Control

Trajectory Tracking

Trajectory tracking is the control problem of making a robot follow a time-parameterized reference trajectory, driving the error between actual and reference states toward zero despite disturbances and model error. Standard solutions include PID and computed-torque control for manipulators, and feedback linearization or nonlinear MPC for mobile robots and quadrotors, with tracking error metrics used to quantify performance. It contrasts with path following, where timing along the route is free.

Why it matters for physical AI

Learned policies typically emit setpoints or short reference segments that low-level tracking controllers execute, so tracking bandwidth and accuracy bound the precision of the entire autonomy stack on real hardware.