Robot Learning

Dropout

Dropout is a neural network regularization technique that randomly zeroes a fraction of unit activations during each training step, preventing co-adaptation of features and reducing overfitting, introduced by Srivastava, Hinton, and colleagues in 2014. At inference, all units are active with appropriately scaled weights. Monte Carlo dropout, which keeps sampling at test time, provides an inexpensive approximation of Bayesian predictive uncertainty.

Why it matters for physical AI

Robot datasets are small relative to model capacity, making regularization consequential for policy generalization. Monte Carlo dropout additionally offers a cheap uncertainty signal usable for failure detection and human handover triggers.