
# ManiSkill2 Data

![teaser](https://github.com/haosulab/ManiSkill2/blob/main/figures/teaser_v2.jpg?raw=true)

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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)

**Note that to download the data you must use the mani_skill2 package to do so as shown below, currently loading through HuggingFace datasets does not work as intended just yet**

## 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}
}
```
task_categories:reinforcement-learning task_categories:robotics size_categories:1M<n<10M language:en license:apache-2.0 robotics reinforcement learning embodied ai computer vision simulation Embodied AI arxiv:2302.04659 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["reinforcement-learning", "robotics"], "tags": ["robotics", "reinforcement learning", "embodied ai", "computer vision", "simulation", "Embodied AI"], "viewer": false}
TAGS
#task_categories-reinforcement-learning #task_categories-robotics #size_categories-1M<n<10M #language-English #license-apache-2.0 #robotics #reinforcement learning #embodied ai #computer vision #simulation #Embodied AI #arxiv-2302.04659 #region-us 


# ManiSkill2 Data

!teaser

![PyPI version](URL ![Open In Colab](URL
![Docs status](URL
![Discord](URL


ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by SAPIEN. 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, 
including assets, robot demonstrations, and pretrained models

For detailed information about ManiSkill2, head over to our GitHub repository, website, or ICLR 2023 paper
documentation

Note that to download the data you must use the mani_skill2 package to do so as shown below, currently loading through HuggingFace datasets does not work as intended just yet

## Assets

Some environments require you to download additional assets, which are stored here.

You can download all the assets by running



or download task-specific assets by running 



## 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.



To learn how to use the demonstrations and what environments are available, go to the demonstrations documentation page: URL


## 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.

The assets are licensed under CC BY-NC 4.0.

If you use ManiSkill2 or its assets, models, and demonstrations, please cite using the following BibTeX entry:


# ManiSkill2 Data

!teaser

![PyPI version](URL ![Open In Colab](URL
![Docs status](URL
![Discord](URL


ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by SAPIEN. 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, 
including assets, robot demonstrations, and pretrained models

For detailed information about ManiSkill2, head over to our GitHub repository, website, or ICLR 2023 paper
documentation

Note that to download the data you must use the mani_skill2 package to do so as shown below, currently loading through HuggingFace datasets does not work as intended just yet ## Assets

Some environments require you to download additional assets, which are stored here.

You can download all the assets by running



or download task-specific assets by running ## 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.



To learn how to use the demonstrations and what environments are available, go to the demonstrations documentation page: URL ## 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.

The assets are licensed under CC BY-NC 4.0.

If you use ManiSkill2 or its assets, models, and demonstrations, please cite using the following BibTeX entry:
TAGS
#task_categories-reinforcement-learning #task_categories-robotics #size_categories-1M<n<10M #language-English #license-apache-2.0 #robotics #reinforcement learning #embodied ai #computer vision #simulation #Embodied AI #arxiv-2302.04659 #region-us 
 # ManiSkill2 Data

!teaser

![PyPI version](URL ![Open In Colab](URL
![Docs status](URL
![Discord](URL


ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by SAPIEN. 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, 
including assets, robot demonstrations, and pretrained models

For detailed information about ManiSkill2, head over to our GitHub repository, website, or ICLR 2023 paper
documentation

Note that to download the data you must use the mani_skill2 package to do so as shown below, currently loading through HuggingFace datasets does not work as intended just yet ## Assets

Some environments require you to download additional assets, which are stored here.

You can download all the assets by running



or download task-specific assets by running ## 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.



To learn how to use the demonstrations and what environments are available, go to the demonstrations documentation page: URL ## 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.

The assets are licensed under CC BY-NC 4.0.

If you use ManiSkill2 or its assets, models, and demonstrations, please cite using the following BibTeX entry:
passage: TAGS
#task_categories-reinforcement-learning #task_categories-robotics #size_categories-1M<n<10M #language-English #license-apache-2.0 #robotics #reinforcement learning #embodied ai #computer vision #simulation #Embodied AI #arxiv-2302.04659 #region-us 
# ManiSkill2 Data

!teaser

![PyPI version](URL ![Open In Colab](URL
![Docs status](URL
![Discord](URL


ManiSkill2 is a unified benchmark for learning generalizable robotic manipulation skills powered by SAPIEN. 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, 
including assets, robot demonstrations, and pretrained models

For detailed information about ManiSkill2, head over to our GitHub repository, website, or ICLR 2023 paper
documentation

Note that to download the data you must use the mani_skill2 package to do so as shown below, currently loading through HuggingFace datasets does not work as intended just yet## Assets

Some environments require you to download additional assets, which are stored here.

You can download all the assets by running



or download task-specific assets by running## 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.



To learn how to use the demonstrations and what environments are available, go to the demonstrations documentation page: URL