Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 license: apache-2.0
+language:
+- en
+tags:
+- robotics
+- reinforcement learning
+- embodied ai
+- computer vision
+- simulation
+size_categories:
+- 1M<n<10M
 ---
+
+# ManiSkill2 Data
+
+![teaser](figures/teaser_v2.jpg)
+
+[![PyPI version](https://badge.fury.io/py/mani-skill2.svg)](https://badge.fury.io/py/mani-skill2) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/haosulab/ManiSkill2/blob/main/examples/tutorials/1_quickstart.ipynb)
+[![Docs status](https://img.shields.io/badge/docs-passing-brightgreen.svg)](https://haosulab.github.io/ManiSkill2)
+[![Discord](https://img.shields.io/discord/996566046414753822?logo=discord)](https://discord.gg/x8yUZe5AdN)
+<!-- [![Docs](https://github.com/haosulab/ManiSkill2/actions/workflows/gh-pages.yml/badge.svg)](https://haosulab.github.io/ManiSkill2) -->
+
+ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by [SAPIEN](https://sapien.ucsd.edu/). **It features 20 out-of-box task families with 2000+ diverse object models and 4M+ demonstration frames**. Moreover, it empowers fast visual input learning algorithms so that **a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a workstation**. The benchmark can be used to study a wide range of algorithms: 2D & 3D vision-based reinforcement learning, imitation learning, sense-plan-act, etc.
+This is the huggingface datasets page for all data related to [ManiSkill2](https://github.com/haosulab/ManiSkill2), 
+including **assets, robot demonstrations, and pretrained models**
+
+For detailed information about ManiSkill2, head over to our [GitHub repository](https://github.com/haosulab/ManiSkill2), [website](https://maniskill2.github.io/), or [ICLR 2023 paper](https://arxiv.org/abs/2302.04659)
+[documentation](https://haosulab.github.io/ManiSkill2/index.html)
+
+## Assets
+
+Some environments require you to download additional assets, which are stored here.
+
+You can download all the assets by running
+
+```
+python -m mani_skill2.utils.download_asset all
+```
+
+or download task-specific assets by running 
+
+```
+python -m mani_skill2.utils.download_asset ${ENV_ID}
+```
+
+## Demonstration Data
+
+The robot demonstrations consist of 4 million+ frames across 20+ robot manipulation tasks.
+
+We provide a command line tool (mani_skill2.utils.download_demo) to download demonstrations from here.
+
+```
+# Download all the demonstration datasets
+python -m mani_skill2.utils.download_demo all
+# Download the demonstration dataset for a specific task
+python -m mani_skill2.utils.download_demo ${ENV_ID}
+# Download the demonstration datasets for all rigid-body tasks to "./demos"
+python -m mani_skill2.utils.download_demo rigid_body -o ./demos
+# Download the demonstration datasets for all soft-body tasks
+python -m mani_skill2.utils.download_demo soft_body
+```
+
+To learn how to use the demonstrations and what environments are available, go to the demonstrations documentation page: https://haosulab.github.io/ManiSkill2/concepts/demonstrations.html
+
+
+## License
+
+All rigid body environments in ManiSkill are licensed under fully permissive licenses (e.g., Apache-2.0).
+
+However, the soft body environments will follow Warp's license. Currently, they are licensed under
+[NVIDIA Source Code License for Warp](https://github.com/NVIDIA/warp/blob/main/LICENSE.md).
+
+The assets are licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
+
+## Citation
+
+If you use ManiSkill2 or its assets, models, and demonstrations, please cite using the following BibTeX entry:
+
+@inproceedings{gu2023maniskill2,
+  title={ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills},
+  author={Gu, Jiayuan and Xiang, Fanbo and Li, Xuanlin and Ling, Zhan and Liu, Xiqiaing and Mu, Tongzhou and Tang, Yihe and Tao, Stone and Wei, Xinyue and Yao, Yunchao and Yuan, Xiaodi and Xie, Pengwei and Huang, Zhiao and Chen, Rui and Su, Hao},
+  booktitle={International Conference on Learning Representations},
+  year={2023}
+}