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Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

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arxiv 2309.02685 v3 pith:CBMBHBGX submitted 2023-09-06 cs.RO cs.AIcs.LG

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
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Jongeun Choi Roberto Horowitz
This is my paper
classification cs.RO cs.AIcs.LG
keywords approachdiffusion-edfsmanipulationroboticdemonstrationsexperimentsgenerativehuman
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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. Project Website: https://sites.google.com/view/diffusion-edfs/home

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