Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Keywords: SE(3)-equivariance, Representation theory, Diffusion models, Robotic manipulation, Lie group, Lie algebra, Generative modeling, Point cloud
TL;DR: We propose a bi-equivariant diffusion generative model on SE(3) for robotic manipulation learning with point cloud vision.
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.
Submission Number: 7
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