Overview#
Isaac Lab is a unified and modular framework for robot learning that aims to simplify common workflows in robotics research (such as reinforcement learning, learning from demonstrations, and motion planning). It is built upon NVIDIA Isaac Sim to leverage the latest simulation capabilities for photo-realistic scenes, and fast and efficient simulation.
The core objectives of the framework are:
Modularity: Easily customize and add new environments, robots, and sensors.
Agility: Adapt to the changing needs of the community.
Openness: Remain open-sourced to allow the community to contribute and extend the framework.
Battery-included: Include a number of environments, sensors, and tasks that are ready to use.
Key features available in Isaac Lab include fast and accurate physics simulation provided by PhysX, tiled rendering APIs for vectorized rendering, domain randomization for improving robustness and adaptability, and support for running in the cloud.
Additionally, Isaac Lab provides over 26 environments, and we are actively working on adding more environments to the list. These include classic control tasks, fixed-arm and dexterous manipulation tasks, legged locomotion tasks, and navigation tasks. A complete list is available in the environments section.
The framework also includes over 16 robots. If you are looking to add a new robot, please refer to the How-to Guides section. The current list of robots includes:
Classic Cartpole, Humanoid, Ant
Fixed-Arm and Hands: UR10, Franka, Allegro, Shadow Hand
Quadrupeds: Anybotics Anymal-B, Anymal-C, Anymal-D, Unitree A1, Unitree Go1, Unitree Go2, Boston Dynamics Spot
Humanoids: Unitree H1, Unitree G1
Quadcopter: Crazyflie
For more information about the framework, please refer to the paper [MYY+23]. For clarifications on NVIDIA Isaac ecosystem, please check out the Frequently Asked Questions section.
License#
The Isaac Lab framework is open-sourced under the BSD-3-Clause license. Please refer to License for more details.
Acknowledgement#
Isaac Lab development initiated from the Orbit framework. We would appreciate if you would cite it in academic publications as well:
@article{mittal2023orbit,
author={Mittal, Mayank and Yu, Calvin and Yu, Qinxi and Liu, Jingzhou and Rudin, Nikita and Hoeller, David and Yuan, Jia Lin and Singh, Ritvik and Guo, Yunrong and Mazhar, Hammad and Mandlekar, Ajay and Babich, Buck and State, Gavriel and Hutter, Marco and Garg, Animesh},
journal={IEEE Robotics and Automation Letters},
title={Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments},
year={2023},
volume={8},
number={6},
pages={3740-3747},
doi={10.1109/LRA.2023.3270034}
}
Table of Contents#
Getting Started
Overview
Features
Resources
Migration Guides
Source API
References