DiffuserLite: Towards Real-time Diffusion Planning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914987506663424 |
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| author | Dong, Zibin Hao, Jianye Yuan, Yifu Ni, Fei Wang, Yitian Li, Pengyi Zheng, Yan |
| author_facet | Dong, Zibin Hao, Jianye Yuan, Yifu Ni, Fei Wang, Yitian Li, Pengyi Zheng, Yan |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15443 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffuserLite: Towards Real-time Diffusion Planning Dong, Zibin Hao, Jianye Yuan, Yifu Ni, Fei Wang, Yitian Li, Pengyi Zheng, Yan Artificial Intelligence 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/. |
| title | DiffuserLite: Towards Real-time Diffusion Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2401.15443 |