Executive Summary
A practical guide to overcoming the sim-to-real gap using GPU-accelerated environments in Isaac Sim and MuJoCo with systematic domain randomization.
Key Takeaways
- ✓The Causes of the Reality Gap — covered in depth with practical examples, formulas, and code.
- ✓Systematic Domain Randomization (DR) — covered in depth with practical examples, formulas, and code.
Sim-to-Real Transfer: Bridging Simulation and Physical Reality
Training reinforcement learning policies on physical robots causes gear wear, motor overheating, and room destruction. Simulators allow collecting 10,000 hours of robot experience in 20 minutes on GPU clusters.
However, policies that achieve 100% success in simulation frequently fail on real hardware. This is the Sim-to-Real Gap.
#1. The Causes of the Reality Gap
#2. Systematic Domain Randomization (DR)
During training across 4,096 parallel environments in NVIDIA Isaac Lab, we systematically randomize every physical parameter at each reset:
By training the policy to succeed across all variations, the physical world simply appears as another randomized instance of the simulator!