NVIDIA Isaac Lab
NVIDIA Isaac™ Lab is an open source, GPU-accelerated, modular framework for robot learning designed to train robot policies at scale.
Built on Omniverse™ libraries, its modular architecture allows developers to choose their physics engine, camera sensors, and rendering pipeline. This flexibility enables training workflows across a wider range of compute, bridging the gap between high-fidelity simulation and scalable robot training.
How Isaac Lab Works
Isaac Lab’s modular architecture and NVIDIA GPU-based parallelization make it ideal for building robot policies that cover a wide range of embodiments — including humanoid robots, manipulators, and autonomous mobile robots (AMRs).
This gives you a comprehensive framework for robot learning, covering everything from environment setup to policy training. It supports both imitation and reinforcement learning methods. Plus, you can further customize and extend Isaac Lab capabilities with a variety of physics engines, such as Newton, PhysX®, NVIDIA Warp, and MuJoCo.
Isaac Lab is also the foundational robot learning framework of the NVIDIA Isaac GR00T platform.
Introductory Resources
Isaac Lab Whitepaper
See how the combination of advanced simulation capabilities and data center scale execution unlock breakthroughs in robotics research.
NVIDIA Isaac Lab-Arena
Built on Isaac Lab, Isaac Lab-Arena is an open-source framework for scalable policy evaluation in simulation.
Isaac Lab Courses
Explore the fundamentals of robot learning and Isaac Lab, a powerful tool for developing robotic applications.
Isaac Lab Office Hours
Stay informed with our recurring office hours that cover in-depth topics with experts answering questions about Isaac Lab.
Key Features
Flexible Robot Learning
Customize workflows with robot training environments, tasks, learning techniques, and the ability to integrate custom libraries (e.g., skrl, RLLib, rl_games, and more).
Reduced Sim-to-Real Gap
Train policies with higher-fidelity physics using Newton, PhysX, or any physics engine, enabling stronger contact modeling and more realistic interactions for a broader class of tasks.
