NVIDIA Isaac Lab
NVIDIA Isaac™ Lab is an open-source, unified framework for robot learning designed to help train robot policies.
It’s built on NVIDIA Isaac Sim™, delivering high-fidelity physics simulation using NVIDIA PhysX® and physically based rendering with NVIDIA RTX™. This bridges the gap between high-fidelity simulation and perception-based robot training, helping developers and researchers build more robots, more efficiently.
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 its capabilities with a variety of physics engines, such as PhysX, NVIDIA Warp, and MuJoCo.
Isaac Lab is also the foundational robot learning framework of the NVIDIA Isaac GR00T platform.
Introductory Resources
A Simulation Framework for Multi-Modal Robot Learning
See how Isaac Lab’s combination of advanced simulation capabilities and data-center scale execution will help unlock breakthroughs in robotics research.
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
The GPU-accelerated PhysX version provides accurate, high-fidelity physics simulations. This include support for deformables that allows for more realistic modeling of robot interactions with the environment.