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 NVIDIA PhysX, Warp, and MuJoCo.
NVIDIA Isaac Lab is also the foundational robot learning framework of the NVIDIA Isaac GR00T platform.
Teach Robots New Skills
Create more robust, efficient, and capable robotic systems by teaching robots new skills in simulation. Robot learning in simulation helps reduce the need for extensive hardware expenses and time-intensive policy training iterations.
Training in Isaac Lab using Imitation Learning
Fast-track humanoid motion policy learning through synthetically generated motion data.
Open-Source Physics Engine for Robotics Simulation
Built on NVIDIA Warp, Newton is optimized for robotics and compatible with learning frameworks such as MuJoCo Playground or NVIDIA Isaac Lab.
Read BlogIsaac Lab Courses
Explore the fundamentals of robot learning and Isaac Lab, a powerful tool for developing robotic applications.
Take the Introductory CourseIsaac Lab Office Hours
Stay informed with our recurring office hours that cover in-depth topics with experts answering questions about Isaac Lab.
Watch the LivestreamsKey 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. These include support for deformables that allows for more realistic modeling of robot interactions with the environment.
