NVIDIA Isaac Lab
NVIDIA Isaac™ Lab is an open-source, unified framework for robot learning designed to help train robot policies.
Isaac Lab is developed on NVIDIA Isaac Sim™, providing high-fidelity physics simulation using NVIDIA® PhysX® and physically based NVIDIA RTX™ rendering. It 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 Isaac Lab ideal for building robot policies that cover a wide range of embodiments, including humanoid robots, manipulators, and autonomous mobile robots (AMRs).
This is a comprehensive framework for robot learning—from environment setup to policy training and deployment. You can customize and extend its capabilities with various physics engines, including NVIDIA PhysX, Warp, and MuJoCo.
NVIDIA Isaac Lab is also the foundational robot learning framework used by the NVIDIA Research and engineering teams developing NVIDIA Isaac GR00T.
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 Humanoids for Real-World Roles
Fourier was able to simulate real-world conditions, minimizing the time and cost of testing and maintenance.
Building a Large-Scale Dexterous Hand Dataset
Galbot built a simulation test environment for dexterous hand grasping models with Isaac Lab and Isaac Sim.
Quadruped Locomotion Policy Training
Learn how Boston Dynamics trains the Spot quadruped locomotion policy using Isaac Lab.
Sample Code: Teaching a Robot to Climb
See Lightweight Berkeley Humanoid training in Isaac Lab to quickly climb the staircase. The training code is available on GitHub.