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A curated list of Multi-Modal Reinforcement Learning resources (continually updated)
| Date | Stars |
|---|---|
| 2026-07-31 | 617 |
| 2026-08-01 | 617 |
| 2026-08-02 | 617 |
| 2026-08-04 | 618 |
| 2026-08-05 | 619 |
| 2026-08-06 | 619 |
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# Awesome Multi-Modal Reinforcement Learning
[](https://github.com/sindresorhus/awesome)
[](https://github.com/opendilab/awesome-multi-modal-reinforcement-learning)
[](https://github.com/opendilab/awesome-multi-modal-reinforcement-learning/blob/main/LICENSE)
This is a collection of research papers for **Multi-Modal reinforcement learning (MMRL)**.
And the repository will be continuously updated to track the frontier of MMRL.
Some papers may not be relevant to RL, but we include them anyway as they may be useful for the research of MMRL.
Welcome to follow and star!
## Introduction
Multi-Modal RL agents focus on learning from video (images), language (text), or both, as humans do. We believe that it is important for intelligent agents to learn directly from images or text, since such data can be easily obtained from the Internet.
## Table of Contents
- [Awesome Multi-Modal Reinforcement Learning](#awesome-multi-modal-reinforcement-learning)
- [Introduction](#introduction)
- [Table of Contents](#table-of-contents)
- [Papers](#papers)
- [ICML 2026](#icml-2026)
- [ICLR 2026](#iclr-2026)
- [NeurIPS 2025](#neurips-2025)
- [ICML 2025](#icml-2025)
- [ICLR 2025](#iclr-2025)
- [ICLR 2024](#iclr-2024)
- [ICLR 2023](#iclr-2023)
- [ICLR 2022](#iclr-2022)
- [ICLR 2021](#iclr-2021)
- [ICLR 2019](#iclr-2019)
- [NeurIPS 2024](#neurips-2024)
- [NeurIPS 2023](#neurips-2023)
- [NeurIPS 2022](#neurips-2022)
- [NeurIPS 2021](#neurips-2021)
- [NeurIPS 2018](#neurips-2018)
- [ICML 2024](#icml-2024)
- [ICML 2022](#icml-2022)
- [ICML 2019](#icml-2019)
- [ICML 2017](#icml-2017)
- [CVPR 2024](#cvpr-2024)
- [CVPR 2022](#cvpr-2022)
- [CoRL 2022](#corl-2022)
- [Other](#other)
- [ArXiv](#arxiv)
- [Contributing](#contributing)
- [License](#license)
## Papers
```
format:
- [title](paper link) [links]
- authors.
- key words.
- experiment environment.
```
### ICML 2026
- [AIR-VLA: Vision-Language-Action Systems for Aerial Manipulation](https://arxiv.org/abs/2601.21602)
- Jianli Sun, Bin Tian, Qiyao Zhang, Chengxiang Li, Zihan Song, Zhiyong Cui, Yisheng Lv, Yonglin Tian
- Keywords: aerial manipulation benchmark, VLA system study, multimodal dataset, long-horizon planning
- ExpEnv: physics-based aerial-manipulation simulator and 3,000 teleoperated demonstrations covering manipulation, spatial understanding, semantic reasoning, and long-horizon planning
- [Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds](https://arxiv.org/abs/2602.00807)
- Xianzhe Fan, Shengliang Deng, Xiaoyang Wu, Yuxiang Lu, Zhuoling Li, Mi Yan, Yujia Zhang, Zhizheng Zhang, He Wang, Hengshuang Zhao
- Keywords: 3D-enhanced VLA, point clouds, domain-agnostic representation learning, robustness to domain gap
- ExpEnv: simulation and real-world VLA experiments with simulator, sensor, and model-estimated point clouds
- [Mixture of Horizons in Action Chunking](https://arxiv.org/abs/2511.19433)
- Dong Jing, Gang Wang, Jiaqi Liu, Weiliang Tang, Zelong Sun, Yunchao Yao, Zhenyu Wei, Yunhui Liu, Zhiwu Lu, Mingyu Ding
- Keywords: action chunking, mixture of horizons, adaptive inference, flow-based VLA pExcerpt of 51,933 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1c90e8d71acef2b4, name:reinforcement learning, desc:reinforcement learning
