A novel robot-task-description for a variety of dynamic behaviors

This paper aims to unify the “robot language” approach and the reinforcement learning (RL) framework in order to design behaviors of robots with a simple description. We develop a kind of robot language where we describe a robot task, then the robot employs an RL method to acquire the corresponding behavior. The remarkable feature of this approach is that we do not have to specify the procedure of the behavior, and the models of the environment and the robot. To accomplish this approach, we employ the C++ RL library SkyAI as the base system, then we extend the SkyAI´s script interface so that we can describe tasks simply. In this mechanism, a task is described with several event-driven functions where the reward and the end-of-episode condition are defined. As the demonstration, we design six kinds of behaviors for a humanoid robot; a crawling, a handstanding, a jumping, a forward rolling, a backward rolling, and a turning task.