https://github.com/ARISE-Initiative/robomimic
robomimic: A Modular Framework for Robot Learning from Demonstration
https://github.com/ARISE-Initiative/robomimic
Last synced: 8 days ago
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robomimic: A Modular Framework for Robot Learning from Demonstration
- Host: GitHub
- URL: https://github.com/ARISE-Initiative/robomimic
- Owner: ARISE-Initiative
- License: mit
- Created: 2021-08-06T06:24:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2026-02-05T19:40:44.000Z (3 months ago)
- Last Synced: 2026-02-06T05:14:36.764Z (3 months ago)
- Language: Python
- Size: 59.8 MB
- Stars: 1,270
- Watchers: 11
- Forks: 344
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-robotics - robomimic
README
# robomimic
[**[Homepage]**](https://robomimic.github.io/) [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html) [**[Study Paper]**](https://arxiv.org/abs/2108.03298) [**[Study Website]**](https://robomimic.github.io/study/) [**[ARISE Initiative]**](https://github.com/ARISE-Initiative)
-------
## Latest Updates
- [06/20/2025] **v0.5.0**: Diffusion Policy, multi-dataset training, language-conditioned policies, and more!
- [03/11/2025] **v0.4.0**: support for [robosuite v1.5](https://github.com/ARISE-Initiative/robosuite/tree/v1.5.1) and migrate robomimic datasets to HuggingFace
- [10/11/2023] **v0.3.1**: support for extracting, training on, and visualizing depth observations for robosuite datasets
- [07/03/2023] **v0.3.0**: BC-Transformer and IQL :brain:, support for DeepMind MuJoCo bindings :robot:, pre-trained image reps :eye:, wandb logging :chart_with_upwards_trend:, and more
- [05/23/2022] **v0.2.1**: Updated website and documentation to feature more tutorials :notebook_with_decorative_cover:
- [12/16/2021] **v0.2.0**: Modular observation modalities and encoders :wrench:, support for [MOMART](https://sites.google.com/view/il-for-mm/home) datasets :open_file_folder: [[release notes]](https://github.com/ARISE-Initiative/robomimic/releases/tag/v0.2.0) [[documentation]](https://robomimic.github.io/docs/v0.2/introduction/overview.html)
- [08/09/2021] **v0.1.0**: Initial code and paper release
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## Colab quickstart
Get started with a quick colab notebook demo of robomimic without installing anything locally.
[](https://colab.research.google.com/drive/1b62r_km9pP40fKF0cBdpdTO2P_2eIbC6?usp=sharing)
-------
**robomimic** is a framework for robot learning from demonstration.
It offers a broad set of demonstration datasets collected on robot manipulation domains and offline learning algorithms to learn from these datasets.
**robomimic** aims to make robot learning broadly *accessible* and *reproducible*, allowing researchers and practitioners to benchmark tasks and algorithms fairly and to develop the next generation of robot learning algorithms.
## Core Features
## Reproducing benchmarks
The robomimic framework also makes reproducing the results from different benchmarks and datasets easy. See the [datasets page](https://robomimic.github.io/docs/datasets/overview.html) for more information on downloading datasets and reproducing experiments.
## Docker
You can use the `Dockerfile` to easily build a containerized environment for setting up robomimic with Python 3.9, Miniconda, robosuite, and PyTorch (CPU/GPU support).
To build, run:
`docker build -t robomimic .`
To run without GPU (CPU only), run:
`docker run -it robomimic`
To run with GPU (if available), run:
`docker run --gpus all -it robomimic`
## Troubleshooting
Please see the [troubleshooting](https://robomimic.github.io/docs/miscellaneous/troubleshooting.html) section for common fixes, or [submit an issue](https://github.com/ARISE-Initiative/robomimic/issues) on our github page.
## Contributing to robomimic
This project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics.
The project originally began development in late 2018 by researchers in the [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL).
Now it is actively maintained and used for robotics research projects across multiple labs.
We welcome community contributions to this project.
For details please check our [contributing guidelines](https://robomimic.github.io/docs/miscellaneous/contributing.html).
## Citation
Please cite [this paper](https://arxiv.org/abs/2108.03298) if you use this framework in your work:
```bibtex
@inproceedings{robomimic2021,
title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation},
author={Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese and Yuke Zhu and Roberto Mart\'{i}n-Mart\'{i}n},
booktitle={Conference on Robot Learning (CoRL)},
year={2021}
}
```