Robotic Writing based on Trust Region Optimization and Imitation Learning

As the use of robots in service area increases, it is necessary to replace human tasks in daily life with robots. Among them, this study focuses on writing and aims to write alphabets on the blackboard with chalk using a 7-DOF robot arm. In chalk writing, it is necessary to regulate the contact force measured between chalk and blackboard within appropriate value, so that the desired letters can be written consistently in the desired thickness. To this end, the deep reinforcement learning algorithm, proximal policy optimization (PPO) was used as a height regulator for the end-effector of a robot to control the contact force between the chalk and the blackboard. Moreover, imitation learning network which is trained using human handwritten data, was used as a planar path generator. The performance of the proposed height regulator and planar path generator was verified by experiments.