Control

System Identification

System identification is the construction of mathematical models of a dynamical system from measured input-output data, estimating parameters such as link masses, inertias, friction coefficients, and actuator characteristics, or fitting non-parametric and learned dynamics models. In robotics it calibrates model-based controllers and narrows the sim-to-real gap by tuning simulator parameters to match hardware — the real-to-sim direction — with excitation trajectories designed to make parameters observable.

Why it matters for physical AI

Accurate dynamics models multiply the value of simulation and model-based control alike; identification is frequently the cheapest single step toward transferring a simulation-trained policy onto hardware.