Poster
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 · Jongeun Choi · Roberto Horowitz
Arch 4A-E Poster #329
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Poster]
Thu 20 Jun 5 p.m. PDT
— 6:30 p.m. PDT
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. The codes will be released upon acceptance.
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