Linux / amd64
Isaac Lab is a unified and modular framework for robot learning that aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and motion planning). It is built upon NVIDIA Isaac Sim to leverage the latest simulation capabilities for photo-realistic scenes and fast and accurate simulation.
Please refer to our documentation page to learn more about the installation steps, features, tutorials, and how to set up your project with Isaac Lab.
Isaac Lab 2.2 brings major upgrades across simulation capabilities, tooling, and developer experience. It expands support for advanced physics features, new environments, and improved testing and documentation workflows. This release includes full compatibility with Isaac Sim 5.0 as well as backwards compatibility with Isaac Sim 4.5.
- Enhanced Physics Support: Updated joint friction modeling using the latest PhysX APIs, added support for spatial tendons, and improved surface gripper interactions.
- New Environments for Imitation Learning: Introduction of two new GR1 mimic environments, with domain randomization and visual robustness evaluation, and improved pick-and-place tasks.
- New Contact-Rich Manipulation Tasks: Integration of FORGE and AutoMate tasks for learning fine-grained contact interactions in simulation.
- Teleoperation Improvements: Teleoperation tools have been enhanced with configurable parameters and CloudXR runtime updates, including head tracking and hand tracking.
- Performance & Usability Improvements: Includes support for Stage in Memory and Cloning in Fabric for faster scene creation, new OVD recorder for large-scene GPU-based animation recording, and FSD (Fabric Scene Delegate) for improved rendering speed.
- Improved Documentation: The documentation has been extended and updated to cover new features, resolve common issues, and streamline setup, including updates to teleop system requirements, VS Code integration, and Python environment management.
Detailed Release Notes can be found HERE
By pulling and using the container, you accept the terms and conditions of the NVIDIA Software License Agreement.
Sources for OSS packages used in this container can be found here.