Robot Learning

Weight Initialization

Weight initialization is the choice of initial parameter values for a neural network before training, which governs early signal propagation and gradient scale. Schemes such as Xavier (Glorot) and Kaiming (He) initialization set layer variances to keep activations stable in deep networks. In robot learning the term also covers warm-starting: initializing a policy from pretrained vision or vision-language weights rather than from random values.

Why it matters for physical AI

Initialization from pretrained foundation-model weights, rather than from scratch, is the single biggest lever behind sample-efficient robot policies, letting small demonstration datasets fine-tune capabilities learned from web-scale corpora.