isaac-sim/IsaacLab
Unified framework for robot learning with multi-physics/renderer support
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 14 days ago, with 285 contributors on GitHub.
Peaked at #80 in the weekly ranking the week of Oct 12, its best in the past year.
Use Cases & Benefits
- Provides a GPU-accelerated framework for unified robot learning with multi-physics and sensor simulation support.
- Enables fast, accurate sim-to-real robotics research by combining physics, sensors, and reinforcement learning in one platform.
- Use for developing and testing reinforcement learning algorithms on diverse robotic platforms with realistic sensor feedback.
- Use for simulating multi-agent robotic environments with complex physics for collaborative task learning.
- Use for accelerating robotics research workflows locally or distributed in the cloud with NVIDIA Isaac Sim integration.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Other
GitHub could not identify this license; read the LICENSE file before depending on it.
Community files
- README: present
- Contributing guide: present
Watch-outs
- License not identified by GitHub; read the LICENSE file before depending on it.
General guidance from repository metadata, not legal advice. How we score
From the README
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.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
4.0K+190
Open issues
404-340
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| anthropics/skills | 187.8K | +2.9K | 15 | — |
| DuarteSantos8/openGym | 19.7K | +2.4K | 60 | 2 days ago |
| shiyu-coder/Kronos | 47.0K | +2.3K | 16 | — |
| NationalSecurityAgency/ghidra | 98.2K | +2.3K | 64 | 14 days ago |
| microsoft/VibeVoice | 56.0K | +1.1K | 29 | — |
| isaac-sim/IsaacLab | 8.6K | — | 59 | 14 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
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