DiffuserLite: Towards Real-time Diffusion Planning

Diffusion planning has been recognized as an effective decision-making paradigm in various domains. The capability of 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 alleviate this, we introduce DiffuserLite, a super fast and lightweight diffusion planning framework, which 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 $122. 2$Hz ($112. 7$x faster than predominant frameworks) and reaches state-of-the-art performance on D4RL, Robomimic, and FinRL benchmarks. In addition, DiffuserLite can also serve as a flexible plugin to increase the decision-making frequency of other diffusion planning algorithms, providing a structural design reference for future works. More details and visualizations are available at https: //diffuserlite. github. io/.

Authors

• Zibin Dong
• Jianye Hao Tianjin University
• Yifu Yuan
• Fei Ni College of Intelligence and Computing, Tianjin University
• Yitian Wang
• Pengyi Li Tianjin University
• Yan Zheng College of Intelligence and Computing, Tianjin University

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