Robots Learn to Write via Human-Robot Interaction

Human-Robot Interaction (HRI) has been extensively investigated in academia, industries, and technology companies. How to teach a robot to write is recognized as a difficult task, involving perception, learning and control of the overall robotic system. This paper develops a robotic handwriting system on a NAO robot by using HRI and Q-learning. In the simulator setup, the human demonstrator first writes to form a desired writing content, with the coordinates of each stroke writing saved; then, by applying the Q-learning algorithm, the virtual NAO automatically learns to find the correct writing order and to optimize the strokes. With the learned knowledge, the real NAO uses its inverse kinematics for generating the joint values from the stroke trajectories, and thus can quickly replicate the letters or words written by the human demonstrator. It is shown that by using the proposed HRI-based handwriting system, the human can naturally and conveniently teach NAO to write many characters and NAO can also have an acceptable writing quality close to the human.