# Overview
**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](../datasets/overview.html) for more information on downloading datasets and reproducing experiments.
## Troubleshooting
Please see the [troubleshooting](../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](../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}
}
```