Paul Maria Scheikl

I am an applied scientist at Amazon Robotics in Berlin, working on the Vulcan project. I was a postdoc at Johns Hopkins University working under the guidance of Axel Krieger in the Laboratory for Computational Sensing and Robotics (LCSR). I received my PhD (Dr.-Ing.) from the Department of Artificial Intelligence in Biomedical Engineering at the Friedrich-Alexander University Erlangen-Nürnberg in Germany, where I was advised by Franziska Mathis-Ullrich.

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Research

My research interests lie at the intersection of robotics, computer vision, and machine learning. I am particularly interested in learning visumotor policies for complex object manipulation tasks. During my doctoral studies, I worked on imitation learning, reinforcement learnig, semantic segmentation, deformable object simulation, and sim-to-real transfer.

Learns a hierarchical policy for autonomous surgery using language-conditioned imitation learning. The high-level policy receives image data and produces a natural language instruction. The low-level policy receives the language instruction and image data and generates robotic motions. The framework is evaluated on the da Vinci Research Kit on ex-vivo porcine organs for laparoscopic cholecystectomy.

Presents the first robotic system to demonstrate autonomous clip positioning on a physical phantom in laparoscopic surgery. The system segments a colorless point cloud from a single camera, extracts target positions for the clips using spline interpolation, and allows human operators to adjust the targets. The segmentation model is trained on only 60 hand-labeled real point clouds, reflecting data scarcity in the surgical domain. The system achieves a localization precision of 0.75 mm at a 95% success rate and executes autonomous clip positioning with a 100% success rate.

Vision-guided, autonomous approach for palliative resection of tracheal tumors. Models the tracheal surface with a fifth-degree polynomial to plan tool trajectories. Aegmentation pipeline identifies the trachea and tumor boundaries.

Reconstructs a complete deformable object, including deformed internal structures, from a single point cloud observation. We use the reconstructed object to plan a robotic path to puncture internal regions of interest. Deformable object reconstruction eliminates the need for deformable object registration!

Demonstrates simulation and policy learning of lens capsule tearing in cataract surgery using reinforcement learning.

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.

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.

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).

Integrate sensory information to ground Graph Network Simulators on real world observations. Predict the mesh state of deformable objects by utilizing point cloud data.

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.

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.

Evaluates several architectures and training strategies for semantic segmentation of organs and tissues in laparoscopic surgery.