  In this article we consider the nonparametric robust estimation problem for
regression models in continuous time with semi-Markov noises observed in
discrete time moments. An adaptive model selection procedure is proposed. A
sharp non-asymptotic oracle inequality for the robust risks is obtained. We
obtain sufficient conditions on the frequency observations under which the
robust efficiency is shown. It turns out that for the semi-Markov models the
robust minimax convergence rate may be faster or slower than the classical one.
