Maximum likelihood estimation (MLE) is often used to estimate the parameters of the circular logistic regression model due to its efficiency under a parametric model. However, evidence has shown that the classical MLE extremely affects the parameter estimation in the presence of outliers. )is article discusses the effect of outliers on circular logistic regression and extends four robust estimators, namely, Mallows, Schweppe, Bianco and Yohai estimator (BY), and weighted BY estimators, to the circular logistic regression model. )ese estimators have been successfully used in linear logistic regression models for the same purpose. )e four proposed robust estimators are compared with the classical MLE through simulation studies. )ey demonstrate satisfactory finite sample performance in the presence of misclassified errors and leverage points. Meteorological and ecological datasets are analyzed for illustration.