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A fundamental step toward broadening the use of real-world image-based visual servoing is to deal with the important issue of reliability and robustness. In order to address this issue, a closed-loop control law is proposed that simultaneously accomplishes a visual servoing task and is robust to a general class of image processing errors. This is achieved with the application of widely accepted statistical techniques such as robust M-estimation and LMedS. Experimental results are presented which demonstrate visual servoing tasks that resist severe outlier contamination.