Simulation

Digital Twin

Digital Twin is a virtual replica of a physical asset, process, or environment that is kept synchronized with its real counterpart through sensor data and state updates. In robotics, digital twins span calibrated simulation models of individual robots to full work cells and warehouses, supporting virtual commissioning, what-if analysis, monitoring, and policy training. Fidelity requirements differ by use: control-grade twins demand accurate dynamics, while planning twins prioritize geometry and throughput statistics.

Why it matters for physical AI

High-fidelity twins let operators validate robot behavior and train or evaluate policies against a faithful stand-in before touching production, shrinking the sim-to-real gap and de-risking deployment changes.