Summary of Diffusion Model For Planning: a Systematic Literature Review, by Toshihide Ubukata et al.
Diffusion Model for Planning: A Systematic Literature Review
by Toshihide Ubukata, Jialong Li, Kenji Tei
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 The paper presents a systematic literature review of recent advancements in applying diffusion models to planning tasks, focusing on datasets, benchmarks, sampling efficiency, adaptability, safety, and domain-specific applications. The authors categorize the current literature into five perspectives: relevant datasets, fundamental studies, skill-centric planning, uncertainty managing mechanisms, and domain-specific applications. The review aims to help researchers better understand the field and promote its development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how diffusion models are used in planning tasks, a growing area of research since 2023. The authors look at different aspects like datasets, sampling efficiency, adaptability, safety, and specific applications like autonomous driving. They categorize the current research into five areas to help researchers understand this field better. |
Keywords
» Artificial intelligence » Diffusion
