Overfitting

When a model performs well on training data but poorly on unseen data, having memorized training-specific patterns rather than learning generalizable features. Overfitting is a critical concern in robot learning where datasets are small. Countermeasures include data augmentation, dropout, weight decay, early stopping, and using pre-trained encoders.