Sequence modeling approaches have shown promising results in robot imitation learning. Recently, diffusion models have been adopted for behavioral cloning in a sequence modeling fashion, benefiting from their exceptional capabilities in modeling complex data distributions. The standard diffusion-based policy iteratively generates action sequences from random noise conditioned on the input states. Nonetheless, the model for diffusion policy can be further improved in terms of visual representations. In this work, we propose Crossway Diffusion, a simple yet effective method to enhance diffusion-based visuomotor policy learning via a carefully designed state decoder and an auxiliary self-supervised learning (SSL) objective. The state decoder reconstructs raw image pixels and other state information from the intermediate representations of the reverse diffusion process. The whole model is jointly optimized by the SSL objective and the original diffusion loss. Our experiments demonstrate the effectiveness of Crossway Diffusion in various simulated and real-world robot tasks, confirming its consistent advantages over the standard diffusion-based policy and substantial improvements over the baselines.

翻译：序列建模方法在机器人模仿学习中展示了令人瞩目的成果。近期，扩散模型凭借其在建模复杂数据分布方面的卓越能力，被以序列建模方式应用于行为克隆。标准的基于扩散策略从随机噪声中迭代生成动作序列，该序列以输入状态为条件。然而，扩散策略模型在视觉表征方面仍有进一步提升的空间。本文提出了一种简单而有效的方法——交叉扩散，通过精心设计的状态解码器与辅助自监督学习目标，增强基于扩散的视觉运动策略学习。该状态解码器从反向扩散过程的中间表示中重建原始图像像素及其他状态信息。整个模型通过自监督学习目标与原始扩散损失进行联合优化。实验结果表明，交叉扩散在多种模拟与真实机器人任务中均具有有效性，证实了其相较于标准基于扩散策略的持续优势，以及相较于基线方法的显著改进。