Nicolas Schreiber
Papers
3
Total Citations
58
H-Index
2
About
Nicolas Schreiber is at the forefront of robotic learning for delicate manipulation, with a particular focus on robot-assisted surgery (RAS). His primary research areas span imitation learning, movement primitives, and human-robot interaction, with an emphasis on enabling robots to handle deformable objects with the gentleness required for surgical interventions. Schreiber’s most impactful contribution is the introduction of Movement Primitive Diffusion (MPD), a novel imitation learning method that combines diffusion models with movement primitives to produce smooth, gentle, and data-efficient policies for surgical robotics. This work, published in 2024, has already garnered 48 citations, reflecting its significance in addressing the critical challenge of motion quality in RAS. Additionally, Schreiber has advanced the field of data collection for robot learning through a comprehensive user study on augmented reality-based interfaces (8 citations), exploring how AR can streamline the acquisition of high-quality demonstration data. His work bridges the gap between data efficiency and motion quality, positioning him as a rising voice in the quest for safer, more capable surgical robots.
Research Focus
Key Achievements
Top Papers
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