A Generalized Robotic Handwriting Learning System based on Dynamic Movement Primitives (DMPs)
This work addresses the challenge of robotic handwriting for end-users by allowing robots to learn from demonstrations, making them more accessible for real-world applications.
This paper presents a robotic handwriting learning system that enables a robot to learn from human demonstrations to draw alphanumeric characters. The system can rewrite letters imitating human writing styles and create new letters in a similar style.
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.