论文标题
基于动态运动原语（DMP）的广义机器人手写学习系统
A Generalized Robotic Handwriting Learning System based on Dynamic Movement Primitives (DMPs)
论文作者
论文摘要
从示范中学习（LFD）是一种强大的学习方法，可以使机器人推断如何在某个或多个人的人为示范所需任务的情况下执行任务。通过从最终用户演示中学习，而不是要求手动编程每种技能的域专家，机器人可以更容易地应用于更广泛的现实世界应用程序。由于人类手写轨迹的复杂性，写作机器人作为LFD的一种应用已成为一个充满挑战的研究主题。在本文中，我们介绍了一个广义的手写学习系统，用于物理机器人，以从人类手写示例中学习以绘制字母数字字符。我们的机器人系统能够重写字母，以模仿人类示威者以类似的写作方式编写和创建新字母的方式。对于该系统，我们开发了增强的动态运动原始（DMP）算法DMP*，从而增强了我们的机器人系统的鲁棒性和泛化能力。
Learning from demonstration (LfD) is a powerful learning method to enable a robot to infer how to perform a task given one or more human demonstrations of the desired task. By learning from end-user demonstration rather than requiring that a domain expert manually programming each skill, robots can more readily be applied to a wider range of real-world applications. Writing robots, as one application of LfD, has become a challenging research topic due to the complexity of human handwriting trajectories. In this paper, we introduce a generalized handwriting-learning system for a physical robot to learn from examples of humans' handwriting to draw alphanumeric characters. Our robotic system is able to rewrite letters imitating the way human demonstrators write and create new letters in a similar writing style. For this system, we develop an augmented dynamic movement primitive (DMP) algorithm, DMP*, which strengthens the robustness and generalization ability of our robotic system.
