Robot Learning

Heteroscedastic Uncertainty

Heteroscedastic uncertainty is input-dependent noise, where the variance of a predicted quantity changes across the input space rather than remaining constant (homoscedastic). Models capture it by predicting a variance alongside each mean output and training with a negative log-likelihood loss, letting the network report higher aleatoric uncertainty for ambiguous observations such as occluded objects.

Why it matters for physical AI

Policies that know when their predictions are unreliable can slow down, gather information, or request help, which is central to safe autonomy and to deciding when human intervention is needed.