The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers’ implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity. In particular, the system innovatively applies a gated recurrent unit (GRU) network to generate robotic writing actions with the support of a prelabeled trajectory sequence vector. Also, a new evaluation method is proposed that considers the shape, trajectory sequence, and structural information of the writing outcome, thereby helping ensure the writing quality. A swarm optimization algorithm is exploited to create an optimal set of parameters of the proposed system. The proposed approach is evaluated using Arabic numerals, and the experimental results demonstrate the competitive writing performance of the system against state-of-the-art approaches regarding multiple criteria (including FID, MAE, PSNR, SSIM, and PerLoss), as well as diversity performance concerning variance and entropy. Importantly, the proposed GRU-based robotic motion planning system, supported with swarm optimization can learn from a small dataset, while producing calligraphy writing with diverse and aesthetically pleasing outcomes.

中文翻译：

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通过学习角色的结构和顺序信息解决机器人轨迹顺序写入问题


数字或字母的书写顺序通常会影响书写结果的美观。因此，机器人书法系统模仿人类作家的隐含意图仍然是一个挑战。本文提出了一种新的机器人书法系统，它能够学习有限顺序信息的书写序列，产生与人类书写者兼容且具有良好多样性的书写结果。特别是，该系统创新性地应用门控循环单元（GRU）网络，在预标记轨迹序列向量的支持下生成机器人书写动作。此外，还提出了一种新的评估方法，考虑书写结果的形状、轨迹序列和结构信息，从而有助于保证书写质量。利用群体优化算法来创建所提出系统的一组最佳参数。使用阿拉伯数字对所提出的方法进行评估，实验结果证明了系统在多个标准（包括 FID、MAE、PSNR、SSIM 和 PerLoss）以及关于方差和熵的多样性性能。重要的是，所提出的基于 GRU 的机器人运动规划系统在群体优化的支持下可以从小型数据集中学习，同时产生具有多样化且美观的结果的书法作品。