Robot Learning

Action Space Normalization

Action space normalization is the rescaling of robot action values into a standard numeric range, commonly minus one to one or zero mean and unit variance, using statistics computed over the training dataset. Normalization equalizes the loss contribution of dimensions with very different physical units, such as gripper width versus joint radians, and is required when actions are discretized into fixed bins for token-based policies. Statistics must be stored and inverted exactly at deployment.

Why it matters for physical AI

Mismatched normalization statistics between training and inference are a notorious silent failure mode when fine-tuning or deploying pretrained policies, producing erratic motions that resemble hardware faults.