Search Results for author: Paul Maria Scheikl

Found 3 papers, 1 papers with code

Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects

no code implementations15 Dec 2023 Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas, Niklas Freymuth, Gerhard Neumann, Rudolf Lioutikov, Franziska Mathis-Ullrich

Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.

Imitation Learning

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud

no code implementations13 Nov 2023 Pit Henrich, Balázs Gyenes, Paul Maria Scheikl, Gerhard Neumann, Franziska Mathis-Ullrich

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object.

Deformable Object Manipulation Object +1

Grounding Graph Network Simulators using Physical Sensor Observations

1 code implementation23 Feb 2023 Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann

Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.

Imputation Motion Planning +1

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