phantom/submodules/phantom-robosuite/README.md

# robosuite

![gallery of_environments](docs/images/gallery.png)

[**[Homepage]**](https://robosuite.ai/)   [**[White Paper]**](https://arxiv.org/abs/2009.12293)   [**[Documentations]**](https://robosuite.ai/docs/overview.html)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative)

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## Latest Updates
- [11/15/2022] **v1.4**: Backend migration to DeepMind's official [MuJoCo Python binding](https://github.com/deepmind/mujoco), robot textures, and bug fixes :robot: [[release notes]](https://github.com/ARISE-Initiative/robosuite/releases/tag/v1.4.0) [[documentation]](http://robosuite.ai/docs/v1.4/)

- [10/19/2021] **v1.3**: Ray tracing and physically based rendering tools :sparkles: and access to additional vision modalities 🎥 [[video spotlight]](https://www.youtube.com/watch?v=2xesly6JrQ8) [[release notes]](https://github.com/ARISE-Initiative/robosuite/releases/tag/v1.3) [[documentation]](http://robosuite.ai/docs/v1.3/)

- [02/17/2021] **v1.2**: Added observable sensor models :eyes: and dynamics randomization :game_die: [[release notes]](https://github.com/ARISE-Initiative/robosuite/releases/tag/v1.2)

- [12/17/2020] **v1.1**: Refactored infrastructure and standardized model classes for much easier environment prototyping :wrench: [[release notes]](https://github.com/ARISE-Initiative/robosuite/releases/tag/v1.1)

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**robosuite** is a simulation framework powered by the [MuJoCo](http://mujoco.org/) physics engine for robot learning. It also offers a suite of benchmark environments for reproducible research. The current release (v1.4) features long-term support with the official MuJoCo binding from DeepMind. 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.

Data-driven algorithms, such as reinforcement learning and imitation learning, provide a powerful and generic tool in robotics. These learning paradigms, fueled by new advances in deep learning, have achieved some exciting successes in a variety of robot control problems. However, the challenges of reproducibility and the limited accessibility of robot hardware (especially during a pandemic) have impaired research progress. The overarching goal of **robosuite** is to provide researchers with:

* a standardized set of benchmarking tasks for rigorous evaluation and algorithm development;
* a modular design that offers great flexibility to design new robot simulation environments;
* a high-quality implementation of robot controllers and off-the-shelf learning algorithms to lower the barriers to entry.

This framework was originally developed since late 2017 by researchers in [Stanford Vision and Learning Lab](http://svl.stanford.edu) (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the [UT Robot Perception and Learning Lab](http://rpl.cs.utexas.edu) (RPL). We welcome community contributions to this project. For details please check out our [contributing guidelines](CONTRIBUTING.md).

This release of **robosuite** contains seven robot models, eight gripper models, six controller modes, and nine standardized tasks. It also offers a modular design of APIs for building new environments with procedural generation. We highlight these primary features below:

* **standardized tasks**: a set of standardized manipulation tasks of large diversity and varying complexity and RL benchmarking results for reproducible research;
* **procedural generation**: modular APIs for programmatically creating new environments and new tasks as combinations of robot models, arenas, and parameterized 3D objects;
* **robot controllers**: a selection of controller types to command the robots, such as joint-space velocity control, inverse kinematics control, operational space control, and 3D motion devices for teleoperation;
* **multi-modal sensors**: heterogeneous types of sensory signals, including low-level physical states, RGB cameras, depth maps, and proprioception;
* **human demonstrations**: utilities for collecting human demonstrations, replaying demonstration datasets, and leveraging demonstration data for learning. Check out our sister project [robomimic](https://arise-initiative.github.io/robomimic-web/);
* **photorealistic rendering**: integration with advanced graphics tools that provide real-time photorealistic renderings of simulated scenes.

## Citation
Please cite [**robosuite**](https://robosuite.ai) if you use this framework in your publications:
```bibtex
@inproceedings{robosuite2020,
  title={robosuite: A Modular Simulation Framework and Benchmark for Robot Learning},
  author={Yuke Zhu and Josiah Wong and Ajay Mandlekar and Roberto Mart\'{i}n-Mart\'{i}n and Abhishek Joshi and Soroush Nasiriany and Yifeng Zhu},
  booktitle={arXiv preprint arXiv:2009.12293},
  year={2020}
}
```