robomimic is a framework for robot learning from demonstration. It offers a broad set of demonstration datasets collected on robot manipulation domains, and learning algorithms to learn from these datasets. This project is part of the broader Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative, with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics.

Video Overview

Team

Core Developers

Ajay Mandlekar

Danfei Xu

Josiah Wong

Soroush Nasiriany

Chen Wang

Matthew Bronars

Project Advisors

Yuke Zhu

Roberto Martín-Martín

Silvio Savarese

Fei-Fei Li

Citation

@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={arXiv preprint arXiv:2108.03298},
  year={2021}
}