Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning
Learning from demonstration often uses generative models that outperform direct action regression methods, but this gap is explained by how they handle prediction errors. The paper found that standard MSE regression disproportionately focuses on large errors, while generative methods distribute gradients better. It introduces Heteroscedastic Student-t action regression (HT-Policies) to model input-dependent scales and heavy tails in residuals, closing the performance gap.