Robot Learning

Loss Function

A loss function is the scalar objective that quantifies the discrepancy between a model's predictions and desired outputs, whose gradient drives parameter updates during training. Robot learning employs mean squared error or L1 losses for continuous action regression, cross-entropy for tokenized actions in models like RT-2, denoising objectives for diffusion policies, and temporal-difference losses in reinforcement learning, often combined with auxiliary representation losses.

Why it matters for physical AI

The loss encodes what a policy is actually optimized to do; mismatches between a convenient training objective and true task success are a persistent source of gap between benchmark scores and real-world reliability.