In　this　paper,　we　present　a　new　robust　estimation　procedure　for　semi-functional　linear　regression　models　by　using　exponential　squared　loss.　The　outstanding　advantage　of　the　proposed　method　is　the　resulting　estimators　are　more　efficient　than　the　least　squares　estimators　in　the　presence　of　outliers　or　heavy-tail　error　distributions.　The　slope　function　and　functional　predictor　variable　are　approximated　by　functional　principal　component　basis　functions.　Under　some　regularity　conditions,　we　obtain　the　optimal　convergence　rate　of　slope　function,　and　the　asymptotic　normality　of　parameter　vector　and　variance　estimator.　Finally,　we　investigate　the　finite　sample　performance　of　the　proposed　method　through　a　simulation　study　and　real　data　analysis.