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Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
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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
Forward citations
Cited by 2 Pith papers
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Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects
A heterogeneous SE(3)-equivariant graph policy (HEPi) outperforms Transformer and homogeneous equivariant baselines on a new seven-task manipulation reinforcement learning benchmark.
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