Simulation

Dynamics Randomization

Dynamics Randomization is the randomization of physical simulation parameters, including link masses, friction coefficients, motor strength, damping, latency, and sensor noise, during policy training so the learned controller is robust to the true system's unknown dynamics. Peng et al. (2018) showed it enables sim-to-real transfer of manipulation policies without real-world fine-tuning, and it is standard in learned quadruped and humanoid locomotion pipelines, sometimes paired with online system identification.

Why it matters for physical AI

Contact dynamics and actuator behavior are the hardest quantities to simulate faithfully, so randomizing over them is often the difference between a policy that walks on hardware and one that falls immediately.