# Tasks

[asset-badge]: https://img.shields.io/badge/download%20asset-yes-blue.svg
[dense-reward-badge]: https://img.shields.io/badge/dense%20reward-yes-green.svg
[no-dense-reward-badge]: https://img.shields.io/badge/dense%20reward-no-red.svg
[sparse-reward-badge]: https://img.shields.io/badge/sparse%20reward-yes-green.svg
[no-sparse-reward-badge]: https://img.shields.io/badge/sparse%20reward-no-red.svg
[demos-badge]: https://img.shields.io/badge/demos-yes-green.svg
ManiSkill features a number of built-in rigid-body tasks, all GPU parallelized and demonstrating a range of features. They are generally categorized into a few categories. Soft-body tasks are provided in the ManiSkill2 codebase. They have yet to be fully integrated into ManiSkill3.

For each task documented in these sections we provide a "Task Card" which briefly describes all the important aspects of the task, including task description, supported robots, randomizations, success/fail conditions, and how goals are specified in observations (if they are non-visual). We further show tags describing whether there are dense rewards provided, sparse rewards (same as success/fail conditions), demonstrations available, and if assets need to be downloaded to run the environment.

![dense-reward][dense-reward-badge]
![sparse-reward][sparse-reward-badge]
![demos][demos-badge]
![download-asset][asset-badge]

If assets do need to be downloaded you can just run

```python -m mani_skill.utils.download_asset <env_id>```

To download demonstrations for a task you can run

```python -m mani_skill.utils.download_demo <env_id>```

Note that some tasks have different state observation data provided depending on the observation mode. In general when the observation mode is "state" or "state_dict" then all ground truth data necessary to solve the task is given. If the observation mode is any of the visual ones, we remove any ground truth observation data (like an object's pose) that would normally be unattainable in the real world.

```{toctree}
:maxdepth: 1

control/index
table_top_gripper/index
quadruped/index
humanoid/index
mobile_manipulation/index
dextrous/index
digital_twins/index
drawing/index
external/index
```