👀 Overview
Welcome to the first official release of Isaac Lab!
Building upon the foundation of the Orbit framework, we have integrated the RL environment designing workflow from OmniIsaacGymEnvs. This allows users to choose a suitable task-design approach for their applications.
While we maintain backward compatibility with Isaac Sim 2023.1.1, we highly recommend using Isaac Lab with Isaac Sim 4.0.0 version for the latest features and improvements.
Full Changelog: v0.3.1...v1.0.0
✨ New Features
- Integrated CI/CD pipeline, which is triggered on pull requests and publishes the results publicly
- Extended support for Windows OS platforms
- Added tiled rendered based Camera sensor implementation. This provides optimized RGB-D rendering throughputs of up to 10k frames per second.
- Added support for multi-GPU and multi-node training for the RL-Games library
- Integrated APIs for environment designing (direct workflow) without relying on managers
- Added implementation of delayed PD actuator model
- Added various new learning environments :
- Cartpole balancing using images
- Shadow hand cube reorientation
- Boston Dynamics Spot locomotion
- Unitree H1 and G1 locomotion
- ANYmal-C navigation
- Quadcopter target reaching
🔧 Improvements
- Reduced start-up time for scripts (inherited from Isaac Sim 4.0 improvements)
- Added lazy buffer implementation for rigid object and articulation data. Instead of updating all the quantities at every step call, the lazy buffers are updated only when the user queries them
- Added SKRL support to more environments
💔 Breaking Change
For users coming from Orbit, this release brings certain breaking changes. Please check the migration guide for more information.
✈️ Migration Guide
Please find detailed migration guides as follows:
🤗 New Contributors
- @abizovnuralem made their first contribution in #452
- @eltociear made their first contribution in #460
- @zoctipus made their first contribution in #486
- @JunghwanRo made their first contribution in #497
🌟 Acknowledgements
We wholeheartedly thank @Mayankm96, @kellyguo11 and the Boston Dynamics AI Institute for their significant contributions to the framework.
