Robust Optimization is a common paradigm to consider uncertainty in the parameters of an optimization problem. The traditional way to find robust solutions requires solving the robust counterpart of an optimiza- tion problem, which, in practice, can often be prohibitively costly. In this work, we study iterative methods to approximately solve Robust Convex Optimization problems, which do not require solving the robust counter- part. We use concepts from the Online Learning framework to propose a new algorithm that performs constraint aggregation, and we demonstrate theoretical convergence guarantees. We then develop a modification of this algorithm that, although without such guarantees, obtains better practical performance. Finally, we implement other classical iterative methods from the Robust Optimization literature and present a computational study of their performances.