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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.
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.
| Date | Stars |
|---|---|
| 2026-08-03 | 1394 |
| 2026-08-06 | 1394 |
| 2026-08-18 | 1396 |
| 2026-08-23 | 1397 |
| 2026-08-24 | 1398 |
| 2026-08-28 | 1400 |
| 2026-08-29 | 1401 |
| 2026-09-01 | 1402 |
| 2026-09-04 | 1403 |
| 2026-09-06 | 1404 |
| 2026-09-07 | 1403 |
| 2026-09-08 | 1404 |
| 2026-09-12 | 1405 |
| 2026-09-13 | 1406 |
| 2026-09-14 | 1408 |
| 2026-09-21 | 1409 |
| 2026-09-22 | 1410 |
| 2026-09-23 | 1411 |
| 2026-09-29 | 1412 |
| 2026-09-30 | 1413 |
| 2026-10-01 | 1414 |
| 2026-10-06 | 1415 |
| 2026-10-07 | 1418 |
| 2026-10-08 | 1420 |
| 2026-10-09 | 1421 |
| 2026-10-12 | 1422 |
| 2026-10-18 | 1421 |
| 2026-10-20 | 1422 |
| 2026-10-31 | 1423 |
| 2026-11-04 | 1423 |
Today
— stars today
This week
+1 stars this week
This month
+9 stars this month
Momentum
0.0
growth rate 0.07%/day
# Awesome-Robotics-Foundation-Models
[](https://awesome.re)
This is the partner repository for the survey paper "Foundation Models in Robotics: Applications, Challenges, and the Future". The authors hope this repository can act as a quick reference for roboticists who wish to read the relevant papers and implement the associated methods. The organization of this readme follows Figure 1 in the paper (shown above) and is thus divided into foundation models that have been applied to robotics and those that are relevant to robotics in some way.
We welcome contributions to this repository to add more resources. Please submit a pull request if you want to contribute!
## Table of Contents
- [Survey](#survey)
- [Robotics](#robotics)
- [Neural Scaling Laws for embodied AI](#neural-scaling-laws-for-embodied-AI)
- [Robot Policy Learning for Decision Making and Controls](#robot-policy-learning-for-decision-making-and-controls)
- [Language-Image Goal-Conditioned Value Learning](#language-image-goal-conditioned-value-learning)
- [Robot Task Planning Using Large Language Models](#robot-task-planning-using-large-language-models)
- [Robot Transformers](#robot-transformers)
- [In-context Learning for Decision-Making](#in-context-learning-for-decision-making)
- [Open-Vocabulary Robot Navigation and Manipulation](#open-vocabulary-robot-navigation-and-manipulation)
- [Relevant to Robotics](#relevant-to-robotics)
- [Open-Vocabulary Object Detection and 3D Classification](#open-vocabulary-object-detection-and-3D-classification)
- [Open-Vocabulary Semantic Segmentation](#open-vocabulary-semantic-segmentation)
- [Open-Vocabulary 3D Scene Representations](#open-vocabulary-3D-scene-representations)
- [Open-Vocabulary Object Representations](#open-vocabulary-object-representations)
- [Affordance Information](#affordance-information)
- [Predictive Models](#predictive-models)
- [Generalist AI](#generalist-AI)
- [Simulators](#simulators)
## Survey
This repository is largely based on the following paper:
**[Foundation Models in Robotics: Applications, Challenges, and the Future]()**
<br />
Roya Firoozi,
Johnathan Tucker,
Stephen Tian,
Anirudha Majumdar,
Jiankai Sun,
Weiyu Liu,
Yuke Zhu,
Shuran Song,
Ashish Kapoor,
Karol Hausman,
Brian Ichter,
Danny Driess,
Jiajun Wu,
Cewu Lu,
Mac Schwager
<br />
If you find this repository helpful, please consider citing:
```bibtex
@article{firoozi2024foundation,
title={Foundation Models in Robotics: Applications, Challenges, and the Future},
author={Firoozi, Roya and Tucker, Johnathan and Tian, Stephen and Majumdar, Anirudha and Sun, Jiankai and Liu, Weiyu and Zhu, Yuke and Song, Shuran and Kapoor, Ashish and Hausman, Karol and others},
journal={The International Journal of Robotics Research},
year={2024},
doi= {https://doi.org/10.1177/02783649241281508}
}
```
```bibtex
@article{firoozi2023foundation,
title={Foundation Models in Robotics: Applications, Challenges, and the Future},
author={Firoozi, Roya and Tucker, Johnathan and Tian, Stephen and Majumdar, Anirudha and Sun, Jiankai and Liu, Weiyu and Zhu, Yuke and Song, Shuran and Kapoor, Ashish and Hausman, Karol and others},
journal={arXiv preprint arXiv:2312.07843},
year={2023}
}
```
## Robotics
### Neural Scaling Laws
* Neural Scaling Laws for Embodied AI: Neural Scaling Laws for Embodied AI [[Paper]](https://arxiv.org/abs/2405.14005)
### Robot Policy Learning for Decision-Making and Controls
#### Language-Conditioned Imitation Learning
* CLIPort: What and Where Pathways for Robotic Manipulation [[Paper]](https://arxiv.org/abs/2109.12098)[[Project]](https://cliport.github.io/)[[Code]](https://github.com/cliport/cliport)
* Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation [[Paper]](https://arxiv.org/abs/2209.05451)[[Project]](https://peract.github.io/)[[Code]](https://githExcerpt of 26,998 characters
Read on GitHub
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Jack · Stanford University · United States
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Jack · Imperial College London · United Kingdom
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Xiongkun Linghu · Beijing Academy of Artificial Intelligence · China
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Enshen Zhou · Beihang University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:88f67af3f6827126, name:robotics