Repository organization

Repository organization#

IsaacLab
├── .vscode
├── .flake8
├── 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 isaaclab.

  • environments: Contains applications for running environments defined in 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.