Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects

Scheikl P, Schreiber N, Haas C, Freymuth N, Neumann G, Lioutikov R, Mathis-Ullrich F (2024)

Publication Type: Journal article

Publication year: 2024

Journal

IEEE Robotics and Automation Letters IEEE

Pages Range: 1-8

DOI: 10.1109/LRA.2024.3382529

Abstract

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 and real world robotic tasks on both state and image observations. MPD outperforms state-of-the-art DIL methods in success rate, motion quality, and data efficiency. Project page: https://scheiklp.github.io/movement-primitive-diffusion/

Authors with CRIS profile

Paul Scheikl Professur für Robotische Planung und Kognition in der Chirurgie Franziska Mathis-Ullrich Professur für Robotische Planung und Kognition in der Chirurgie

Involved external institutions

Karlsruhe Institute of Technology (KIT) DE Germany (DE)

How to cite

APA:

Scheikl, P., Schreiber, N., Haas, C., Freymuth, N., Neumann, G., Lioutikov, R., & Mathis-Ullrich, F. (2024). Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects. IEEE Robotics and Automation Letters, 1-8. https://doi.org/10.1109/LRA.2024.3382529

MLA:

Scheikl, Paul, et al. "Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects." IEEE Robotics and Automation Letters (2024): 1-8.

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