Simulation

Terrain Generation

Terrain generation is the procedural creation of simulated ground geometry, such as slopes, stairs, steps, gaps, rough noise fields, and stepping stones, used to train and evaluate locomotion policies. Modern reinforcement learning pipelines pair generated terrains with curricula that escalate difficulty as the policy improves, a recipe central to massively parallel training in simulators like Isaac Gym. Terrain diversity in simulation is a primary driver of real-world robustness.

Why it matters for physical AI

Locomotion policies generalize roughly to the distribution of terrain they trained on, so scalable procedural generation directly determines whether quadrupeds and humanoids can handle stairs, rubble, and slopes outside the lab.