Diffusion-EDFs: Bi-Equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Abstract
Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper we present Diffusion-EDFs a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly we validate our methods with real hardware experiments.
Cite
Text
Ryu et al. "Diffusion-EDFs: Bi-Equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.01705Markdown
[Ryu et al. "Diffusion-EDFs: Bi-Equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/ryu2024cvpr-diffusionedfs/) doi:10.1109/CVPR52733.2024.01705BibTeX
@inproceedings{ryu2024cvpr-diffusionedfs,
title = {{Diffusion-EDFs: Bi-Equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation}},
author = {Ryu, Hyunwoo and Kim, Jiwoo and An, Hyunseok and Chang, Junwoo and Seo, Joohwan and Kim, Taehan and Kim, Yubin and Hwang, Chaewon and Choi, Jongeun and Horowitz, Roberto},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {18007-18018},
doi = {10.1109/CVPR52733.2024.01705},
url = {https://mlanthology.org/cvpr/2024/ryu2024cvpr-diffusionedfs/}
}