Robot Learning

Uncertainty Quantification

Uncertainty quantification is the estimation of how confident a model's predictions are, conventionally divided into aleatoric uncertainty, inherent in noisy data, and epistemic uncertainty, stemming from limited knowledge that more data could reduce. Practical methods include deep ensembles, Monte Carlo dropout, Bayesian neural networks, and conformal prediction, which wraps any model with distribution-free coverage guarantees. Well-calibrated uncertainty lets a system know when it does not know.

Why it matters for physical AI

Robots acting on wrong-but-confident predictions cause physical damage, so calibrated uncertainty is the trigger for safe fallbacks, human handoffs, and targeted data collection in deployed learning systems.