autonomous Navigation for Endovascular Interventions in the Real World”. The examination committee consisted of Prof. Franziska Mathis-Ullrich (FAU)...
SPARC member Pit Henrich was awarded first place for his presentation at AIBEConf, held at the Digital Health Innovation Platform (d.hip) in Erlangen. His research focuses on advancing robotic interaction with deformable tissue and objects (a critical challenge in surgical robotics) by providing str...
We are pleased to share that our paper, “LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions,” has been accepted for publication in the IEEE Transactions on Robotics.
In this work, we present LUDO, a method for low-latency understanding of deformable object...
