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# autosim
# AutoDataGen

**autosim: An automated data generation pipeline based on NVIDIA Isaac Lab**
**AutoDataGen: An automated data generation pipeline based on NVIDIA Isaac Lab**

An automatic simulation data generation pipeline built on Isaac Lab, integrating LLM-based task decomposition, motion planning, and navigation capabilities.
An automated simulation data generation pipeline built on Isaac Lab, integrating LLM-based task decomposition, motion planning, and navigation capabilities.

## Overview

`autosim` provides an extensible automated data generation pipeline for NVIDIA Isaac Lab environments:
`AutoDataGen` provides an extensible automated data generation pipeline for NVIDIA Isaac Lab environments:

- Starting from **task code and scene information**, it uses an LLM to **decompose high-level tasks** automatically.
- Maps the decomposition result into a sequence of **atomic skills**.
- Invokes **motion planning (based on cuRobo)** and **navigation** to execute these skills in simulation.
- Produces unified **action sequences / trajectory data** for downstream robot learning and research.

> In short: `autosim` helps you automatically turn “a task in Isaac Lab” into an executable sequence of skills, and generates data that can be used for training or evaluation.
> In short: `AutoDataGen` helps you automatically turn “a task in Isaac Lab” into an executable sequence of skills, and generates data that can be used for training or evaluation.

## Installation

Below is a typical setup workflow. `autosim` can be installed as a submodule inside an environment that already contains Isaac Lab.
Below is a typical setup workflow.

> AutoDataGen uses `autosim` as a Python package to organize the code. `autosim` can be installed as a submodule within an environment that already includes Isaac Lab.

```bash
conda create -n autosim python=3.11
conda create -n AutoDataGen python=3.11

conda activate autosim
conda activate AutoDataGen

git clone https://github.com/LightwheelAI/autosim.git
git clone https://github.com/LightwheelAI/AutoDataGen.git

cd autosim
cd AutoDataGen

git submodule update --init --recursive
```

### IsaacLab Installation

`autosim` depends on Isaac Lab. You can follow the official installation guide [here](https://isaac-sim.github.io/IsaacLab/v2.2.1/source/setup/installation/pip_installation.html), or use the commands below. If you already have an environment with Isaac Lab installed, you can reuse it and skip this step.
`AutoDataGen` depends on Isaac Lab. You can follow the official installation guide [here](https://isaac-sim.github.io/IsaacLab/v2.2.1/source/setup/installation/pip_installation.html), or use the commands below. If you already have an environment with Isaac Lab installed, you can reuse it and skip this step.

```bash
# Install CUDA toolkit
Expand Down Expand Up @@ -80,7 +82,7 @@ pip install -e source/autosim

After completing the installation and configuration steps above, you can directly run the built-in example.

First, install the `autosim` example package:
First, install the `autosim_examples` package:

```bash
pip install -e source/autosim_examples
Expand Down Expand Up @@ -113,7 +115,7 @@ For a task that has already been defined in Isaac Lab, you can use `autosim` to

Inherit from `AutoSimPipeline` and implement:

- `load_env(self) -> ManagerBasedEnv`: load the environment based on Isaac Lab / Gymnasium; this environment should correspond to the pre-defined Task.
- `load_env(self) -> ManagerBasedEnv`: load the environment using Isaac Lab; this environment should correspond to the pre-defined task.
- `get_env_extra_info(self) -> EnvExtraInfo`: provide the task name, robot name, end-effector link, and reach targets expressed as poses relative to objects, etc.

3. **Register the pipeline in the package’s `__init__.py`**
Expand Down Expand Up @@ -141,6 +143,23 @@ For a task that has already been defined in Isaac Lab, you can use `autosim` to

> You can refer to `source/autosim_examples/autosim_examples/autosim/pipelines/franka_lift_cube.py` for a minimal working example.

## Using with LW-BenchHub

[LW-BenchHub](https://github.com/LightwheelAI/LW-BenchHub) includes several usage examples of AutoDataGen. You can try them by following the steps below.

First, set up the LW-BenchHub environment according to its [documentation](https://docs.lightwheel.net/lw_benchhub/usage/Installation), and then install `autosim` into that environment. Since Isaac Lab has already been installed in the previous step, you only need to complete the **cuRobo Installation** and **autosim Installation** steps above.

Then you can launch it with:

```bash
cd lw_benchhub

python scripts/autosim/run_autosim_example.py --pipeline_id=LWBenchhub-Autosim-CoffeeSetupMugPipeline-v0
```

We also support tasks such as CheesyBread, CloseOven, OpenFridge, and KettleBoiling. You can find the corresponding [implementations](https://github.com/LightwheelAI/LW-BenchHub/tree/main/lw_benchhub/autosim) in LW-BenchHub.


## Contributing

Issues, feature requests, and pull requests are all welcome!
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