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Unified framework for robot learning built on NVIDIA Isaac Sim

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Omniverse Isaac Orbit

IsaacSim Python Linux platform pre-commit Docs status License

Isaac Orbit (or orbit in short) is a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes, and fast and accurate simulation.

Please refer our documentation page to learn more about the installation steps, features and tutorials.

Contributing to Orbit

We wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone. These may happen in form of bug reports, feature requests, or code contributions. For details, please check our contribution guidelines.

Troubleshooting

Please see the troubleshooting section for common fixes or submit an issue.

For issues related to Isaac Sim, we recommend checking its documentation, or opening a question on its forums.

Support

  • Please use GitHub Discussions for discussing ideas, asking questions, and requests for new features.
  • Github Issues should only be used to track executable pieces of work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features, or general updates.

Acknowledgement

NVIDIA Isaac Sim is available freely under individual license. For more information about its license terms, please check here.

ORBIT framework is released under BSD-3 License. The license files of its dependencies and assets are present in the docs/licenses directory.

Citation

Please cite this paper if you use this framework in your work:

@misc{mittal2023orbit,
	author = {Mayank Mittal and Calvin Yu and Qinxi Yu and Jingzhou Liu and Nikita Rudin and David 	Hoeller and Jia Lin Yuan and Pooria Poorsarvi Tehrani and Ritvik Singh and Yunrong Guo and Hammad Mazhar and Ajay Mandlekar and Buck Babich and Gavriel State and Marco Hutter and Animesh Garg},
	title = {ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments},
	year = {2023},
	eprint = {arXiv:2301.04195},
}

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