Paul Maria Scheikl

I'm a doctoral candidate in the Surgical Planning and Robotic Cognition Lab (SPARC) at the Friedrich-Alexander-University Erlangen-Nürnberg in Germany.

Email / Scholar / Github

Research

I'm interested in robotics, visuomotor learning, and deformable object simulation. Most of my research is about learning visuomotor policies for deformable object manipulation for robot-assisted surgery.

	Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas, Niklas Freymuth, Gerhard Neumann, Rudolf Lioutikov, Franziska Mathis-Ullrich. IEEE Robotics and Automation Letters (RA-L), 2024 code / website Combines the versatility of diffusion-based imitation learning with the high-quality motion generation capabilities of Probabilistic Dynamic Movement Primitives. Achieves gentle manipulation of deformable objects, while maintaining data efficiency critical for surgical applications where demonstration data is scarce. Evaluated in simulation and on real robotic hardware.
	Registered and Segmented Deformable Object Reconstruction From a Single View Point Cloud Pit Henrich, Balázs Gyenes, Paul Maria Scheikl, Gerhard Neumann, Franziska Mathis-Ullrich. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024 3D reconstruction and segmentation of deformable objects from a single view point cloud. Also introduces a simple sampling algorithm to generate better training data for occupancy learning.
	LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery Paul Maria Scheikl, Balázs Gyenes, Rayan Younis, Christoph Haas, Gerhard Neumann, Martin Wagner, Franziska Mathis-Ullrich. Journal of Machine Learning Research (JMLR), 2023 code: lap_gym / sofa_env Reinforcement learning framework for robot-assisted laparoscopic surgery. Builds on the open-source, fast, interactive FEM simulation backend SOFA. Deformable object manipulation, topological changes (cutting), grasping, image observation modalities (RGB, depth, segmentation, point clouds).
	Grounding Graph Network Simulators using Physical Sensor Observations Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann. International Conference on Learning Representations (ICLR), 2023 Integrate sensory information to ground Graph Network Simulators on real world observations. Predict the mesh state of deformable objects by utilizing point cloud data.
	Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery Paul Maria Scheikl, Eleonora Tagliabue, Balázs Gyenes, Martin Wagner, Diego Dall'Alba, Paolo Fiorini, Franziska Mathis-Ullrich. IEEE Robotics and Automation Letters (RA-L), 2022 Training a visumotor policy for deformable object manipulation in simulation with reinforcement learning. Transferring the policy to the real world with the daVinci Research Kit using unpaired image-to-image translation.
	Cooperative assistance in robotic surgery through multi-agent reinforcement learning Paul Maria Scheikl, Balázs Gyenes, Tornike Davitashvili, Rayan Younis, André Schulze, Beat P Müller-Stich, Gerhard Neumann, Martin Wagner, Franziska Mathis-Ullrich. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 Learns decentralized policies without a human in the loop with multi-agent reinforcement learning. Evaluates the learned policies in cooperation with a human surgeon for deformable object manipulation.
	Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery Paul Maria Scheikl, Stefan Laschewski, Anna Kisilenko, Tornike Davitashvili, Benjamin Müller, Manuela Capek, Beat P Müller-Stich, Martin Wagner, Franziska Mathis-Ullrich. Current Directions in Biomedical Engineering (CDBE), 2020 Evaluates several architectures and training strategies for semantic segmentation of organs and tissues in laparoscopic surgery.

The website is based on the code from Jon Barron