| # Overview |
|
|
| <p align="center"> |
| <img width="24.0%" src="../images/task_lift.gif"> |
| <img width="24.0%" src="../images/task_can.gif"> |
| <img width="24.0%" src="../images/task_tool_hang.gif"> |
| <img width="24.0%" src="../images/task_square.gif"> |
| <img width="24.0%" src="../images/task_lift_real.gif"> |
| <img width="24.0%" src="../images/task_can_real.gif"> |
| <img width="24.0%" src="../images/task_tool_hang_real.gif"> |
| <img width="24.0%" src="../images/task_transport.gif"> |
| </p> |
|
|
| **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. |
|
|
| ## Core Features |
|
|
|
|
| <!-- <div style="box-sizing:border-box;" > |
| <section class="page-section" style="box-sizing:border-box;display:block;" > |
| <div class="container" style="box-sizing:border-box;width:100%;padding-right:0.75rem;padding-left:0.75rem;margin-right:auto;margin-left:auto;" > |
| <div class="row text-center" style="box-sizing:border-box;display:flex;flex-wrap:wrap;margin-right:-0.75rem;margin-left:-0.75rem;" > |
| <div class="col-lg-3 col-md-6" style="box-sizing:border-box;position:relative;width:100%;padding-right:0.75rem;padding-left:0.75rem;flex:0 0 50%;max-width:50%;" > |
| <div class="feature-box" style="box-sizing:border-box;background-attachment:scroll;padding-top:30px;padding-bottom:30px;padding-right:20px;padding-left:20px;margin-bottom:50px;text-align:center;border-width:1px;border-style:solid;border-color:#e6e6e6;height:400px;position:relative;background-color:#DEEEFB;background-image:none;background-repeat:repeat;background-position:top left;" > |
| <h3 style="box-sizing:border-box;margin-top:0;margin-bottom:0.5rem;" >Standardized Datasets</h3> |
| <p class="text-muted" style="box-sizing:border-box;margin-top:0;margin-bottom:1rem;" >Datasets collected from different sources (single proficient human, multiple humans, and machine-generated) across simulated and real-world tasks spanning multiple robots and environments</p> |
| </div> |
| </div> |
| <div class="col-lg-3 col-md-6" style="box-sizing:border-box;position:relative;width:100%;padding-right:0.75rem;padding-left:0.75rem;flex:0 0 50%;max-width:50%;" > |
| <div class="feature-box" style="box-sizing:border-box;background-attachment:scroll;padding-top:30px;padding-bottom:30px;padding-right:20px;padding-left:20px;margin-bottom:50px;text-align:center;border-width:1px;border-style:solid;border-color:#e6e6e6;height:400px;position:relative;background-color:#DEEEFB;background-image:none;background-repeat:repeat;background-position:top left;" > |
| <h3 style="box-sizing:border-box;margin-top:0;margin-bottom:0.5rem;" >Suite of Learning Algorithms</h3> |
| <p class="text-muted" style="box-sizing:border-box;margin-top:0;margin-bottom:1rem;" >High-quality implementations of offline learning algorithms, including BC, BC-RNN, HBC, IRIS, BCQ, CQL, and TD3-BC</p> |
| </div> |
| </div> |
| <div class="col-lg-3 col-md-6" style="box-sizing:border-box;position:relative;width:100%;padding-right:0.75rem;padding-left:0.75rem;flex:0 0 50%;max-width:50%;" > |
| <div class="feature-box" style="box-sizing:border-box;background-attachment:scroll;padding-top:30px;padding-bottom:30px;padding-right:20px;padding-left:20px;margin-bottom:50px;text-align:center;border-width:1px;border-style:solid;border-color:#e6e6e6;height:400px;position:relative;background-color:#DEEEFB;background-image:none;background-repeat:repeat;background-position:top left;" > |
| <h3 style="box-sizing:border-box;margin-top:0;margin-bottom:0.5rem;" >Modular Design</h3> |
| <p class="text-muted" style="box-sizing:border-box;margin-top:0;margin-bottom:1rem;" >Support for learning both low-dimensional and visuomotor policies, diverse network architectures, and interface to easily use external datasets</p> |
| </div> |
| </div> |
| <div class="col-lg-3 col-md-6" style="box-sizing:border-box;position:relative;width:100%;padding-right:0.75rem;padding-left:0.75rem;flex:0 0 50%;max-width:50%;" > |
| <div class="feature-box" style="box-sizing:border-box;background-attachment:scroll;padding-top:30px;padding-bottom:30px;padding-right:20px;padding-left:20px;margin-bottom:50px;text-align:center;border-width:1px;border-style:solid;border-color:#e6e6e6;height:400px;position:relative;background-color:#DEEEFB;background-image:none;background-repeat:repeat;background-position:top left;" > |
| <h3 style="box-sizing:border-box;margin-top:0;margin-bottom:0.5rem;" >Flexible Experiment Workflow</h3> |
| <p class="text-muted" style="box-sizing:border-box;margin-top:0;margin-bottom:1rem;" >Utilities for running hyperparameter sweeps, visualizing demonstration data and trained policies, and collecting new datasets using trained policies</p> |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
| </div> --> |
| |
| <p align="center"> |
| <img style="width:100.0%;height:auto;" src="../images/core_features.png"> |
| </p> |
|
|
| <!-- |
| <style> |
| .column { |
| width: 45%; |
| float: left; |
| margin-right: 3%; |
| margin-bottom: 20px; |
| text-align: center; |
| padding: 20px; |
| height: 250px; |
| } |
| |
| .column:last-child { |
| margin-right: 0; |
| } |
| |
| .clear { |
| clear: both; |
| } |
| |
| @media screen and (max-width : 1024px) { |
| .column { |
| width: 50%; |
| float: left; |
| margin-right: 0; |
| padding: 15px; |
| } |
| } |
| |
| @media screen and (max-width : 767px) { |
| .column { |
| width: 100%; |
| float: none; |
| padding: 15px 0; |
| } |
| } |
| </style> |
| |
| <div class="row"> |
| <div class="column" style="background-color:#DEEEFB;"> |
| <h4>Suite of Learning Algorithms</h4> |
| <p>High-quality implementations of offline learning algorithms, including BC, BC-RNN, HBC, IRIS, BCQ, CQL, and TD3-BC</p> |
| </div> |
| <div class="column" style="background-color:#DEEEFB;"> |
| <h4>Standardized Datasets</h4> |
| <p>Datasets collected from different sources (single proficient human, multiple humans, and machine-generated) across simulated and real-world tasks spanning multiple robots and environments</p> |
| </div> |
| <div class="column" style="background-color:#DEEEFB;"> |
| <h4>Modular Design</h4> |
| <p>Support for learning both low-dimensional and visuomotor policies, diverse network architectures, and interface to easily use external datasets</p> |
| </div> |
| <div class="column" style="background-color:#DEEEFB;"> |
| <h4>Flexible Experiment Workflow</h4> |
| <p>Utilities for running hyperparameter sweeps, visualizing demonstration data and trained policies, and collecting new datasets using trained policies</p> |
| </div> |
| </div> |
| --> |
| |
| <!-- 1. **Offline Learning Algorithms** |
| High-quality implementations of offline learning algorithms, including BC, BC-RNN, HBC, IRIS, BCQ, CQL, and TD3-BC |
| 2. **Standardized Datasets** |
| Datasets collected from different sources (single proficient human, multiple humans, and machine-generated) across simulated and real-world tasks spanning multiple robots and environments |
| 3. **Modular Design** |
| Support for learning both low-dimensional and visuomotor policies, diverse network architectures, and interface to easily use external datasets |
| 4. **Flexible Experiment Workflow** |
| Utilities for running hyperparameter sweeps, visualizing demonstration data and trained policies, and collecting new datasets using trained policies --> |
|
|
|
|
| ## Reproducing benchmarks |
|
|
| The robomimic framework also makes reproducing the results from different benchmarks and datasets easy. See the [datasets page](../datasets/overview.html) for more information on downloading datasets and reproducing experiments. |
|
|
| ## Troubleshooting |
|
|
| Please see the [troubleshooting](../miscellaneous/troubleshooting.html) section for common fixes, or [submit an issue](https://github.com/ARISE-Initiative/robomimic/issues) on our github page. |
|
|
| ## Contributing to robomimic |
| This 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. |
| The project originally began development in late 2018 by researchers in the [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL). |
| Now it is actively maintained and used for robotics research projects across multiple labs. |
| We welcome community contributions to this project. |
| For details please check our [contributing guidelines](../miscellaneous/contributing.html). |
|
|
| ## Citation |
|
|
| Please cite [this paper](https://arxiv.org/abs/2108.03298) if you use this framework in your work: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
