Repository organization ----------------------- .. code-block:: bash IsaacLab ├── .vscode ├── CONTRIBUTING.md ├── CONTRIBUTORS.md ├── LICENSE ├── isaaclab.bat ├── isaaclab.sh ├── pyproject.toml ├── README.md ├── docs ├── docker ├── source │   ├── isaaclab │   ├── isaaclab_assets │   ├── isaaclab_mimic │   ├── isaaclab_rl │   └── isaaclab_tasks ├── scripts │   ├── benchmarks │   ├── demos │   ├── environments │   ├── imitation_learning │   ├── reinforcement_learning │   ├── tools │   ├── tutorials ├── tools └── VERSION Isaac Lab is built on the same back end as Isaac Sim. As such, it exists as a collection of **extensions** that can be assembled into **applications**. The ``source`` directory contains the majority of the code in the repository and the specific extensions that compose Isaac lab, while ``scripts`` containing python scripts for launching customized standalone apps (Like our workflows). These are the two primary ways of interacting with the simulation and Isaac lab supports both! Checkout this `Isaac Sim introduction to workflows `__ for more details. Extensions ~~~~~~~~~~ The extensions that compose Isaac Lab are kept in the ``source`` directory. To simplify the build process, Isaac Lab directly use `setuptools `__. It is strongly recommend that you adhere to this process if you create your own extensions using Isaac Lab. The extensions are organized as follows: * **isaaclab**: Contains the core interface extension for Isaac Lab. This provides the main modules for actuators, objects, robots and sensors. * **isaaclab_assets**: Contains the extension with pre-configured assets for Isaac Lab. * **isaaclab_tasks**: Contains the extension with pre-configured environments for Isaac Lab. * **isaaclab_mimic**: Contains APIs and pre-configured environments for data generation for imitation learning. * **isaaclab_rl**: Contains wrappers for using the above environments with different reinforcement learning agents. Standalone ~~~~~~~~~~ The ``scripts`` directory contains various standalone applications written in python. They are structured as follows: * **benchmarks**: Contains scripts for benchmarking different framework components. * **demos**: Contains various demo applications that showcase the core framework :mod:`isaaclab`. * **environments**: Contains applications for running environments defined in :mod:`isaaclab_tasks` with different agents. These include a random policy, zero-action policy, teleoperation or scripted state machines. * **tools**: Contains applications for using the tools provided by the framework. These include converting assets, generating datasets, etc. * **tutorials**: Contains step-by-step tutorials for using the APIs provided by the framework. * **workflows**: Contains applications for using environments with various learning-based frameworks. These include different reinforcement learning or imitation learning libraries.