Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning

This research addresses the performance gap between regression and generative policies in robot learning by analyzing action-prediction residuals. The authors introduce HT-Policies, a heteroscedastic Student-t action regression method, which achieves competitive success rates in both simulation and real-robot evaluations, offering an efficient alternative to generative approaches.