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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.
Paper list in the survey paper: Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
| Date | Stars |
|---|---|
| 2026-07-31 | 468 |
| 2026-08-04 | 468 |
| 2026-08-06 | 468 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<img width="1000" src="./assets/teaser.png"/>
</p>
# Paper List in the survey paper
The papers discussed in our survey paper [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://arxiv.org/pdf/2312.08782) are listed in this file. The papers are grouped by the following categories:
- [Foundation models used in Robotics](#foundation-models-used-in-robotics). For these papers, the authors apply existing vision and language foundation models, such as LLM, VLM, vision FM and image and video generation models in modules of robotics, such as perception, action generation, task planning. We also include perspectives such as action grounding, traing data generation, and world modeling in this category.
- [Generalist Robotic Foundation Models](#generalist-robotic-foundation-models). For these papers, the authors propose new Robotic Foundation Models (RFM), these models typically are learning either from imitation learning, such as VLA models, or reinforcement learning.
The taxonomy is shown in this figure,
<p align="center">
<img width="1000" src="./assets/taxonomy.png"/>
</p>
We list the selected papers surveyed in our [survey paper](https://arxiv.org/abs/2312.08782). The dates are based on the first released date on arxiv. This list will be constantly updated.
NOTE: We only include papers with experiments on real physical robotics, in high-fidelity robotic simulation environments, or using real robotics datasets.
## Generalist Robotic Foundation Models
### Vision-Language-Action (VLA) Models
- RT-2 **RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control**, 28 Jul 2023, [paper link](https://robotics-transformer2.github.io/assets/rt2.pdf)
- Octo **Octo: An Open-Source Generalist Robot Policy**, 14 Dec 2023, [paper link](https://octo-models.github.io/paper.pdf)
- OpenVLA **OpenVLA: An Open-Source Vision-Language-Action Model**, 13 June 2024, [paper link](https://arxiv.org/pdf/2406.09246)
- π0 **π0: A Vision-Language-Action Flow Model for General Robot Control**, 31 Oct 2024, [paper link](https://arxiv.org/pdf/2410.24164)
- π_0.5 **π_{0.5}: a Vision-Language-Action Model with Open-World Generalization**, 22 April 2025, [paper link](https://arxiv.org/pdf/2504.16054)
- SmolVLA **SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics**, 6 Jun 2025, [paper link](https://arxiv.org/pdf/2506.01844)
- LBM **A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation**, 7 Jul 2025, [paper link](https://arxiv.org/pdf/2507.05331)
- GEN-0 **GEN-0: Embodied Foundation Models That Scale with Physical Interaction**, 4 Nov 2025, [report link](https://generalistai.com/blog/nov-04-2025-GEN-0)
- GEN-1 **GEN-1: Scaling Embodied Foundation Models to Mastery**, April 2, 2026, [report link](https://generalistai.com/blog/gen-1)
### World Action Models (WAM)
- Cosmos Policy **Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning**, 22 Jan 2026, [paper link](https://arxiv.org/pdf/2601.16163)
- DreamZero **World Action Models are Zero-shot Policies**, 17 Feb 2026, [paper link](https://arxiv.org/pdf/2602.15922)
- Fast-WAM **Fast-WAM: Do World Action Models Need Test-time Future Imagination?**, 17 Mar 2026, [paper link](https://arxiv.org/pdf/2603.16666)
### Memory in Robot Foundation Models
- MEM **MEM: Multi-Scale Embodied Memory for Vision Language Action Models**, 4 Mar 2026, [paper link](https://arxiv.org/pdf/2603.03596)
### Earlier Models:
#### Imitation Learning-based
<!-- - **Pre-Trained Language Models for Interactive Decision-Making**, 2022, [paper link](https://arxiv.org/pdf/2202.01771.pdf) -->
- GATO **A Generalist Agent**, 12 May 2022, [paper link](https://arxiv.org/pdf/2205.06175.pdf)
<!-- - PACT **PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training**, 22 Sep 2022, [paper link](https://arxiv.org/pdf/2209.11133.pdf) -->
- ZeST **Can Foundation Models Perform Zero-SExcerpt of 21,687 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c35b1ee5a04be685, topic:foundation-models