docs/source/setup/ecosystem.rst

.. _isaac-lab-ecosystem:

Isaac Lab Ecosystem
===================

Isaac Lab is built on top of Isaac Sim to provide a unified and flexible framework
for robot learning that exploits latest simulation technologies. It is designed to be modular and extensible,
and aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and
motion planning). While it includes some pre-built environments, sensors, and tasks, its main goal is to
provide an open-sourced, unified, and easy-to-use interface for developing and testing custom environments
and robot learning algorithms.

Working with Isaac Lab requires the installation of Isaac Sim, which is packaged with core robotics tools
that Isaac Lab depends on, including URDF and MJCF importers, simulation managers, and ROS features. Isaac
Sim also builds on top of the NVIDIA Omniverse platform, leveraging advanced physics simulation from PhysX,
photorealistic rendering technologies, and Universal Scene Description (USD) for scene creation.

Isaac Lab not only inherits the capabilities of Isaac Sim, but also adds a number
of new features that pertain to robot learning research. For example, including actuator dynamics in the
simulation, procedural terrain generation, and support to collect data from human demonstrations.

.. image:: ../_static/setup/ecosystem-light.jpg
    :class: only-light
    :align: center
    :alt: The Isaac Lab, Isaac Sim, and NVIDIA Omniverse ecosystem

.. image:: ../_static/setup/ecosystem-dark.jpg
    :class: only-dark
    :align: center
    :alt: The Isaac Lab, Isaac Sim, and NVIDIA Omniverse ecosystem


Where does Isaac Lab fit in the Isaac ecosystem?
------------------------------------------------

Over the years, NVIDIA has developed a number of tools for robotics and AI. These tools leverage
the power of GPUs to accelerate the simulation both in terms of speed and realism. They show great
promise in the field of simulation technology and are being used by many researchers and companies
worldwide.

`Isaac Gym`_ :cite:`makoviychuk2021isaac` provides a high performance GPU-based physics simulation
for robot learning. It is built on top of `PhysX`_ which supports GPU-accelerated simulation of rigid bodies
and a Python API to directly access physics simulation data. Through an end-to-end GPU pipeline, it is possible
to achieve high frame rates compared to CPU-based physics engines. The tool has been used successfully in a
number of research projects, including legged locomotion :cite:`rudin2022learning` :cite:`rudin2022advanced`,
in-hand manipulation :cite:`handa2022dextreme` :cite:`allshire2022transferring`, and industrial assembly
:cite:`narang2022factory`.

Despite the success of Isaac Gym, it is not designed to be a general purpose simulator for
robotics. For example, it does not include interaction between deformable and rigid objects, high-fidelity
rendering, and support for ROS. The tool has been primarily designed as a preview release to showcase the
capabilities of the underlying physics engine. With the release of `Isaac Sim`_, NVIDIA is building
a general purpose simulator for robotics and has integrated the functionalities of Isaac Gym into
Isaac Sim.

`Isaac Sim`_ is a robot simulation toolkit built on top of Omniverse, which is a general purpose platform
that aims to unite complex 3D workflows. Isaac Sim leverages the latest advances in graphics and
physics simulation to provide a high-fidelity simulation environment for robotics. It supports
ROS/ROS2, various sensor simulation, tools for domain randomization and synthetic data creation.
Tiled rendering support in Isaac Sim allows for vectorized rendering across environments, along with
support for running in the cloud using `Isaac Automator`_.
Overall, it is a powerful tool for roboticists and is a huge step forward in the field of robotics
simulation.

With the release of above two tools, NVIDIA also released an open-sourced set of environments called
`IsaacGymEnvs`_ and `OmniIsaacGymEnvs`_, that have been built on top of Isaac Gym and Isaac Sim respectively.
These environments have been designed to display the capabilities of the underlying simulators and provide
a starting point to understand what is possible with the simulators for robot learning. These environments
can be used for benchmarking but are not designed for developing and testing custom environments and algorithms.
This is where Isaac Lab comes in.

Isaac Lab is built on top of Isaac Sim to provide a unified and flexible framework
for robot learning that exploits latest simulation technologies. It is designed to be modular and extensible,
and aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and
motion planning). While it includes some pre-built environments, sensors, and tasks, its main goal is to
provide an open-sourced, unified, and easy-to-use interface for developing and testing custom environments
and robot learning algorithms. It not only inherits the capabilities of Isaac Sim, but also adds a number
of new features that pertain to robot learning research. For example, including actuator dynamics in the
simulation, procedural terrain generation, and support to collect data from human demonstrations.

Isaac Lab replaces the previous `IsaacGymEnvs`_, `OmniIsaacGymEnvs`_ and `Orbit`_ frameworks and will
be the single robot learning framework for Isaac Sim. Previously released frameworks are deprecated
and we encourage users to follow our migration guides to transition over to Isaac Lab.


Is Isaac Lab a simulator?
-------------------------

Often, when people think of simulators, they think of various commonly available engines, such as
`MuJoCo`_, `Bullet`_, and `Flex`_. These engines are powerful and have been used in a number of
research projects. However, they are not designed to be a general purpose simulator for robotics.
Rather they are primarily physics engines that are used to simulate the dynamics of rigid and
deformable bodies. They are shipped with some basic rendering capabilities to visualize the
simulation and provide parsing capabilities of different scene description formats.

Various recent works combine these physics engines with different rendering engines to provide
a more complete simulation environment. They include APIs that allow reading and writing to the
physics and rendering engines. In some cases, they support ROS and hardware-in-the-loop simulation
for more robotic-specific applications. An example of these include `AirSim`_, `DoorGym`_, `ManiSkill`_,
`ThreeDWorld`_ and lastly, `Isaac Sim`_.

At its core, Isaac Lab is **not** a robotics simulator, but a framework for building robot learning
applications on top of Isaac Sim. An equivalent example of such a framework is `RoboSuite`_, which
is built on top of `MuJoCo`_ and is specific to fixed-base robots. Other examples include
`MuJoCo Playground`_ and `Isaac Gym`_ which use `MJX`_ and `PhysX`_ respectively. They
include a number of pre-built tasks with separated out stand-alone implementations for individual
tasks. While this is a good starting point (and often convenient), a lot of code
repetition occurs across different task implementations, which can reduce code-reuse for larger
projects and teams.

The main goal of Isaac Lab is to provide a unified framework for robot learning that includes
a variety of tooling and features that are required for robot learning, while being easy to
use and extend. It includes design patterns that simplify many of the common requirements for
robotics research. These include simulating sensors at different frequencies, connecting to different
teleoperation interfaces for data collection, switching action spaces for policy learning,
using Hydra for configuration management, supporting different learning libraries and more.
Isaac Lab supports designing tasks using *manager-based (modularized)* and *direct (single-script
similar to Isaac Gym)* patterns, leaving it up to the user to choose the best approach for their
use-case. For each of these patterns, Isaac Lab includes a number of pre-built tasks that can be
used for benchmarking and research.


Why should I use Isaac Lab?
---------------------------

Isaac Lab provides an open-sourced platform for the community to drive progress with consolidated efforts
toward designing benchmarks and robot learning systems as a joint initiative. This allows us to reuse
existing components and algorithms, and to build on top of each other's work. Doing so not only saves
time and effort, but also allows us to focus on the more important aspects of research. Our hope with
Isaac Lab is that it becomes the de-facto platform for robot learning research and an environment *zoo*
that leverages Isaac Sim. As the framework matures, we foresee it benefitting hugely from the latest
simulation developments (as part of internal developments at NVIDIA and collaborating partners)
and research in robotics.

We are already working with labs in universities and research institutions to integrate their work into Isaac Lab
and hope that others in the community will join us too in this effort. If you are interested in contributing
to Isaac Lab, please reach out to us.


.. _PhysX: https://developer.nvidia.com/physx-sdk
.. _Isaac Sim: https://developer.nvidia.com/isaac-sim
.. _Isaac Gym: https://developer.nvidia.com/isaac-gym
.. _IsaacGymEnvs: https://github.com/isaac-sim/IsaacGymEnvs
.. _OmniIsaacGymEnvs: https://github.com/isaac-sim/OmniIsaacGymEnvs
.. _Orbit: https://isaac-orbit.github.io/
.. _Isaac Automator: https://github.com/isaac-sim/IsaacAutomator
.. _AirSim: https://microsoft.github.io/AirSim/
.. _DoorGym: https://github.com/PSVL/DoorGym/
.. _ManiSkill: https://github.com/haosulab/ManiSkill
.. _ThreeDWorld: https://www.threedworld.org/
.. _RoboSuite: https://robosuite.ai/
.. _MuJoCo: https://mujoco.org/
.. _MuJoCo Playground: https://playground.mujoco.org/
.. _MJX: https://mujoco.readthedocs.io/en/stable/mjx.html
.. _Bullet: https://github.com/bulletphysics/bullet3
.. _Flex: https://developer.nvidia.com/flex