Reinforcement Learning · advanced · concept 129 of 176
Sim-to-Real Transfer
Training RL agents in simulation and deploying them in the real world. Overcomes the cost and danger of real-world training. Domain randomization helps bridge the gap between sim and reality. The wider framing is physical AI: models trained largely in simulated worlds and digital twins before they ever touch hardware.
Key terms
Domain randomizationSimulationReality gapDigital twinPhysical AI
Learn these first
Videos
▶ Bridging the Sim-to-Real Gap for Accelerated Robot Training ↗
NVIDIA · YouTube
Guides and articles
Domain Randomization for Sim2Real Transfer | Lil'Log ↗
Lil'Log (OpenAI researcher)
Courses, papers, and more