Diffusion planning has been recognized as an effective decision-making paradigm in various domains. The capability of conditionally generating high-quality long-horizon trajectories makes it a promising research direction. However, existing diffusion planning methods suffer from low decision-making frequencies due to the expensive iterative sampling cost. To address this issue, we introduce DiffuserLite, a super fast and lightweight diffusion planning framework. DiffuserLite employs a planning refinement process (PRP) to generate coarse-to-fine-grained trajectories, significantly reducing the modeling of redundant information and leading to notable increases in decision-making frequency. Our experimental results demonstrate that DiffuserLite achieves a decision-making frequency of $122$Hz ($112.7$x faster than previous mainstream frameworks) and reaches state-of-the-art performance on D4RL benchmarks. In addition, our neat DiffuserLite framework can serve as a flexible plugin to enhance decision frequency in other diffusion planning algorithms, providing a structural design reference for future works. More details and visualizations are available at https://diffuserlite.github.io/.

翻译：扩散规划已被视为多个领域中一种有效的决策范式。其有条件地生成高质量长时域轨迹的能力使其成为一个颇具前景的研究方向。然而，现有扩散规划方法因迭代采样成本高昂而面临决策频率低下的问题。为解决此问题，我们提出了DiffuserLite——一个超快速且轻量级的扩散规划框架。DiffuserLite采用规划精化过程（PRP）来生成从粗到细的轨迹，显著减少了冗余信息的建模，从而大幅提升了决策频率。实验结果表明，DiffuserLite实现了$122$Hz的决策频率（比先前主流框架快$112.7$倍），并在D4RL基准测试上达到了最先进的性能。此外，我们简洁的DiffuserLite框架可作为灵活插件，提升其他扩散规划算法的决策频率，为未来工作提供了结构设计参考。更多详情和可视化内容请访问https://diffuserlite.github.io/。