Summary of Diffuserlite: Towards Real-time Diffusion Planning, by Zibin Dong et al.
DiffuserLite: Towards Real-time Diffusion Planning
by Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, Yan Zheng
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DiffuserLite, a fast and lightweight diffusion planning framework that efficiently generates high-quality long-horizon trajectories. The existing methods suffer from low decision-making frequencies due to the expensive iterative sampling cost. To overcome this limitation, DiffuserLite employs a planning refinement process (PRP) that reduces redundant information modeling and increases decision-making frequency by 112.7x compared to predominant frameworks. The framework achieves state-of-the-art performance on D4RL, Robomimic, and FinRL benchmarks, with a decision-making frequency of 122.2Hz. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion planning is like making decisions quickly and accurately. This paper shows how to make it even faster by creating a new way called DiffuserLite. Right now, other methods take too long because they keep repeating themselves. DiffuserLite solves this problem by being more efficient and accurate. It works really well on three different tests, making 122 decisions per second. This is important for robots and other machines that need to make quick decisions. |
Keywords
* Artificial intelligence * Diffusion
