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NVIDIA Isaac Lab
NVIDIA Isaac™ Lab is an open-source, agent-ready modular framework for training and refining robot policies using reinforcement learning and imitation learning.
This unified framework lets you rapidly prototype policies in lightweight simulation environments before deploying to production systems.
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, NVIDIA® PhysX®, NVIDIA Warp, and MuJoCo.
Explore our reference workflow that packages supported models, libraries, frameworks, and a task into a single reproducible workflow for a humanoid robot.
Choose the physics, rendering, and visualization backends that best fit your robot learning workload. Train and evaluate policies across simulation backends to build robustness for real-world transfer.
Richer Physics with Newton
Train complex robot skills with Newton. Mix and couple MJWarp, MPM, VBD, and Kamino solvers to simulate articulated rigid bodies, particles, deformables, and closed-chain mechanisms.
Scale Training Anywhere
Scale training from a single GPU to an entire cluster. Massively parallel simulation supports demanding perception-in-the-loop workloads and diverse experiences for more generalizable robot skills.
RL Post-training for Robot Foundation Models
Refine pretrained robot foundation models such as Isaac GR00T with Isaac RL Post Training (RLPT). Use reinforcement learning and parallel simulation to practice at scale and improve existing skills.
Multiple Rendering Options
Choose rendering options from high throughput to photorealism with Newton and OVRTX. Bring reconstructed scenes into simulation with 3D Gaussian rendering or turn simulated depth into photorealistic video with NVIDIA Cosmos 3-Nano Transfer.
Simulation to Deployment
Connect simulation training to real-world deployment with LEAPP. Package a policy’s compute graph and metadata into a portable deployment bundle to bring learned robot skills onto real hardware.
Get Started With Isaac Lab
Download
Get started with the latest version of Isaac Lab by following the installation guides on GitHub.
View more tutorials and how-to guides in the documentation.
Accelerate Robot Learning
Choose from reinforcement learning and imitation learning to train AI robots. Easily bring your custom libraries and use the direct agent-environment or hierarchical-manager development workflows.
Tiled rendering reduces rendering time by consolidating input from multiple cameras into a single large image. With a streamlined API for handling vision data, the rendered output directly serves as observational data for simulation learning.
Scale up training of cross-embodied models for complex reinforcement learning environments across multiple GPUs and nodes. Deploy locally and on the cloud (AWS, GCP, Azure, and Alibaba Cloud) by integrating with NVIDIA OSMO.
Accurate High-Fidelity Physics Simulation and Rendering in Omniverse
Tap into the latest GPU-accelerated PhysX version through Isaac Lab, including support for deformables, ensuring quick and accurate physics simulations augmented by domain randomizations.
A GPU Accelerated Simulation Framework For Multi-Modal Robot Learning
NVIDIA Isaac Lab
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning.
Tech Blog
Streamline Robot Learning with Whole-Body Control and Enhanced Teleoperation in NVIDIA Isaac Lab 2.3
NVIDIA Isaac Lab
The latest version of Isaac Lab 2.3, in early developer preview, improves humanoid robot capabilities with advanced whole-body control, enhanced imitation learning, and better locomotion.
Tech Blog
Quadruped Robot Locomotion and Multiphysics Simulation Using Newton in NVIDIA Isaac Lab
NVIDIA Isaac Lab
Walks through how to train a quadruped robot to move from one point to another and how to set up a multiphysics simulation with an industrial manipulator to fold clothes. This tutorial uses Newton within NVIDIA Isaac Lab.
Ecosystem
Our industry partners and collaborators are integrating NVIDIA Isaac Lab-Arena into their platforms and solutions.
Isaac Sim™ is a comprehensive robotics simulation platform built on NVIDIA Omniverse that provides high-fidelity simulation with advanced physics and photorealistic rendering. It focuses on synthetic data generation (SDG) and testing and validation (SIL/HIL) and is a reference template for custom robotics simulators.
In contrast, Isaac Lab is a lightweight, open-source framework built on top of Isaac Sim, specifically optimized for robot learning workflows and designed to simplify common tasks in robotics research like reinforcement learning, imitation learning, and motion planning.
If you’re an existing NVIDIA Isaac Gym (predecessor of Isaac Lab) user, we recommend migrating to Isaac Lab to ensure you have access to the latest advancements in robot learning and a powerful development environment to accelerate your robot training efforts. Check out the migration guide from Isaac Gym environments to Isaac Lab.
Yes, Isaac Lab and MuJoCo are complementary. MuJoCo's ease of use and lightweight design allow for rapid prototyping and deployment of policies and Isaac Lab can complement it when you want to create more complex scenes, scaling massively parallel environments with GPUs and high-fidelity sensor simulations with RTX rendering. NVIDIA and MuJoCo are actively exploring advancing technical collaborations, stay tuned for future announcements.
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