Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects-Reference-Cited by-同舟云学术

Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects

Published:2024-06 Issue:6 Volume:9 Page:5338-5345
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Scheikl Paul Maria¹^ORCID,Schreiber Nicolas²^ORCID,Haas Christoph²^ORCID,Freymuth Niklas²^ORCID,Neumann Gerhard²^ORCID,Lioutikov Rudolf²^ORCID,Mathis-Ullrich Franziska¹^ORCID

Affiliation:

1. Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany

2. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany

Funder

Erlangen National High Performance Computing Center

Deutsche Forschungsgemeinschaft

HoreKa supercomputer

Research and the Arts Baden-Württemberg

Bundesministerium für Bildung und Forschung

DFG

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7083369/10504377/10480552.pdf?arnumber=10480552

Reference25 articles.

1. Information maximizing curriculum: A curriculum-based approach for learning versatile skills;Blessing,2023

2. Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

3. Multimodal diffusion transformer for learning from play;Reuss,2023

4. Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

5. Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds

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