Robot Learning

Overfitting

Overfitting is the phenomenon in which a model fits its training data, including noise and incidental correlations, so closely that performance degrades on new data. In robot learning it appears as policies that succeed on training objects, positions, and lighting but fail under small variations, or that latch onto spurious cues such as backgrounds. Remedies include more diverse data, augmentation, regularization, and early stopping.

Why it matters for physical AI

Robot datasets are small and homogeneous compared with web corpora, making overfitting to a lab's specific scenes one of the most common reasons policies fail to transfer to the field.