Robot Learning

Noise Injection

Noise injection is the deliberate addition of perturbations to actions, observations, or system parameters during training or data collection. In imitation learning, methods such as DART inject noise into expert demonstrations so the dataset covers states near, but off, the expert trajectory, mitigating covariate shift. In reinforcement learning and sim-to-real transfer, action and dynamics noise improve exploration and robustness.

Why it matters for physical AI

Policies trained only on clean expert states fail once small errors compound; training with realistic perturbations produces controllers that recover instead of drifting further off-distribution.