Simulation

Domain Gap

Domain Gap is the mismatch between the data distribution a model was trained on and the distribution it encounters at deployment, causing performance degradation. In robotics the most studied instance is the sim-to-real gap, spanning visual discrepancies in rendering, lighting, and textures as well as physical discrepancies in contact dynamics, actuation latency, and sensor noise. Gaps also arise between labs and deployment sites, and between embodiments.

Why it matters for physical AI

The size of the domain gap determines how much simulation training, offline data, and cross-robot data actually transfer to a target platform, making gap measurement and mitigation central to scaling physical AI economically.