Diffusion Model for Planning: A Systematic Literature Review

• URL: http://arxiv.org/abs/2408.10266v1
• Date: Fri, 16 Aug 2024 08:37:01 GMT
• Title: Diffusion Model for Planning: A Systematic Literature Review
• Authors: Toshihide Ubukata, Jialong Li, Kenji Tei,
• Abstract summary: Diffusion models leverage processes to capture complex data distributions effectively. Recent advancements in the application of diffusion models for planning have led to a significant growth in related publications since 2023.
• Score: 0.5879683541040848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models, which leverage stochastic processes to capture complex data distributions effectively, have shown their performance as generative models, achieving notable success in image-related tasks through iterative denoising processes. Recently, diffusion models have been further applied and show their strong abilities in planning tasks, leading to a significant growth in related publications since 2023. To help researchers better understand the field and promote the development of the field, we conduct a systematic literature review of recent advancements in the application of diffusion models for planning. Specifically, this paper categorizes and discusses the current literature from the following perspectives: (i) relevant datasets and benchmarks used for evaluating diffusion modelbased planning; (ii) fundamental studies that address aspects such as sampling efficiency; (iii) skill-centric and condition-guided planning for enhancing adaptability; (iv) safety and uncertainty managing mechanism for enhancing safety and robustness; and (v) domain-specific application such as autonomous driving. Finally, given the above literature review, we further discuss the challenges and future directions in this field.

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