LAUSR

Abstract The concept of reinforcement learning enables an agent to learn a task based on trial and error. Especially for the automation of industrial processes, this approach promises significant advantages in terms of flexibility and adaptability when compared to rule-based solutions. While previous works have uncovered the potential of reinforcement learning and the applicability to real-world scenarios was shown, the algorithm relies on a discretization of time, where every time step comprises a self-contained sequence of observation, execution and feedback. However, this design poses a major obstacle for tasks, which do not allow for a distinct separation of steps. A prominent example is motion planning for industrial robotics, where reinforcement-learning solutions to date result in non-fluent trajectories. In this work, we address this shortcoming of reinforcement learning by presenting an asynchronous update strategy, which enables the agent to plan its next trajectory while executing the previous one. We use Bezier curves as actions due to the ability to characterize complex trajectories with relatively few parameters. We show that our modifications further improve the smoothness of the robot’s motion and allow for a smoother velocity profile without a drop in performance when compared to previous solutions.