Robot Learning

Batch Normalization

Batch normalization is a neural network layer, introduced by Ioffe and Szegedy in 2015, that normalizes activations using the mean and variance of the current mini-batch, then rescales them with learned parameters, stabilizing and accelerating training of deep networks. Its dependence on batch statistics complicates recurrent models, small batches, and reinforcement learning, so transformers and many policy networks favor layer normalization or group normalization instead.

Why it matters for physical AI

Normalization layers whose behavior differs between training and inference are a recurring source of sim-to-real and deployment bugs in visuomotor policies, motivating the field's drift toward batch-independent alternatives.