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.. code-block:: bash
IsaacLab
├── .vscode
├── CONTRIBUTING.md
├── CONTRIBUTORS.md
├── LICENSE
├── isaaclab.bat
├── isaaclab.sh
├── pyproject.toml
├── README.md
├── docs
├── docker
├── source
│ ├── isaaclab # core framework
│ ├── isaaclab_physx # PhysX backend (requires Isaac Sim)
│ ├── isaaclab_ovphysx # standalone PhysX backend (requires ovphysx)
│ ├── isaaclab_ov # OVRTX renderer backend (requires ovrtx)
│ ├── isaaclab_newton # Newton backend (kit-less)
│ ├── isaaclab_assets # pre-configured robot & sensor assets
│ ├── isaaclab_tasks # pre-built RL/IL environments
│ ├── isaaclab_tasks_experimental # Warp-accelerated environments
│ ├── isaaclab_rl # RL library wrappers
│ ├── isaaclab_mimic # imitation learning & data generation
│ ├── isaaclab_teleop # teleoperation & XR
│ ├── isaaclab_visualizers # external visualizer backends
│ ├── isaaclab_contrib # community-contributed extensions
│ ├── isaaclab_experimental # Warp-accelerated manager and environment variants
│ ├── extensions # legacy Omniverse extension wrappers
│ └── standalone # standalone tutorials & workflows
├── scripts
│ ├── benchmarks
│ ├── demos
│ ├── environments
│ ├── imitation_learning
│ ├── reinforcement_learning
│ ├── sim2sim_transfer
│ ├── tools
│ └── tutorials
├── tools
└── VERSION
Isaac Lab supports the **PhysX** and **Newton** physics engines through backend packages. The
default PhysX path runs through Isaac Sim, while ``ovphysx`` supports standalone PhysX workflows
without launching Isaac Sim and Newton provides a Warp-native kit-less backend. The ``source``
directory contains all packages that compose Isaac Lab, while ``scripts`` contains standalone
Python applications for training, evaluation, and tooling.
See :doc:`/source/overview/core-concepts/multi_backend_architecture` for details on the backend
system, and :doc:`/source/setup/ecosystem` for a full package-layer overview.
Submodules
~~~~~~~~~~
The packages under ``source/`` are installed as Python packages using
`setuptools <https://setuptools.readthedocs.io/en/latest/>`__. They are organized into three
groups:
**Core, physics backends, and renderers**
* **isaaclab**: The core framework. Provides :mod:`~isaaclab.sim` (simulation context and
configuration), :class:`~isaaclab.scene.InteractiveScene`, asset and sensor base classes and
factory interfaces (:mod:`~isaaclab.assets`, :mod:`~isaaclab.sensors`), environment base
classes (:mod:`~isaaclab.envs`), the manager system (:mod:`~isaaclab.managers`), composable
MDP term library (:mod:`~isaaclab.envs.mdp`), actuator models (:mod:`~isaaclab.actuators`),
low-level controllers (:mod:`~isaaclab.controllers`), procedural terrain generation
(:mod:`~isaaclab.terrains`), and human-input device support (:mod:`~isaaclab.devices`).
* **isaaclab_physx**: PhysX-backed implementations of articulations, rigid bodies, deformable
objects, Fabric views, the Isaac RTX renderer, and USD spawners. Requires Isaac Sim.
* **isaaclab_ovphysx**: Standalone PhysX backend variant using ``ovphysx`` and the
TensorBindingsAPI. Requires the ``ovphysx`` package and can run without launching Isaac Sim.
* **isaaclab_ov**: OVRTX renderer backend for RTX-based tiled camera rendering. Requires the
``ovrtx`` package and can run without Isaac Sim.
* **isaaclab_newton**: Newton-backed implementations of articulations, rigid bodies, rigid
object collections, cameras, USD spawners, and the Warp renderer. Supports
:ref:`kit-less installation <isaaclab-installation-root>` without Isaac Sim.
**Tasks and assets**
* **isaaclab_assets**: Pre-configured :class:`~isaaclab.utils.configclass` dataclasses for a
wide range of robots (Franka, Unitree, ANYmal, Spot, Allegro, humanoids, quadcopters, and
more) and sensors (Velodyne, GelSight).
* **isaaclab_tasks**: Registered `gymnasium <https://gymnasium.farama.org/>`__ environments for
reinforcement and imitation learning, organized as *manager-based* and *direct* tasks across
locomotion, manipulation, navigation, and classic control domains.
* **isaaclab_tasks_experimental**: Experimental task implementations under active development,
not yet part of the stable task suite.
**Optional Additions**
* **isaaclab_rl**: Thin wrappers that adapt Isaac Lab environments to the interfaces expected
by `RSL-RL <https://github.com/leggedrobotics/rsl_rl>`__,
`skrl <https://skrl.readthedocs.io/>`__,
`Stable Baselines 3 <https://stable-baselines3.readthedocs.io/>`__, and
`RL Games <https://github.com/Denys88/rl_games>`__.
* **isaaclab_mimic**: APIs and pre-configured environments for data generation and imitation
learning, including cuRobo-based motion planners and a dataset-generation pipeline.
* **isaaclab_teleop**: Teleoperation session orchestration with OpenXR / CloudXR support,
device retargeters for manipulators and humanoids, and gamepad / spacemouse / keyboard input.
* **isaaclab_visualizers**: Supplementary visualizer backends (Isaac Sim Kit, Newton, Rerun, Viser) that
work with any physics backend.
* **isaaclab_contrib**: Community-contributed features: multirotor assets, TacSL
visuo-tactile sensors, drone thrust controllers, and more.
* **isaaclab_experimental**: Pre-production core experiments including Warp-accelerated manager
and environment variants.
Standalone
~~~~~~~~~~
The ``scripts`` directory contains standalone Python applications.
They are structured as follows:
* **benchmarks**: Scripts for benchmarking different framework components.
* **demos**: Demo applications that showcase the core framework :mod:`isaaclab`.
* **environments**: Scripts for running environments defined in :mod:`isaaclab_tasks` with
different agents (random policy, zero-action policy, teleoperation, scripted state machines).
* **imitation_learning**: Applications for training and evaluating policies with imitation
learning libraries (e.g. robomimic).
* **reinforcement_learning**: Applications for training and evaluating policies with RL
libraries (e.g. rsl_rl, rl_games, sb3, skrl).
* **sim2sim_transfer**: Scripts for transferring policies trained in one simulator to another.
* **tools**: Applications for using framework tools such as converting assets and generating
datasets.
* **tutorials**: Step-by-step tutorials for using the APIs provided by the framework.
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