@inproceedings{
dong2024diffuserlite,
title={DiffuserLite: Towards Real-time Diffusion Planning},
author={Zibin Dong and Jianye HAO and Yifu Yuan and Fei Ni and Yitian Wang and Pengyi Li and YAN ZHENG},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2TXDHUqyrQ}
}
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/.
DiffuserLite: Towards Real-time Diffusion Planning
Zibin Dong Jianye HAO Yifu Yuan Fei Ni Yitian Wang Pengyi Li YAN ZHENG