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.

翻译：近期研究已证实，等变方法能显著提升机器人学习中的数据效率、泛化能力与鲁棒性。与此同时，基于去噪扩散的生成建模因其在随机行为演示中学习机器人操作任务的潜力而备受关注。本文提出扩散-EDFs（Diffusion-EDFs），一种将空间旋转-平移等变性（即SE(3)-等变性）融入扩散生成建模的新方法。通过将SE(3)-等变性集成至模型架构中，我们证明所提方法展现出卓越的数据效率——仅需5至10个任务演示即可实现有效的端到端训练。此外，相比于先前基于扩散的操作方法，我们的方法在泛化能力方面表现更为优异。