Isaac Lab Ecosystem

Isaac Lab is built on top of Isaac Sim to provide a unified and flexible framework for robot learning that exploits latest simulation technologies. It is designed to be modular and extensible, and aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and motion planning). While it includes some pre-built environments, sensors, and tasks, its main goal is to provide an open-sourced, unified, and easy-to-use interface for developing and testing custom environments and robot learning algorithms.

Working with Isaac Lab requires the installation of Isaac Sim, which is packaged with core robotics tools that Isaac Lab depends on, including URDF and MJCF importers, simulation managers, and ROS features. Isaac Sim also builds on top of the Nvidia Omniverse platform, leveraging advanced physics simulation from PhysX, photorealistic rendering technologies, and Universal Scene Description (USD) for scene creation.

Isaac Lab not only inherits the capabilities of Isaac Sim, but also adds a number of new features that pertain to robot learning research. For example, including actuator dynamics in the simulation, procedural terrain generation, and support to collect data from human demonstrations.

For a detailed explanation of Nvidia’s development journey of robot learning frameworks, please visit the FAQ page.