Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Hyunwoo Ryu, Jiwoo Kim, Hyunseok An, Junwoo Chang, Joohwan Seo, Taehan Kim, Yubin Kim, Chaewon Hwang
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.
BibTeX
@inproceedings{cvpr2024_diffusionedfsbie,
title = {Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation},
author = {Hyunwoo Ryu and Jiwoo Kim and Hyunseok An and Junwoo Chang and Joohwan Seo and Taehan Kim and Yubin Kim and Chaewon Hwang and Jongeun Choi and Roberto Horowitz},
booktitle = {CVPR 2024},
year = {2024}
}