# Demo Scripts

There are a number of useful/quick scripts you can run to do a quick test/demonstration of various features of ManiSkill.

## Demo Random Actions
The fastest and quickest demo is the random actions demo.
Run 
```bash
python -m mani_skill.examples.demo_random_action -h
```
for a full list of available commands.

Some recommended examples that cover a number of features of ManiSkill

Tasks in Realistic Scenes (ReplicaCAD dataset example)
```bash
python -m mani_skill.utils.download_asset "ReplicaCAD"
python -m mani_skill.examples.demo_random_action -e "ReplicaCAD_SceneManipulation-v1" \
  --render-mode="rgb_array" --record-dir="videos" # run headless and save video
python -m mani_skill.examples.demo_random_action -e "ReplicaCAD_SceneManipulation-v1" \
  --render-mode="human" # run with GUI
```

To turn ray-tracing on for more photo-realistic rendering, you can add `--shader="rt"` or `--shader="rt-fast"`

```bash
python -m mani_skill.examples.demo_random_action -e "ReplicaCAD_SceneManipulation-v1" \
  --render-mode="human" --shader="rt-fast" # faster ray-tracing option but lower quality
```

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/fetch_random_action_replica_cad_rt.mp4" type="video/mp4">
</video>

Tasks with multiple robots
```bash
python -m mani_skill.examples.demo_random_action -e "TwoRobotStackCube-v1" \
  --render-mode="human"
```

```{figure} images/tworobotstackcube.png
---
alt: SAPIEN GUI two robot stack cube task
---
```


Tasks with dextrous hand
```bash
python -m mani_skill.examples.demo_random_action -e "RotateValveLevel2-v1" \
  --render-mode="human"
```

```{figure} images/rotatevalvelevel2.png
---
alt: SAPIEN GUI showing the rotate valve level 2 task
---
```


Tasks with simulated tactile sensing
```bash
python -m mani_skill.examples.demo_random_action -e "RotateSingleObjectInHandLevel3-v1" \
  --render-mode="human"
```

```{figure} images/rotatesingleobjectinhand.png
---
alt: SAPIEN GUI showing the rotate single object in hand task
---
```

To quickly demo tasks that support simulating different objects and articulations (with different dofs) across parallel environments see the [GPU Simulation section](#gpu-simulation)

<!-- 
AI2THOR related scenes
```bash
python -m mani_skill.utils.download_asset "AI2THOR"
python -m mani_skill.examples.demo_random_action -e "ArchitecTHOR_SceneManipulation-v1" --render-mode="rgb_array" --record-dir="videos" # run headless and save video
python -m mani_skill.examples.demo_random_action -e "ArchitecTHOR_SceneManipulation-v1" --render-mode="human" # run with GUI
``` -->

## GPU Simulation

To benchmark the GPU simulation on the PickCube-v1 task with 4096 parallel tasks you can run
```bash
python -m mani_skill.examples.benchmarking.gpu_sim -e "PickCube-v1" -n 4096
```

To save videos of the visual observations the agent would get (in this case it is just rgb and depth) you can run
```bash
python -m mani_skill.examples.benchmarking.gpu_sim -e "PickCube-v1" -n 64 \
  --save-video --render-mode="sensors"
```
it should run quite fast! (3000+ fps on a 4090, you can increase the number envs for higher FPS). You can change `--render-mode="rgb_array"` to render from higher quality cameras.

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/mani_skill_gpu_sim-PickCube-v1-num_envs=16-obs_mode=state-render_mode=sensors.mp4" type="video/mp4">
</video>


To try out the diverse parallel simulation features you can run
```bash
python -m mani_skill.examples.benchmarking.gpu_sim -e "PickSingleYCB-v1" -n 64 \
  --save-video --render-mode="sensors"
python -m mani_skill.examples.benchmarking.gpu_sim -e "OpenCabinetDrawer-v1" -n 64 \
  --save-video --render-mode="sensors"
```
which shows two tasks that have different objects and articulations in every parallel environment. Below is an example for the OpenCabinetDrawer task.

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/mani_skill_gpu_sim-OpenCabinetDrawer-v1-num_envs=16-obs_mode=state-render_mode=sensors.mp4" type="video/mp4">
</video>


<!-- TODO show mobile manipulation scene gpu sim stuff -->

More details and performance benchmarking results can be found on [this page](../additional_resources/performance_benchmarking.md)

## Interactive Control

Click+Drag Teleoperation:

Simple tool where you can click and drag the end-effector of the Panda robot arm to solve various tasks. You just click+drag, press "n" to move to the position you dragged to, "g" to toggle on/off grasping, and repeat. Press "q" to quit and save a video of the result.

```bash
python -m mani_skill.examples.teleoperation.interactive_panda -e "StackCube-v1" 
```

See [main page](../data_collection/teleoperation.md#clickdrag-system) for more details about how to use this tool (for demo and data collection). The video below shows the system running.

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/teleop-stackcube-demo.mp4" type="video/mp4">
</video>

## Motion Planning Solutions

We provide some motion planning solutions/demos for the panda arm on some tasks, you can try it now and record demonstrations with the following:

```bash
python -m mani_skill.examples.motionplanning.panda.run -e "PickCube-v1" # runs headless and only saves video
python -m mani_skill.examples.motionplanning.panda.run -e "StackCube-v1" --vis # opens up the GUI
python -m mani_skill.examples.motionplanning.panda.run -h # open up a help menu and also show what tasks have solutions
```

Example below shows what it looks like with the GUI:

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/motionplanning-stackcube.mp4" type="video/mp4">
</video>

For more details check out the [motion planning page](../data_collection/motionplanning.md)

## Real2Sim Evaluation

ManiSkill3 supports extremely fast real2sim evaluation via GPU simulation + rendering of policies like RT-1 and Octo. See [this page](../../tasks/digital_twins/index.md) for more details on which environments are supported. To run inference of RT-1 and Octo, see the `maniskill3` branch of the [SimplerEnv Project](https://github.com/simpler-env/SimplerEnv/tree/maniskill3).

## Visualize Pointcloud Data

You can run the following to visualize the pointcloud observations (require's a display to work)

```bash
pip install "pyglet<2" # make sure to install this dependency
python -m mani_skill.examples.demo_vis_pcd -e "StackCube-v1"
```


```{figure}  images/pcd_vis.png
```

## Visualize Segmentation Data

You can run the following to visualize the segmentation data

```bash
python -m mani_skill.examples.demo_vis_segmentation -e "StackCube-v1"
python -m mani_skill.examples.demo_vis_segmentation -e "StackCube-v1" \
  --id id_of_part # mask out everything but the selected part
```


```{figure}  images/seg_vis.png
```

## Visualize Camera Textures (RGB, Depth, Albedo etc.)

You can run the following to visualize any number of textures generated by the camera. Note that by default the shader used is the "default" shader which outputs just about every possible texture you might need. See [the page on cameras and shaders](../../)

```bash
python -m mani_skill.examples.demo_vis_textures -e "StackCube-v1" -o rgb+depth
python -m mani_skill.examples.demo_vis_textures -e "OpenCabinetDrawer-v1" -o rgb+depth+albedo+normal
```


```{figure}  images/rgbd_vis.png
```

## Visualize Reset Distributions

Determining how difficult a task might be for ML algorithms like reinforcement learning and imitation learning can heavily depend on the reset distribution of the task. To see what the reset distribution of any task (the result of repeated env.reset calls) looks like you can run the following to save a video to the `videos` folder

```bash
python -m mani_skill.examples.demo_reset_distribution -e "PegInsertionSide-v1" --record-dir="videos"
```

<video preload="auto" controls="True" width="100%">
<source src="https://github.com/haosulab/ManiSkill/raw/main/docs/source/_static/videos/PegInsertionSide-v1_reset_distribution.mp4" type="video/mp4">
</video>

## Visualize Any Robot

Run the following to open a viewer displaying any robot given in a empty scene with just a floor. You can also specify different keyframes if there are any pre-defined to visualize.

```bash
python -m mani_skill.examples.demo_robot -r "panda"
```