Papers
14
Total Citations
344
H-Index
7
About
Paul Maria Scheikl is a leading researcher at the intersection of robotics, artificial intelligence, and minimally invasive surgery, whose work is pioneering the next generation of autonomous surgical systems. His research focuses on enabling robots to perform delicate, dexterous tasks in the unpredictable environment of the human body, moving beyond simple automation toward true cognitive assistance. Scheikl’s major contributions include the development of **SRT-H**, a hierarchical framework for language-conditioned imitation learning that allows surgical robots to generalize across complex, long-duration procedures (70 citations). He also advanced sim-to-real transfer for reinforcement learning, enabling robots to master deformable object manipulation—a critical skill for surgery—in simulation before deployment (66 citations). His work on the first self-learning, context-sensitive camera-guiding robot for minimally invasive surgery (55 citations) and the **Movement Primitive Diffusion** method for gentle robotic manipulation (48 citations) further demonstrates his impact. With over 300 total citations, Scheikl has also developed open-source frameworks like **LapGym** to accelerate research in the field. His achievements signal a future where robots act as capable, cooperative partners in the operating room, reducing surgeon fatigue and improving patient outcomes.
Research Focus
Key Achievements
Top Papers
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- 3A learning robot for cognitive camera control in minimally invasive surgery55 citations · 2021
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- 8Evaluation of Domain Randomization Techniques for Transfer Learning5 citations · 2019
- 9Autonomous Vision-Guided Resection of Central Airway Obstruction5 citations · 2025
- 10Robotik im Operationssaal – (Ko‑)Operieren mit Kollege Roboter5 citations · 2020