Loss Function

A mathematical function that quantifies the discrepancy between model predictions and ground truth targets. The loss is minimized during training. Common losses in robot learning: MSE (continuous actions), cross-entropy (discrete actions), diffusion loss (denoising score matching), and contrastive loss (representation learning). The choice of loss function directly shapes what the model learns.