robomimic

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 if you use this framework in your work:

@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}
}