Simulation

Sim-to-Real Transfer

Sim-to-real transfer is the practice of training robot policies in simulation and deploying them on physical hardware, exploiting simulation's speed, safety, and parallelism while confronting the mismatch between simulated and real dynamics and sensing. Standard techniques include domain randomization (Tobin et al., 2017), system identification, teacher-student distillation from privileged simulator state, and real-world fine-tuning. Landmark successes include OpenAI's dexterous in-hand manipulation and learned locomotion controllers for ANYmal and other legged robots.

Why it matters for physical AI

Simulation offers effectively unlimited experience at near-zero marginal cost; transfer techniques determine how much of that experience survives contact with reality, especially for locomotion and dexterity.