What Is Isaac Lab?
Isaac Lab is NVIDIA's GPU-accelerated robot simulation and learning framework, built on Isaac Sim 4.0 (Omniverse-based). It supports up to 4,000+ parallel environments on a single A100, enabling RL policy training that would take weeks on CPU-based simulators to complete in hours. It's MIT licensed, which means no restrictions on commercial use — a significant advantage over some competing platforms.
Isaac Lab is not primarily a physics simulator — it delegates simulation to Isaac Sim's PhysX backend. It's a training framework: it manages environment vectorization, observation/action space definitions, reward computation, and the training loop interface with standard RL libraries (RSL-RL, RL-Games, Stable Baselines3).
Installation
Prerequisites: Ubuntu 22.04, CUDA 12.1+, NVIDIA driver ≥530. Minimum GPU: RTX 3090 (24GB VRAM) for small-scale experiments; A100 80GB for production training runs.
- Step 1: Install Isaac Sim 4.0 via pip: pip install isaacsim==4.0.0 --extra-index-url https://pypi.nvidia.com. This pulls approximately 15GB of packages. Expect 20–30 minutes on a fast connection.
- Step 2: Clone Isaac Lab and run the installer: git clone https://github.com/isaac-sim/IsaacLab && cd IsaacLab && ./isaaclab.sh --install. This creates a virtual environment with all dependencies and runs a verification test.
- Step 3: Verify with a headless test: ./isaaclab.sh -p source/standalone/tutorials/00_sim/create_empty.py --headless. Total setup time: approximately 30–45 minutes on a fresh machine with fast internet.
Built-In Environments Overview
| Category | Environment | Robot | Task |
|---|---|---|---|
| Manipulation | Isaac-Reach-Franka-v0 | Franka Research 3 | End-effector reach to target pose |
| Manipulation | Isaac-Lift-Cube-Franka-v0 | Franka Research 3 | Grasp and lift cube |
| Manipulation | Isaac-Open-Drawer-Franka-v0 | Franka Research 3 | Pull drawer open to target position |
| Locomotion | Isaac-Velocity-Rough-Anymal-C-v0 | ANYmal C | Velocity tracking on rough terrain |
| Locomotion | Isaac-Walk-Unitree-G1-v0 | Unitree G1 | Forward walking velocity tracking |
| Navigation | Isaac-Navigation-Flat-v0 | Generic diff-drive | Goal-conditioned navigation |
RL Training Walkthrough
Training a lift-cube policy from scratch with PPO on 2,048 parallel environments:
- Command: ./isaaclab.sh -p source/standalone/workflows/rsl_rl/train.py --task Isaac-Lift-Cube-Franka-v0 --num_envs 2048 --headless
- Expected runtime: Approximately 8 hours on a single A100 80GB to reach 80% success rate. On RTX 4090 (24GB), reduce num_envs to 512 and expect 20–30 hours.
- What to watch: Monitor episode_rew_mean (should increase monotonically after 500K steps), success_rate (target >70% before exporting), and value_loss (should stabilize; if diverging, reduce learning rate).
Key Hyperparameters
| Parameter | Default Value | Effect of Increasing |
|---|---|---|
| num_envs | 2048 | Better gradient estimates, higher GPU memory use |
| learning_rate | 1e-3 | Faster early learning, instability risk |
| gamma (discount) | 0.99 | Longer horizon planning, slower propagation |
| clip_param (PPO) | 0.2 | Less conservative updates, instability risk |
| num_mini_batches | 4 | Smaller batches, noisier gradients |
Sim-to-Real Export
Isaac Lab supports ONNX export for trained policies: ./isaaclab.sh -p source/standalone/workflows/rsl_rl/export.py --task Isaac-Lift-Cube-Franka-v0 --checkpoint path/to/model.pth. The exported ONNX model can be converted to TensorRT for deployment on Jetson AGX Orin using TensorRT's trtexec tool.
Typical inference latency on Jetson AGX Orin after TensorRT conversion: 5–15ms for policies up to 10M parameters — well within real-time control requirements. For policies with image observations (ResNet encoder + MLP policy), expect 30–80ms depending on image resolution.
Performance Comparison
| Simulator | Max Parallel Envs (A100 80GB) | Physics Accuracy | RL Training Speed |
|---|---|---|---|
| Isaac Lab (PhysX) | 4,000+ | Medium-high | Fastest |
| MuJoCo (CPU) | 50–100 | High (contact) | Slowest |
| PyBullet | 10–20 | Medium | Slow |
| IsaacGym (legacy) | 8,192 | Medium | Fast (deprecated) |
SVRC provides pre-configured Isaac Lab environments for custom manipulation tasks as part of our simulation services. See the RL environment documentation for available configurations.