Cybernetic learning

Cybernetic learning refers to feedback-driven adaptation, where a robot uses continuous sensing to compare outcomes with goals and adjust behavior in real time. Unlike static policies or offline training, it embeds control within a loop that monitors performance and corrects errors as they emerge.

In humanoid systems, cybernetic learning connects low-level reflexes with high-level objectives. It enables balance, compliance, or energy optimization by aligning perception, control, and actuation in a closed loop. This approach is used in fall recovery, adaptive locomotion, and joint-level correction, where decisions must respond to shifting terrain, payloads, or intent.

Cybernetic learning draws from classical control theory but extends it through modern policy updates, real-time inference, and onboard feedback. It turns learning into a regulatory function, giving robots the capacity to self-correct.