Recent studies have verified that equivariant methods can significantly improve the data efficiency, generalizability, and robustness in robot learning. Meanwhile, denoising diffusion-based generative modeling has recently gained significant attention as a promising approach for robotic manipulation learning from demonstrations with stochastic behaviors. In this paper, we present Diffusion-EDFs, a novel approach that incorporates spatial roto-translation equivariance, i.e., SE(3)-equivariance to diffusion generative modeling. By integrating SE(3)-equivariance into our model architectures, we demonstrate that our proposed method exhibits remarkable data efficiency, requiring only 5 to 10 task demonstrations for effective end-to-end training. Furthermore, our approach showcases superior generalizability compared to previous diffusion-based manipulation methods.

本文介绍了Diffusion-EDFs，一种新颖的方法，将SE(3)-等变性（roto-translation equivariance）引入扩散生成模型中，展示出了卓越的数据效率，只需要5至10个任务演示进行有效的端到端训练，并且相比以前的基于扩散的操作方法，具有更好的泛化能力。

Diffusion-EDFs: SE(3)上的双等变去噪生成建模，用于视觉机器人操作