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
Policy learning in robot-assisted surgery (RAS) lacks data efficient and
versatile methods that exhibit the desired motion quality for delicate surgical
interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a
novel method for imitation learning (IL) in RAS that focuses on gentle
manipulation of deformable objects. The approach combines the versatility of
diffusion-based imitation learning (DIL) with the high-quality motion
generation capabilities of Probabilistic Dynamic Movement Primitives (ProDMPs).
This combination enables MPD to achieve gentle manipulation of deformable
objects, while maintaining data efficiency critical for RAS applications where
demonstration data is scarce. We evaluate MPD across various simulated tasks
and a real world robotic setup on both state and image observations. MPD
outperforms state-of-the-art DIL methods in success rate, motion quality, and
data efficiency.