Control

Closed-Loop Control

Closed-loop control is a control strategy in which the system's output is measured and fed back to compute corrective commands, continuously driving the error between desired and actual state toward zero. Unlike open-loop execution, feedback rejects disturbances, model errors, and drift, at the cost of requiring sensing and raising stability considerations. PID, impedance, and model predictive controllers are all closed-loop, and the term also describes policies that re-observe before every action.

Why it matters for physical AI

Reactive, closed-loop visuomotor policies distinguish modern robot learning from plan-then-execute pipelines: re-observing at every step lets policies correct slips, moved objects, and perception errors during execution.