This branch targets Isaac Sim 6.2. For installation instructions, see the Isaac Lab documentation.

Note that this branch is currently under active development and may experience breaking changes or error messages. Performance issues and regressions may also be observed in some use cases.

Isaac Lab is a GPU-accelerated, open-source framework designed to unify and simplify robotics research workflows, such as reinforcement learning, imitation learning, and motion planning. Built on NVIDIA Isaac Sim, it combines fast and accurate physics and sensor simulation, making it an ideal choice for sim-to-real transfer in robotics.

Isaac Lab provides developers with a range of essential features for accurate sensor simulation, such as RTX-based cameras, LIDAR, or contact sensors. The framework's GPU acceleration enables users to run complex simulations and computations faster, which is key for iterative processes like reinforcement learning and data-intensive tasks. Moreover, Isaac Lab can run locally or be distributed across the cloud, offering flexibility for large-scale deployments.

A detailed description of Isaac Lab can be found in our arXiv paper.

Isaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning:

• Robots: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with more than 16 commonly available models.
• Environments: Ready-to-train implementations of more than 30 environments, which can be trained with popular reinforcement learning frameworks such as RSL RL, SKRL, RL Games, or Stable Baselines. We also support multi-agent reinforcement learning.
• Physics: Rigid bodies, articulated systems, deformable objects
• Sensors: RGB/depth/segmentation cameras, camera annotations, IMU, contact sensors, ray casters.

Our documentation page provides everything you need to get started, including detailed tutorials and step-by-step guides. Follow these links to learn more about: