Generative art has drawn increased attention in recent AI applications. Traditional approaches of robot calligraphy have faced challenges in achieving style consistency, line smoothness and high-quality structural uniformity. To address the limitation of existing methods, we propose a dual generator framework based on deep adversarial networks for robotic calligraphy reproduction. The proposed model utilizes a encoder-decoder module as one generator for style learning and a robot arm as the other generator for motion learning to optimize the networks and obtain the best robot calligraphy works. Based on the enhanced datasets, multiple evaluation metrics including coverage rate, structural similarity index measure, intersection over union and Turing test are employed to perform the experimental validation. The evaluations demonstrate that the proposed method is highly effective and applicable in robot calligraphy and achieves state-of-the-art results with the average structural similarity index measure 75.91%, coverage rate 70.25%, and intersection over union 80.68%, which provides a paradigm for evaluation in the field of art.