| | --- |
| | license: mit |
| | --- |
| | |
| | **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. |
| |
|
| | This repository contains some of the datasets and model checkpoints released with the robomimic framework. |
| |
|
| | ## 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} |
| | } |
| | ``` |
