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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of recent robot learning papers incorporating diffusion models for robotics tasks.
| Date | Stars |
|---|---|
| 2026-07-31 | 351 |
| 2026-08-06 | 352 |
Today
+1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome Robot Diffusion [](https://github.com/sindresorhus/awesome) <!-- omit in toc -->
A curated list of recent robot learning papers incorporating diffusion models for manipulation, navigation, planning etc.
The paper list is structured so that each paper falls into only one place. While some methods could fit into multiple places, we place each one in the most relevant class.
Please refer to our survey paper below for detailed review, which has been accepted to International Journal of Computer Vision (IJCV).
<div align="center">
<h3>Diffusion Models in Robotics: A survey</h3>
[Xiaokang Liu](https://scholar.google.com/citations?user=dAEHm8AAAAAJ&hl=zh-CN&oi=ao)<sup>1</sup>
[Yuchen Ma](https://scholar.google.com/citations?user=_6xM_IcAAAAJ&hl=zh-CN&oi=ao)<sup>1</sup>
[Chen Gao](https://chengaopro.github.io/)<sup>1</sup>
[Mike Zheng Shou](https://scholar.google.com/citations?user=h1-3lSoAAAAJ&hl=zh-CN&oi=ao)<sup>1</sup>
<sup>1</sup>[Show Lab](https://sites.google.com/view/showlab), National University of Singapore
[](https://www.researchgate.net/profile/Xiaokang-Liu-15/publication/390661359_Diffusion_Models_in_Robotics_A_Survey/links/67f7b329401b473b8b988f2a/Diffusion-Models-in-Robotics-A-Survey.pdf)
</div>
<br>
If our work helps you in your research, please kindly cite our paper 😀.
```bibtex
@article{liu2026diffusion,
title={Diffusion models in robotics: A survey},
author={Liu, Xiaokang and Ma, Kevin Yuchen and Gao, Chen and Shou, Mike Zheng},
journal={International Journal of Computer Vision},
volume={134},
number={6},
pages={299},
year={2026},
publisher={Springer}
}
```
----
<!-- <p align="center">
<img src="https://makeavideo.studio/assets/overview.webp" width="240px"/>
<img src="https://makeavideo.studio/assets/A_teddy_bear_painting_a_portrait.webp" width="240px"/>
</p>
<p align="center">
<img src="https://tuneavideo.github.io/assets/teaser.gif" width="480px"/>
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<img src="https://github.com/ChenyangQiQi/FateZero/blob/main/docs/gif_results/17_car_posche_01_concat_result.gif?raw=true" width="240px"/>
<img src="https://github.com/ChenyangQiQi/FateZero/blob/main/docs/gif_results/3_sunflower_vangogh_conat_result.gif?raw=true" width="240px"/>
</p>
<p align="center">
(Source: <a href="https://makeavideo.studio/">Make-A-Video</a>, <a href="https://tuneavideo.github.io/">Tune-A-Video</a>, and <a href="https://fate-zero-edit.github.io/">Fate/Zero</a>.)
</p> -->
## Table of Contents <!-- omit in toc -->
- [Benchmarks](#benchmarks)
- [Diffusion as Policy](#diffusion-as-policy)
- [Diffusion as Synthesizer](#diffusion-as-synthesizer)
- [Task Objectives and Applications](#task-objectives-and-applications)
- [Robot Learning Utilizing Diffusion Model Properties](#robot-learning-utilizing-diffusion-model-properties)
- [Citation](#citation)
### Benchmarks
+ [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://arxiv.org/abs/1709.10087) (RSS 2018)
[](https://github.com/aravindr93/hand_dapg)
[](https://arxiv.org/abs/1709.10087)
[](https://sites.google.com/view/deeprl-dexterous-manipulation)
+ [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://arxiv.org/abs/1910.10897) (CoRL 2020)
[](https://github.com/Farama-Foundation/Metaworld)
[](https://arxiv.org/abs/1910.10897)
[![Website](https://img.shields.io/badge/WebsiteExcerpt of 46,699 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:79358b2fa1cd9ac3, name:robotics, desc:robotics, desc:robot learning