In　this　article,　we　consider　a　new　robust　estimation　procedure　for　the　partial　functional　linear　model　(PFLM)　with　the　slope　function　approximated　by　spline　basis　functions.　This　robust　estimation　procedure　applies　a　modified　Huber＇s　function　with　tail　function　replaced　by　the　exponential　squared　loss　(ESL)　to　achieve　robustness　against　outliers.　A　data-driven　procedure　is　presented　for　selecting　the　tuning　parameters　of　the　new　estimation　method,　which　enables　us　to　reach　better　robustness　and　efficiency　than　other　methods　in　the　presence　of　outliers　or　non-normal　errors.　We　construct　robust　estimators　of　both　parametric　coefficients　and　function　coefficient　in　the　PFLM.　Moreover,　some　asymptotic　properties　of　the　resulting　estimators　are　established.　The　finite　sample　performance　of　our　proposed　method　is　studied　through　simulations　and　illustrated　with　a　data　example.