Perception

Calibration

Calibration is the process of estimating the parameters that relate a robot's sensors, actuators, and kinematic model to physical reality, such as camera intrinsics, sensor-to-robot transforms, joint offsets, and force sensor biases. Accurate calibration ensures that measurements from different sensors can be fused in a common reference frame and that commanded motions match real-world outcomes. Calibration typically drifts over time due to wear, thermal effects, and mechanical shocks, requiring periodic re-estimation.

Why it matters for physical AI

Learned policies trained on one robot's data implicitly bake in its calibration; miscalibrated cameras or kinematics shift the observation distribution and silently degrade policy performance across a fleet.