Robot Learning

Normalization Statistics

Normalization statistics are the per-dimension values, typically means and standard deviations or quantile bounds, used to scale observations and actions before training a policy and to unscale predicted actions at inference time. When training on mixed datasets such as Open X-Embodiment, statistics are often computed per dataset or per embodiment. Checkpoints must ship with the exact statistics used during training.

Why it matters for physical AI

Mismatched normalization is a common silent failure when fine-tuning or deploying pretrained robot policies, producing wrongly scaled actions despite a correctly loaded model.