An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the gradient-sampling methodology, is designed specifically for cases when objective and generalized gradient values might be subject to bounded, uncontrollable errors. A novel assumption pertaining to such errors in nonsmooth settings is introduced that decouples the noise that may be present due to typical errors in derivative estimates with noise that may be due to misidentification of segments of differentiability of the objective. Similarly to state-of-the-art guarantees for noisy smooth optimization, it is proved for the algorithm that, with probability one, either the sequence of objective values will decrease without bound or the algorithm will generate an iterate at which a measure of stationarity is below a threshold that depends proportionally on the error bounds for the objective function and generalized gradient values. The results of numerical experiments are presented, which show that the algorithm can indeed perform approximate optimization robustly despite errors in objective and generalized gradient values.