DiffuserLite: Towards Real-time Diffusion Planning

• URL: http://arxiv.org/abs/2401.15443v5
• Date: Fri, 25 Oct 2024 03:36:07 GMT
• Title: DiffuserLite: Towards Real-time Diffusion Planning
• Authors: Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, Yan Zheng,
• Abstract summary: We introduce a super fast and lightweight diffusion planning framework, which employs a planning refinement process (PRP) to generate coarse-to-fine-grained trajectories. Our experimental results demonstrate that diffuserLite achieves a decision-making frequency of 122.2Hz 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.
• Score: 39.93614402208524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 122.2Hz (112.7x 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/.

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