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license: mit |
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--- |
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**robomimic** is a framework for robot learning from demonstration. |
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It offers a broad set of demonstration datasets collected on robot manipulation domains and offline learning algorithms to learn from these datasets. |
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**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. |
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This repository contains some of the datasets released with the robomimic framework. |
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## Citation |
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Please cite [this paper](https://arxiv.org/abs/2108.03298) if you use this framework in your work: |
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```bibtex |
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@inproceedings{robomimic2021, |
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title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}, |
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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}, |
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booktitle={Conference on Robot Learning (CoRL)}, |
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year={2021} |
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} |
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``` |
