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robomimic\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_lift.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_can.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_tool_hang.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_square.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_lift_real.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_can_real.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_tool_hang_real.gif\"\u003e\n  \u003cimg width=\"24.0%\" src=\"docs/images/task_transport.gif\"\u003e\n \u003c/p\u003e\n\n[**[Homepage]**](https://robomimic.github.io/) \u0026ensp; [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html) \u0026ensp; [**[Study Paper]**](https://arxiv.org/abs/2108.03298) \u0026ensp; [**[Study Website]**](https://robomimic.github.io/study/) \u0026ensp; [**[ARISE Initiative]**](https://github.com/ARISE-Initiative)\n\n-------\n## Latest Updates\n- [06/20/2025] **v0.5.0**: Diffusion Policy, multi-dataset training, language-conditioned policies, and more! \n- [03/11/2025] **v0.4.0**: support for [robosuite v1.5](https://github.com/ARISE-Initiative/robosuite/tree/v1.5.1) and migrate robomimic datasets to HuggingFace\n- [10/11/2023] **v0.3.1**: support for extracting, training on, and visualizing depth observations for robosuite datasets\n- [07/03/2023] **v0.3.0**: BC-Transformer and IQL :brain:, support for DeepMind MuJoCo bindings :robot:, pre-trained image reps :eye:, wandb logging :chart_with_upwards_trend:, and more\n- [05/23/2022] **v0.2.1**: Updated website and documentation to feature more tutorials :notebook_with_decorative_cover:\n- [12/16/2021] **v0.2.0**: Modular observation modalities and encoders :wrench:, support for [MOMART](https://sites.google.com/view/il-for-mm/home) datasets :open_file_folder: [[release notes]](https://github.com/ARISE-Initiative/robomimic/releases/tag/v0.2.0) [[documentation]](https://robomimic.github.io/docs/v0.2/introduction/overview.html)\n- [08/09/2021] **v0.1.0**: Initial code and paper release\n\n-------\n\n## Colab quickstart\nGet started with a quick colab notebook demo of robomimic without installing anything locally.\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1b62r_km9pP40fKF0cBdpdTO2P_2eIbC6?usp=sharing)\n\n\n-------\n\n**robomimic** is a framework for robot learning from demonstration.\nIt offers a broad set of demonstration datasets collected on robot manipulation domains and offline learning algorithms to learn from these datasets.\n**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.\n\n## Core Features\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"50.0%\" src=\"docs/images/core_features.png\"\u003e\n \u003c/p\u003e\n\n\u003c!-- **Standardized Datasets**\n- Simulated and real-world tasks\n- Multiple environments and robots\n- Diverse human-collected and machine-generated datasets\n\n**Suite of Learning Algorithms**\n- Imitation Learning algorithms (BC, BC-RNN, HBC)\n- Offline RL algorithms (BCQ, CQL, IRIS, TD3-BC)\n\n**Modular Design**\n- Low-dim + Visuomotor policies\n- Diverse network architectures\n- Support for external datasets\n\n**Flexible Workflow**\n- Hyperparameter sweep tools\n- Dataset visualization tools\n- Generating new datasets --\u003e\n\n\n## Reproducing benchmarks\n\nThe robomimic framework also makes reproducing the results from different benchmarks and datasets easy. See the [datasets page](https://robomimic.github.io/docs/datasets/overview.html) for more information on downloading datasets and reproducing experiments.\n\n## Docker\n\nYou can use the `Dockerfile` to easily build a containerized environment for setting up robomimic with Python 3.9, Miniconda, robosuite, and PyTorch (CPU/GPU support).\n\nTo build, run:\n`docker build -t robomimic .`\n\nTo run without GPU (CPU only), run:\n`docker run -it robomimic`\n\nTo run with GPU (if available), run:\n`docker run --gpus all -it robomimic`\n\n## Troubleshooting\n\nPlease see the [troubleshooting](https://robomimic.github.io/docs/miscellaneous/troubleshooting.html) section for common fixes, or [submit an issue](https://github.com/ARISE-Initiative/robomimic/issues) on our github page.\n\n## Contributing to robomimic\nThis project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics.\nThe project originally began development in late 2018 by researchers in the [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL).\nNow it is actively maintained and used for robotics research projects across multiple labs.\nWe welcome community contributions to this project.\nFor details please check our [contributing guidelines](https://robomimic.github.io/docs/miscellaneous/contributing.html).\n\n## Citation\n\nPlease cite [this paper](https://arxiv.org/abs/2108.03298) if you use this framework in your work:\n\n```bibtex\n@inproceedings{robomimic2021,\n  title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation},\n  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},\n  booktitle={Conference on Robot Learning (CoRL)},\n  year={2021}\n}\n```\n","funding_links":[],"categories":["🧠 Robot Learning \u0026 RL"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FARISE-Initiative%2Frobomimic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FARISE-Initiative%2Frobomimic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FARISE-Initiative%2Frobomimic/lists"}